research paper on food supply

• Open access
• Published: 23 August 2023

Meta-analysis of food supply chain: pre, during and post COVID-19 pandemic

• Abdul Kafi   ORCID: orcid.org/0000-0002-7300-6898 1 ,
• Nizamuddin Zainuddin 1 ,
• Adam Mohd Saifudin 1 ,
• Syairah Aimi Shahron 1 ,
• Mohd Rizal Razalli 1 ,
• Suria Musa 1 &
• Aidi Ahmi 2  

Agriculture & Food Security volume  12 , Article number:  27 ( 2023 ) Cite this article

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Metrics details

Despite the unprecedented impact of COVID-19 on the food supply chain since 2020. Understanding the current trends of research and scenarios in the food supply chain is critical for developing effective strategies to address the present issue. This study aims to provide comprehensive insights into the pre, during, and post COVID-19 pandemic in the food supply chain.

Methodology

This study used the Scopus database from 1995 to November 6, 2022, to analyse the food supply chain. Bibliometric analysis was conducted using VOSviewer software to create knowledge maps and visualizations for co-occurrence, co-authorship, and country collaboration. Biblioshiny, a shiny app for the Bibliometrix R package, was then used to explore theme evaluation path maps in the research domain.

The bibliometric analysis of 2523 documents provides important insights into present and future publication trends. Top author keywords included blockchain, traceability, food safety, sustainability, and supply chain management. The Sustainability (Switzerland) journal ranked first in productivity, and the International Journal of Production Economics received the highest citations. The United Kingdom was the most productive country, collaborating with partners in Europe, Asia, and North America. The Netherlands had the highest percentage of documents with international authors, while India and China had the lowest. The thematic evaluation maps revealed that articles focused on important research topics including food processing industry, information sharing, risk assessment, decision-making, biodiversity, food safety, and food waste.

This study contribute to the growing body of literature on the food supply chain by providing a comprehensive analysis of research trends during different phases of the pandemic. The findings can be used to inform policymakers and industry leaders about the measures required to build a more resilient and sustainable food supply chain infrastructure for the future. This study considered only Scopus online database for bibliometric analysis, which may have limited the search strategy. Future studies are encouraged to consider related published articles by linking multiple databases.

Introduction

Food supply chains are extensively explored and dependent on worldwide situations. The COVID-19 pandemic has addressed the deficiency of flexibility in food supply chain, resulting in financial and social disasters with global implications [ 1 ]. Similarly, the COVID-19 pandemic has impacted the food supply chain, from field to consumer, which represents an important sector in any country. The discriminatory nature of the pandemic had a remarkable impact on people's lives and health standards, as well as global business, supply chains, and major economies [ 2 ]. Associated restrictions imposed during the pandemic affected the food safety of household by directly disrupting the food supply chain [ 3 , 4 ]. As a consequence of the severity, the stability of the food supply chain is essential to prevent interruption towards the national economy, social security and public health. Significant association of food supply chain occurred at the combination of dynamic, fragile and complicated that can simultaneously be influenced by the regulator actions, such as road closures, lockdown, and vehicle movement control. While such restrictions prevent the spread of a disease, they can also disrupt the trade of agricultural products and market chains [ 5 ]. Since the restriction was imposed by governments around the world, food distribution declined to 60% [ 6 ]. While, the COVID-19 pandemic also created serious obstruction in many sectors such as livestock production, vegetable production, plantation, cultivation and harvesting due to a shortage of labour as these sectors are comparatively labour-concentric [ 7 ]. Although, labour shortages also represented a crucial problem even before the beginning of the COVID-19 pandemic [ 8 ]. This issue is undermining the capability of farmers and agriculture enterprises to operate because of scarcity of workers owing to illness and physical distance during harvesting. These circumstances delayed the provision of food and agriculture input and integrated issues in the food supply chain to market [ 9 ]. However, several firms depend on their key inputs, whereby the maximum are more vulnerable to disruptions since domestic markets have to meet their requirements. Barriers to logistics which interfere food supply chain further weaken high-value commodities because of their limited shelf life [ 6 , 10 ]. Therefore, maintenance of logistical efficiency, particularly during and after the global crisis is a vital aspect of the food sector. Correspondingly, raw material procurement from suppliers is the biggest bottleneck in the food supply chain and ensuring a continuous flow of food from producers to end users [ 11 ]. The challenge is to risk the capacity of agriculture producers to operate as usual which can negatively affect freshness and food safety, food quality, and limit market entrance and pricing [ 10 ]. The effects on agricultural systems due to the pandemic depend heavily on the composition and intensity of agricultural activity and vary according to the product manufactured and the country. In low-income countries, productivity is mostly labour oriented, whereas, in high-income countries, capital-intensive practices are generally dependent on agricultural production. Therefore, the supply chain should remain operational with a focus on crucial logistical problems [ 6 ]. In addition, the supply chain involves not just producers, distributors and customers, but also labour concerted food processing plants. Production in several plants has been limited, interrupted or temporarily stopped owing to workers who have been identified COVID -19 positive and who have hesitated to go to work presuming they are sick, especially in meeting processing firms at the time of the pandemic outbreak [ 12 , 13 ]. Besides, another important factor that caused food supply chin disruption during COVID -19 pandemic is centralized food production. This approach has contributed to the production and cost reduction of food processors. Centralisation has some limitation such as inflexible and long supply chain problems. Furthermore, it might cause challenges to meet demand through a small numbers of very big production facilities [ 14 ] such as closing the full process if a pandemic leads to high volume production lines with less options. In the face of these problems, food supply chain have shown tolerable resilience while supermarket shelves were refilled with the disappearance of hoarding behaviour and the demand growth response to supply chains [ 15 ].

Although several articles were published and included the aspect of COVID -19 impact on the food supply chain but are primarily concerned with the focus on the subject. Direct studies were related to bibliometric analysis to know the trend and scenario of research were not known. Thus, it is crucial to provide information with a clear direction of the present research status and future trends in the COVID-19 food supply chain. Evidence from several cohort studies in the bibliometric analysis was conducted on food safety governance [ 16 ], food supply chain safety research [ 17 ], and agri-food value chain [ 18 ]. As far as we know, there are no bibliometric analyses conducted related to COVID-19 food supply chain pre, during and post-pandemic.

After identifying this gap, this study tries to fill the gap by developing a detailed analysis of COVID-19 food supply chain studies pre, during and post-pandemic. As a quantitative analysis method, a bibliometric technique is adopted to develop a trend in various domains and utilized to uncover the present status [ 19 , 20 , 21 ]. In bibliometric analysis, researchers can define fields of research, looking at the further direction of research, and getting involved with other institutes and countries [ 22 ]. Hence, this study specifically focused to answer the pertaining research questions to present the trend of the previous studies on COVID-19 and the food supply chain with the global development of the field.

What are the dynamics and trends of COVID-19 food supply chain literature?

What are the highly-cited documents in COVID-19 food supply chain research through time?

What are the most productive authors, countries, institutions, and source titles in terms of publication numbers?

What are the more productive keywords in the COVID-19 supply chain research pre, during and post-pandemic?

What is the current knowledge formation status relating to co-occurrence, collaboration and co-authorship linkage in COVID-19 food supply chain research?

Which are the most productive research themes, and how did they evolve through time?

This study adopted a bibliometric technique to analyse the publication of COVID-19 on food supply chain research extracted from the Scopus database to provide a functional overview of the current trend of predictable research throughout the world. Scopus is considered the largest cited and referenced abstract of literature containing a wide range of subject areas. Employing Scopus is, therefore, an attempt to comprise more subjects which are not explored in WoS [ 23 , 24 ]. This study will help researchers, policymakers and individuals to support food supply chain research trends and to explore the possibilities and opportunities for future study.

To answer the research questions, the paper is organized into the following sections. “Introduction” Section discusses a brief introduction to the topic including the current knowledge on the impact of COVID-19 on the food supply chain, the research gap, and the purpose of the study. “Literature review” Section discusses the literature review in the field of the COVID-19 food supply chain in general. “Bibliometric analysis and methods” Section describes the methodology used in this study which includes Bibliometric, Biblioshiny, and presents the flowchart and data analysis structure. “Results” Section covers the discussion to answer the above research questions and future research directions. “Conclusion” Section describes the conclusion that covers the contributions, limitations and future research scopes.

Literature review

The COVID-19 pandemic triggered disruptions to the food supply chains which included purchaser concern buying attitude for key items, as well as a rapid change in consumption trends, which shifts away from the food service industry and toward meals ready and consumed at home [ 25 ]. A similar study was focused on the supply chain and the food industry is no exception. Due to a decrease in demand, the closure of food production services, and financial constrain, the businesses are unable to continue supplying their products to stores[ 26 ], and the current status of the COVID-19 pandemic has put unprecedented pressure on food supply chains. These include bottlenecks in transportation, logistics, farm labour, and food processing [ 15 ]. This behaviour of the food supply chain (FSC) is identified a main reason worldwide [ 27 ]. The scenario is that the food supply chain is disrupted with a potential breakdown in food freight borders, increasing business flexibility and social capital through real-time business communication [ 28 ]. Because of emerging COVID-19 pandemic issues, there are major concerns about food production, manufacture, delivery, and consumption in the food supply chain [ 2 ]. Therefore, a lack of consumer access to food poses the ultimate threat to food security. There is also a massive rise in global food issue with the appearance of COVID-19 and the advent of numerous difficulties in all sectors of the food supply chain (such as production, distribution, and transportation), and this problem has taken on added significance [ 29 ].

COVID-19 pandemic has had a significant effect on the global food supply chain, raising public awareness about the constancy of the global food network and the significant interruption to food availability [ 30 ]. It is proven the COVID-19 pandemic has an effect on the agricultural and food supply chain from two angles, which are "food supply and food demand" [ 31 , 32 ]. In current months, the COVID -19 pandemic had threaten the global food supply chain creating economic instability, constraints to food accessibility, restrictions on farm commodity shipping, limitations on food production, difficulties in food product transit, evolving consumer demand, food production facility closures, shortages of farm workers to harvest vegetables, farm worker travel restrictions, and fruit deficiencies [ 33 , 35 , 35 ]. Therefore, numerous countries adopted diverse strategies to reduce the impact of COVID -19 on the food supply chain [ 34 ]. However, a potential problem still exists that need to be addressed to gradually resolve the present crisis. Hence, the global food supply chain is facing many issues resulting from the continuing COVID-19 pandemic around the world, which has prompted serious issues about food supply, distribution, processing, and demand [ 35 ]. Accordingly, the COVID -19 pandemic has increased disruption and damage to the global food supply chain in the following areas, which are (i) logistics (ii) harvest (iii) processing (iv) sourcing, and (v) go-to-market [ 36 ].

Bibliometric analysis and methods

Bibliometric analysis.

Bibliometric studies provide a wide range of options for understanding the significance of all studies. A quantitative and qualitative technique of bibliometric analysis is used for the publication of journals and articles, including their corresponding citations over time [ 37 ]. It differentiates the present status of research by measuring the scientific outcome of a country and institution and has played a major role in the past in influencing policymaking and improving the knowledge of science [ 38 , 39 ]. This also allows researchers to identify and help them to determine the scope of study topics, and plan their focused mind and projection of trends [ 40 ]. This method can provide a statistical output for calculating and estimating the number and development trends of a particular field [ 41 , 42 ]. Several studies have explored the food supply chain using the bibliometric technique [ 43 , 45 , 45 ]. This research provides a quantitative literature review by drawing connections between various keywords related to the food supply chain. It is a standardized technique for calculating and assessing written communication among authors [ 46 ], quantifying the trend and characteristics of a certain research area based on several measures [ 47 , 48 ], and focusing on research titles, keywords, affiliations, authors, and article publication [ 49 , 50 ], network and countries [ 51 ], co-authorship links, co-citation links, bibliographic coupling links that may be used in citation mapping to visualize a cluster or theme [ 51 ], and supply chain management [ 52 ]. In this study, Scopus databases are considered to extract necessary information. It is chosen as the source of the largest abstract indexing database and it is recommended by the previous studies that would cover a wide range of areas and provide comprehensive search options to help researchers develop search strings with accurate results, especially in broad areas of the research [ 44 , 53 ].

Thematic evaluation

Thematic evolution is a new research technique that is currently the widest accepted method for using many disciplines to measure the topic growth, evolution, and flow of a specific research area over time, supporting scholars in understanding the growth of a particular research area more methodically. This study used Biblioshiny, a shiny application for the Bibliometrix R package, to performthe thematic evaluation mapping [ 38 , 54 ]. To analyze the evaluation theme, the proportion of total authors' keywords indicated by drawing the range of the subject direction on the coordinate axis. The growth and decrease of the alluvial area represent the change in scale over time.

Data collection

This study has chosen the online Scopus database from 1995 to 6 November 2022 in food supply chain because it is the world's largest citation and abstract database of scholarly works from international publishers which provides a one-stop platform for scientific scholars [ 55 ]. Especially, compared to other databases like Web of Science (WoS), Google Scholar (GS), and PubMed (PubMed), Scopus has a wider variety of publications and helps with both keyword searches and bibliographic analysis [ 56 ]. Scopus has 20% higher coverage than WoS in terms of citation analysis, however, Google Scholar produces inconsistent results. PubMed is a database that is commonly utilised in scientific research [ 56 ]. Figure  1 shows the search strategy and detailed steps for the data collection for this study.

Flow diagram of article searching strategy of food supply chain documents

Search strategy

In a bibliometric study, it is important to choose the appropriate keywords. Based on the research questions, this study limited the search to two main title keywords: “food” and “supply chain”. Therefore, this research encompassed two possible combination strings of keywords that are relevant to the study’s topic. The title of an article should incorporate information that can be used to capture the attention of readers since it is the first element that readers will observe first [ 57 ]. Finally, this study comprises two search query strings TITLE (“food”) AND TITLE (“supply chain”). A total of 2523 research documents between 1995 and 6 November 2022 were obtained from the Scopus database (Additional file 1 ). There were no excluded methods applied during the search of the document as shown in Fig.  1 .

Tools and data analysis

Numerous disciplines had adopted VOSviewer to perform bibliometric analysis, e.g., social media in knowledge management [ 58 ], supply chain and logistics [ 59 ], presumption [ 58 ], business intelligence [ 60 ], health [ 61 ], and brand personality analysis [ 62 ]. To achieve the research objectives and research questions, this study adopted VOSviewer software to visualize the geographical distribution, authorship, citations, keywords, collaboration among countries specifically on COVID-19 food supply chain topics. The VOSviewer visualises bibliometric maps in different methods to present various features of literature structure. The VOSviewer employs an integrated approach to mapping and clustering that is constructed on the normalised term co-occurrence matrix and a similarity measure that determines the intensity of association between terms [ 51 , 63 ]. Based on citations and bibliographic coupling links, the VOSviewer creates clusters of authors' keywords, countries, and organizations. These clusters indicate the compactness of articles, keywords, countries, and organizations in specific research. In addition, Microsoft Excel 2013 software tools were used to analyze the primary data collected from Scopus (CSV format). Finally, R studio explores the evolutionary themes of COVID-19 food supply chain research topics pre, during and post-pandemic. Figure  2 portrays the different steps and analyses performed in this study. To address the research questions, the study is divided into two parts: descriptive analysis and network analysis.

Framework for bibliometric analysis

Descriptive analysis

This section explores the COVID-19 food supply chain research profile from 1995 to 2022, these include all current publication information, research trends, prolific authors, highly cited papers, publication sources, most productive institutions and countries, and the authors’ keywords as shown in Table 1 .

Yearly publication trend

The total publication, total citation, citation per article, and citation per year of the articles published between 1995 and 2022 were used to analyzed the yearly publication trends. Table 2 and Fig.  3 describe the yearly publication trend on the food supply chain pre, during and post COVID-19. In general, the number of publications on the COVID-19 food supply chain significantly increased from 217 articles in 2019 to 419 articles in 2021. The rate of development after 2020 was rather drastic and the number of publications increased to almost double that of the previous year. The growth of published articles indicates that the topic is beginning a stage of development. As a result, as of November 2022, 345 articles were published that have undertaken and explored new related topics attributed to the worldwide pandemic issues which simultaneously disrupted the supply chain.

The trend of publications per year of COVID-19 food supply chain

Most productive authors

The number of total publications, total citations, and h- index are analyzed to understand the most influential authors in COVID-19 food supply chain research domain. There are 5839 single authors devoting to food supply chain research from 1995 to 2022. Table 3 shows the twenty most prolific authors and found that Van Der Vorst, J.G.A.J has received the highest number of publications at 26 publications, 1945 total citations and h - index of 18 in this domain. Results revealed that Kumar, A. and Li, D are among the most prominent authors in the COVID-19 food supply chain field.

Highly cited papers

Table 4 shows associated information (authors, article title, total citations, and citations per year) of the top 20 most productive journals. The paper titled “Food waste within food supply chains: Quantification and potential for change to 2050” had 1699 total citations and 141.58 citations per year, followed by “Understanding alternative food networks: Exploring the role of short food supply chains in rural development” with a total citation of 1033 and 54.37 citations per year, and “An agri-food supply chain traceability system for China based on RFID & blockchain technology” had a total citation of 751 and 125.17 citations per year, respectively. In addition, the core journals in COVID-19 food supply chain studies are multidisciplinary, referring to traceability, corporate social responsibilities, modelling approach, sustainability, bioavailability and human health, blockchain technology, fresh food quality etc.

Most productive source titles

There are 2523 articles published in different journals . Table 5 shows the top twenty source titles that published ten or more documents from 1995 to 2022. Sustainability (Switzerland), Journal of Cleaner Production and British Food Journal are the top three publishers with a total publication of 102, 67, and 52 on the COVID -19 food supply chain and total citations of 1571, 2669 and 1394, respectively.

Most productive countries

According to the Scopus database, COVID-19 food supply chain documents were extracted from 127 countries. Table 6 shows the top thirty most prolific countries with at least 25 papers published. Among the highest thirty countries, the United Kingdom is the most productive country with a leading publication of 400 articles, accounting for 15.85% followed by China with 327 articles (12.96%) ranked the second position, United States ranks third with 272 articles (10.78%), Italy ranks fourth with 234 articles (9.27%) and India ranks fifth with 230 articles (9.12%), respectively. As indicated, these productive countries have a greater concern about COVID-19 food supply chain research pre, during and post-pandemic than other countries.

Most prolific institutions

Table 7 lists the top ten most prolific institutions for the 327 articles studied accounting for 12.95% of total documents relating to COVID–19 food supply chain research. Wageningen University & amp; Research, Alma Mater Studiorum Università di Bologna and Cranfield University are the core contributor to this research domain. These institutes have published 183 articles, which interprets for 7.27% of the entire publications. The results indicate that the productive documents are extremely intense among a few institutes only.

Top frequent authors’ keywords

Table 8 presents the top frequently used authors' keywords in the food supply chain before the COVID-19 pandemic. There are 34 occurrences of the food supply chain put in the first place, followed by 24 and 10 occurrences in supply chain and supply chain management, respectively.

In bibliometric analysis, a word cloud of author's keywords is a visual representation of the most commonly used words in an article's list of keywords to identify the most common themes in an author's work. The size of each word in the cloud represents its frequency in the list of keywords [ 84 ]. Figure  4 shows the word cloud map of the top author’s keywords before the COVID-19 pandemic. In the map, cloud-found food supply chain, supply chain, supply chain management, food industry, food safety, and traceability are the always core analyse research topics. Based on this analysis, it was found that a relationship was established linking food safety, agri-food supply chain and sustainability. This proves the importance of research in linking these three keywords and their impact on the COVID-19 food supply chain are interconnected.

Word cloud of top author’s keywords (BEFORE COVID-19)

In addition, this study used Multiple Correspondence Analysis (MCA) with the R package bibliometrix to investigate the author's keywords. The MCA is a data analysis method that could be applied to the graphical analysis of categorical data [ 38 ]. This study chose MCA because this analysis can identify the underlying themes established on the author’s keywords. Using the MCA method, related keywords are grouped, providing a hierarchical display of how frequently used terms are typically employed [ 83 ]. If two separate terms (like food and supply chain) appear in the same number of articles, then the two terms can be grouped [ 38 ].

Figure  5 maps the authors’ keywords conceptual structure associated with the “food” and “supply chain” publications domain before the COVID-19 pandemic. This map shows that the publications included in the analysis are categorized into two major groups which are red and blue. In each group, some words are connected. The red cluster shows more different words to which many research publications connect the words organized in this field. The conceptual structure is appeared in the keyword ‘co-occurrence’. For the red cluster, food is linked to food safety, organic food, short food supply, agri-food supply chain, and supply chain management. For the blue cluster, the supply chain is linked to food industry, sustainability, green supply chain management. Future research focuses on service oriented architecture and traceability, which follow the food supply chain stability to protect the disruption.

Conceptual structure map based on author’s keywords (BEFORE COVID-19)

Table 9 shows the most commonly used author’s keywords in food supply chain after COVID-19. Food supply chain appeared is in first place with 61 occurrences, followed by the supply chain, and sustainability with 44 and 31 occurrences, respectively. Following Fig.  6 , Table 9 indicates the top ranked keywords based on their co-occurrence.

Word cloud of author’s keywords (AFTER COVID-19)

Figure  6 shows the word cloud map of the author’s keywords after COVID-19 pandemic. Among the top word, cloud focused food supply chain, supply chain, blockchain, sustainability, short food supply chain, supply chain management, food industry, and agri-food supply chain are the core analyse research topics. Based on this analysis, it was found that a relationship has been established linking food safety, food security, food waste, traceability, and resilience. These top words are not expected and not to consider the searching string. However, these associations clearly indicated that there are severe influences on food supply chain as a whole after COVID-19 pandemic.

Figure  7 presents the author’s keywords conceptual structure involved with the food supply chain research domain after COVID-19 pandemic. The figure explores that the publications evolved in the analysis are clustered into two key groups, which directs the logical construct of food supply chain studies. For the red group, food is linked to food system, food loss and waste, short food supply chain, and food supply chain performance. For the blue group, supply chain is linked to total interpretative structuring modelling, digital technology and internet of things. Future research focuses on food supply chain study related to blockchain, traceability, resilience, innovation, sustainable development, consumer behaviour and crucial economy, which is the way of comprehensive understanding of food supply chain research trends.

Conceptual structure map based on author’s keywords (AFTER COVID-19)

Based on the analysis of word clouds and conceptual structure maps, it has been found that certain words may appear frequently in a word cloud of an author's keywords but not be represented in the author's conceptual structure of keywords. This is because a word cloud simply represents the frequency of occurrence of individual words or phrases, while an author's keywords conceptual structure focuses on the relationships between those words and phrases. For example, a word like "food industry” may appear frequently in an author's list of keywords and thus have a comparatively large size in the word cloud, but it may not be a central concept in the author's research, and therefore may not have a visible position in the author's keywords conceptual structure. On the other hand, a less frequently occurring word like "consumer behaviour” may have a more central position in the author's conceptual structure, even though it appears less frequently in the word cloud. Overall, both tools serve different purposes in bibliometric analysis and provide complementary insights into an author's research focus [ 50 ].

Bibliometric mapping analysis of COVID-19 food supply chain

A common application of bibliometric mapping analysis is to recognize particular research fields to gain an outline of the topology of the study area, its themes, topics, and terms, and how they connect closely [ 85 ]. Furthermore, to visualise the output of bibliometric mapping, a worldwide mapping analysis method is visualization of similarities (VOS) [ 46 , 51 , 83 ] has been adopted through a computer aided program called VOSviewer (Leiden University, Netherlands) [ 63 ]. The VOSviewer visualizes bibliometric maps in a range of methods in accordance with emphasising unique factors regarding the literature production. VOSviewer applied a combined method for both clustering and mapping or it is mainly created on the standardised term co-occurrence which estimates relationship strength between terms and is also an effective tool for conducting network analysis [ 86 ]. Furthermore, VOSviewer Version 1.6.2 [ 63 ] allows the construction of sceneries in which terms are coloured based on the year of their first presence in scientific publication. The size of the font and the enclosing rectangle indicates the popularity of a term; bigger rectangles and fonts indicate more productive terms. This study used VOSviewer to visualise co-authorship and collaboration networks and R studio to visualise co-occurrence of keywords and thematic evaluation of COVID-19 food supply chain topics.

Co-authorship analysis

Co-authorship network visualisation revealed knowledge domain maps of major authors groups in the COVID-19 food supply chain research. Figure  8 , each node shows an author, and the size of the nodes indicates the number of published articles. The link connecting two nodes represents the collaborative relationship between two authors, and the thickness of the link indicates the degree of association. Based on the knowledge domain maps of the co-authorship network, potential authors can deliver important information for a research institute to improve collaboration groups, for individual researchers to seek collaboration scopes and for the publisher to gather editorial teams to publish special issues in journals or books. It can be seen from Fig.  8 , the cooperation among prolific authors is intense. Co-authorship network formed several groups, such as the yellow group comprising Van Der Vorst (documents 26, links 26), followed by Kumar (documents 19, links 36) in the green group, and Liu (documents 16, links 16) in the yellow group as the core. Based on the results among the research groups, most productive authors mainly work independent or in collaboration inside the same organization, but the scale of such collaboration is small and not firm resulting to a lack of effective international exchange and cooperation.

Co-authorship network among productive authors

Countries collaboration network

This study established a collaboration network among countries through VOSviewer software in the research domain knowledge map in the field of COVID-19 on the food supply chain. A network visualisation map is presented in Fig.  9 . The co-authorship collaboration was established among 127 countries whereas articles were contributed from 67 countries (minimum threshold document 5). The thickness of the line indicates with each country can be determined by the frequency of co-authorship. This map indicates that a satisfactory collaboration network was established between the United Kingdom, China, United States, Italy, India, Netherlands, Germany, and France. More importantly, the United Kingdom collaboration developed with the European, Asian and North American countries and received the highest citations of 14712, links 301 and 400 documents. However, Singapore, Peru, Israil, Algeria, Romania, Lativia, Ukraine and Qatar have had less cooperation with other countries. In our opinion, there are two significances of the high proportion of articles in countries. First, it contributes to the delivery of more detailed study topics, and secondly, provides a window of opportunity for the collaboration of new countries; in other words, it enables the collaboration of other researchers and institutions in these fields.

Co-authorship collaboration network among countries

Keyword co-occurrence network

Keywords are the major content of publications, and the purpose of keyword analysis is to identify important research compositions in COVID-19 food supply chain. A co-occurrence network of author’s keywords was used to highlight research topics in the field. The method refers to the most commonly used keywords represented by the font size and larger circles [ 61 ]. The lines between the keywords reflect their correlation strength [ 20 , 61 ]. For a better understanding, the related keywords are commonly listed, as indicated by the same colour. Keywords without lines between them indicate that no connection has been developed. Considering that the closer to the centre of the network map terms appear, the more co-occurrence together. A closer connection indicates a stronger association.

To explore associated keywords to COVID-19 food supply chain research, the results in Fig.  10 indicate that two thematic clusters have been identified such as (i) food supply chain, and (ii) Supply chain and each cluster is denoted in a different colour. As shown in Fig.  10 , food supply chain covers the network centre, demonstrated by the greater cluster theme studied by previous scholars. The keyword ‘food supply chain’ is superposed on sustainability, food waste, COVID-19, circular economy, resilience, small and medium enterprise and food loose representing the closeness between them, further, COVID-19 impact on the food supply chain cannot be separated. Though in the second network of the supply chain, the linked keywords are supply chain management, blockchain, food industry, traceability, food safety, agri-food supply chain and short food supply chain. These highlight the COVID-19 pandemic impact and implies the rising attraction in this research field.

Author’s keywords co-occurrence analysis

This study used Biblioshiny software to analyse the author's keywords pre, during and post-COVID-19 food supply chain research to draw the evaluation of core research themes from 1995 to 2022. Figure  11 shows a Sankey diagram to analyse the journal's thematic evolution for the readers related to COVID-19 food supply chain. A Sankey diagram is used to demonstrate how various themes are related and have evolved in the past [ 54 ]. Each box on the map represents a theme, and the size of the boxes is relational to the frequency with which the theme occurs [ 87 ]. The flows connect each box, displaying the theme's evolution traces, and the thicker the connecting line, the stronger the link between the two themes. Overall, themes in COVID-19 supply chain are becoming more diverse over time, probably because more scholars from various fields are attracted to this theme. These indicated that the COVID-19 food supply chain gradually intersects with various fields such as e-government, traceability, information sharing, risk assessment, food waste, and blockchain. As shown in Fig.  11 , COVID-19 research in the food supply chain has evolved into six new themes from 1995 to 2019 and three themes from 2020 to 2022. Furthermore, it shows how the six themes exhibited with three themes before and after the COVID-19 pandemic.

Thematic evaluation based on authors’ keywords (Pre and Post COVID-19)

While Fig.  12 shows the thematic map before the COVID-19 pandemic mainly the upper-right quadrant reveals the motor theme which is high centrality and density because these themes are well developed and significant to the structure of a research field. The lower-right quadrant shows the basic themes. They are categorized by moderate centrality, which is resilience and the main focus in COVID-19 food supply chain research. This quadrant consists both transversal and general themes. The upper-left quadrant shows the niche themes which are strongly interconnected among themselves and have strong centrality to outside research. These concerns are extremely broader and significant. The theme related in the lower-left shows the growing or decreasing themes which are minimal and underdeveloped. The themes in this quadrant have a low density and centrality and they commonly reveal new theme.

The strategic thematic map of the author’s keywords (BEFORE COVID-19)

Figure  13 provides an overview and future trends of academic research on the food supply chain after the COVID-19 pandemic through the themes presented in the four quadrants. The motor themes have been developed extensively in both food supply and supply chain management. Food waste and catering services are well developed and isolated, and they occupy the basic theme. This is practically after COVID-19 pandemic, these themes gained importance for the food supply chain. Relatively emerging theme have currently been discussed comprehensively the subject of blockchain and food safety included in the third quadrant of cointegration has been on the boundary between basic and emerging themes, which is high centrality and density to the structure of a research field. Finally, the fourth quadrant is the food chain and food contamination themes are relatively wider and have a strong connection with the food supply chain. The most important rising topics in this period were the blockchain and food safety, which is on the border between basic and emerging or declining themes. Strongly interconnected niche theme is food contamination as a new problem in the food supply chain.

The strategic thematic map of the author’s keywords (Post COVID-19)

Discussion and future research direction

The descriptive analysis simplifies the present trend of research on the food supply chain by analysing pre, before and -post-COVID-19 data. It shows growing interest in COVID-19 on the food supply chain from the scientific community, which is an attractive issue in research, with a significant publication. Still, the COVID-19 pandemic led to high incidents in the food supply chain and the investigation and development strategies might be the result of this interest. It is proven that the current trend of COVID-19 impact is a major challenge to food supply chain analysis research and the pertaining term in the food supply chain has been encouraged in terms of COVID-19 impact reduction. By analysing the top keywords, many prospects for future study that have been disclosed by the COVID-19 pandemic. The COVID-19 has exposed supply chain vulnerability, confirming the importance of optimization and simulation. Implementing new technologies can improve efficiency, save costs, and increase customer satisfaction, allowing businesses to remain competitive. Similarly, Keywords on short food supply address these difficulties, there may be a shift towards shorter, more localized food supply chains can improve resilience and reduce the risk of disruptions caused by global crises or natural disasters. This approach may also provide potential synergy between sustainability practices and the need for more robust and sustainable food supply systems [ 88 , 89 ]. The use of blockchain technology can enhance traceability in supply chains, reducing the risk of food fraud and improving food safety. Blockchain can also support the adoption of sustainable and effective supply chain management strategies, particularly for short food supply chains. However, further research is necessary to develop new models for direct sales and blockchain-based systems that can validate the sustainability of food products. To minimize waste and increase efficiency, it is essential to integrate blockchain technology into food supply chains in a way that considers both economic and environmental impacts [ 91 ]. Finally, the Covid-19 outbreak has emphasized the significance of collaboration and innovation in the food industry. In the coming years, there may be a surge in investments in research and development, along with increased partnerships between industry players to discover new ways to address the issues affecting the food sector. Through these efforts, the industry can build a more resilient, sustainable, and secure food system for the future [ 92 ]. This also might lead to different directions for research, which include exploring new related topics and exploring less studied fields through a new framework. It might also be beneficial to provide new collaboration opportunities to widen the scope of the study.

Then, this study found the most productive countries, institutes and productive authors. The results explored that the United Kingdom is the most emerging country, but at the growing stages, China, the United States and Italy are also effective contributors in this field. The Wageningen University & amp; Research and Alma Mater Studiorum Università di Bologna have been the most productive institutes with one hundred twenty (120) and thirty-three (33) publications. Looking at the prolific authors, Van Der Vorst received the highest citation with 1945 citations. These countries and institutions have produced more in-depth and critical research in this area. The results of this analysis help governments, institutions, and authors work together, share knowledge of the COVID -19 food supply chain, and employing comprehensive strategies and efficient methods to address supply chain-related issues.

To develop an in-depth understanding of the results, this study used bibliometric mapping to provide a more comprehensive visualization of the results. The author’s keywords and co-occurrence (or co-word) analysis demonstrated that more research was focused on COVID-19 food supply chain and its impact on food security, food safety, traceability, food industry and sustainability which is closer to the circle. This finding addresses the issue of the COVID-19 food supply chain is linked to agriculture production and policy for food sustainability and its outcome is rational on the implication of food safety and food security to protect regular demand [ 87 , 88 ]. Another important issue suggests that creating more supply chain resilience may initiate a better initiative to managing and reducing challenges and risks faced pre and post COVID-19 pandemic [ 89 , 90 ]. Meanwhile, the co-authorship network shows that comparatively less collaboration occurs among authors on COVID-19 food supply chain research. This implies that the findings of Van Der Vorst and Kumar play an important role in the collaboration network. Although having a low level of relationship act as knowledge breakers among groups. The findings of the study also show that Li, D. Manning, L. and Accorsi, R have the same number of publications but few relational ties. This outcome could be influenced by limited collaborations within a closed group. Therefore, effective collaboration among scholars is required to widen the scope of COVID-19 food supply chain research.

By analysing thematic evaluation using the author’s keywords and compiling the results that postulate further direction that the research prolific topics of COVID-19 food supply chain are mainly connected to the food processing industry, traceability enterprise performance, information sharing and dissemination, decision making, risk assessment, food waste, food loose, and food traceability system. These eight primary directions are captured with each other in the evolutionary process. Research on the COVID-19 food supply chain still has significant potential for development because the integration, intensity, and reorganization of themes are more pronounced, showing that the articles are closely related and the degree of diversity is not high. This interdisciplinary research indicates that the COVID -19 food supply chain has an impact on food security and triggered disruption to respond to the current pandemic food security and caused disruption to food supply chain and suggests developing a framework for smooth resilience in the food supply chain system [ 33 ].

Furthermore, this study develops a basic structure for the most affected themes within the global food chain created by the post-COVID-19 pandemic. Generally, the global food chain combines production, processing, distribution, and consumption on a large scale [ 15 , 92 ]. The intensity of the impact may vary among sectors and goods at various stages of the supply chain for various products. There are four significant issues raised in the thematic analysis that the global food chain should address in the post-COVID-19 pandemic. The following diagram (Fig. 14 ) presents the basic structure of the global food chain that can help the food industry adapt to the post-pandemic situations caused by COVID-19.

Proposed framework for building robust and resilience global food supply chain in the post-COVID-19 pandemic

Source: Author(s): Author own construction

Firstly, COVID-19 has highlighted the crucial importance of food safety in the global food chain. Ensuring safe food from farm to fork is now a top priority for governments, food manufacturers, and consumers worldwide. Therefore a robust food safety system (e.g., processing, distribution and preparation activities, consumption, and delivery) and a strong food safety culture would safeguard public health and prevent future pandemics [ 93 ]. Secondly, COVID-19 disruptions in transportation, distribution, and consumption have led to increased food waste, resulting in significant losses in food supply chains. These relevancy have encouraged the implementation of Industry 4.0 technologies and practices, aimed at addressing the widely recognized issue of food waste and loss. Specifically, advanced technologies such as Internet of Things (IoT) platforms, BIG Data, artificial intelligence, and information and communication technologies (ICTs) can be leveraged to obtain up-to-date information, enhance communication between suppliers and buyers, and rationalise the distribution of food supply chain [ 90 ]. Thirdly, the COVID-19 pandemic has disrupted the food supply chain, raising concerns about the need for increased transparency, efficiency, and safety in the food industry. Consequently, there is a growing interest in employing blockchain technology to resolve these issues. Blockchain provides a secure and transparent way to monitor food products from the farm to the table, thereby reducing food waste and ensuring food safety and quality. With its potential to improve supply chain management and food safety, blockchain is anticipated to play a crucial role in the post-COVID-19 pandemic food industry [ 94 ]. Fourthly, the COVID-19 pandemic has resulted in an increase in pressure on natural resources and a decline in biodiversity. The post-COVID-19 pandemic presents an opportunity to construct more resilient and sustainable food systems that promote both human welfare and biodiversity conservation. By creating a secure and transparent record of food production and distribution, stakeholders will be able to better comprehend the environmental impact of food systems and work towards more sustainable practices. In addition, the use of blockchain technology in the food supply chain can help conserve biodiversity by encouraging sustainable agricultural practises and reducing food waste [ 95 ].

The findings of the global food supply chain's main themes of the post-COVID-19 pandemic emphasise food safety, the reduction of food waste, and the conservation of biodiversity. Tracking food products throughout the supply chain, blockchain technology has emerged as an effective instrument for enhancing food safety, quality, and transparency. It is essential to manage the food supply chain sustainably in order to safeguard public health, the environment, and social and economic development [ 96 ].

Another important issue relevant to COVID-19 food supply chain is the gap of strategic intervention in the study. Specifically, the role of the government and country policies in controlling the disruption and ensuring an effective food supply chain that is raised on lean strategic principles is to be considered extensively. For example, only the country's national policy for the food sector to re-design and re-shape their food supply chains before and after the COVID-19 resilience strategy and how it helps to build a smooth food supply chain that is capable of managing the further pandemics. However, the previous study on bibliometric analysis focused on food supply chain safety [ 17 ], and agri-food value chain, this study establishes the novel analysis of the prior study on the COVID-19 food supply chain.

In this study, a total of 2523 papers were published in the field of the COVID-19 food supply chain, and the evolution of the current state of trend during and after the COVID-19 pandemic was scientifically mapped. The COVID-19 food supply chain bibliometric mapping trend was conducted using Vosviewer software and thematic evolution trend analysis using R software to measure the most rising subjects topic. In bibliometric analysis, this study explored key information such as yearly publication trends, article sources and document contents, prolific authors, highly cited papers, most productive institutions, most productive countries, most productive source titles, top authors keyword, co-occurrence, co-authorship network, and country collaboration network. Next, this study highlighted the thematic evaluation, and thematic maps provide an opportunity in specific areas related to the COVID-19 food supply chain pre, during and post-pandemic, thus building subject knowledge importance and how its various aspects have been used before and post COVID-19 pandemic. Thus, this contribution to more empirical policy-related research is encouraged to robust the food supply chain and reduce the impact of the COVID-19 pandemic connected to food safety, agriculture production, resilience solution, mitigate supply chain disruptions, and improve sustainability as shown in Figs.  12 and 13 . Another important contribution, this study revealed specific research gaps in the present literature and proposes a scope for the specific research areas to fill these gaps. Finally, a co-occurrence analysis with the author’s keywords is conducted to stipulate the trend of research pre, before and post-COVID-19 pandemic.

One of the limitations of this study is considering Scopus online database which is the main source for bibliometric analysis. In this case, the selection of data sources may limit the search strategy. Future studies are encouraged for considering other sectors to know how the food supply chain of those sectors is impacted by the COVID-19 pandemic by linking articles from other data sources such as the Google Scholar and Web of Science.

As the pandemic has a massive impact on the world's food supply chain, the study is expected to provide new insight by evolving all the related documents published in this field with a systematic review method. Scholars and policymakers can use this research to know current developments in the food supply chain, and adopt various resilience strategies as discussed to mitigate the impact. However, the pandemic has a severe impact on the global food supply chain. The study is expected to provide more insights by compiling all relevant literature published in this domain using bibliometric analysis. Therefore, this study will be helpful for scholars and policymakers to understand what is happening in the food supply chain pre, during and post pandemic, and policymakers may apply different development strategies as explored to reduce the COVID-19 pandemic impact.

Availability of data and materials

All data presented in this manuscript are available on Scopus database using the search query highlighted in the “Methodology” section. Raw data are attached to the manuscript.

Teigiserova DA, Hamelin L, Thomsen M. Towards transparent valorization of food surplus, waste and loss: clarifying definitions, food waste hierarchy, and role in the circular economy. Sci Total Environ. 2020;706:136033. https://doi.org/10.1016/j.scitotenv.2019.136033 .

Article   CAS   PubMed   Google Scholar  

Din AU, Han H, Ariza-Montes A, Vega-Muñoz A, Raposo A, Mohapatra S. The impact of COVID-19 on the food supply chain and the role of E-commerce for food purchasing. Sustain. 2022. https://doi.org/10.3390/su14053074 .

Article   Google Scholar  

Fan S, Si W, Zhang Y. How to prevent a global food and nutrition security crisis under COVID-19? China Agric Econ Rev. 2020;12(3):471–80. https://doi.org/10.1108/CAER-04-2020-0065 .

Wang Y, Wang J, Wang X. COVID-19, supply chain disruption and China’s hog market: a dynamic analysis. China Agric Econ Rev. 2020;12(3):427–43. https://doi.org/10.1108/CAER-04-2020-0053 .

Article   CAS   Google Scholar  

Bourlakis M, Weightman PW. Food Supply Chain Management. Oxford: Blackwell Pub; 2004.

Google Scholar  

FAO. Policy responses to keep input markets flowing in times of COVID-19. 2020.

Stephens EC, Martin G, van Wijk M, Timsina J, Snow V. Editorial: impacts of COVID-19 on agricultural and food systems worldwide and on progress to the sustainable development goals. Agric Syst. 2020;183:102873. https://doi.org/10.1016/j.agsy.2020.102873 .

Article   PubMed   PubMed Central   Google Scholar  

Richards TJ, Rickard B. COVID-19 impact on fruit and vegetable markets. Can J Agric Econ. 2020;68(2):189–94. https://doi.org/10.1111/cjag.12231 .

ILO. COVID-19 and the impact on agriculture and food security. 2019. 2020.

FAO. Responding to the impact of the COVID-19 outbreak on food value chains through efficient logistics. 2020. https://doi.org/10.4060/ca8466en

Alonso E, Gregory J, Field F, Kirchain R. Material availability and the supply chain: risks, effects, and responses. Environ Sci Technol. 2007;41(19):6649–56. https://doi.org/10.1021/es070159c .

Devereux S, Béné C, Hoddinott J. Conceptualising COVID-19’s impacts on household food security. Food Secur. 2020;12(4):769–72. https://doi.org/10.1007/s12571-020-01085-0 .

Flynn D. CDC provides first guidance to a specific meat plant for combating COVID-19 among employees | Food Safety News. Food Saf News. 2020;2:7.

Almena A, Fryer PJ, Bakalis S, Lopez-Quiroga E. Centralized and distributed food manufacture: a modeling platform for technological, environmental and economic assessment at different production scales. Sustain Prod Consum. 2019;19(i):181–93. https://doi.org/10.1016/j.spc.2019.03.001 .

OECD. Food supply chains and Covid-19: impacts and policy lessons. Euro Choices. 2020;19(3):34–9. https://doi.org/10.1111/1746-692X.12297 .

Shen C, Wei M, Sheng Y. A bibliometric analysis of food safety governance research from 1999 to 2019. Food Sci Nutr. 2021;9(4):2316–34. https://doi.org/10.1002/fsn3.2220 .

Luo J, Leng S, Bai Y. Food supply chain safety research trends from 1997–2020: a bibliometric analysis. Front Public Heal. 2022. https://doi.org/10.3389/fpubh.2021.742980 .

Das NK, Roy A. COVID-19 and agri-food value chain: a systematic review and bibliometric mapping. J Agribus Dev Emerg Econ. 2021. https://doi.org/10.1108/JADEE-07-2021-0188 .

Ellis LA, Churruca K, Clay-Williams R, Pomare C, Austin EE, Long JC, et al. Patterns of resilience: a scoping review and bibliometric analysis of resilient health care. Saf Sci. 2019;118:241–57. https://doi.org/10.1016/j.ssci.2019.04.044 .

Danvila-del-Valle I, Estévez-Mendoza C, Lara FJ. Human resources training: a bibliometric analysis. J Bus Res. 2019;101(February):627–36. https://doi.org/10.1016/j.jbusres.2019.02.026 .

Bartolini M, Bottani E, Grosse EH. Green warehousing: Systematic literature review and bibliometric analysis. J Clean Prod. 2019;226:242–58. https://doi.org/10.1016/j.jclepro.2019.04.055 .

Boudry C, Baudouin C, Mouriaux F. International publication trends in dry eye disease research: a bibliometric analysis. Ocul Surf. 2018;16(1):173–9. https://doi.org/10.1016/j.jtos.2017.10.002 .

Article   PubMed   Google Scholar  

Mercuri F, Graciliano E, Kumata J, Yuji A, Amaral EB, Simões Vitule JR. Energy by microbial fuel cells: scientometric global synthesis and challenges. Renew Sustain Energy Rev. 2016;65:832–40.

Wang J, Zheng T, Wang Q, Xu B, Wang L. A bibliometric review of research trends on bioelectrochemical systems. Current Sci. 2015. https://doi.org/10.18520/cs/v109/i12/2204-2211 .

Hobbs JE. Food supply chains during the COVID-19 pandemic. Can J Agric Econ. 2020;68(2):171–6.

Barman A, Das R, De PK. Impact of COVID-19 in food supply chain: Disruptions and recovery strategy. Curr Res Behav Sci. 2021;2. https://doi.org/10.1016/j.crbeha.2021.100017 .

Khan SAR, Razzaq A, Yu Z, Shah A, Sharif A, Janjua L. Disruption in food supply chain and undernourishment challenges: an empirical study in the context of Asian countries. Socioecon Plann Sci. 2021. https://doi.org/10.1016/j.seps.2021.101033 .

Godrich SL, Kent K, Lo J. Food supply impacts and solutions associated with the COVID-19 pandemic : a regional Australian case study. Int J Environ Res Public Health. 2022. https://doi.org/10.3390/ijerph19074116 .

Azani M, Shaerpour M, Yazdani MA, Aghsami A, Jolai F. A novel scenario-based bi-objective optimization model for sustainable food supply chain during the COVID-19: a case study. Process Integr Optim Sustain. 2022. https://doi.org/10.1007/s41660-021-00203-5 .

Holland K. Canada’s food security during the COVID-19 pandemic. Sch Public Policy Publ. 2020;13(13):1–14.

FAO. Adjusting business models to sustain agri-food enterprises during COVID-19. Adjust Bus Model to Sustain agri-food Enterp Dur COVID-19. 2020. https://doi.org/10.4060/ca8996en .

Laborde D, Martin W, Swinnen J, Vos R. COVID-19 risks to global food security. Science. 2020;369(6503):500–2. https://doi.org/10.1126/science.abc4765 .

Nasereldin YA, Brenya R, Bassey AP, Ibrahim IE, Alnadari F, Nasiru MM, et al. Is the global food supply chain during the COVID-19 pandemic resilient? A review paper. Open J Bus Manag. 2021;09(01):184–95. https://doi.org/10.4236/ojbm.2021.91010 .

Aday S, Aday MS. Impact of COVID-19 on the food supply chain. Food Qual Saf. 2020;4(4):167–80. https://doi.org/10.1093/fqsafe/fyaa024 .

Jagt R, Van Houten S., Lestiboudois S. COVID-19 has broken the global food supply chain”, So Now What?. 2020. https://www2.deloitte.com/nl/nl/pages/consumer/articles/%0Afood-covid-19-reshaping-supply-chains.html .

Agarwal A, Durairajanayagam D, Tatagari S, Esteves SC, Harlev A. Bibliometrics: tracking research impact by selecting the appropriate metrics. Asian J Androl. 2016. https://doi.org/10.4103/1008-682X.171582 .

Aria M, Cuccurullo C. bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr. 2017;11(4):959–75. https://doi.org/10.1016/j.joi.2017.08.007 .

Bar-Ilan J. Informetrics at the beginning of the 21st century—a review. J Informetr. 2008;2(1):1–52.

Chang YW, Huang MH, Lin CW. Evolution of research subjects in library and information science based on keyword, bibliographical coupling, and co-citation analyses. Scientometrics. 2015;105(3):2071–87.

Mao G, Huang N, Chen L, Wang H. Research on biomass energy and environment from the past to the future: a bibliometric analysis. Sci Total Environ. 2018;635:1081–90. https://doi.org/10.1016/j.scitotenv.2018.04.173 .

Soosaraei M, Khasseh AA, Fakhar M, Hezarjaribi HZ. A decade bibliometric analysis of global research on leishmaniasis in web of science database. Ann Med Surg. 2018;26:30–7. https://doi.org/10.1016/j.amsu.2017.12.014 .

Barbosa MW. Uncovering research streams on agri-food supply chain management: a bibliometric study. Glob Food Sec. 2021;28:100517. https://doi.org/10.1016/j.gfs.2021.100517 .

Ittmann H. A bibliometric analysis of the journal of transport and supply chain management. J Transport Supply Chain Manag. 2021;2005:1–15.

Luo J, Ji C, Qiu C, Jia F. Agri-food supply chain management: Bibliometric and content analyses. Sustain. 2018. https://doi.org/10.3390/su10051573 .

Van Eck NJ, Waltman L. Bibliometric mapping of the computational intelligence field. Int J Uncertainty Fuzziness Knowlege-Based Syst. 2007;15(5):625–45.

Kumar B, Sharma A, Vatavwala S, Kumar P. Digital mediation in business-to-business marketing: a bibliometric analysis. Ind Mark Manag. 2019. https://doi.org/10.1016/j.indmarman.2019.10.002 .

Monasterolo I, Pasqualino R, Janetos AC, Jones A. Sustainable and inclusive food systems through the lenses of a complex system thinking approach—a bibliometric review. Agriculture. 2016. https://doi.org/10.3390/agriculture6030044 .

Gupta BM, Bhattacharya S. A bibliometric approach towards mapping the dynamics of science and technology. DESIDOC Bull Inform Technol. 2004. https://doi.org/10.14429/dbit.24.1.3616 .

Ding Y, Chowdhury GG, Foo S. Bibliometric cartography of information retrieval research by using co-word analysis. Inf Process Manag. 2001;37(6):817–42.

Bonilla CA, Merigó JM, Torres-Abad C. Economics in Latin America: a bibliometric analysis. Scientometrics. 2015;105(2):1239–52.

van Eck NJ, Waltman L. Software survey: VOS viewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523–38. https://doi.org/10.1007/s11192-009-0146-3 .

Charvet F. The intellectual structure of supply chain management: A bibliometric approach. J Bus Logist. 2008. https://doi.org/10.1002/j.2158-1592.2008.tb00068.x .

Tuyon J, Peter O, Aidi O, Chia A, Huang H. Sustainable financial services: reflection and future perspectives. J Financ Serv Mark. 2022. https://doi.org/10.1057/s41264-022-00187-4 .

Aria M, Misuraca M, Spano M. Mapping the evolution of social research and data science on 30 years of social indicators research. Soc Indic Res. 2020;149(3):803–31. https://doi.org/10.1007/s11205-020-02281-3 .

Heldens W, Burmeister C, Kanani-Sühring F, Maronga B, Pavlik D, Sühring M, et al. Geospatial input data for the PALM model system 6.0: model requirements, data sources and processing. Geosci Model Dev. 2020;13(11):5833–73.

Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, scopus, web of science, and google scholar: strengths and weaknesses. FASEB J. 2008;22(2):338–42.

Chen J. Perspective Innovation Research of Global. Acad Manag Annu Meet. 2010.

Noor S, Guo Y, Shah SHH, Saqib Nawaz M, Butt AS. Bibliometric analysis of social media as a platform for knowledge management. Int J Knowl Manag. 2020;16(3):33–51.

Fahimnia B, Tang CS, Davarzani H, Sarkis J. Quantitative models for managing supply chain risks: a review. Eur J Oper Res. 2015;247(1):1–15. https://doi.org/10.1016/j.ejor.2015.04.034 .

Liang TP, Liu YH. Research landscape of business intelligence and big data analytics: a bibliometrics study. Expert Syst Appl. 2018;111:2–10. https://doi.org/10.1016/j.eswa.2018.05.018 .

Vošner HB, Kokol P, Bobek S, Železnik D, Završnik J. A bibliometric retrospective of the journal computers in human behavior (1991–2015). Comput Human Behav. 2016;65:46–58.

Llanos-Herrera GR, Merigo JM. Overview of brand personality research with bibliometric indicators. Kybernetes. 2019;48(3):546–69.

van Eck NJ, Waltman L. VOSviewer Manual—version 1.6.8. 2018. http://www.vosviewer.com/documentation/Manual_VOSviewer_1.5.4.pdf

Parfitt J, Barthel M, Macnaughton S, Parfitt J. Food waste within food supply chains: quantification and potential for change to 2050. Phil Trans R Soc B. 2016;365(1554):3065–81.

Renting H, Marsden TK, Banks J. Understanding alternative food networks: exploring the role of short food supply chains in rural development. Environ Plan A. 2003;35(3):393–411.

Tian F. An agri-food supply chain traceability system for China based on RFID & blockchain technology. 2016 13th Int Conf Serv Syst Serv Manag ICSSSM 2016. 2016.

Kummu M, De Moel H, Porkka M, Siebert S, Varis O, Ward PJ. Lost food, wasted resources: global food supply chain losses and their impacts on freshwater, cropland, and fertiliser use. Sci Total Environ. 2012;438:477–89. https://doi.org/10.1016/j.scitotenv.2012.08.092 .

Aung MM, Chang YS. Traceability in a food supply chain : Safety and quality perspectives. Food Control. 2014;39:172–84. https://doi.org/10.1016/j.foodcont.2013.11.007 .

Marsden T, Banks J, Bristow G. Food supply chain approaches: exploring their role in rural development. Sociol Ruralis. 2000;40(4):424–38. https://doi.org/10.1111/1467-9523.00158 .

Maloni MJ, Brown ME. Corporate social responsibility in the supply chain : an application in the food industry. 2006;35–52. https://doi.org/10.1007/s10551-006-9038-0 .

Ahumada O, Villalobos JR. Application of planning models in the agri-food supply chain: a review. Eur J Oper Res. 2009;196(1):1–20. https://doi.org/10.1016/j.ejor.2008.02.014 .

Beske P, Land A, Seuring S. Sustainable supply chain management practices and dynamic capabilities in the food industry: a critical analysis of the literature. Intern J Prod Econ. 2014;152:131–43. https://doi.org/10.1016/j.ijpe.2013.12.026 .

Verkerk R, Schreiner M, Krumbein A, Ciska E, Holst B. Glucosinolates in Brassica vegetables: the influence of the food supply chain on intake, bioavailability and human health. Mol Nutr Food Res. 2009. https://doi.org/10.1002/mnfr.200800065 .

Tian F. A Supply Chain Traceability System for Food Safety Based on HACCP, Blockchain & Internet of Things. In: International Conference on Service Systems and Service Management. 2012. https://doi.org/10.1109/ICSSSM.2017.7996119 .

Govindan K, Jafarian A, Khodaverdi R, Devika K. Two-echelon multiple-vehicle location—routing problem with time windows for optimization of sustainable supply chain network of perishable food. Int J Prod Econ. 2014;2009:1–20. https://doi.org/10.1016/j.ijpe.2013.12.028 .

Rong A, Akkerman R, Grunow M. An optimization approach for managing fresh food quality throughout the supply chain. Intern J Prod Econ. 2011;131(1):421–9. https://doi.org/10.1016/j.ijpe.2009.11.026 .

Caro MP, Ali MS, Vecchio M, Giaffreda R. Blockchain-based traceability in Agri-food supply chain management: a practical implementation 2018 IoT vert top summit Agric—Tuscany, IOT Tuscany 2018. IEEE: Piscataway; 2018.

Bosona T, Gebresenbet G. Food traceability as an integral part of logistics management in food and agricultural supply chain. Food Control. 2013;33(1):32–48. https://doi.org/10.1016/j.foodcont.2013.02.004 .

Roth AV, Tsay AA, Gray JV. Unraveling the food supply chain: Strategic insights from China and the 2007 recalls. J Supply Chain Manag. 2007. https://doi.org/10.1111/j.1745-493X.2008.00043.x .

Kamilaris A, Fonts A, Prenafeta-bold FX. The rise of blockchain technology in agriculture and food supply chains. Trends Food Sci Technol. 2019;91(July):640–52.

Doukidis GI, Matopoulos A, Vlachopoulou M, Manthou V, Manos B. A conceptual framework for supply chain collaboration: Empirical evidence from the agri-food industry. Supply Chain Manag An Int J. 2007;12(3):177–86.

Grimm JH, Hofstetter JS, Sarkis J. Critical factors for sub-supplier management: a sustainable food supply chains perspective. Int J Prod Econ. 2014. https://doi.org/10.1016/j.ijpe.2013.12.011 .

Demiroz F, Haase TW. The concept of resilience : a bibliometric analysis of the emergency and disaster management literature. Local Gov Stud. 2018;00(00):1–20. https://doi.org/10.1080/03003930.2018.1541796 .

Depaolo BCA, Wilkinson K. Get Your Head into the Clouds : Using Word Clouds for Analyzing Qualitative Assessment Data. TechTrends. 2014;58(3):38-44. https://doi.org/10.1007/s11528-014-0750-9 .

van Eck. Methodological Advances in Bibliometric Mapping of Science. 2011.

Reardon T, Mishra A, Nuthalapati CSR, Bellemare MF, Zilberman D. Covid-19’s disruption of India’s transformed food supply chains. Econ Polit Wkly. 2020;55(18):18–22.

Abhishek Srivastava M. Mapping the influence of influencer marketing: a bibliometric analysis. Mark Intell Plan. 2021;39(7):979–1003. https://doi.org/10.1108/MIP-03-2021-0085 .

Michel-villarreal R. Towards sustainable and resilient short food supply chains: a focus on sustainability practices and resilience capabilities using case study. Br Food J. 2022;(2020). https://doi.org/10.1108/BFJ-09-2021-1060 .

Bygballe LE, Dubois A, Jahre M. The importance of resource interaction in strategies for managing supply chain disruptions. J Bus Res. 2023;154:113333. https://doi.org/10.1016/j.jbusres.2022.113333 .

Galanakis CM. The Food Systems in the Era of the Coronavirus (COVID-19) Pandemic Crisis. Foods. 2020;9(523):1–10. https://doi.org/10.3390/foods9040523 .

Prosser L, Lane ET, Jones R. Collaboration for innovative routes to market : COVID-19 and the food system. Agric Syst. 2021;188:103038. https://doi.org/10.1016/j.agsy.2020.103038 .

Aday S, Aday MS. Impact of COVID-19 on the food supply chain. Food Qual Saf. 2020;4(4):167–180. https://doi.org/10.1093/fqsafe/fyaa024 .

European Union. Recipe for Change: An Agenda for Sustainable Food System for a Healthy Europe.; 2018. https://doi.org/10.2777/84024 .

Kamble SS, Gunasekaran A, Subramanian N. Blockchain technology ’ s impact on supply chain integration and sustainable supply chain performance : evidence from the automotive industry. Ann Oper Res. 2021. https://doi.org/10.1007/s10479-021-04129-6 .

FAO. Sustainable Food Systems Concept and Framework.; 2018. https://www.fao.org/3/ca2079en/CA2079EN.pdf .

Kafi MA, Adam S, Zainuddin N, Shahron S, Abualrejal H, Mohamad MM. Essential of RFID Technology in Supply Chain Management : A review on Digital Perspective. In: International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE). IEEE; 2022. https://doi.org/10.1109/ITSS-IoE56359.2022.9990933 .

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Acknowledgements

The authors sincerely thank Universiti Utara Malaysia (UUM) for allowing us to access the most recent information from Scopus data sources.

The authors would like to thank the Perusahaan Saudee (Ref Kod S/O: 21069) Sdn Bhd for giving us support to publish this article.

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MAK; started the idea: developed the methodology: data extraction: data analysis: visualization and data interpretation: manuscript written: editing and submitted: NZ; AMS; SAS; and MRR; Guided and provided insightful thought to improve the manuscript. AA; reviewed and provided intellectual support in the writing of this manuscript. All authors read and approved the final manuscript.

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Additional file 1: table s1..

Overview of the bibliographic information’s for the food supply chain domain recovered from Scopus database. Table S2. Trends in yearly publications. Table S3. Most productive authors that published 10 and more publications in the food supply chain. Table S4. Top twenty highly cited documents published in the COVID-19 food supply chain domain. Table S5. Most productive source title. Table S6. Most productive countries published twenty-five and more documents. Table S7. Top ten most prolific institutions. Table S8. Top frequent author’s keywords (BEFORE COVID-19). Table S9. Top author’s keywords (AFTER COVID-19). Figure S1. Flow diagram of article searching strategy of food supply chain documents. Figure S2. Framework for bibliometric analysis. Figure S3. The trend of publications per year of COVID - 19 food supply chain. Figure S4. Word cloud of top author’s keywords (BEFORE COVID-19). Figure S5. Conceptual structure map based on author’s keywords (BEFORE COVID-19). Figure S6. Word cloud of author’s keywords (AFTER COVID-19). Figure S7. Conceptual structure map based on author’s keywords (AFTER COVID-19). Figure S8. Co-authorship network among productive authors. Figure S9. Co-authorship collaboration network among countries. Figure S10. Author’s keywords co-occurrence analysis. Figure S11. Thematic evaluation based on authors’ keywords (Pre and Post COVID-19). Figure S12. The strategic thematic map of the author’s keywords (BEFORE COVID-19). Figure S13. The strategic thematic map of the author’s keywords (Post COVID-19). Figure S14. Proposed framework for building robust and resilience global food supply chain in the post-COVID-19 pandemic.

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Kafi, A., Zainuddin, N., Saifudin, A. et al. Meta-analysis of food supply chain: pre, during and post COVID-19 pandemic. Agric & Food Secur 12 , 27 (2023). https://doi.org/10.1186/s40066-023-00425-5

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• Published: 09 October 2018

Review of the sustainability of food systems and transition using the Internet of Food

• Nicholas M. Holden 1 ,
• Eoin P. White 2 ,
• Matthew. C. Lange   ORCID: orcid.org/0000-0002-6148-7962 3 &
• Thomas L. Oldfield 1  

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Environmental impact

Many current food systems are unsustainable because they cause significant resource depletion and unacceptable environmental impacts. This problem is so severe, it can be argued that the food eaten today is equivalent to a fossil resource. The transition to sustainable food systems will require many changes but of particular importance will be the harnessing of internet technology, in the form of an ‘Internet of Food’, which offers the chance to use global resources more efficiently, to stimulate rural livelihoods, to develop systems for resilience and to facilitate responsible governance by means of computation, communication, education and trade without limits of knowledge and access. A brief analysis of the evidence of resource depletion and environmental impact associated with food production and an outline of the limitations of tools like life cycle assessment, which are used to quantify the impact of food products, indicates that the ability to combine data across the whole system from farm to human will be required in order to design sustainable food systems. Developing an Internet of Food, as a precompetitive platform on which business models can be built, much like the internet as we currently know it, will require agreed vocabularies and ontologies to be able to reason and compute across the vast amounts of data that are becoming available. The ability to compute over large amounts of data will change the way the food system is analysed and understood and will permit a transition to sustainable food systems.

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Introduction.

The food we eat today is unsustainable for two reasons: the food system causes unacceptable environmental impacts and it is depleting non-renewable resources. Our food can be regarded as ‘fossil food’ because its production relies on fossil fuel, non-renewable mineral resources, depletion of groundwater reserves and excessive soil loss. The idea of sustainable food systems is at the heart of global efforts to manage and regulate human food supply. 1 The sustainable development goals focus on a number of critical global issues, but Goal 2 (‘end hunger, achieve food security and improved nutrition and promote sustainable agriculture’), Goal 12 (‘ensure sustainable consumption and production patterns’) and Goal 13 (‘take urgent action to combat climate change and its impacts’) are intimately related to the need to transition global food systems from fossil to sustainable. To understand how to meet the challenge presented by these goals, it is necessary to consider what is meant by ‘sustainable’ in the context of a food system. In 1989, the Food and Agriculture Organisation (FAO) council defined sustainable development as ‘the management and conservation of the natural resource base, and the orientation of technological and institutional change in such a manner as to ensure the attainment and continued satisfaction of human needs for present and future generations. Such sustainable development (in the agriculture, forestry and fisheries sectors) conserves land, water, plant and animal genetic resources, is environmentally non-degrading, technically appropriate, economically viable and socially acceptable’. 2 The important ideas in this definition are working within the planetary boundary (‘the natural resource base’), having a future-proof system (‘continued satisfaction’, ‘present and future generations’), limiting impacts to those manageable by the buffering capacity of the planet (‘environmentally non-degrading’), considering the financial needs of business stakeholders (‘economically viable’) and compatible with local needs and customs (‘socially acceptable’).

Five principles have been identified to support a common vision for sustainable agriculture and food. 3 These are: (1) resource efficiency; (2) action to conserve, protect and enhance natural resources; (3) rural livelihood protection and social well-being; (4) enhanced resilience of people, communities and ecosystems; and (5) responsible governance. The aim of this paper is to outline the case for why food systems are not sustainable and to define the case for using technology, specifically internet technologies (hardware and software combined to make the ‘Internet of Food’) to enable the transition of the food system from fossil to sustainable. Increasing population, increasing consumption, a billion malnourished people and agriculture that is concurrently degrading land, water, biodiversity and climate on a global scale 4 combine to indicate that the fossil food systems we currently rely on are not fit-for-purpose. It is suggested that halting agricultural expansion, closing yield gaps, increasing efficiency, changing diets and reducing waste could lead to a doubling of food production with reduced environmental impacts of agriculture. 4 To achieve these changes, it is going to be necessary to harness internet technology, in the form of an ‘Internet of Food’, which offers the chance to use global resources more efficiently, to stimulate rural livelihoods, to develop systems for resilience and to facilitate responsible governance by means of computation, communication, education and trade without limits of knowledge and access.

The concept of ‘Internet of Food’ first appeared in peer-reviewed literature in 2011 (based on a search of scopus.com using ‘Internet of Food’ as the search term). It was described by the idea of food items having an ‘IP identify’, which raised the question of how this might influence our eating habits. 5 Their focus was very much on how the technology could influence food choices and predicted that by 2020 it would be possible to monitor and control food objects remotely through the Internet. It is this technological control of the food system that has real potential to help societal stakeholders (consumers, retailers, processors, producers, shareholders, landowners, indigenous peoples and so on) to engage in the transition of our food system from being fossil to sustainable. The ubiquitous physical tagging and sensing of mass and energy flow in the food system linked to a formal semantic web will allow computation over the whole system to answer questions such as: What was the resource depletion of this product? What is the social impact of eating this product? What food safety procedures have been employed for this product? What and where has wealth been created by the value chain of this product? When these questions can be answered for specific instances of food product types and predicted for new products, then it will be possible to determine whether a specific food system is sustainable or not. The stakeholders demanding answers to these questions are likely to be governance and policy makers and consumers. When these questions can be answered, it will be possible to plan how to manage the evolution of the fundamental life support system (food) from fossil to sustainable in order to support a growing global population.

Current food systems

To understand the need for a systematic transformation of the food system, it is necessary to detail exactly why it is unsustainable. An overwhelming case can be made for the environmental dimension of the system, but there are also social and economic issues as well. This paper will focus the environmental case (resource depletion and adverse environmental impact that relate to the ‘continued satisfaction’ and ‘environmentally non-degrading’ criteria for sustainable food systems), but similar cases can be made for important social and economic issues as well.

Resource depletion

The resource depletion case can be made with respect to energy, nutrients, water, soil and land. Each will be summarised in turn. To date, the agri-food system has converted non-renewable fossil fuel energy into food by enabling mechanisation, amplified fertiliser production, improved food processing and safe global transportation. 6 According to FAO, 7 the agri-food sector accounts for around 30% of the world’s total energy consumption, with Europe alone accounting for 17% of gross energy consumption in 2013. 8 Agriculture, including crop cultivation and animal rearing, is the most energy-intense phase of the food system, accounting for nearly one third of the total energy consumed in the food production chain. 9 To date, renewable energy has had limited penetration of the agri-food sector with fossil fuels accounting for almost 79% of the energy consumed by the food sector. 8 From an energy perspective, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement) due to its reliance on fossil energy sources.

By the end of the 20th Century, it was estimated that US-produced ammonia represented 32% of fertiliser nitrogen (N) demand, which was produced by extracting N from the atmosphere as ammonia by a process using hydrogen from natural gas (fossil fuel). 9 The vast majority of N fertilisers consumed today are still created using fossil fuels and cannot be regarded as sustainable until such times as new technological approaches emerge, which are currently in their infancy. 10 , 11 A review of mineral fertiliser reserves concluded that potash reserves (the source of most potassium (K) fertilisers) are of great concern and that it is time to start evaluating other sources of K for agriculture 12 but others concluded that ‘modern agriculture is currently relying on a non-renewable resource and future phosphate rock is likely to yield lower quality P at a higher price’. 13 If significant physical and institutional changes are not made to the way we currently use and source P, agricultural yields will be severely compromised in the future. Estimates for when world peak P will be reached range from 2027 14 to 2033. 13 Variations in estimations of when peak P will occur are due the constant changing of reserve levels. 12 The ‘power imbalance’ where just three countries controlling >85% of the known global phosphorus reserves, 15 a concentration of power far greater than that of crude oil, is also of concern, and it has been concluded that the combined impact increasing demand, dwindling reserves and geopolitical constraints could result in reduced production and supply of chemical P fertilisers and increased global P price. 16 It is clear that over time horizons of around 50 years the agri-food system is going to face a major nutrient crisis unless reliance of fossil mineral resources is significantly reduced and ultimately eliminated. From a nutrient management point-of-view, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement) due to its reliance on fossil mineral resources.

Modern food production is reliant on irrigation to a great extent, which according to the UN water programme, accounts for 70% of freshwater withdrawals worldwide. 17 Excessive removal of groundwater for irrigation is leading to rapid depletion of aquifers in key food-producing regions, such as North-Western India, the North China Plain, Central USA and California. 18 Aquifers replenish so slowly that they are effectively a non-renewable resource. The depletion of these large freshwater stocks threatens food production and security locally and globally via international food trade. Non-sustainable groundwater abstraction contributed to 20% of global gross irrigation water demand in the year 2000 with this demand having tripled over the period 1960–2000. 19 For many countries, irrigation is sustained by non-renewable groundwater, and it has been highlighted that ‘a vast majority of the world’s population lives in countries sourcing nearly all their staple crop imports from partners who deplete groundwater to produce these crops’. 18 Countries who both produce and import food irrigated from rapidly depleting aquifers are particularly at risk, such as USA, Mexico, Iran and China. It has been estimated that India, soon to be the most populous country in the world, will be unable to meet water requirements within 300 years and emerging pressures may reduce this time horizon considerably. 20 Given the interaction of water supply with energy, this situation may become even worse. For example, in California, 20% of electricity production is used for moving and pumping water for agriculture, 21 and as water becomes more difficult to access, the energy demand will increase. From a water management point-of-view, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement) due to its reliance on non-renewable water resources.

Over 20 years ago, it was estimated that around one third of the world’s agricultural land had been lost to erosion and the rate of loss was about 10 Mha/year 22 Calculations suggest that soil erosion rates under ploughed cultivation are one to two orders of magnitude greater than soil production rates. 23 This rate of soil loss is not compatible with the ‘continued satisfaction’ requirement for a sustainable food system. It is also linked with other environmental impacts, such as loss of carbon, gaseous emissions, non-point source pollution and sedimentation of waterways, 24 therefore it is not compatible with the ‘environmentally non-degrading’ criteria as well. Given projections for expansion of dryland areas to around 50% of total land surface, with 78% of dryland expansion in areas supporting 50% of population growth in the coming decades, 25 the control of soil erosion and its related impacts is going to be a major requirement for sustainable food systems. From a soil management perspective, the food system can be regarded as unsustainable (cannot meet the ‘continued satisfaction’ requirement).

Having considered the energy, nutrient, water, soil and land requirements for food production, it must be concluded that the food system is unsustainable and needs to change because the natural resource base, future satisfaction and environmentally non-degrading requirements cannot be met. It is reasonable to describe food as ‘fossil food’ because of the reliance of non-renewable (and rapidly depleting) resources to supply much of the world’s population. A complete transformation of the food system is required, one that can perhaps be best driven by harnessing appropriate technology to monitor, control and regulate the different types of food system by unleashing the potential benefits of being able to compute over the vast amounts of data that can be obtained from the activities along the food value chain.

Modern industrial agriculture was made possible through land clearing and habitat disruption. Some recognised consequences of this were fragmentation and loss of biodiversity, significant greenhouse gas (GHG) emissions from land clearing and adverse impact on marine and freshwater ecosystems. 26 An estimate suggests that the global food system, from fertiliser manufacture to food storage and packaging, is responsible for up to one third of all human-caused GHG emissions. 27 Using data from 2005, 2007 and 2008, agricultural production is also estimated to be responsible for a significant share of GHG emissions from the food system, releasing ~12,000 Mt CO 2 e/year representing about 86% of all food-related anthropogenic GHG emissions, followed by fertiliser manufacture at ~575 Mt CO 2 e/year and refrigeration at ~490 Mt CO 2 e/year. 28 The impacts of such emissions are already being felt 29 including negative feedbacks on crop yield and health. Reducing this impact will be critical to transitioning from unsustainable fossil food to a sustainable future-proof food system. 28 , 30

The eutrophication of surface waters has become an endemic global problem. 31 From the 1950s to the 1990s, agriculture was associated with a 6.87-fold increase in nitrogen fertilisation, a 3.48-fold increase in phosphorus fertilisation, a 1.68-fold increase in the amount of irrigated cropland and a 1.1-fold increase in land in cultivation. 26 Agricultural production has been identified as a major underlying and persistent cause of eutrophication in many catchments around the world 32 , 33 Nutrient loadings from agriculture are a major driver of water quality deterioration, but it is unclear what level of on-farm control is necessary to achieve water quality improvements. 31 Smart agriculture and precision farming will drive improvement by increasing resource use efficiency and by harnessing technology to determine current conditions, future weather conditions and the correct intervention. 34 , 35

Similar cases can be made for acidification, 36 biodiversity, 37 ecosystem toxicity 38 and other environmental impacts. 39 Taking just the limited number of examples presented above, it is clear that the ‘environmentally non-degrading’ requirement for a sustainable food system is not being met by current food supply systems and a radical change is needed. From an environmental perspective (resource depletion and adverse impact), it can be concluded that food systems are not sustainable (in general), and if we work from a strong sustainability perspective of working within planetary boundaries, 40 they cannot become sustainable until this adverse situation is rectified.

Life cycle thinking methods and the need for an Internet of Food

Life cycle thinking is increasingly being used to assess food system sustainability. 41 It is an approach used to assess products, processes or services in terms of their place in the world, the full life cycle that is required for them to serve human society and environmental, social and economic consequences of that service. The method has been recognised as the leading approach for including sustainability in decision-making in the United States of America, 42 Europe 43 and elsewhere in the world. The quantitative tool used to implement life cycle thinking is life cycle assessment (LCA), which is formalised by international standard (ISO 14040/14044) 44 and has been widely used to assess food production systems. 45 LCA is one of the most important methodologies used to assess the impact (pollution and resource depletion) of the food system by using mass and energy flow accounting to model the system and agreed scientific models to calculate resource depletion and specific types of environmental impacts.

It has been suggested that LCA can lead to practitioners focus on the ‘eco-efficiency’ of inherently unsustainable products, and this can lead to increased consumption, because of the LCA paradigm of reducing negatives rather than increasing positives 46 The cradle-to-cradle (C2C) concept tends to focus more on linking resource consumption and waste creation with sustainability status rather than minimisation of specific impacts. One conclusion is that the best attributes of both approaches should be harnessed. 46 All such methods (e.g. LCA, C2C) depend on being able to collect sufficient data to characterise a system of interest or the use of publicly funded or commercial databases when site-specific data are not available. It was noted that ‘the practicality of adopting LCA to support decision-making can be limited by the generic nature of the assessment and the resource-intensive nature of data collection and life cycle inventory modelling’, 47 which is the key limitation for developing tools to facilitate the transition from fossil to sustainable food. The need to share data between stakeholders in increasingly important for the creation of useful information about the food system.

A number of issues associated with using LCA to better understand and manage food systems have been noted, 41 including (i) the variability of food production, supply chain and consumption globally; (ii) uncertainty related to the specification of data 48 and the system; 49 (iii) identifying the boundary between technosphere and ecosphere because agriculture relies on exploiting the ecosphere; 50 (iv) correctly identifying the real function 51 of the food system in order to select the most useful functional unit; (v) the multi-functionality of the system; (vi) capturing or modelling inventory data (which requires cooperation between stakeholders for food system applications); (vii) the geo-temporal specificity of background data from LCA databases; (viii) capturing the role of different stakeholders (e.g. consumers, government, industry); (ix) the role of diet choices and (x) handling ‘waste’. These issues are seen in the lack of comparability of LCA studies of the same type of product. 52 Furthermore, the scope of LCA as a global tool to quantify environmental impacts over the whole life cycle creates limitations. 53 LCA by its nature, focusses on the global scale and on steady-state, linear homogenous modelling, making it ‘very difficult to include varying spatial and temporal characteristics and nonlinear characteristics of large numbers of processes that occur all over the world’. 53 There are inherent limitations of inventory because of loss of spatial, temporal, dose–response and threshold information, which reduced the accuracy of impact assessments. 54 The ‘Internet of Food’ would transform our understanding of the food system and how they are modelled using LCA, provided data sharing is possible. Of the issues affecting food LCA, 41 most could be directly addressed by the ability to collect data and compute across the whole food chain: variability, uncertainty, multi-functionality, inventory data, databases, stakeholder influence, diet and waste, and the other two, boundary and function, could probably be better understood based on discernible activity. The examples of data mining of U.S. Environmental Protection Agency (EPA) data sets, 47 potential for avoiding excessive simplification 55 and use of big data in industrial ecology 56 indicate that this is the way forward.

Internet of Food: an enabling technology for the transition from fossil to sustainable

The deployment of sensor networks in the food system have historically been stage-specific and typically designed for monitoring and decision-making at a specific site and time, despite the potential for system integration having been recognised more than a decade ago. 57 Many sensors have been developed that could be used for the food chain, for example, for soil monitoring, for precision agriculture purposes, 58 for post-harvest storage monitoring, 59 for process control, 60 for retail logistics monitoring 61 and in some cases for domestic use. 62 A key requirement to create an ‘Internet of Food’ will be to make the data from these sensors interoperable and to be able to compute across the data set they create. A notable limitation is lack of integration caused by the current mix of open and closed data, communications, hardware standards and a lack of willingness to share data between stakeholders. It has been noted that an ‘…ontology-driven architecture for developing hybrid systems [that] consists of various entities including software components, hardware components (sensors, actuators and controllers), datastores (knowledge base, raw data, metadata), biological elements (plants[or animals]) and environmental context…’ 63 would permit the development of precision agriculture applications, and by logical extension this is required to utilise information across the whole food system (i.e. the Internet of Food). The proposal here is that the ‘hybrid-system’ needs to be extended to cover the whole food system, thus permitting production, process, logistics, retail, purchasing, consumption, nutrition and health outcomes to be integrated through information and computation. Where it is not possible to integrate data of the whole system that delivers a product, it will be very difficult to use Internet of Food for best advantage because its strength is determined by the data available.

A critical requirement will be the development of related ontologies. An ontology is the formal naming of concepts (e.g. types, properties, inter-relationships) within a domain and it is used to describe or infer properties of that domain. In order to be able to draw upon a range of data sources (sensors) and databases (knowledge silos), it is necessary to label data with unique identifiers that permit computers to reason with or compute over those data sets. This is where the real value of Internet of Things, and more specifically Internet of Food lies. To achieve the paradigm shift from fossil food to sustainable food systems, such a shift is needed, facilitated by the ability to reason with such data. As noted, 63 an ontology-driven architecture is needed to enable the ‘Internet of Food’. Ecologists have recognised the importance of big data in ecological research 64 in order to address major scientific and societal issues, and to answer the major question facing food (how to achieve a sustainable food system?), an agreed vocabulary and language structure (ontology) is needed. To take simple examples, the word ‘buttermilk’ originally referred to liquid left after churning butter is now also used to describe a fermented or cultured milk drink, so until the language describing these two concepts is standardised it is not possible to compute from diverse data sets within the domain of dairy processing, never mind across domains, where words such as slurry, matrix and texture all mean very different things depending on context. A noted rapid growth of Internet of Things requires standardisation to lower the entry barriers for the new services, to improve interoperability of systems and to allow better services performance. 65 They noted that this is particularly important for security, communication and identification where interoperability, and particularly semantic interoperability, will be critical. It has been recognised that a proliferation of ad hoc coded data systems will be an impediment to developing data-centric systems that can transform farming, 66 so sharing of data, agreement of standards and stakeholder cooperation will be required to achieve food systems transformation.

Food ontologies can be used with the specific aim of identifying gaps and for purposes beyond the initial, relatively simple applications, such as recognising foods, 67 with a contextual focus on diet, food selection, health and wellbeing being possible, 68 which is a critical component of a sustainable food system, and just as important are the social, economic and environmental impacts and benefits. There are untold opportunities to develop specific services targeted in these areas as well as the potential for integration, with tools such as life cycle sustainability assessment to evaluate the true sustainability of specific food products, meal combinations, whole diets and food systems. These ideas have been evaluated in the context of mining U.S. EPA data for assessing chemical manufacturing, 47 which identified that automating data access was a challenge because the data are incompatible with semantic queries. Data need to be described using ontologies to relate those data that need to be linked and to introduce LCA concepts to the descriptions. A framework for integrating ‘big data’ with LCA has been suggested 69 and it was also noted that development of semantic web standards for ecological data have greatly enhanced interoperability in that domain. 70 The same is required for the food system. It has been suggested that when food (and water) domain descriptors have been developed, this will enable ‘IT support [for] improved production, distribution and sales of foodstuffs [and water]’, 71 but the development of the domain models for the food chain is perhaps not a task for commerce or industry, rather for public, international research.

The opportunities that will be created by the Internet of Food are immense. One important shift will be from a descriptive, inferential approach to analysing food systems to a ‘big data’ approach. 68 ‘Big data’ can be characterised in terms of volume (data sets too large for conventional database management), velocity (acquiring, understanding and interpreting data in real time) and variety (the vast array of sources and types of data beyond the conventional rows and columns of numbers describing transactions). 72 Examples have already emerged where ‘big data’ has been used to provide data useful for LCA including agricultural resource survey 73 and resource use and emissions associated with U.S. electric power generation. 74 It is worth pointing out that much of the data relied on for LCA studies is drawn from commonly used databases (e.g. EcoInvent, ELCD, NREL) and are reliant on ‘small data’ and limited observations, which has resulted in reported error (multiple orders of magnitude), 47 while ‘big data’ offers a means to answering questions about environmental impact or food safety that simply cannot be contemplated in the context of controlled experiments. 71

Authors have considered ‘big data’ and ‘internet of things’ in the context of specific parts of the food chain. For example, ‘big data’ in ‘smart farming’ (i.e. the production stage of the food system) is now being used to make predictive insights about farm operations, to support operational decisions, to redesign business processes and to change business models. 75 To leverage this value at the farm level required extension along the food chain beyond the farm, but two scenarios are emerging: closed proprietary systems and open collaborative systems, 74 such as Food Industry Intelligence Network, 76 Food Innovation Network 77 and European Institute of Innovation & Technology (EIT) Food. 78 Priority should be given to development of data and applications infrastructure and at the same time to organisational issues concerning governance and business models for data sharing. 74 In the context of circular economy (i.e. the end of life, non-consumed part of the food system), it was found that, despite the concept of circular economy being discussed for decades, it has not become an adopted business model. 79 An analysis of literature from 2006 to 2015 found only 70 publications at the intersection of circular economy and ‘big data’/‘internet of things’, but nearly half (34) had been published in 2015. 75 It was suggested that technology encompassed by ‘big data’ and ‘internet of things’ is what is needed to enable such change, 75 which is the same argument being put forward here for the Internet of Food in the context of sustainable food systems. Two implications of relevance for the Internet of Food are: there is a gap between scientific research and corporate initiatives, which needs to be closed, and the search of literature was limited by the keywords available, and more specifically the lack of structured taxonomy to describe the circular economy. It is reasonable to conclude that if these ideas are relevant to one small component of the Internet of Food, then they are probably relevant to the concept as a whole.

These two recent reviews highlight the importance of developing the Internet of Food as a precompetitive platform on which business models can be built, much like the internet as we currently know it, and to achieve this we need to define agreed vocabularies and ontologies to be able to reason and compute across the vast amounts of data that are and will be available in the future. The ability to compute over large amounts of data will change the way the food system is analysed and understood. Biological scientists have noted how important data curation is, because as curated data become available the way science is conducted changes. 80 A key requirement of data curation is the connection of data from different sources in a human- and machine-comprehensible way. Another key change is the processing of multiple sources of complex data (‘big data’) using inference programs. While this might lead to new ways of conducting experimental (hypothesis driven) research, it is also unlocking the door to data-driven research, i.e. extracting new knowledge and understanding from data without experimentation or preconceived ideas, and providing new management approaches based on information and better decision-making capabilities. 71

The Internet of Food offers substantial opportunities for understanding the limits and constraints to sustainable food systems and thus supporting decisions about the transition from fossil to sustainable food. It is essential that all stakeholders engage with the development of Internet of Food to ensure harmonious development of a technology that can be used for both pre-competitive applications and commercial exploitation, if it is to be fully developed over the coming 5–10 years. In addition to the technical issues highlighted here, there are considerations of data ownership, privacy, ethical use of data, market control and other application domains (e.g. food safety, traceability, personal nutrition, security, fraud and policy) that need to be developed with stakeholder contributions alongside the technical advances considered here.

Conclusions

In order to transition to a sustainable food system, we need specific technology infrastructure to allow high-quality data to be collected about the food system that will permit the best possible decision-making. Key requirements are: standard vocabularies and ontologies to allow integration of data sets across the internet; proliferation of low cost sensing to allow orders of magnitude change in the supply of empirical observation data into LCA models; and new analytical methods to collate, curate, analyse and utilise data across the whole food production system. We need an Internet of Food to monitor conditions and analyse data to derive knowledge that can be combined with the means to implement control of the system to enable a step change in how we think about food systems. This technology will give us the chance to transition from fossil food to sustainable food systems.

United Nations. Transforming our world: the 2030 Agenda for Sustainable Development, Resolution adopted by the General Assembly on 25 September 2015, available at https://www.un.org/ga/search/view_doc.asp?symbol=A/RES/70/1&Lang=E , Accessed 16 Oct 2017.

The State of Food and Agriculture 1998 (Food and Agriculture Organization of the United Nations, Rome, 1998).

Building a Common Vision for Sustainable Food and Agriculture: Principles and Approaches (Food and Agriculture Organization of the United Nations, Rome, 2014).

Foley, J. A. et al. Solutions for a cultivated planet. Nature 478 , 337–342 (2011).

Article   CAS   Google Scholar  

Kouma, J. P. & Lui, L. Internet of Food. In 2011 IEEE International Conferences on Internet of Things, and Cyber, Physical and Social Computing . https://doi.org/10.1109/iThings/CPSCom.2011.120 , 2011.

Kucukvar, M. & Samadi, H. Linking national food production to global supply chain impacts for the energy-climate challenge: the cases of the EU-27 and Turkey. J. Clean. Prod. 108 , 395–408 (2015).

Article   Google Scholar  

FAO. Energy Smart Food for People and Climate - Issue Paper (Food and Agriculture Organization of the United Nations, Rome, 2011).

Google Scholar  

Monforti-Ferrario, F. et al. Energy Use in the EU Food Sector: State of Play and Opportunities for Improvement (Publications Office, Luxembourg, 2015).

Jasinski, S. M., Kramer, D. A., Ober, J. A. & Searls, J. P. Fertilizers—sustaining global food supplies. United States Geological Survey Fact Sheet FS-155-99 (1999).

Zou, Z., Ye, J., Sayama, K. & Arakawa, H. Direct splitting of water under visible light irradiation with an oxide semiconductor photocatalyst. Nature 414 , 625–627 (2001).

Kudo, A. & Miseki, Y. Heterogeneous photocatalyst materials for water splitting. Chem. Soc. Rev. 38.1 , 253–278 (2009).

Manning, D. A. C. How will minerals feed the world in 2050? Proc. Geologists Assoc. 126 , 14–17 (2015).

Cordell, D., Drangert, J.-O. & White., S. The story of phosphorus: global food security and food for thought. Glob. Environ. Change 19 , 292–305 (2009).

Mohr, S. & Evans, G. Projections of future phosphorus production. Philica - The Instant, Open access Journal of Everything (2013).

Elser, J. & Bennett, E. Phosphorus cycle: a broken biogeochemical cycle. Nature 478 , 29–31 (2011).

Chowdhury, R. B., Moore, G. A., Weatherley, A. J. & Arora, M. Key sustainability challenges for the global phosphorus resource, their implications for global food security, and options for mitigation. J. Clean. Prod. 140 , 945–963 (2017).

Water and Jobs - The United Nations World Water Development Report 2016 (United Nations Educational, Scientific and Cultural Organization, Paris, 2016).

Dalin, C., Wada, Y., Kastner, T. & Puma., M. J. Groundwater depletion embedded in international food trade. Nature 543 , 700–704 (2017).

Wada, Y., Van Beek, L. P. H. & Bierkens, F. P. M. Nonsustainable groundwater sustaining irrigation: a global assessment. Water Resourc. Res. https://doi.org/10.1029/2011WR010562 (2012).

Goswami, P. & Nishad, S. Virtual water trade and time scales for loss of water sustainability: a comparative regional analysis. Sci. Rep. 5 , 1 (2015).

Schwabe, K., Knapp, K. & Luviano, I. In Competition for Water Resources - Experiences and Management Approaches in the US and Europe (eds Ziolkowska, J. & Peterson, J.) Ch. 2.1.2 (Elsevier, Cambridge, United States, 2017).

Pimentel, D. et al. Environmental and economic costs of soil erosion and conservation benefits. Science 267.5201 , 1117–1123 (1995).

Montgomery, D. R. Soil erosion and agricultural sustainability. Proc. Natl. Acad. Sci. 104 , 13268–13272 (2007).

Lal, R. Soil degradation by erosion. Land Degrad. Dev. 12 , 519–539 (2001).

Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6 , 166–171 (2015).

Tilman, D. Global environmental impacts of agricultural expansion: the need for sustainable and efficient practices. Proc. Natl. Acad. Sci. 96.11 , 5995–6000 (1999).

Gilbert, N. One-third of our greenhouse gas emissions come from agriculture. Nature (31 Oct 2012).

Vermeulen, S. J., Campbell, B. M. & Ingram., J. S. Climate change and food systems. Annu. Rev. Environ. Resour. 37 , 195–222 (2012).

Intergovernmental Panel on Climate Change [Core Writing Team, Pachauri, R. K. & Meyer, L]. (2014) CLIMATE CHANGE 2014 Synthesis Report Summary for Policymakers . https://www.ipcc.ch/pdf/assessment-report/ar5/syr/AR5_SYR_FINAL_SPM.pdf , Accessed 01 Oct 2018.

Thornton, P. K. Recalibrating food production in the developing world: global warming will change more than just the climate. Handle Proxy 2012. Web. 15 June 2017.

Withers, P., Neal, C., Jarvie, H. & Doody, D. Agriculture and eutrophication: where do we go from here? Sustainability 6 , 5853–5875 (2014).

Carpenter, S. R. Eutrophication of aquatic ecosystems: bistability and soil phosphorus. Proc. Natl. Acad. Sci. 102 , 10002–10005 (2005).

Moss, B. Water pollution by agriculture. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363 , 659–666 (2008).

Ruiz-Garcia, L., Lunadei, L., Barreiro, P. & Robla, I. A review of wireless sensor technologies and applications in agriculture and food industry: state of the art and current trends. Sensors 9 , 4728–4750 (2009).

Rawat, P., Singh, K. D., Chaouchi, H. & Bonnin, J. M. Wireless sensor networks: a survey on recent developments and potential synergies. J. Supercomput. 68 , 1–48 (2013).

Bouwman, A. F., Van Vuuren, D. P., Derwent, R. G. & Posch, M. A global analysis of acidification and eutrophication of terrestrial ecosystems. Water Air Soil Pollut. 141 , 349–382 (2002).

Green, R. E. Farming and the fate of wild nature. Science 307 , 550–555 (2005).

Nagajyoti, P. C., Lee, K. D. & Sreekanth, T. V. M. Heavy metals, occurrence and toxicity for plants: a review. Environ. Chem. Lett. 8.3 , 199–216 (2010).

Foley, J. A. Global consequences of land use. Science 309 , 570–574 (2005).

Rockström, J. et al. Planetary boundaries: exploring the safe operating space for humanity. Ecol. Soc. 14 , 32 (2009).

Notarnicola, B., Sala, S., Anton, A., Mclaren, S. J., Saouter, E. & Sonesson, U. The role of life cycle assessment in supporting sustainable agri-food systems: a review of the challenges. J. Clean. Prod. 140 , 399–409 (2017).

National Research Council. Sustainability Concepts in Decision-Making: Tools and Approaches for the US Environmental Protection Agency (The National Academies Press, Washington, DC, 2014). https://doi.org/10.17226/18949 .

Wolf M.-A., Pant, R., Chomkhamsri, K., Sala, S. & Pennington, D. The International Reference Life Cycle Data System (ILCD) Handbook (European Commission, Joint Research Centre, Ispra, Italy, 2012).

ISO. Environmental Management: Life Cycle Assessment- Illustrative Examples on How to Apply ISO14044 to Impact Assessment Situations (International Organization for Standardization, Geneva, 2012).

Roy, P. et al. A review of life cycle assessment (LCA) on some food products. J. Food Eng. 90 , 1–10 (2009).

Bjørn, A. & Michael, Z. H. Absolute versus relative environmental sustainability. J. Ind. Ecol. 17 , 321–332 (2012).

Cashman, S. A. et al. Mining available data from the united states environmental protection agency to support rapid life cycle inventory modeling of chemical manufacturing. Environ. Sci. Technol. 50 , 9013–9025 (2016).

Groen, E. A. & Heijungs, R. Ignoring correlation in uncertainty and sensitivity analysis in life cycle assessment: what is the risk? Environ. Impact Assess. Rev. 62 , 98–109 (2017).

Li, T., Zhang, H., Liu, Z., Ke, Q. & Alting, L. A system boundary identification method for life cycle assessment. Int. J. Life Cycle Assess. 19 , 646–660 (2013).

Tillman, A.-M., Ekvall, T., Baumann, H. & Rydberg, T. Choice of system boundaries in life cycle assessment. J. Clean. Prod. 2 , 21–29 (1994).

Cooper, J. S. Specifying functional units and reference flows for comparable alternatives. Int. J. Life Cycle Assess. 8 , 337–349 (2003).

Yan, M.-J., Humphreys, J. & Holden, N. M. An evaluation of life cycle assessment of european milk production. J. Environ. Manag. 92 , 372–379 (2011).

Udo de Haes, H. A., Heijungs, R., Suh, S. & Huppes, G. Three strategies to overcome the limitations of life-cycle assessment. J. Ind. Ecol. 8.3 , 19–32 (2004).

Owens, J. W. Life-cycle assessments: constraints on moving from inventory to impact assessment. J. Ind. Ecol. 1 , 37–50 (1997).

Hellweg, S., Mila, L. & Canals, I. Emerging approaches, challenges and opportunities in life cycle assessment. Science 344 , 1109–1113 (2014).

Xu, M., Cai, H. & Liang, S. Big data and industrial ecology. J. Ind. Ecol. 19 , 205–210 (2015).

Wang, N., Zhang, N. & Wang, M. Wireless sensors in agriculture and food industry—recent development and future perspective. Comput. Electron. Agric. 50 , 1–14 (2006).

Corwin, D. L. & Lesch, S. M. Application of soil electrical conductivity to precision agriculture. Agron. J. 95 , 455 (2003).

Pérez-Marín, D., Paz, P., Guerrero, J.-E., Garrido-Varo, A. & Sánchez., M.-T. Miniature handheld NIR sensor for the on-site non-destructive assessment of post-harvest quality and refrigerated storage behavior in plums. J. Food Eng. 99 , 294–302 (2010).

Woodcock, T., Fagan, C. C., O’Donnell, C. P. & Downey, G. Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food Bioprocess Technol. 1 , 117–129 (2007).

Abad, E. et al. RFID smart tag for traceability and cold chain monitoring of foods: demonstration in an intercontinental fresh fish logistic chain. J. Food Eng. 93 , 394–399 (2009).

Chi, P.-Y. (Peggy)., Chen, J.-H., Chu, H.-H. & Lo, J.-L. Enabling calorie-aware cooking in a smart kitchen. In Persuasive Technology . PERSUASIVE 2008 (eds Oinas-Kukkonen, H., Hasle, P., Harjumaa, M., Segerståhl, K. & Øhrstrøm, P.) 116–127 (Springer, Berlin, Hiedelberg, 2008).

Goumopoulos, C., Achilles, D. K. & Cassells, A. An ontology-driven system architecture for precision agriculture applications. Int. J. Metadata Semant. Ontol. 4 , 72 (2009).

Hampton, S. et al. Big data and the future of ecology. Front. Ecol. Environ. 11 , 156–162 (2013).

Xu, L. D., He, W. & Li, S. Internet of Things in industries: a survey. IEEE Trans. Ind. Inform. 10 , 2233–2243 (2014).

Iftikhar, N., Pedersen, T. B. Flexible exchange of farming device data. Comput. Electron. Agric. 75 , 52–63 (2011).

Boulos, M., Yassine, A., Shirmohammadi, S., Namahoot, C. & Brückner, M. Towards an “Internet of Food”: food ontologies for the Internet of Things. Future Internet 7 , 372–392 (2015).

Kouma, J.-P. & Liu, L. Internet of Food. In International Conference on Internet of Things and 4th International Conference on Cyber , Physical and Social Computing (eds. Xia, F., Chen, Z., Pan, G., Yang, L. T. & Ma, J.) (IEEE Computer Society, Los Alamitos, California, Washington, Tokyo, 2011). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6142168 , Accessed 01 Oct 2018.

Cooper, J., Noon, M., Jones, C., Kahn, E. & Arbuckle, P. Big data in life cycle assessment. J. Ind. Ecol. 17 , 796–799 (2013).

Hampton, S. E. et al. Big data and the future of ecology. Front. Ecol. Environ. 11 , 156–162 (2013).

Zander, J. & Mosterman, P. J. Computation for Humanity: Information Technology to Advance Society (CRC, Taylor & Francis Group, Boca Raton, 2014).

Sonka, S. Big data and the Ag sector: more than lots of numbers. Int. Food Agribusiness Manage. Rev. 17 , 2014.

ARMS Farm Financial and Crop Production Practices. USDA ERS - ARMS Farm Financial and Crop Production Practices . Web. 07 June 2017.

Emissions & Generation Resource Integrated Database (eGRID). EPA. Environmental Protection Agency . 01 June 2017. Web. 07 June 2017.

Wolfert, S., Ge, L., Verdouw, C. & Bogaardt, M.-J. Big data in smart farming – a review. Agric. Syst. 153 , 69–80 (2017).

Food Industry Intelligence Network, available at http://www.campdenbri.co.uk/news/fiin.php , Accessed 25th May 2018

Food Innovation Network, available at http://foodinnovationnetwork.co.uk , Accessed 29th March 2018.

European Institute of Innovation & Technology Food, available at http://www.eitfood.eu , Accessed 29th March 2018.

Nobre, G. C. & Tavares, E. Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study. Scientometrics 111 , 463–492 (2017).

Howe, D. et al. Big data: the future of biocuration. Nature 455 , 47–50 (2008).

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Acknowledgements

The authors would like to acknowledge the support and funding from the UCD Institute of Food and Health, UC Davis, Food for Health Institute and the IC3-Foods Conference.

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The ideas and initial draft for this paper were drawn together by N.M.H. and consolidated at the inaugural IC3-Foods Conference in UC Davis, November 2016, following extensive discussion with M.C.L. T.L.O. and E.P.W. contributed to background research and additional draft text. M.C.L. refined the technology discussion.

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Holden, N.M., White, E.P., Lange, M.C. et al. Review of the sustainability of food systems and transition using the Internet of Food. npj Sci Food 2 , 18 (2018). https://doi.org/10.1038/s41538-018-0027-3

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Sustainable Supply Chain Management in the Food Industry: A Conceptual Model from a Literature Review and a Case Study

Associated data.

The new data that were created and analyzed in this study are the ones presented in the article. Interview or field notes data sharing is not applicable to this article.

The purpose of this study is twofold: firstly, to provide a literature review of sustainable supply chain management (SSCM) critical factors, practices and performance; and secondly, to develop a comprehensive and testable model of SSCM in the food industry. The research conducted comprises a literature review and a case study. The literature review findings propose a theoretical framework linking SSCM critical factors, practices and performance. The case study comprises two sustainability leaders in the Greek food supply chain in order to investigate the three SSCM constructs. A new set of pioneering SSCM practices in the Greek food industry is identified, including daily conversation, local sourcing and HR investments. The end result of this research proposes a testable model that sheds light on SSCM in the food industry and is based on a set of propositions.

1. Introduction

Over the past decades, sustainable supply chain management (SSCM) has attracted much attention from academics and practitioners [ 1 , 2 ]. Globalisation allowed processes to be dispersed around the world, linking all supply chain members, from suppliers to end customers, through information sharing and material and capital flows [ 2 ]. As a result, pressures from stakeholders, such as regulatory bodies, non-governmental organisations (NGOs), community organizations, suppliers, customers and global competition, have prompted companies to reconsider the balance of environmental, social and economic issues in their supply chains [ 3 ] and adopt sustainable supply chain management practices. SSCM is defined as “the management of material, information and capital flows as well as cooperation among companies along the supply chain while taking goals from all three dimensions of sustainable development, i.e., economic, environmental and social, into account which are derived from customer and stakeholder requirements” [ 2 ] (p. 1700).

As in all business operations, SSCM tries to achieve clearly defined performance goals [ 4 ]. However, this is not an easy task due to the complexity of supply chains, where individual members have different and often conflicting goals from other members of the chain and hence different performance measures. Different measures are not always seen as positive regarding the entire chain’s performance, because a single company’s outcomes may be harmful for other supply chain members. Hence, the performance of the entire chain can only be improved if the supply chain is conceptualized as a whole, outside the boundaries of the firm level [ 5 ].

SSCM practices such as environmental purchasing and sustainable packaging often have positive outcomes regarding supply chain sustainability performance [ 6 ]. The development of SSCM practices can either be enabled or inhibited by various contingent factors. A variety of industries face specific enabling or inhibiting factors from different points of view based on their size, culture, location and supply chain partners. Many researchers have studied SSCM in several sectors such as manufacturing [ 2 , 7 ], the automotive industry [ 8 ], oil and gas [ 9 ], energy [ 10 ] and the food industry [ 11 ]. The food industry is one of the most important sectors that faces significant environmental, economic, social and political challenges. This is due to the focus of public attention on food safety, production practices, environmental issues such as deforestation, climate change and energy consumption and social issues such as fair wages and population growth [ 11 , 12 ]. Furthermore, globalization, technological advances, the use of agricultural chemicals and improved transportation have simultaneously raised concerns regarding the sustainability of food supply chains [ 13 , 14 ], since “changes at one stage in a supply chain will have knock-on effects on other stages in the chain” [ 14 ] (p. 97).

Other critical issues are related to the measurement of supply chain impacts, to supply chain collaboration and networking, to stakeholder engagement, to sustainable development goals, etc. [ 15 ]. These challenges confirm the differentiability of food supply chains, which lies upon variability and risk factors due to the product-specific characteristics such as perishability, seasonality in production, transportation and storage conditions [ 16 ]. In addition, customers and firms have raised their concerns regarding the origin of products, food safety, quality and sustainable production [ 17 ], including animal welfare and environmental pressure [ 16 ].

Numerous studies have investigated the relationship between SSCM practices and sustainability performance. However, limited work has been conducted on the empirical investigation of industry and location-specific SSCM critical factors and practices and their relationship to sustainability performance [ 11 , 18 , 19 , 20 , 21 ]. The food industry is characterised by enhanced supply chain relationships that aim at achieving high sustainability performance [ 11 ]. Earlier findings from ref. [ 6 ] demonstrate that environmentally friendly purchasing and sustainable packaging result in improved economic and social performance. Direct and indirect impacts between the dimensions of sustainability performance are also observed in the literature. A positive relationship is found between corporate social performance and financial performance [ 22 ]. In the wine industry, ref. [ 23 ] found that employee practices related to social sustainability result in reduced costs; ref. [ 24 ] found that environmental practices have positive environmental performance outcomes and indirect impacts on cost performance based on quality improvements. The authors of ref. [ 25 ] suggest an alignment between goals that lead to improved environmental and financial performance. On the other hand, ref. [ 24 ] highlights “the complexity of sustainability impacts on performance and suggest that performance benefits from sustainability programs may be difficult to recognize” [ 24 ] (p. 38).

With the above in mind, the aim of this study is to gain insight into the SSCM critical factors and practices that are implemented in the food industry and their possible relationship to sustainability performance. To support the purpose of this research, two methods were used. A literature review of the key SSCM topics and a case study to demonstrate the experience of two leaders in SSCM. The aim of this research will be achieved by addressing the following research questions (RQ):

RQ1: What are the factors that influence the adoption of SSCM practices in the food industry?

RQ2: Which practices do companies in the food industry adopt to develop SSCM?

RQ3: What measures can be used to measure SSCM performance in the food industry?

The rest of the paper is organized as follows. The next section presents the literature review and the case study methods. The results of the literature review and the case study are presented and discussed in conjunction with previous research in Section 3 . Finally, the conclusions are drawn in Section 4 , including the study limitations as well as future research opportunities.

2. Materials and Methods

The research methodology that was applied in this study is based on the following steps [ 15 ]: (i) a literature review; (ii) identification of the gaps; (iii) concepts synthesis; and (iv) a case study.

2.1. Literature Review Method

Because the identification and conceptualisation of SSCM is still unclear, a literature review was conducted on the key sustainable supply chain management topics, such as critical factors for implementation, practices and performance. Despite the fact that other reviews on the SSCM are already published, this review is required in order provide an up-to-date report and understanding of the current SSCM research. The search for related scientific articles was based on keywords and authors’ names, in major bibliographical databases and publishers such as Scopus, Elsevier, Emerald, Springer, Wiley, Taylor & Francis, Springer, Sage Publications and Inderscience, over a twenty-year period since 2000. The keywords search included “sustainable supply chain management”, “drivers”, “barriers”, “enablers”, “motivators”, “critical factors”, “sustainable supply chain management practices”, “sustainability performance” and “food industry”. The authors search included Seuring S., Beske P., Gualandirs J., Govindan, K, Pagell M., etc., since these authors have repeatedly focused their research on SSCM topics [ 1 ]. A secondary search was also carried out using the cited references. Only papers in peer-reviewed English scientific journals are reviewed. This research includes articles with a focus on the food industry as a field of application but is not limited to that. Articles from other sectors were also included in the study.

The measures identified by the comprehensive literature review were named and grouped based on the affinity method, which is utilized to organize into categories common themes from a large amount of information [ 26 ]. In addition to the affinity method, the naming and grouping of the constructs were based on interviews of five professionals of the food industry and five academics.

2.2. Case Study Method

Taking into account that the analysis of a supply chain as a whole is a complex and difficult task and in order to explore the SSCM critical factors, practices and performance in the food industry, a case study was selected as the most appropriate research method [ 27 , 28 ]. This study investigates a sustainable supply chain in order to capture the critical factors of SSCM, the SSCM practices adopted and their influence on sustainability performance. The research has been carried out in a supply chain that is comprised of two SSCM leaders that operate in Greece ( Table 1 ). This is particularly useful, because it offers empirical contributions within the Greek-business context, where SSCM literature is limited. The names of the companies were not disclosed in order to protect confidentiality and encourage the openness of responses. The unit of analysis in this study is the food supply chain. The study investigates the particular food supply chain, comprised of two companies, and the findings will concern the supply chain as a whole.

Sample characteristics.

The authors of ref. [ 29 ] propose a five-stage process for case studies that is used for structuring this research. Figure 1 depicts the various research steps.

The five stages of the research process model.

• The first step is related to the research objective. This research uses a single case study to investigate the critical factors that influence companies in the food industry to implement SSCM practices, which are these practices and how do they influence sustainability performance.
• The second step is related to the research instrument development. A single case research design is used to guide this study and provide an in-depth understanding of a complex phenomenon, through the observation of actual practices in real-world settings, without any kind of control or manipulation, considering both temporal and contextual dimensions [ 30 , 31 ]. Case studies provide researchers the opportunity to closely analyse the data within a specific context. In ref. [ 32 ] (p. 18), the authors define the case study research method “as an empirical inquiry that investigates a contemporary phenomenon within its real-life context; when the boundaries between phenomenon and context are not clearly evident; and in which multiple sources of evidence are used.” Furthermore, the detailed qualitative accounts often produced in case studies not only help to explore or describe the data in real-life environments but also help to explain the complexities of real-life situations, which may not be captured through experimental or survey research [ 33 ]. For the reasons referred to above, a single case study comprised by two leaders in the food industry was selected as the most appropriate research method for this study. The firms are both sustainability leaders in the Greek food industry and members of multinational groups. The companies were selected as they have received a series of recognitions regarding sustainability, such as Environmental Awards, Supply Chain Sustainability Awards, distinctions in CSR actions, etc. Furthermore, both companies play a crucial role in the Greek industry, society and economy. An interview protocol [ 27 ] was developed on the basis of the reviewed literature and closely following previous research on SSCM [ 34 , 35 ] (see Appendix A ). The authors of ref. [ 36 ] highlight that using existing questions enables the comparability of results. Furthermore, ref. [ 28 ] points out that using interview protocols assures the reliability of data. The interviews ranged from 70 to 90 min.
• (1) The CSR Manager of the SB company;
• (2) The Quality Manager of the SB company;
• (3) The Manager of the distribution centre of the SM company;
• (4) Two Logistics Project Managers of the SM company;
• (5) Three Retail Store Managers of the SM company.

Field notes were typed up during each interview. Repeated contacts by phone or e-mails were needed to confirm the chain of evidence. Except for the data drawn from interviews, the analysis of the sustainability reports in combination with the website information and other news were important secondary sources.

• 4. The fourth step refers to the data analysis. The data analysis was filtered and guided by the identified SSCM constructs.

The fifth step is related to assuring the quality of the research process: Multiple sources of data were collected, including archival data (financial reports, CSR reports, website material and company records), on-site observations and semi-structured interviews, in order to achieve data source triangulation and ensure construct validity ([ 37 ], p. 68; [ 28 ], p. 36). The internal validity of the case was assured by doing pattern matching with other studies identified in previous research [ 28 ]. Regarding the external validity, the case study was designed and conducted based on the gathering of as many data as possible in order to attain a deeper knowledge of the complex background of SSCM and to identify the more analytical and general theoretical implications [ 28 ].

3. Results and Discussion

This section begins with the literature review results, highlighting the concepts of the sustainable supply chain management and continues with the case study results of the food supply chain.

3.1. Literature Review Results

The results of the literature review are classified in three main SSCM content categories, namely, critical factors, practices and performance.

3.1.1. Critical Factors

In studying the literature, many terms are found to be used interchangeably by researchers. For example, the factor top management commitment is considered as enabler [ 35 , 38 ], driver [ 39 ], success factor [ 40 ], critical factor [ 41 ], enabling factor [ 42 ], reason [ 43 ], motivator [ 19 ] and firm-level strength [ 44 ]. In contrast, most researchers in the SSCM literature use the term barrier when describing factors that inhibit SSCM, such as the lack of top management commitment [ 1 , 35 , 39 ]. As observed, there are several terms to describe the same factor, indicating a lack of agreement on how these terms should be used in SSCM research. Furthermore, these factors are classified in more than one category, such as internal and external [ 35 , 45 , 46 ], regulatory, resource, market and social [ 47 ], stakeholder, process or product [ 48 ].

The identified factors are named critical factors, including enablers, drivers, success factors, motives as well as barriers and inhibiting factors. More specifically, in this study critical factors are defined as the factors that are responsible for enabling or inhibiting the successful implementation of SSCM. This is the rationale for grouping the enablers, drivers, success factors, motives, barriers and inhibiting factors in one group. This approach is also applied in other studies that investigate SSCM [ 41 ]. A total of 83 critical factors were identified in the literature from 34 papers. The critical factors are classified into three groups. The first group is related to firm-level critical factors (FLCF), the second to supply chain-level critical factors (SCLCF) and the third to external critical factors (ECF). All three groups of factors play a major role in the success or failure of the implementation of SSCM [ 1 ].

Firm-Level Critical Factors (FLCF)

Sustainable supply chain management scholars have asserted that firms should consider multiple factors that will enable or hinder the successful implementation of SSCM practices [ 1 ]. Several critical factors from various industries and countries have been identified in the literature [ 1 ]. Top management commitment and support is considered the most common FLCF [ 35 , 38 , 40 ]. In ref. [ 49 ], the authors have highlighted that top management is responsible for directing sustainability efforts; [ 50 ] also found that senior corporate management’s attitude can foster plant-level sustainability management. Indeed, the implementation of SSCM is an internal decision that has to be supported at the firm level [ 43 ]. From a supply chain-level perspective, ref. [ 39 ] have found that top management is a factor that drives purchasing and supply management sustainability initiatives. On the other hand, low or lack of top management commitment is considered by many researchers as a barrier for the successful implementation of SSCM [ 1 , 46 ]. In the food industry, ref. [ 34 ] have found that the most common critical factors for SSCM adoption are the operational cost reduction and market drivers, such as customer requirements, retailer pressure and brand image and corporate reputation. Meeting customer demands, expectations and requirements is one of the most cited critical factors for the implementation of SSCM [ 35 , 39 , 47 ]. It is widely accepted that customers are the stakeholder group that influences most a company’s performance by buying or rejecting a specific product [ 51 ]. For example, there are customers that desire to have environmentally friendly products and services and they are willing to pay more for their demand. If companies fail to meet this specific requirement, they may face customer boycotts [ 43 ]. In ref. [ 39 ], the authors identified knowledge and expertise regarding sustainability as a driving force for developing an organisation’s SSCM strategy, while ref. [ 47 ] highlighted knowledge as a critical intangible asset for SSCM implementation. Indeed, competences, knowledge and expertise are crucial factors for the successful or unsuccessful implementation of SSCM [ 39 , 41 , 43 , 47 ]. The recent study of ref. [ 18 ] has shown that companies that invest in human capital with professional expertise and capabilities on sustainability issues can enable the implementation of SSCM practices. In the same line, ref. [ 47 ] mentions that the lack of knowledge about sustainability issues hinders the development of SSCM. Training and education are other key firm-level critical factors that are closely related to sustainability performance [ 18 ]. Training and development about sustainability allow for sustainability improvements in job performance and helps companies minimise errors and waste [ 18 ]. A lack of training and education, on the other hand, hinders successful SSCM implementation [ 1 ]. In ref. [ 24 ], the authors found that despite the fact that social sustainability practices, including participation and training of employees, indirectly impact firm performance, they are positively related. More specifically, social sustainability practices are considered quality-enablers in the food sector [ 24 ]. Reputation critical factors are related to brand name and reputation, or minimization of the risk of negative publicity [ 47 ]. The authors of ref. [ 47 ] highlight that corporate reputation and image are positively related to eco-brand developments. Being proactive regarding sustainability issues can bring a good reputation and image and offer easier market access and develop a good network of suppliers and partners [ 52 ]. In ref. [ 53 ] (p. 325), the authors further explain that “organizations build a reputation of ‘good citizen’ by promoting environmental and social sustainability in their supply chain. This reputation improves legitimacy and access to key resources”. Firm-level critical factors related to financial issues include cost savings from operational and material efficiencies [ 47 ] and the increased resource utilization [ 39 ]. On the opposite side, companies that desire to adopt SSCM practices often struggle to overcome the high costs related to the upstream supply chain greening [ 47 ] or the development of supply chain infrastructure, systems and processes [ 19 ].

Supply Chain-Level Critical Factors (SCLCF)

Supply chain-level CFs are closely linked to firm-level CFs. The literature posits that firm level and supply chain-level alignment strongly affect their successful integration [ 54 ]. Information sharing has been identified as one of the most important enablers to adopt SSCM practices [ 38 , 40 , 41 ]. In ref. [ 18 ], the authors suggest that information sharing enables the development of new ideas regarding sustainability and enhances collaboration throughout the supply chain. In the food industry, information sharing among supply chain members is described as a novel form for traceability and it is linked to improved supply chain performance [ 25 ]. Ref. [ 34 ] mentions that product traceability is strongly related to social sustainability and ensures food safety. The limited or lack of information and transparency on sustainability related issues, on the other hand, has a negative impact on SSCM implementation [ 41 , 42 ]. Trustful relationships and commitment among supply chain partners is mentioned as a key factor for implementing SSCM in the food industry. This is due to the criticality of ingredient quality in the food production [ 41 ]. According to ref. [ 34 ], who investigated sustainability in the Italian meat supply chain, building trust amongst supply chain firms is a core component for implementing exceptional supply chain practices, such as supplier collaboration, for sustainability. On the contrary, ref. [ 1 ] highlights that poor supplier commitment is one of the most common inhibiting factors. In ref. [ 46 ], the authors found that the lack of trust and commitment between supply chain members is an important obstacle, especially when customers audit suppliers. Agreeing on a common SSCM strategy is another important supply chain critical factor. The authors of ref. [ 40 ] found that it is more likely for companies that signal sustainability initiatives to their supply chain partners and stakeholders to develop a common SSCM strategy with them. Developing a common SSCM strategy ensures that all supply chain partners pursue the same strategic goal [ 40 ]. Indeed, policy sharing, and the subsequent establishment of common goals, was found to be a key factor for the implementation of SSCM practices such as environmental collaboration [ 55 ]. Ref. [ 11 ] found that pro-activity is a key factor when pursuing an SSCM strategy in the food industry (e.g., organic food or fair trade) since new processes and technologies need to be established. The lack of agreement on an SSCM strategy hinders the adoption of SSCM. Another factor that significantly affects the adoption of SSCM practices is geographical distance. The findings of ref. [ 56 ] show that when geographical distance between suppliers increases, a negative impact is observed on data gathering, assessment and collaboration. More specifically, ref. [ 41 ] found that when visiting distant farms or manufacturing plants is required, significant travel effort and resources are needed and as a result it is more difficult to check the partners’ operations and processes. On the contrary, shorter supply chains often lead to the successful implementation of sustainability practices [ 57 ].

External Critical Factors

External CFs originate from a variety of stakeholders, such as government, customers, suppliers, media, non-governmental organizations (NGOs), etc. Two of the most common external critical factors for SSCM are the existence of regulatory frameworks [ 38 , 39 , 47 ] and the awareness of and compliance to government policy and legislations [ 18 , 35 , 39 , 58 , 59 ]. Pressure from governments in the form of legislation, such as energy and waste directives, international regulations such as the UN Declaration of Human rights and International Labour Organization conventions, or the EU’s Sustainable Consumption, Production and Sustainable Industrial Policy Action Plan are critical factors for the implementation of SSCM in the food industry [ 47 , 52 ]. Furthermore, pressure from investors [ 35 , 47 ] and interaction with NGOs and other external stakeholders [ 42 ] may exert pressure on companies to implement SSCM. Pressures from investors, such as increased investor appeal on sustainability criteria, are considered a driving force to initiate and maintain SSCM [ 35 , 47 ]. Food scares regarding pesticide residues, unhealthy ingredients, chemical residues, etc., result in cautious measures [ 47 ]. Other studies have identified competitor’s pressure as a market factor that may lead to the development of SSCM practices. Refs. [ 35 , 39 , 40 , 47 ] posit that the adoption of SSCM practices by competitors motivates companies to develop SSCM.

Additional SSCM critical factors are identified in the literature but are not included here, since the concentration in this paper is on those factors that are relevant for sustainable supply chain management in the food industry. A comprehensive list would have to include critical factors such as innovativeness, technology and equipment [ 18 ]; employee involvement and traditional accounting methods [ 35 ]; additional human resources [ 42 ]; personnel commitment [ 41 ]; Industry 4.0 solutions [ 60 , 61 , 62 ], including the Internet of Things (IoT), sustainability data and information [ 42 ]; and the supply chain cultural and language differences [ 41 ]; among others.

3.1.2. Practices

In ref. [ 63 ] (p. 620), supply chain management practice is defined as “a set of activities undertaken in an organization to promote effective management of its supply chain”. In combination with the definition of SSCM that has been provided in the introduction, SSCM practice is defined as a set of sustainability (i.e., economic, environmental and social) activities undertaken in an organization in cooperation with each stakeholders, to promote effective sustainability management of its supply chain. SSCM practices have their origins in green supply chain management (GSCM). Ref. [ 8 ] have examined the relationships between GSCM practices and organizational performance in the Chinese manufacturing and processing sectors. In their study they categorized GSCM practices into four groups: (1) Internal environmental management; (2) External GSCM practices; (3) Investment recovery; and (4) Eco-design. Their results have shown that GSCM practices tend to have a positive relationship with environmental and economic outcomes. The same authors three years later used internal environmental management, green purchasing, eco-design, cooperation with customers and investment recovery to represent GSCM practices in their empirical study [ 64 ]. Ref. [ 65 ] investigated the impact of GSCM practices on organizational performance in the electrical and electronic sector. Their results indicate that green procurement and green manufacturing practices have a positive influence on environmental and financial performance. The authors in ref. [ 66 ] identified 47 different logistics social responsibility (LSR) practices and developed a taxonomy of five categories including socially responsible purchasing, sustainable transportation, reverse logistics, sustainable packaging and sustainable warehousing. The authors in ref. [ 67 ] have empirically investigated the influence of environmental collaboration practices in the supply chain on environmental and manufacturing performance. In ref. [ 25 ], five bundles of SSCM practices were identified through case studies of ten exemplar firms: (1) commonalities, cognitions and orientations; (2) ensuring supplier continuity; (3) re-conceptualize the chain; (4) supply chain management practices including sourcing management, operations and investments in human capital; and (5) measurement. In their list of SSCM practices in the food industry, ref. [ 24 ] included both social and environmental issues. More specifically, they have identified four types of SSCM practices, namely, land management, recycling, facility conservation and social practices, and tested their relationships to environmental, quality and cost performance. Focusing on a more social perspective of supply chains, ref. [ 56 ] developed a construct of supplier socially responsible practices, including human rights, labour practices, codes of conduct and social audits. In ref. [ 6 ], the authors suggest that a positive effect on supply chain sustainability performance could be achieved when firms adopt environmental purchasing and sustainable packaging practices.

The concept of SSCM includes material, information and capital flows; cooperation across the supply chain; economic, environmental and social performance; and customer and stakeholder requirements [ 2 ]. The extant body of literature portrays a variety of different SSCM practices, but all have one central objective, namely, the improvement of supply chain sustainability performance. A total of 96 SSCM practices were identified in the literature from 21 papers. In order to conceptualize and develop a sound construct based on the literature and on [ 11 ], five practices that cover the aspects of SSCM emerged: (1) strategic orientation; (2) supply chain continuity; (3) collaboration; (4) risk management; and (5) pro-activity. This set of practices emphasizes enhancing the relationships among supply chain partners, the flow of goods and information, and the sustainability aspects.

Despite the major aspects of SSCM that the above practices cover, it should be highlighted that the set of practices that will be described below is not considered complete. Several other practices that have been discussed previously are investigated in the extant literature. In this paper, the SSCM practices as proposed by ref. [ 11 ] are used for two reasons: (1) these practices are applied to food supply chains; and (2) the aim of this paper is to further enhance the empirical content of these practices.

Strategic Orientation

Strategic orientation refers to the commitment of organizations to SCM, as well as to their dedication to the Triple Bottom Line (TBL) concept [ 11 ]. In ref. [ 25 ], the authors proposed that, in order to create a sustainable supply chain, a management orientation towards sustainability is required. The balance of environmental, social and economic issues, i.e., the Triple Bottom Line (TBL), plays a crucial role for companies that want to implement a sustainability strategy [ 68 , 69 , 70 ], and support their decision making [ 11 ]. In ref. [ 21 ], SSCM practices in the automotive sector were investigated and found that supply chain orientation and the TBL approach are the most important practices for supply chain sustainability. Furthermore, ref. [ 20 ] conducted a survey to investigate the impact of SSCM practices from manufacturing companies in various sectors on dynamic capabilities and enterprise performance. Their results showed a positive relationship between supply chain strategic orientation and sustainability performance. In the food industry, ref. [ 11 ] found that TBL orientation, which is driven by the consumer’s demand, the company’s motivation and the stakeholders’ pressure, is addressing the sustainability needs of the food industry.

Supply chain continuity is related to the design and structure of the supply chain network [ 11 ]. Ensuring supplier continuity is identified as one of the top sustainable supply chain management practices for exemplar firms [ 25 ]. Continuity has to do with the interaction of supply chain members on a permanent base [ 11 ]. The core elements of supply chain continuity are the long-term relationships with supply chain partners, the supply chain partner development and the partner selection. Long-term relationships include trust and commitment among the supply chain members [ 25 ], which endeavours information sharing [ 71 ] and enhances the collaborative design of products or processes [ 55 ]. Supplier development refers to the improvement in supplier environmental and social performance [ 25 ]. In traditional supply chain management, the development of suppliers is found to be one of the best practices [ 72 ], which is also connected to sustainability through mentoring approaches [ 73 ]. In the food industry, for example, the assistance and teaching of new farming methods or the funding of costs related to more sustainable farming practices are included in the development of partners [ 74 ]. Partner selection is based on their supply chain competency [ 75 ] and their desire to develop sustainable practices [ 76 ]. Focusing on activities that enhance transparency, traceability, supplier certification and decommodisation is important for ensuring supplier continuity [ 25 ]. As ref. [ 25 ] (p. 48) describe, organizations that are pursuing continuity in their supply chains, “are trying to ensure that all members of their chain not only stay in business, but that they do so in a manner that allows them to thrive, reinvest, innovate and grow”. Furthermore, focal firms are positively affected by supply chain continuity due to the fact that the supply chain base is stable and capable [ 25 ]. Ref. [ 20 ] also found a positive relationship between supply chain continuity and sustainability performance.

Collaboration

The importance of collaboration in supply chains has been recognized as a key factor but also as a great challenge for supply chain success [ 77 ]. Collaboration goes beyond the traditional modus operandi between organisations. First of all, collaboration as an SSCM practice is not restricted only to new product development but also to the development and enhancement of business processes [ 11 , 67 ]. The literature suggests that efficient and responsive supply chains rely on the creation of close and long-term relationships and partnerships with various members of the supply chain in order to increase the customer value [ 77 , 78 ]. Joint development is a key enabler for long-term partnerships. Reference [ 11 ] defines it as the collaborative development of new technologies, processes and products. As ref. [ 79 ] point out, specific resources from each supply chain partner are required in order to jointly address sustainability issues. The implementation of collaborative development is based on knowledge sharing in order to enable the development of sustainable products and processes [ 55 ]. Moreover, suppliers and customers can jointly plan the decrease of their operations’ impact on the environment or support the information exchange and the logistical and technical integration [ 67 ]. Collaboration is also characterized by enhanced communication—a very important practice regarding the management of supply chain partners. The quality of information sharing is critical in order to achieve transparency in the supply chain [ 80 , 81 ]. Transparency regarding the origin and ingredients of food, the production methods, etc., is also important for consumers [ 82 ]. Despite the need for collaboration to achieve sustainable supply chain management, significant barriers arise that are mainly due to the complexity of supply chains. For example, ref. [ 77 ] found that the structure of the food industry and the nature of products have a negative impact on the intensity of collaboration and restrict it to the more tactical-operational, tactical and logistical level.

Risk Management

Supply chain risk management includes the adoption of risk mitigation practices to avoid exposure to risks [ 2 ]. The adoption of standards and certifications is identified as the most common risk management practice in the literature [ 11 ]. This is due to the fact that standards and certifications such as ISO 9001 and ISO 14001 can be applied to a broad range of sectors and they can also be managed (if companies wish) by external consultants, who enhance the level of credibility [ 83 ]. Monitoring of specific suppliers in order to explore their needs and identify their progress on specific goals [ 84 ] is another practice identified within the risk management category. As authors in ref. [ 11 ] mentioned, individual monitoring of suppliers is particularly important in food supply chains, where traceability is a crucial factor to guarantee sustainable production. Despite this fact, individual monitoring is not frequently addressed in the extant literature [ 11 ]. Pressure group management is another key characteristic of risk management, which can affect the company’s reputation or performance [ 85 ]. In ref. [ 2 ], it is pointed out that stakeholders such as NGOs and government should not only be monitored but actively engaged and managed through the implementation of specific practices that address their pressures. It should be noted that the interests of a company and its stakeholders do not always align, and their pressure is seen from a negative perspective [ 11 ].

• Proactivity

Proactivity refers to the actions taken by a company in order to control and manage a specific situation regarding sustainability before it happens, rather than responding to it after it happens. The literature shows that Life Cycle Assessment (LCA) is the most common tool of the pro-activity practice [ 11 ]. LCA is used to measure the environmental impacts of the life cycle of a product or service. While LCA is a commonly discussed topic in the literature, ref. [ 25 ] found that exemplar firms are using life cycle analysis at the basic level, and only to address the environmental impacts of the chain and not the social ones. Ref. [ 11 ] highlights the necessity of supply chain orientation for LCA. If supply chain orientation is not implemented, the information between the supplier, buyer and focal company will not be shared. As a result, joint contributions should be made by all members of the supply chain [ 11 ]. Stakeholder management is found to be one of the most frequent practices in the literature [ 11 ]. When companies decide to adopt proactive practices, the management of stakeholder requirements is acting as an important factor for performance, products and processes improvement [ 2 , 11 ]. Innovation is another key factor of proactivity and it has been investigated in the field of sustainable supply chain management literature [ 85 ]. Innovation includes the capability of a company to generate and implement new ideas and develop or apply new technologies. It is a prerequisite for dynamic market environments such as sustainable supply chain management [ 11 ]. An example of supply chain innovation is the adoption of new innovative technologies, such as the Internet of Things or Industry 4.0 tools, which make both internal and external processes more efficient and result in improved sustainability performance [ 61 ]. Learning from partners and stakeholders is another important dimension of proactivity. The acquisition of new knowledge is the key characteristic of learning. Companies can learn from supply chain partners, local communities, NGOs, government, researchers, etc. The authors of ref. [ 86 ] showed that when firms wish to implement a sustainability strategy, they should be pro-active in the first steps of the product’s development and in its whole life cycle. Overall, ref. [ 25 ] highlight that proactivity and commitment can only be effective if companies achieve an alignment between business models and environmental and social sustainability aspects. Ref. [ 11 ] further explains that in sustainable food supply chains, such as organic or fair trade, which are dynamic in nature and still young industries, proactive measures are necessary, since many new processes and technologies are under development.

3.1.3. Performance

Sustainability performance refers to how well an organisation achieves its environmental, economic and social goals. Most studies in the literature focus on the economic and environmental performance aspects, whereas the social dimension and the integration of the three sustainability dimensions are still lagging behind [ 2 ]. However, the review of [ 4 ] revealed a rising interest in studies that investigate the social dimension and the combination of all three dimensions; however, more research is needed in the field. The present section proposes sustainability performance as a three-dimensional construct. A more detailed discussion of the environmental, economic and social performance is provided below. In total, 684 SSCM measures were identified from 55 papers, which were grouped in the following three categories.

Environmental Performance

A wide variety of research papers has focused on the environmental performance of supply chains. As ref. [ 2 ] argues, this can be explained due to the fact that environmental issues have been on the research agenda for many years. This could be further supported by the fact that, in many countries, organizations are obliged to meet specific thresholds on their environmental impacts; e.g., toxi-chemical releases [ 87 ]. The most frequently used measure is related to either the reduction or avoidance of hazardous/harmful/toxic materials. The second most cited measure is water consumption, followed by energy consumption, recycled materials, Life Cycle Analysis (LCA) and environmental penalties. Energy efficiency, air emissions and greenhouse gas emissions are also some of the most cited measures in the literature.

A variety of other measures that appear less in the literature have addressed themes such as waste [ 79 , 87 , 88 ], environmental management systems, eco-design [ 89 , 90 ], biodiversity [ 87 , 91 ], etc.

Economic Performance

Economic performance is typically the most important factor that all companies are aiming to improve. Since the focus of this research is on supply chain management, the economic dimension is an integral part. In the context of SSCM, the comprehensive literature review in [ 2 ] shows that economic issues were addressed in all the studied papers. At this point, it should be mentioned that possible trade-offs between the three sustainability dimensions can occur. Especially for the economic dimension, economic incentives could be hidden behind a variety of environmental and social measures [ 87 ]. For example, economic performance measures such as procurement costs might increase when deciding to use environmentally friendly materials [ 4 ]. The most frequent measure regarding the economic performance is quality. Measures that focus on quality may refer to the quality of products provided by suppliers [ 87 ] or to the quality of the production process [ 73 ]. Sales, market share and profit are the second most frequent measures, followed by delivery time and customer satisfaction.

Other measures that appeared less in the literature include responsiveness [ 89 , 90 , 92 ] number of employees [ 93 , 94 , 95 ], transportation costs [ 95 , 96 , 97 ], etc.

Social Performance

As mentioned before, previous studies have revealed that little research has focused on the social performance of supply chains [ 12 , 98 ]. The authors of ref. [ 99 ] argue that this could be due to the fact that social issues are frequently hard to measure. The literature shows that only a few measures are frequently used confirming the fact that little attention has been given to the social dimension of SSCM. The most frequently used measure is recordable accidents followed by training and education and labour practices.

Other social issues that appeared in the literature include human rights [ 100 , 101 , 102 ], local communities influence [ 89 , 90 , 103 ], fair trade [ 57 , 100 , 104 ], philanthropy [ 105 ], etc. A recent study [ 106 ] has shed light on modern slavery in supply chains, a new area in the agenda of SSCM that has gained a lot of attention lately.

Table 2 lists the proposed constructs described in Section 3.1.1 , Section 3.1.2 and Section 3.1.3 , along with their definitions and supporting literature.

Proposed SSCM constructs, along with their definitions and supporting literature.

Figure 2 presents the SSCM theoretical framework developed in this study. A detailed description of the identified constructs is provided in the previous sections. Using literature support, this study has linked the developed constructs and proposed the expected relationships among them. The framework proposes that critical factors are influencing the implementation of SSCM practices, which in turn influence SSCM performance. CF is conceptualized as a three-dimensional construct (firm level, supply chain level and external level); SSCM practice is conceptualized as a five-dimensional construct (strategic orientation, continuity, collaboration, risk management and pro-activity); and SSCM performance is conceptualized as a three-dimensional construct (environmental, economic and social).

Proposed theoretical framework linking critical factors, practices and performance (based on the literature).

3.2. Case Study Results

The empirical results of the food supply chain case study reflect all the SSCM constructs that have been presented in the theoretical framework. In addition, some new “pioneering” SSCM practices emerged from the data. In Section 3.2.1 , the first research question is answered regarding the critical factors for engagement and implementation of SSCM practices. The second and third research questions are answered in Section 3.2.2 and Section 3.2.3 , by addressing which SSCM practices are implemented and what measures can be used for SSCM performance measurement in the food industry.

3.2.1. Critical Factors

The commitment and support of top management is reported as predominant firm-level critical factor for SSCM implementation. As highlighted, “sustainability is seen as an integral part for the future of our business. You cannot produce like there is no tomorrow, you produce because you want tomorrow to exist” (CSR Manager, SB). SSCM requires “proactive top management that understands that sustainability is an organizational commitment” ([ 25 ], p. 40). Indeed, top management is a critical firm-level factor for the promotion of SSCM and its absence may act as an obstacle for SSCM adoption [ 1 , 35 ].

Customer-driven orientations, in order to meet customer demands and needs, have been confirmed as critical factors of SSCM implementation, by all the interviewees. Previous studies have found that customer demands and requirements drive the development and implementation of SSCM practices [ 35 , 39 , 47 ]. For example, ref. [ 19 ] found that customer expectations are some of the most important driving forces for SSCM implementation. Similarly, ref. [ 47 ] have confirmed that customer demand and expectations are market drivers for corporate supply chain responsibility. In other words, adapting to what customers want is necessary for the implementation of SSCM at all supply chain stages.

According to the managers and the companies’ records, expertise and knowledge on environmental and social issues of supply chains is required to implement SSCM practices. Knowledge about how suppliers and other partners work regarding sustainability, is a critical SSCM factor that exemplar firms are adopting to improve their entire supply chains [ 25 ].

Employee training and development was also confirmed by both companies as another important firm-level critical factor. All key informants highlighted the continued efforts of their companies to offer a variety of programs in order to improve employee satisfaction and raise the sustainability awareness. Training and development can lead to engagement in SSCM practices, which, as mentioned by the interviewees, is a crucial part of the corporate strategy. It was evident by both the participants and the companies’ records that training and development programmes improve job performance and reduces errors and waste, which was also confirmed by the study of [ 18 ].

Efficiency in operations and material management was mentioned by the participants. Efficient energy management and electricity generated from renewable energy sources were the top mentioned factors of SSCM. Another element of efficiency is technology. Both companies exploit the available technologies to improve and optimize operational processes. This leads to cost savings and resource reduction and thus offers the ability for new investment plans.

The sampled supply chain is involved in traceability actions with their suppliers. Previous literature suggests that traceability is a new form of information sharing [ 25 ]. There is a requirement for information sharing on the living conditions of the animals, on the production of products, on the materials used, the locality information, information related to product labelling, etc. As reported, clear information about the products and their ingredients are provided on the front and back of the packages.

According to the interview data, the key to successful solutions to the daily problems is trust. Trusted partnerships and long-term cooperation build relationships of trust and confidence with suppliers. In this way, both companies achieve their goals, while at the same time “pushing” their suppliers to develop and improve as individuals. The same logic applies to the customers as well. Several systems are applied in the sampled companies, such as, compliance management system as well as anti-corruption and antifraud systems. In general, both companies are trying to create a climate of mutual trust among their stakeholders (employees, customers, suppliers, local communities, etc.).

Both companies have managed to establish a common supply chain strategy with their supply chain partners. The improvement in environmental and social standards across the supply chain is in the core element of the SSCM strategy. The organisations implement a sustainability strategy in their partnerships that includes goal-oriented actions. As reported, the suppliers are a crucial part of the supply chain, and through continuous dialogue with them, the added value of the products and services reducing one’s environmental footprint and effects on society is enhanced. Geographical distance was not mentioned by the participants. However, both companies use local sourcing in more than 80% of their operations. This creates additional added value in the local economy, with the indirect creation of jobs.

The interview data revealed that legislation requirements very often force companies to transform their business by applying sustainable practices; e.g., water saving. This is further identified in the secondary data, where strong focus is given on information regarding the legal penalties or fines for non-compliance with environmental and social regulations. Information is also provided regarding the compliance to European and national legislation on consumer products and the non-promotion and communication to minors (aged under 18 years old). The literature suggests that regulations and legislations can act as strong driving forces for the implementation of SSCM practices [ 39 , 43 ]. Examples, such as the British Petroleum (BP) oil spill, have shown that there is as huge negative impact on the supply chain economic performance, estimated at around $90 billion, including civil and criminal penalties [ 107 ].

Furthermore, the trends of stakeholders undoubtedly constitute an important pressure as they can also change a company’s strategy. For example, an interviewee mentioned that when there was an intensive debate about obesity, the company realized that it could not ignore it and decided to develop new products for consumers who do not want to get extra calories. In this way, the consumer had the choice of choosing the suitable product regarding his/her wishes. The sampled companies engage stakeholders in active dialogues throughout the year, to determine and redesign their sustainability strategy and actions and understand how to meet their needs and expectations. Stakeholder management is critical for maintaining a healthy and sustainable business. As declared with the CSR reports of the two companies and corroborated by the interview data, producing a positive value for stakeholders and creating the conditions for a healthy competitive environment enhance sustainable development.

The risk of changing product quality after production is mentioned as a key external critical factor for implementing SSCM practices. As reported, a company makes significant investments in order to offer the customer the right product, in the right package, at the right point of sales and at the right price, with its primary concern being safety. Another interviewee highlighted that the company is developing and implementing systems, standards and practices to ensure food quality and safety and avoid actual and reputational risks such as child labour.

3.2.2. Practices

The data analysis suggests the development of two main groups of practices: the traditional SSCM practices and the pioneering SSCM practices. The first group includes the SSCM practices as identified in the literature, while the second group encompasses SSCM practices that are adopted by leaders. The term “pioneering” is used only to describe these practices in the Greek food industry context. In the following sections, a description of both groups of practices is provided.

Traditional SSCM practices

Collaborations with supply chain members such as suppliers and customers as well as with a range of stakeholders such as NGOs and other entities are identified as key practices that help both companies and their supply chains to achieve sustainability goals. Long-term collaborations and contact with suppliers and stakeholders create relationships of trust and confidence. Development and improvement of suppliers as individuals is another characteristic of collaboration that emerged from the data. Joint development and training of suppliers is found to add value in the supply chain management performance. The data revealed that the companies are already deploying traceability practices for specific products. In parallel, they both are in the process of digital transformation, which will help them to increase supply chain traceability, transparency, quality, speed and efficiency.

Practices regarding suppliers’ and external partners’ selection are reported in the continuity category. According to an interviewee from SB, “There are guiding principles for all suppliers which include a wide range of requirements such as the confirmation that children are not working at a supplier’s company”. SB is implementing a “continuous development” approach, which deploys corrective actions to ensure that all suppliers comply with the company’s environmental, social and labour policy. Furthermore, the data suggest that partnering with reliable suppliers, especially in quality and safety issues, is necessary for a continuous relationship.

Strategic orientation

As reported, both companies are engaged in strategic supply chain management, which promotes the balance among environmental, economic and social issues. The data reveal that an SSCM strategy was already in place and three common characteristics were identified. First, a continuous business model alignment with economic, environmental and social issues is in place. For example, SB has re-designed a series of their products towards reducing plastic in packaging and this resulted in environmental and economic benefits, while at the same time allowed the company to apply similar techniques to other products. This is consistent with previous studies that found that alignment of environmental, social and economic goals is needed for managerial orientation towards sustainability [ 25 ]. The second and third component is that both companies treat suppliers as key strategic partners and focus on strategic sustainability issues related to the local communities.

Risk management

The implementation of management systems is used as a risk management tool for both companies. Food quality management systems (e.g., ISO 22000), environmental management systems (e.g., 14001) and health and safety (OHASAS 18001) are identified as key risk analysis tools. Furthermore, a strict supplier selection criteria system is supporting the risk management practices along with supplier monitoring through tactical inspections. Apart from the risk mitigation outcomes, tactical inspections are a pre-requisite for the successful interaction and long-term relationships among the supply chain members.

In this group of practices, the key component is to go beyond compliance with current legislation requirements by engaging in more advanced sustainable practices. Product innovation (e.g., products with reduced calories) and process innovation especially in the logistics domain are identified as key for SSCM. Supplier codes of conduct, including environmental, health and safety, labour and social issues, as well as partners’ coaching to adopt and implement SSCM practices are also included in proactive practices. Finally, energy- and water-saving practices and efficient fleet management are implemented to reduce the negative outcomes. Another set of practices that is related to proactivity, as stated by the CSR Manager of the SB and the Logistics Project Manager of the SM, is employee welfare, human rights practices, and the supporting actions for young people and local communities.

Pioneering SSCM Practices

• Conversation

Sustainability is part of the daily conversation in the two companies. Discussions of noneconomic issues is shared across all departments. As the CSR Manager of the SB company mentioned, “the basic principle in our company is social and environmental responsibility in our daily transactions”. Daily conversations about sustainability issues are part of all decision-making processes in a way that all employees consider social and/or environmental impacts of their decisions. As ref. [ 25 ] (p. 51) proposed, “management orientation is evidenced by sustainability being part of the day-to-day conversation”.

• Local sourcing

Local sourcing was evidenced by a focus on sourcing from Greek suppliers in more than 80%. Clear sustainability benefits of local sourcing include minimization of transport, increase of freshness and contributions to environmental and social improvements.

• Investing in Human Resources

Investing in human resources is considered a key SSCM practice. As in previous studies on sustainability leaders [ 25 ], the internal focus in this sample is on employee investments. Both companies provided information regarding their programmes for employee training, skills development and benefits. They both recognised positive outcomes regarding the employees’ personal development and well-being and their commitment to the organisations’ goals. As an interviewee mentioned, “investing in employee training and development not only serves as a motivation, but it also enables the organization to create a highly skilled workforce”.

3.2.3. Performance

By analysing the companies’ records, it became evident that sustainability performance was measured through specific indicators and standards. More specifically, both companies follow the GRI and UN Global Compact principles. This is evidenced by the sustainability reports, which reveal that the companies are adapting to international sustainability reporting standards. This should be no surprise, since both companies are sustainability leaders.

Both companies have mentioned that SSCM is related to a direct increase in costs. Many of the aforementioned practices, apart from the financial resources, include investments in human, and time resources. For example, practices regarding suppliers’ and external partners’ selection, such as the suppliers guiding principles of the SB, which require the confirmation that children are not working at the supplier’s company, as well as the tactical supplier inspections, increase costs. However, as the CSR Manager of SB mentioned, “sometimes you pay more to have the best suppliers and this contributes to added value for costumers, which increases customer loyalty”. Supporting local suppliers to adopt SSCM practices (employee protection and security, human rights, etc.) also contributes to the local economy through indirect job creation.

On the contrary, energy-saving practices are found to have a positive financial impact by means of cost reduction, which increases the profit rates. This is due to the fact that energy-efficiency investments are producing results from the first day of implementation. For instance, both companies have invested huge amounts in LED lighting, which is considered a highly energy-efficient technology.

Quality improvement is another important economic factor that both companies are engaged in. For instance, SM has mentioned that compliance with quality standards and reduction of defective products are key quality measures.

Not surprisingly, sales and market share, is also found to be a key economic measure. Other measures discussed under the economic dimension are the annual R&D investments, productivity, delivery time and flexibility.

As expected from both companies, as sustainability leaders, they have environmental performance systems in place that manage not only the environmental “basic” indicators (hazardous/harmful/toxic materials, energy, water, CO 2 emissions, compliance to standards, environmental accidents and use of recycled materials) but the advanced ones as well, such as the re-design of products towards a reduction in plastic and the reuse of it through circular processes. A key characteristic of both companies is that most of the indicators are measured at the organizational level. For example, energy use is measured in both companies’ facilities but not in their suppliers’ operations. It is also reported that the energy consumed comes from renewable energy sources at a level of 100% in SB’s facilities and 97% in SM’s facilities. Managers from SM have reported that the company is planning to measure the indirect emissions of its supply chain. As [ 9 ] propose, a useful tool to measure the impact of a supply chain as a whole is life-cycle analysis (LCA).

A variety of other measures have addressed themes such as waste recovery [ 20 ], waste [ 79 , 87 , 88 ], environmental management systems, eco-design [ 7 , 89 , 90 ] biodiversity [ 87 , 91 ], etc.

In the social sustainability dimension, the data suggested indicators such as product safety, employee accident rates, employee training rates, health and safety issues, employment contribution, employee benefits, loyalty and turnover rate, corporate image, human rights screening (suppliers and contractors) and community support. Several projects both internal and external are implemented in both companies. For example, an excellent working environment that is fair, safe and enjoyable with prospects for development (such as job rotation, promotions, new roles, etc.) is a key performance measure for SB. From an external point of view, supplier social assessment is performed from SB regarding the suppliers’ human right policies and broader social issues. Furthermore, SM reported that local community support in the form of volunteering or charity actions is another key performance indicator.

Table 3 presents the SSCM aspects as identified in the case study.

Aspects of SSCM as identified in the case study.

3.3. Discussion

The results of this study offer empirical evidence regarding the identified constructs and their interrelationships. More specifically, the data analysis suggests a model of SSCM in the food industry, providing a first step toward defining three constructs (critical factors, practices and performance) that can create sustainability in the food industry. The proposed model is depicted in Figure 3 .

Conceptual model of sustainable supply chain management in the food industry.

The model is developed based on the extant literature and the case study data. Figure 3 presents specific relationships between the constructs, which contribute to a better understanding of SSCM in the food industry. In the following paragraphs, the relationships of the proposed constructs are conceptualized in propositions that need to be tested in future research.

The ability of a company to identify and understand the factors that enable and inhibit the creation of sustainability across supply chain is critical for SSCM. A variety of SSCM critical factors is identified and categorized at the firm level, the supply chain level and the external level. These factors are linked to the implementation of SSCM practices. In line with prior literature, the commitment of top management or the knowledge and expertise regarding sustainability are identified as important firm-level critical factors for SSCM. For example, ref. [ 1 , 35 ] suggest that the lack of top management commitment and support hinder the development of SSCM. SSCM requires “proactive top management that understands that sustainability is an organizational commitment” [ 25 ] (p. 40).

At the supply chain level there is evidence that information sharing and trust between partners are two of the key critical factors for implementing SSCM. The literature posits that that information sharing enables the development of new ideas regarding sustainability and enhances collaboration throughout the supply chain [ 18 ]. On the opposite side, the lack of information sharing is found to have a negative impact on SSCM implementation [ 41 , 42 ].

Regarding the external environment, three key factors have been confirmed by the dataset: compliance with international and national regulations, stakeholder management and reduction in actual and reputational risk. The identification, engagement and communication with customers, local community and NGOs were reported as critical factors for the successful implementation of SSCM practices. This is consistent with prior literature which confirmed that stakeholders are driving forces for the integration of SSCM practices [ 19 ]. Especially in the food retail industry NGO pressure is critical for the adoption of SSCM [ 53 ].

Based on the above, the first set of propositions is developed below.

SSCM critical factors are directly related to the implementation of SSCM practices.

Firm-level critical factors are directly related to the implementation of SSCM practices.

Supply chain-level critical factors are directly related to the implementation of SSCM practices.

External critical factors are directly related to the implementation of SSCM practices.

Considering the adopted SSCM practices, the findings suggest two main groups, namely, the traditional SSCM practices and the pioneering SSCM practices. Traditional SSCM practices include the five categories proposed in the literature. This is not a surprise, since the sample of this study is comprised by leaders in sustainability. In this case study, the SSCM practices as proposed by [ 11 ] are used as a key starting point and as a guiding tool for developing a model of SSCM in the food industry. What is interesting in this case study, is the possible trade-offs between the SSCM practices. For example, the focus on supplier continuity requires long-term relationships which is a key element of collaboration. This is also consistent with prior literature which suggests that supply base continuity long-term relationships are critical for the successful implementation of SSCM [ 108 ]. Continuity was also evidenced by a focus on supplier risk management. Both companies have in place a supplier selection criteria system, which is also related to the supplier codes of conduct that comprise environmental, health and safety, labour and social issues. Regarding the three identified pioneering SSCM practices (conversation, local sourcing and HR investments), it should be noted that they could have been encompassed in the traditional SSCM practices. However, it was decided to be separately presented since both companies engage in these practices in significant amounts. Furthermore, the purpose was to show what sustainability leaders in the food industry are doing regarding SSCM. In no way do these three practices constitute something new or unique.

The findings underline that SSCM performance is linked to SSCM practices. Despite the fact that all participants agreed on a direct increased cost of implementing SSCM, their general perspective was that SSCM practices have the ability to enhance environmental and social performance. This is also supported by [ 6 ], who found that environmentally friendly purchasing and sustainable packaging have a positive effect on sustainable performance. Another example based on the results is food safety, which is linked to improved sustainability and can be achieved through traceability practices. Evidence of similar results is also provided by [ 34 ], who found that traceability practices in the meat supply chain are closely associated with social sustainability and food safety. It can also be argued that traceability is the end-result of sharing information, which is related to enhanced supply chain performance [ 25 ]. Based on the above observations, the following propositions are developed.

SSCM practices are positively associated with sustainability performance.

Strategic orientation is positively associated with sustainability performance .

Continuity is positively associated with sustainability performance.

Collaboration is positively associated with sustainability performance.

Risk management is positively associated with sustainability performance .

Pro-activity is positively associated with sustainability performance.

Conversation, is positively associated with sustainability performance.

Local sourcing, is positively associated with sustainability performance.

Investing in HR is positively associated with sustainability performance.

Another interesting finding is the interrelationships between the three dimensions of sustainability performance. The data suggest that environmental performance improvements, such as energy efficiency practices, have visible cost reductions in the short term. This contradicts the results of [ 24 ], who found that in the food industry environmental performance is not affecting costs directly. Continuing with a study in the Italian meat supply chain, ref. [ 34 ] found that SSCM practices, such as cleaner technologies, offer a competitive advantage, since they contribute to improved economic and environmental or social performance. Ref. [ 109 ] also found a positive correlation between corporate social performance and corporate financial performance. Based on the above arguments, the following propositions are developed.

Environmental performance is positively associated to economic performance.

Social performance is positively associated to economic performance.

4. Conclusions

4.1. theoretical contributions.

This research has examined the SSCM critical factors, practices and performance through a literature review and a case study comprised of sustainability leaders in the food industry. The study has identified the SSCM critical factors and practices that sustainability leaders implement and what measures are used in sustainability performance in the food industry. In line with ref. [ 32 ], who highlights the deductive nature of case studies, this research investigated the applicability and validity of the three SSCM constructs as identified in the literature review, in a specific Greek food supply chain. The case study implies direct and indirect links among the three key constructs, namely, SSCM critical factors, SSCM practices and sustainability performance. Furthermore, in line with the developed propositions, the three constructs are conceptualised within a model that needs to be quantitatively tested.

It can be argued that it is not a surprise that the two sustainability leaders are more committed to SSCM. Both have identified common factors that are critical for developing SSCM practices. This study has also identified a new set of pioneering SSCM practices in the Greek food industry. Daily conversations, local sourcing and investing in HR are common practices for SSCM leaders in the Greek food supply chain, however industry specific.

The developed SSCM conceptual model can be exploited by researchers that wish to investigate the proposed constructs individually or together, both at the firm level and the supply chain level, and either through quantitative (surveys) or qualitative research methods (replicate the case study in other geographical locations or other industries). Researchers may also take advantage of the developed model and use it as an evaluation framework or as an SSCM roadmap for the design of future research projects.

4.2. Managerial Implications

Apart from the theoretical contributions, this study provides some managerial implications regarding the deployment of the proposed model. While the identified constructs in this research are not new and can be characterized as SSCM traditional, they have been studied in a food supply chain considering all the three sustainability dimensions. The developed model can be used by companies in the food industry that want to promote or determine the best way to develop SSCM and improve their sustainability performance. The results can be utilized by food industry professionals and assist them in the development of SSCM by identifying the critical factors of SSCM implementation, the practices adopted, and the sustainability performance measures.

4.3. Limitations and Future Research Directions

This study, as in any other research, suffers from limitations that will be presented along with future research propositions. First, the sample is small, industry and location specific, and the results cannot be transferred or used to generalize the overall food industry. Future studies may conduct research in other industries or world regions, using larger samples, in order to achieve generalization of the results. Second, this study focused on food sustainability leaders. It is likely that in more typical organisations—not sustainability leaders—different SSCM factors, practices and performance measures will be identified. Third, the traditional and pioneering practices should be investigated in other industries to check their applicability as well as the possible trade-offs. Finally, in this study, specific interrelationships among the constructs are addressed. However, the small sample does not allow for deeper investigations. Future research should examine the importance of each of the constructs and the strength of their inter-relationships.

Interview Protocol

• (1) General information about the company
• What are the factors that push the company to implement SSCM practices?
• What are the factors that hinder the company to implement SSCM practices?

Supply chain continuity

Pro-activity

• What measures/indicators does your company use to measure SSCM performance?
• How has the implementation of SSCM practices affected the environmental, social and economic performance of your company?
• Is there any observed relationship between environmental, social and economic performance (win–win, win–lose)?

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, T.M. and K.G.; methodology, T.M. and K.G.; validation, T.M. and K.G.; formal analysis, T.M.; investigation, T.M.; resources, T.M.; data curation, T.M. and K.G.; writing—original draft preparation, T.M.; writing—review and editing, T.M. and K.G.; visualization, T.M.; supervision, K.G.; project administration, T.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted based on the principles of the Committee for Research Ethics of the University of Macedonia, that was established according to Chapter E’ (Articles 21–27) of Law 4521 (Government Gazette vol. A ’38/2-3-2018).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

A Systematic Review of Sustainable Supply Chain Management Practices in Food Industry

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• First Online: 15 November 2021
• Cite this conference paper

• Federica Minardi 18 ,
• Valérie Botta-Genoulaz 18 &
• Giulio Mangano 19  

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 629))

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The food industry is central to human beings and heavily impacts the lives of the entire society. Nowadays, the sustainable development goal and the introduction of new information and communication technologies has led food companies to deal with this new paradigm. They require sustainable practices that have the dual objective of improving the overall performance of the company itself and fulfilling the sustainability requirement. Research works on sustainable supply chain management practices in the food industry is quite fragmented, as it often considers just a part of the chain. Therefore, through a systematic literature review, this paper aims to provide an up-to-date analysis of supply chain management practices within the scope of sustainability, studying the findings of 224 reviewed papers. The implications of this work are relevant for academic research as they enlarge the body of knowledge and highlight key points where there is the need to investigate further. From a practical point of view this study proposes an overview of the most common and adopted practices that can be implemented in order to achieve sustainable development in the food industry.

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American Productivity and Quality Council (APQC). Available on http://www.apqc.org .

Rohmer, S.U.K., Gerdessen, J.C., Claassen, G.D.H.: Sustainable supply chain design in the food system with dietary considerations: a multi-objective analysis. Eur. J. Oper. Res. 273 , 1149–1164 (2019)

Article   Google Scholar  

Manzini, R., Accorsi, R.: The new conceptual framework for food supply chain assessment. J. Food Eng. 115 , 251–263 (2013)

Nosratabadi, S., Mosavi, A., Shamshirband, S., Kazimieras Zavadskas, E., Rakotonirainy, A., Chau, K.W.: Sustainable business models: a review. Sustainability 11 (6), 1663 (2019)

Google Scholar  

Khan, S.A.R., Yu, Z., Golpîra, H., Sharif, A., Mardani, A.: A state-of-the-art review and meta-analysis on sustainable supply chain management: future research directions. J. Clean. Prod., 123357 (2020)

Emamisaleh, K., Rahmani, K.: Sustainable supply chain in food industries: drivers and strategic sustainability orientation. Cogent Bus. Manag. 4 (1), 1345296 (2017)

Elkington, J.: Partnerships from cannibals with forks: the triple bottom line of 21st-century business. Environ. Qual. Manag. 8 (1), 37–51 (1998)

Tidy, M., Wang, X., Hall, M.: The role of supplier relationship management in reducing greenhouse gas emissions from food supply chains: supplier engagement in the UK supermarket sector. J. Clean. Prod. 112 , 3294–3305 (2016)

Genovese, A., Acquaye, A.A., Figueroa, A., Koh, S.L.: Sustainable supply chain management and the transition towards a circular economy: evidence and some application. Omega 66 , 344–357 (2017)

Pohlmann, C.R., Scavarda, A.J., Alves, M.B., Korzenowski, A.L.: The role of the focal company in sustainable development goals: a Brazilian food poultry supply chain case study. J. Clean. Prod. 245 , 118798 (2020)

Kalmykova, Y., Sadagopan, M., Rosado, L.: Circular economy–from review of theories and practices to development of implementation tools. Resour., Conserv. Recycl. 135 , 190–201 (2018)

Lee, L.S., Misni, F.: A review on strategic, tactical and operational decision planning in reverse logistics of green supply chain network design. J. Comput. Commun. 5 , 83–104 (2015)

Chardine-Baumann, E., Botta-Genoulaz, V.: A framework for sustainable performance assessment of supply chain management practices. Comput. Ind. Eng. 76 , 138–147 (2014)

Sgarbossa, F.: Russo, I: a proactive model in sustainable food supply chain: Insight from a case study. Int. J. Prod. Econ. 183 , 596–606 (2017)

Krishnan, R., Agarwal, R., Bajada, C., Arshinder, K.: Redesigning a food supply chain for environmental sustainability–An analysis of resource use and recovery. J. Clean. Prod. 242 , 118374 (2020)

European Parliament. Regulation (EU) No 1305/2013 of the European Parliament and of the Council of 17 December 2013 on Support for Rural Development by the European Agricultural Fund for Rural Development (EAFRD) and Repealing Council Regulation (EC), No 1698/2005, European Parliament, Brussels, Belgium

Filippini, R., Marraccini, E., Lardon, S., Bonari, E.: Is the choice of a farm’s commercial market an indicator of agricultural intensity? Conventional and short food supply chains in periurban farming systems. Italian J. Agron. 11 (1), 1–5 (2016)

Baez, Y.P., Sequeira, M., Hilletofth, P.: Local and organic food distribution systems: towards a future agenda. Oper. Supply Chain Manag. 13 , 336–348 (2020)

Chan, H.Y., Abdul Halim-Lim, S., Tan, T.B., Kamarulzaman, N.H., Jamaludin, A.A., Wan-Mohtar, W.A.A.Q.I.: Exploring the drivers and the interventions towards sustainable food security in the food supply chain. Sustainability 12 (19), 7890 (2020)

Wohlin, C.: Guidelines for snowballing in systematic literature studies and a replication in software engineering. In: 18th International Conference on Evaluation and Assessment in Software Engineering, EASE’2014, London, England, pp. 1–10 (2014)

Gunasekaran, A., Gawankar, S., Kamble, S.S.: Achieving sustainable performance in a data-driven agriculture supply chain: a review for research and applications. Int. J. Prod. Econ. 219 , 179–194 (2020)

Hox, J.J., Boeije, H.R.: Data collection, primary vs secondary. Ency. Soc. Manag. 1 , 593–599 (2005)

Gustavsson, J., Cederberg, C., Sonesson, U.: Global food losses and food waste—extent, causes and prevention. FAO, Rome, Italy (2011)

FoodDrink Europe. https://www.fooddrinkeurope.eu/uploads/publications_documents/FoodDrinkEurope_-_Data__Trends_2020_digital.pdf . Accessed 13 Jan 2021

Monteiro, C.A.: A new food classification based on the extent and purpose of industrial food processing. In: 8th International Conference on Diet and Activity Methods, FAO, Rome (2012)

Zimon, D., Tyan, J., Sroufe, R.: Drivers of sustainable supply chain management: practices to alignment with UN sustainable development goals. Int. J. Qual. Res. 14 (1), 219–236 (2019)

Papargyropoulou, E., Lozano, R., Steinberger, J.K., Wright, N., Ujang, Z.: The food waste hierarchy as a framework for the management of food surplus and food waste. J. Clean. Prod. 76 , 106–115 (2014)

Mangla, S.K., Rich, N., Luthra, S., Kumar, D., Rana, N.P., Dwivedi, Y.K.: Enablers to implement sustainable initiatives in agri-food supply chains. Int. J. Prod. Econ. 203 , 379–393 (2018)

Govindan, K., Kadziński, M., Sivakumar, R.: Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega 71 , 129–145 (2017)

Luthra, S., Raut, R.D., Narkhede, B.E., Mangla, S.K., Gardas, B.B., Priyadarshinee, P.: Examining the performance oriented indicators for implementing green management practices in the Indian agro sector. J. Clean. Prod. 215 , 926–943 (2019)

Boye, J. I., Yves. A.: Current trends in green technologies in food production and processing. Food Eng. Rev. 5 , 1–17 (2013)

Makinde, O., Mowandia, M.T., Michael, A.: Performance evaluation of the supply chain system of a food product manufacturing system using a questionnaire-based approach. Procedia Manuf. 43 , 751–757 (2020)

Accorsi, R., Baruffaldi, G., Manzini, R., Pini, C.: Environmental impacts of reusable transport items: a case study of pallet pooling in a retailer supply chain. Sustainability 11 (11), 3147 (2019)

Nemecek, T., Kulak, M., Frossard, E., Gaillard, G.: Eco-efficiency improvement by using integrative design and life cycle assessment. The case study of alternative bread supply chains in France. J. Clean. Prod. 112 , 2452–2461 (2016)

Wiese, W., Toporowski, A.: CSR failures in food supply chains – an agency perspective. Br. Food J. 115 (1), 92–107 (2013)

Jaaron, A.A., Zaid, A.A., Bon, A.T.: The impact of green human resource management and green supply chain management practices on sustainable performance: an empirical study. J. Clean. Prod. 204 , 965–979 (2018)

Grimm, J.H., Hofstetter, J.S., Sarkis, J.: Exploring sub-suppliers’ compliance with corporate sustainability standards. J. Clean. Prod. 112 , 1971–1984 (2016)

Azevedo, S.G., Silva, M.E., Matias, J.C., Dias, G.P.: The influence of collaboration initiatives on the sustainability of the cashew supply chain. Sustainability 10 (6), 2075 (2018)

Lutz, J., Smetschka, B., Grima, N.: Farmer cooperation as a means for creating local food systems—potentials and challenges. Sustainability 9 (6), 925 (2017)

El Bilali, H., Allahyari, M.S.: Transition towards sustainability in agriculture and food systems: role of information and communication technologies. Inf. Process. Agri. 5 , 456–464 (2018)

Kummer, S., Milestad, R.: The diversity of organic box schemes in Europe—an exploratory study in four countries. Sustainability 12 (7), 2734 (2020)

Mihailović, B., Jean I. R., Popović, V., Radosavljević, K., Krasavac, B.C., Bradić-Martinović, A.: Farm differentiation strategies and sustainable regional development. Sustainability 12 (7223) (2020)

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Minardi, F., Botta-Genoulaz, V., Mangano, G. (2021). A Systematic Review of Sustainable Supply Chain Management Practices in Food Industry. In: Camarinha-Matos, L.M., Boucher, X., Afsarmanesh, H. (eds) Smart and Sustainable Collaborative Networks 4.0. PRO-VE 2021. IFIP Advances in Information and Communication Technology, vol 629. Springer, Cham. https://doi.org/10.1007/978-3-030-85969-5_2

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Animals searching for food must navigate through complex landscapes with varying terrain, food availability, predator activity, and shelter. One method for analyzing how foraging animals balance these competing interests is Optimal Foraging Theory, which posits that foragers make decisions that maximize some expected reward or utility. However, models have generally focused on simplified settings, especially regarding animal movement decisions, either ignoring spatial variability entirely or limiting the forager’s options to choosing among a few discrete habitat patches (or patch types) that are modeled as spatially uniform. In this paper, we present a model of optimal foraging in a continuous landscape for an animal that is subject to predation. Foraging animals thus must choose not only where to gather food, but also how to (safely) travel across the landscape. Furthermore, we explicitly model stochastic predator interactions, allowing us to predict optimal foraging trajectories conditional on the presence or absence of an immediate threat from a predator. We illustrate our model with numerical examples, one a hypothetical landscape with two regions of high food abundance and two areas of high predation risk on the direct route between the feeding areas and the forager’s overnight refuge, the other inspired by empirical data on foraging Samango monkeys ( Cercopithecus albogularis schwarzi ). We find that the shape of a forager’s utility function affects not only where it chooses to feed, but also the paths it takes to and from the optimal feeding ground. Thus, examining predicted optimal trajectories can provide additional information about what quantity, if any, animals optimize while foraging. We also develop and demonstrate a preliminary model for an animal that depletes the food supply in its local environment.

Competing Interest Statement

The authors have declared no competing interest.

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Two decades of advancements in cold supply chain logistics for reducing food waste: a review with focus on the meat industry.

1. Introduction

Objective and scope of study.

• What is the current state of the art on beef CSCL in terms of management, sustainability, network design, and the use of information technologies for red meat waste reduction?
• To provide an overview of the current state of the art and to identify the gaps and contemporary challenges to red meat waste reduction;
• To identify key research themes and their potential role and associated elements in mitigating red meat waste reduction, especially across the beef CSCL systems;
• To pinpoint the directions in each theme that warrant further research advancement.

2. Materials and Methods

2.1. literature retrieval and selection, 2.2. extracting the research themes, 3.1. the literature review identified themes and subjects, 3.2. the literature’s evolution and descriptive results, 3.3. management, 3.3.1. logistics management and chronological evolution, 3.3.2. management and regulations, 3.3.3. management and collaboration, 3.3.4. management and costs, 3.3.5. management and inventory, 3.3.6. management and decision-making, 3.3.7. management and risks, 3.3.8. management and waste reduction, 3.3.9. management and information, 3.3.10. management and cold chain deficiencies, 3.4. sustainability, 3.4.1. sustainability and closed-loop scs (clscs), 3.4.2. sustainability and business models, 3.4.3. sustainability and wastage hotspots, 3.4.4. sustainability and packing, 3.4.5. sustainability and information flow, 3.5. network design optimisation, 3.5.1. network design and decision levels, 3.5.2. network design and the location–inventory problem, 3.5.3. network design and routing-inventory problem, 3.5.4. network design and the location routing problem, 3.5.5. network design and the integrated location–inventory routing problem, 3.5.6. network design and sustainability, 3.5.7. network design and information flow, 3.6. information technologies, 3.6.1. it and meat sc transformation, 3.6.2. emerging information technologies and meat scs, technical instruments, technological systems, 4. discussion, 4.1. management, 4.2. sustainability, 4.3. network design, 4.4. information technology, 5. conclusions.

• Management: ◦ Effective management practices are crucial for addressing FLW in beef CSCL systems. ◦ There is a notable transition from LM to FLM and SFLM, with the potential for emerging technologies to create an “Intelligent Sustainable Food Logistics Management” phase. ◦ Suboptimal management practices continue to contribute significantly to FLW, underscoring the need for enhanced strategies and adherence to regulations and standards.
• Sustainability: ◦ Sustainability in beef CSCL involves addressing social, economic, and environmental benefits. ◦ Reducing FLW can lead to increased profits, improved customer satisfaction, public health, equity, and environmental conservation by minimising resource use and emissions. ◦ Comprehensive research integrating all sustainability dimensions is needed to fully understand and mitigate FLW. Current efforts often address only parts of sustainability. A more holistic approach is required to balance environmental, economic, and social dimensions effectively.
• Network Design: ◦ Effective network design and optimisation are pivotal in reducing FLW within beef CSCL systems. ◦ There is a necessity for integrating all three levels of management decisions in the logistics network design process. Decision levels in network design must be considered to understand trade-offs among sustainability components in this process. ◦ Future research should focus on integrating management decisions and network design, CSCL uncertainties, sustainability dimensions, and advanced technologies to enhance efficiency and reduce waste in beef CSCL systems.
• Information Technologies: ◦ Information technologies such as Digital Twins (DTs) and Blockchain (BC) play a significant role in improving efficiency and reducing FLW in beef CSCL. ◦ The integration of these technologies can enhance understanding of fluid dynamics, thermal exchange, and meat quality variations, optimising the cooling process and reducing energy usage. ◦ Challenges like data security and management efficiency need to be addressed to maximise the benefits of these technologies.

Author Contributions

Data availability statement, acknowledgments, conflicts of interest.

• Ren, Q.-S.; Fang, K.; Yang, X.-T.; Han, J.-W. Ensuring the quality of meat in cold chain logistics: A comprehensive review. Trends Food Sci. Technol. 2022 , 119 , 133–151. [ Google Scholar ] [ CrossRef ]
• Nastasijević, I.; Lakićević, B.; Petrović, Z. (Eds.) Cold chain management in meat storage, distribution and retail: A review. In IOP Conference Series: Earth and Environmental Science ; IOP Publishing: Bristol, UK, 2017. [ Google Scholar ]
• Brodribb, P. A Study of Waste in the Cold Food Chain and Opportunities for Improvement ; Expert Group: Hefei, China, 2020. [ Google Scholar ]
• Castonguay, A.C.; Polasky, S.; Holden, M.H.; Herrero, M.; Mason-D’Croz, D.; Godde, C.; Chang, J.; Gerber, J.; Witt, G.B.; Game, E.T. Navigating sustainability trade-offs in global beef production. Nat. Sustain. 2023 , 6 , 284–294. [ Google Scholar ] [ CrossRef ]
• FAO. FAOSTAT Online Database. 2021. Available online: http://faostat.fao.org/ (accessed on 20 September 2023).
• Australia, M.L. Global Beef Industry and Trade Report ; Meat & Livestock Australia: Sydney, NSW, Australia, 2022. [ Google Scholar ]
• Juan Ding, M.; Jie, F.; Parton, K.A.; Matanda, M.J. Relationships between quality of information sharing and supply chain food quality in the Australian beef processing industry. Int. J. Logist. Manag. 2014 , 25 , 85–108. [ Google Scholar ] [ CrossRef ]
• Li, H.; Pan, P. (Eds.) Food waste in developed countries and cold chain logistics. In E3S Web of Conferences ; EDP Sciences: Les Ulis, France, 2021. [ Google Scholar ]
• Ishangulyyev, R.; Kim, S.; Lee, S.H. Understanding food loss and waste—Why are we losing and wasting food? Foods 2019 , 8 , 297. [ Google Scholar ] [ CrossRef ]
• National Food Waste Strategy: Halving Australia’s Food Waste by 2030. Department of Climate Change, Energy, the Environment and Water, Canberra, Australia. 2024. Available online: https://www.dcceew.gov.au/environment/protection/waste/food-waste#:~:text=Australia’s%20National%20Food%20Waste%20Strategy,the%20National%20Food%20Waste%20Strategy (accessed on 8 February 2024).
• Keegan, E.; Breadsell, J.K. Food waste and social practices in Australian households. Sustainability 2021 , 13 , 3377. [ Google Scholar ] [ CrossRef ]
• Aschemann-Witzel, J.; De Hooge, I.; Amani, P.; Bech-Larsen, T.; Oostindjer, M. Consumer-related food waste: Causes and potential for action. Sustainability 2015 , 7 , 6457–6477. [ Google Scholar ] [ CrossRef ]
• Gokarn, S.; Kuthambalayan, T.S. Analysis of challenges inhibiting the reduction of waste in food supply chain. J. Clean. Prod. 2017 , 168 , 595–604. [ Google Scholar ] [ CrossRef ]
• Yan, H.; Song, M.-J.; Lee, H.-Y. A Systematic Review of Factors Affecting Food Loss and Waste and Sustainable Mitigation Strategies: A Logistics Service Providers’ Perspective. Sustainability 2021 , 13 , 11374. [ Google Scholar ] [ CrossRef ]
• Brennan, A.; Browne, S. Food waste and nutrition quality in the context of public health: A scoping review. Int. J. Environ. Res. Public Health 2021 , 18 , 5379. [ Google Scholar ] [ CrossRef ]
• Neff, R.A.; Kanter, R.; Vandevijvere, S. Reducing food loss and waste while improving the public’s health. Health Aff. 2015 , 34 , 1821–1829. [ Google Scholar ] [ CrossRef ]
• Luo, N.; Olsen, T.; Liu, Y. A Conceptual Framework to Analyze Food Loss and Waste within Food Supply Chains: An Operations Management Perspective. Sustainability 2021 , 13 , 927. [ Google Scholar ] [ CrossRef ]
• Parashar, S.; Sood, G.; Agrawal, N. Modelling the enablers of food supply chain for reduction in carbon footprint. J. Clean. Prod. 2020 , 275 , 122932. [ Google Scholar ] [ CrossRef ]
• Ma, L.; Qin, W.; Garnett, T.; Zhang, F. Review on drivers, trends and emerging issues of the food wastage in China. Front. Agric. Sci. Eng. 2015 , 2 , 159–167. [ Google Scholar ] [ CrossRef ]
• Lan, S.; Tseng, M.-L.; Yang, C.; Huisingh, D. Trends in sustainable logistics in major cities in China. Sci. Total Environ. 2020 , 712 , 136381. [ Google Scholar ] [ CrossRef ]
• Farooque, M.; Zhang, A.; Liu, Y. Barriers to circular food supply chains in China. Supply Chain Manag. Int. J. 2019 , 24 , 677–696. [ Google Scholar ] [ CrossRef ]
• Han, J.-W.; Zuo, M.; Zhu, W.-Y.; Zuo, J.-H.; Lü, E.-L.; Yang, X.-T. A comprehensive review of cold chain logistics for fresh agricultural products: Current status, challenges, and future trends. Trends Food Sci. Technol. 2021 , 109 , 536–551. [ Google Scholar ] [ CrossRef ]
• Martinez, M.G.; Fearne, A.; Caswell, J.A.; Henson, S. Co-regulation as a possible model for food safety governance: Opportunities for public–private partnerships. Food Policy 2007 , 32 , 299–314. [ Google Scholar ] [ CrossRef ]
• Wang, K.Y.; Yip, T.L. Cold-chain systems in China and value-chain analysis. In Finance and Risk Management for International Logistics and the Supply Chain ; Elsevier: Amsterdam, The Netherlands, 2018; pp. 217–241. [ Google Scholar ]
• Liu, M.; Dan, B.; Zhang, S.; Ma, S. Information sharing in an E-tailing supply chain for fresh produce with freshness-keeping effort and value-added service. Eur. J. Oper. Res. 2021 , 290 , 572–584. [ Google Scholar ] [ CrossRef ]
• An, J.; Wang, L.; Lv, X. Research on agri-food cold chain logistics management system: Connotation, structure and operational mechanism. J. Serv. Sci. Manag. 2015 , 8 , 894–902. [ Google Scholar ] [ CrossRef ]
• Zhang, J.; Cao, W.; Park, M. Reliability analysis and optimization of cold chain distribution system for fresh agricultural products. Sustainability 2019 , 11 , 3618. [ Google Scholar ] [ CrossRef ]
• Zhang, H.; Qiu, B.; Zhang, K. A new risk assessment model for agricultural products cold chain logistics. Ind. Manag. Data Syst. 2017 , 117 , 1800–1816. [ Google Scholar ] [ CrossRef ]
• Chauhan, C.; Dhir, A.; Akram, M.U.; Salo, J. Food loss and waste in food supply chains. A systematic literature review and framework development approach. J. Clean. Prod. 2021 , 295 , 126438. [ Google Scholar ] [ CrossRef ]
• Luo, N.; Olsen, T.; Liu, Y.; Zhang, A. Reducing food loss and waste in supply chain operations. Transp. Res. Part E Logist. Transp. Rev. 2022 , 162 , 102730. [ Google Scholar ] [ CrossRef ]
• Broeze, J.; Guo, X.; Axmann, H. Trade-Off Analyses of Food Loss and Waste Reduction and Greenhouse Gas Emissions in Food Supply Chains. Sustainability 2023 , 15 , 8531. [ Google Scholar ] [ CrossRef ]
• Kumar, A.; Mangla, S.K.; Kumar, P. An integrated literature review on sustainable food supply chains: Exploring research themes and future directions. Sci. Total Environ. 2022 , 821 , 153411. [ Google Scholar ] [ CrossRef ]
• Stindt, D. A generic planning approach for sustainable supply chain management-How to integrate concepts and methods to address the issues of sustainability? J. Clean. Prod. 2017 , 153 , 146–163. [ Google Scholar ] [ CrossRef ]
• Tranfield, D.; Denyer, D.; Smart, P. Towards a methodology for developing evidence-informed management knowledge by means of systematic review. Br. J. Manag. 2003 , 14 , 207–222. [ Google Scholar ] [ CrossRef ]
• Cerchione, R.; Esposito, E. A systematic review of supply chain knowledge management research: State of the art and research opportunities. Int. J. Prod. Econ. 2016 , 182 , 276–292. [ Google Scholar ] [ CrossRef ]
• Ali, I.; Gölgeci, I. Where is supply chain resilience research heading? A systematic and co-occurrence analysis. Int. J. Phys. Distrib. Logist. Manag. 2019 , 49 , 793–815. [ Google Scholar ] [ CrossRef ]
• VOSviewer. Available online: https://www.vosviewer.com/getting-started (accessed on 20 May 2023).
• Chopra, S. Designing the distribution network in a supply chain. Transp. Res. Part E Logist. Transp. Rev. 2003 , 39 , 123–140. [ Google Scholar ] [ CrossRef ]
• Gunasekaran, A.; Lai, K.-h.; Cheng, T.E. Responsive supply chain: A competitive strategy in a networked economy. Omega 2008 , 36 , 549–564. [ Google Scholar ] [ CrossRef ]
• Dabbene, F.; Gay, P.; Sacco, N. Optimisation of fresh-food supply chains in uncertain environments, Part I: Background and methodology. Biosyst. Eng. 2008 , 99 , 348–359. [ Google Scholar ] [ CrossRef ]
• Trienekens, J.; Zuurbier, P. Quality and safety standards in the food industry, developments and challenges. Int. J. Prod. Econ. 2008 , 113 , 107–122. [ Google Scholar ] [ CrossRef ]
• Lipinski, B.; Hanson, C.; Lomax, J.; Kitinoja, L.; Waite, R.; Searchinger, T. Reducing Food Loss and Waste ; World Resources Institute: Washington, DC, USA, 2013. [ Google Scholar ]
• van der Vorst, J.G.; van Kooten, O.; Luning, P.A. Towards a diagnostic instrument to identify improvement opportunities for quality controlled logistics in agrifood supply chain networks. Int. J. Food Syst. Dyn. 2011 , 2 , 94–105. [ Google Scholar ]
• Wognum, P.N.; Bremmers, H.; Trienekens, J.H.; Van Der Vorst, J.G.; Bloemhof, J.M. Systems for sustainability and transparency of food supply chains–Current status and challenges. Adv. Eng. Inform. 2011 , 25 , 65–76. [ Google Scholar ] [ CrossRef ]
• Soysal, M.; Bloemhof-Ruwaard, J.M.; Meuwissen, M.P.; van der Vorst, J.G. A review on quantitative models for sustainable food logistics management. Int. J. Food Syst. Dyn. 2012 , 3 , 136–155. [ Google Scholar ]
• Koberg, E.; Longoni, A. A systematic review of sustainable supply chain management in global supply chains. J. Clean. Prod. 2019 , 207 , 1084–1098. [ Google Scholar ] [ CrossRef ]
• Brandenburg, M.; Govindan, K.; Sarkis, J.; Seuring, S. Quantitative models for sustainable supply chain management: Developments and directions. Eur. J. Oper. Res. 2014 , 233 , 299–312. [ Google Scholar ] [ CrossRef ]
• Bettley, A.; Burnley, S. Towards sustainable operations management integrating sustainability management into operations management strategies and practices. In Handbook of Performability Engineering ; Springer: Berlin/Heidelberg, Germany, 2008; pp. 875–904. [ Google Scholar ]
• Zhong, R.; Xu, X.; Wang, L. Food supply chain management: Systems, implementations, and future research. Ind. Manag. Data Syst. 2017 , 117 , 2085–2114. [ Google Scholar ] [ CrossRef ]
• Van der Meulen, B.M. The structure of European food law. Laws 2013 , 2 , 69–98. [ Google Scholar ] [ CrossRef ]
• Kayikci, Y.; Ozbiltekin, M.; Kazancoglu, Y. Minimizing losses at red meat supply chain with circular and central slaughterhouse model. J. Enterp. Inf. Manag. 2020 , 33 , 791–816. [ Google Scholar ] [ CrossRef ]
• Jedermann, R.; Nicometo, M.; Uysal, I.; Lang, W. Reducing food losses by intelligent food logistics. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 2014 , 372 , 20130302. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, Z.; Liu, Y.; Wang, Q.; Sun, L.; Sun, S. (Eds.) Research on food safety and security of cold chain logistics. In IOP Conference Series: Earth and Environmental Science ; IOP Publishing: Bristol, UK, 2021; p. 012176. [ Google Scholar ]
• Centobelli, P.; Cerchione, R.; Ertz, M. Food cold chain management: What we know and what we deserve. Supply Chain Manag. Int. J. 2021 , 26 , 102–135. [ Google Scholar ]
• Lee, J.C.; Daraba, A.; Voidarou, C.; Rozos, G.; Enshasy, H.A.E.; Varzakas, T. Implementation of food safety management systems along with other management tools (HAZOP, FMEA, Ishikawa, Pareto). The case study of Listeria monocytogenes and correlation with microbiological criteria. Foods 2021 , 10 , 2169. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Singh, R.K.; Luthra, S.; Mangla, S.K.; Uniyal, S. Applications of information and communication technology for sustainable growth of SMEs in India food industry. Resour. Conserv. Recycl. 2019 , 147 , 10–18. [ Google Scholar ] [ CrossRef ]
• Nayak, R.; Waterson, P. Global food safety as a complex adaptive system: Key concepts and future prospects. Trends Food Sci. Technol. 2019 , 91 , 409–425. [ Google Scholar ] [ CrossRef ]
• Tseng, M.-L.; Ha, H.M.; Tran, T.P.T.; Bui, T.-D.; Lim, M.K.; Lin, C.-W.; Helmi Ali, M. Data-driven on sustainable food supply chain: A comparison on Halal and non-Halal food system. J. Ind. Prod. Eng. 2022 , 39 , 430–457. [ Google Scholar ] [ CrossRef ]
• Bortolini, M.; Faccio, M.; Ferrari, E.; Gamberi, M.; Pilati, F. Fresh food sustainable distribution: Cost, delivery time and carbon footprint three-objective optimization. J. Food Eng. 2016 , 174 , 56–67. [ Google Scholar ] [ CrossRef ]
• Chan, F.T.; Wang, Z.; Goswami, A.; Singhania, A.; Tiwari, M.K. Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations. Int. J. Prod. Res. 2020 , 58 , 5155–5174. [ Google Scholar ] [ CrossRef ]
• Fikar, C. A decision support system to investigate food losses in e-grocery deliveries. Comput. Ind. Eng. 2018 , 117 , 282–290. [ Google Scholar ] [ CrossRef ]
• Soysal, M.; Bloemhof-Ruwaard, J.M.; Haijema, R.; van der Vorst, J.G. Modeling a green inventory routing problem for perishable products with horizontal collaboration. Comput. Oper. Res. 2018 , 89 , 168–182. [ Google Scholar ] [ CrossRef ]
• Liljestrand, K. Logistics solutions for reducing food waste. Int. J. Phys. Distrib. Logist. Manag. 2017 , 47 , 318–339. [ Google Scholar ] [ CrossRef ]
• Halloran, A.; Clement, J.; Kornum, N.; Bucatariu, C.; Magid, J. Addressing food waste reduction in Denmark. Food Policy 2014 , 49 , 294–301. [ Google Scholar ] [ CrossRef ]
• Cattaneo, A.; Sánchez, M.V.; Torero, M.; Vos, R. Reducing food loss and waste: Five challenges for policy and research. Food Policy 2021 , 98 , 101974. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, X. Research on Food Cold Chain Logistics System Collaboration. Carpathian J. Food Sci. Technol. 2016 , 8 , 131. [ Google Scholar ]
• Weng, X.; An, J.; Yang, H. The analysis of the development situation and trend of the city-oriented cold chain logistics system for fresh agricultural products. Open J. Soc. Sci. 2015 , 3 , 70–80. [ Google Scholar ] [ CrossRef ]
• Dania, W.A.P.; Xing, K.; Amer, Y. Collaboration behavioural factors for sustainable agri-food supply chains: A systematic review. J. Clean. Prod. 2018 , 186 , 851–864. [ Google Scholar ] [ CrossRef ]
• Seuring, S.; Brix-Asala, C.; Khalid, R.U. Analyzing base-of-the-pyramid projects through sustainable supply chain management. J. Clean. Prod. 2019 , 212 , 1086–1097. [ Google Scholar ] [ CrossRef ]
• Yu, Y.; Jaenicke, E.C. Estimating food waste as household production inefficiency. Am. J. Agric. Econ. 2020 , 102 , 525–547. [ Google Scholar ] [ CrossRef ]
• Huang, H.; He, Y.; Li, D. Pricing and inventory decisions in the food supply chain with production disruption and controllable deterioration. J. Clean. Prod. 2018 , 180 , 280–296. [ Google Scholar ] [ CrossRef ]
• Li, X.; Zhou, K. Multi-objective cold chain logistic distribution center location based on carbon emission. Environ. Sci. Pollut. Res. 2021 , 28 , 32396–32404. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Dai, J.; Che, W.; Lim, J.J.; Shou, Y. Service innovation of cold chain logistics service providers: A multiple-case study in China. Ind. Mark. Manag. 2020 , 89 , 143–156. [ Google Scholar ] [ CrossRef ]
• Al Theeb, N.; Smadi, H.J.; Al-Hawari, T.H.; Aljarrah, M.H. Optimization of vehicle routing with inventory allocation problems in Cold Supply Chain Logistics. Comput. Ind. Eng. 2020 , 142 , 106341. [ Google Scholar ] [ CrossRef ]
• Zhao, H.; Liu, S.; Tian, C.; Yan, G.; Wang, D. An overview of current status of cold chain in China. Int. J. Refrig. 2018 , 88 , 483–495. [ Google Scholar ] [ CrossRef ]
• Tang, J.; Zou, Y.; Xie, R.; Tu, B.; Liu, G. Compact supervisory system for cold chain logistics. Food Control 2021 , 126 , 108025. [ Google Scholar ] [ CrossRef ]
• Badia-Melis, R.; Mc Carthy, U.; Ruiz-Garcia, L.; Garcia-Hierro, J.; Villalba, J.R. New trends in cold chain monitoring applications-A review. Food Control 2018 , 86 , 170–182. [ Google Scholar ] [ CrossRef ]
• Liu, G.; Hu, J.; Yang, Y.; Xia, S.; Lim, M.K. Vehicle routing problem in cold Chain logistics: A joint distribution model with carbon trading mechanisms. Resour. Conserv. Recycl. 2020 , 156 , 104715. [ Google Scholar ] [ CrossRef ]
• Esmizadeh, Y.; Bashiri, M.; Jahani, H.; Almada-Lobo, B. Cold chain management in hierarchical operational hub networks. Transp. Res. Part E Logist. Transp. Rev. 2021 , 147 , 102202. [ Google Scholar ] [ CrossRef ]
• Chen, J.; Dan, B.; Shi, J. A variable neighborhood search approach for the multi-compartment vehicle routing problem with time windows considering carbon emission. J. Clean. Prod. 2020 , 277 , 123932. [ Google Scholar ] [ CrossRef ]
• Wu, J.; Haasis, H.-D. The freight village as a pathway to sustainable agricultural products logistics in China. J. Clean. Prod. 2018 , 196 , 1227–1238. [ Google Scholar ] [ CrossRef ]
• Tsamboulas, D.A.; Kapros, S. Freight village evaluation under uncertainty with public and private financing. Transp. Policy 2003 , 10 , 141–156. [ Google Scholar ] [ CrossRef ]
• Pekkaya, M.; Keleş, N. Determining criteria interaction and criteria priorities in the freight village location selection process: The experts’ perspective in Turkey. Asia Pac. J. Mark. Logist. 2022 , 34 , 1348–1367. [ Google Scholar ] [ CrossRef ]
• Shashi, S.; Cerchione, R.; Singh, R.; Centobelli, P.; Shabani, A. Food cold chain management: From a structured literature review to a conceptual framework and research agenda. Int. J. Logist. Manag. 2018 , 29 , 792–821. [ Google Scholar ] [ CrossRef ]
• Magalhães, V.S.; Ferreira, L.M.D.; da Silva César, A.; Bonfim, R.M.; Silva, C. Food loss and waste in the Brazilian beef supply chain: An empirical analysis. Int. J. Logist. Manag. 2020 , 32 , 214–236. [ Google Scholar ] [ CrossRef ]
• Hülsmann, M.; Brenner, V. Causes and Effects of Cold Chain Ruptures: Performance of Fragmented Versus Integrated Cold Chains ; Jacobs University, School of Engineering and Science, Internat. Logistics, Systems Management: Bremen, Germany, 2011. [ Google Scholar ]
• Buisman, M.; Haijema, R.; Bloemhof-Ruwaard, J. Discounting and dynamic shelf life to reduce fresh food waste at retailers. Int. J. Prod. Econ. 2019 , 209 , 274–284. [ Google Scholar ] [ CrossRef ]
• Kibler, K.M.; Reinhart, D.; Hawkins, C.; Motlagh, A.M.; Wright, J. Food waste and the food-energy-water nexus: A review of food waste management alternatives. Waste Manag. 2018 , 74 , 52–62. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Herron, C.B.; Garner, L.J.; Siddique, A.; Huang, T.-S.; Campbell, J.C.; Rao, S.; Morey, A. Building “First Expire, First Out” models to predict food losses at retail due to cold chain disruption in the last mile. Front. Sustain. Food Syst. 2022 , 6 , 1018807. [ Google Scholar ] [ CrossRef ]
• Mendes, A.; Cruz, J.; Saraiva, T.; Lima, T.M.; Gaspar, P.D. (Eds.) Logistics strategy (FIFO, FEFO or LSFO) decision support system for perishable food products. In Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain, 8–9 November 2020; IEEE: New York, NY, USA, 2020; pp. 173–178. [ Google Scholar ]
• Plan, W.R.A. Reducing Food Waste through Retail Supply Chain Collaboration ; WRAP: Banbury, UK, 2011. [ Google Scholar ]
• Nikolicic, S.; Kilibarda, M.; Maslaric, M.; Mircetic, D.; Bojic, S. Reducing food waste in the retail supply chains by improving efficiency of logistics operations. Sustainability 2021 , 13 , 6511. [ Google Scholar ] [ CrossRef ]
• Kaipia, R.; Dukovska-Popovska, I.; Loikkanen, L. Creating sustainable fresh food supply chains through waste reduction. Int. J. Phys. Distrib. Logist. Manag. 2013 , 43 , 262–276. [ Google Scholar ] [ CrossRef ]
• Govindan, K.; Kadziński, M.; Sivakumar, R. Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain. Omega 2017 , 71 , 129–145. [ Google Scholar ] [ CrossRef ]
• Rahbari, M.; Hajiagha, S.H.R.; Mahdiraji, H.A.; Dorcheh, F.R.; Garza-Reyes, J.A. A novel location-inventory-routing problem in a two-stage red meat supply chain with logistic decisions: Evidence from an emerging economy. Kybernetes 2021 , 51 , 1498–1531. [ Google Scholar ] [ CrossRef ]
• Yazdani, M.; Chatterjee, P.; Zavadskas, E.K.; Zolfani, S.H. Integrated QFD-MCDM framework for green supplier selection. J. Clean. Prod. 2017 , 142 , 3728–3740. [ Google Scholar ] [ CrossRef ]
• Kieu, P.T.; Nguyen, V.T.; Nguyen, V.T.; Ho, T.P. A spherical fuzzy analytic hierarchy process (SF-AHP) and combined compromise solution (CoCoSo) algorithm in distribution center location selection: A case study in agricultural supply chain. Axioms 2021 , 10 , 53. [ Google Scholar ] [ CrossRef ]
• Mihajlović, J.; Rajković, P.; Petrović, G.; Ćirić, D. The selection of the logistics distribution center location based on MCDM methodology in southern and eastern region in Serbia. Oper. Res. Eng. Sci. Theory Appl. 2019 , 2 , 72–85. [ Google Scholar ] [ CrossRef ]
• Zanoni, S.; Zavanella, L. Chilled or frozen? Decision strategies for sustainable food supply chains. Int. J. Prod. Econ. 2012 , 140 , 731–736. [ Google Scholar ] [ CrossRef ]
• Aravendan, M.; Panneerselvam, R. Literature review on network design problems in closed loop and reverse supply chains. Intell. Inf. Manag. 2014 , 6 , 104–117. [ Google Scholar ] [ CrossRef ]
• Mejjaouli, S.; Babiceanu, R.F. Cold supply chain logistics: System optimization for real-time rerouting transportation solutions. Comput. Ind. 2018 , 95 , 68–80. [ Google Scholar ] [ CrossRef ]
• Titiyal, R.; Bhattacharya, S.; Thakkar, J.J. The distribution strategy selection for an e-tailer using a hybrid DANP VIKOR MCDM model. Benchmarking Int. J. 2019 , 26 , 395–433. [ Google Scholar ] [ CrossRef ]
• Gallo, A.; Accorsi, R.; Baruffaldi, G.; Manzini, R. Designing sustainable cold chains for long-range food distribution: Energy-effective corridors on the Silk Road Belt. Sustainability 2017 , 9 , 2044. [ Google Scholar ] [ CrossRef ]
• Yakavenka, V.; Mallidis, I.; Vlachos, D.; Iakovou, E.; Eleni, Z. Development of a multi-objective model for the design of sustainable supply chains: The case of perishable food products. Ann. Oper. Res. 2020 , 294 , 593–621. [ Google Scholar ] [ CrossRef ]
• Sel, Ç.; Pınarbaşı, M.; Soysal, M.; Çimen, M. A green model for the catering industry under demand uncertainty. J. Clean. Prod. 2017 , 167 , 459–472. [ Google Scholar ] [ CrossRef ]
• Van Der Vorst, J.G.; Tromp, S.-O.; Zee, D.-J.v.d. Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. Int. J. Prod. Res. 2009 , 47 , 6611–6631. [ Google Scholar ] [ CrossRef ]
• Bortolini, M.; Galizia, F.G.; Mora, C.; Botti, L.; Rosano, M. Bi-objective design of fresh food supply chain networks with reusable and disposable packaging containers. J. Clean. Prod. 2018 , 184 , 375–388. [ Google Scholar ] [ CrossRef ]
• Li, Q.; Yu, P.; Wu, X. Shelf life extending packaging, inventory control and grocery retailing. Prod. Oper. Manag. 2017 , 26 , 1369–1382. [ Google Scholar ] [ CrossRef ]
• Verghese, K.; Lewis, H.; Lockrey, S.; Williams, H. Packaging’s role in minimizing food loss and waste across the supply chain. Packag. Technol. Sci. 2015 , 28 , 603–620. [ Google Scholar ] [ CrossRef ]
• Ghadge, A.; Kaklamanou, M.; Choudhary, S.; Bourlakis, M. Implementing environmental practices within the Greek dairy supply chain: Drivers and barriers for SMEs. Ind. Manag. Data Syst. 2017 , 117 , 1995–2014. [ Google Scholar ] [ CrossRef ]
• Sajjad, A.; Eweje, G.; Tappin, D. Managerial perspectives on drivers for and barriers to sustainable supply chain management implementation: Evidence from New Zealand. Bus. Strategy Environ. 2020 , 29 , 592–604. [ Google Scholar ] [ CrossRef ]
• Hien, D.N.; Thanh, N.V. Optimization of cold chain logistics with Fuzzy MCDM Model. Processes 2022 , 10 , 947. [ Google Scholar ] [ CrossRef ]
• Saaty, T.L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 2008 , 1 , 83–98. [ Google Scholar ] [ CrossRef ]
• Karanam, M.; Krishnanand, L.; Manupati, V.K.; Antosz, K.; Machado, J. Identification of the critical enablers for perishable food supply chain using deterministic assessment models. Appl. Sci. 2022 , 12 , 4503. [ Google Scholar ] [ CrossRef ]
• Kutlu Gündoğdu, F.; Kahraman, C. A novel VIKOR method using spherical fuzzy sets and its application to warehouse site selection. J. Intell. Fuzzy Syst. 2019 , 37 , 1197–1211. [ Google Scholar ] [ CrossRef ]
• Ali, S.M.; Rahman, M.H.; Tumpa, T.J.; Rifat, A.A.M.; Paul, S.K. Examining price and service competition among retailers in a supply chain under potential demand disruption. J. Retail. Consum. Serv. 2018 , 40 , 40–47. [ Google Scholar ] [ CrossRef ]
• Ali, S.M.; Nakade, K. Optimal ordering policies in a multi-sourcing supply chain with supply and demand disruptions-a CVaR approach. Int. J. Logist. Syst. Manag. 2017 , 28 , 180–199. [ Google Scholar ] [ CrossRef ]
• Singh, A.; Shukla, N.; Mishra, N. Social media data analytics to improve supply chain management in food industries. Transp. Res. Part E Logist. Transp. Rev. 2018 , 114 , 398–415. [ Google Scholar ] [ CrossRef ]
• Tang, C.S. Perspectives in supply chain risk management. Int. J. Prod. Econ. 2006 , 103 , 451–488. [ Google Scholar ] [ CrossRef ]
• Ali, S.M.; Moktadir, M.A.; Kabir, G.; Chakma, J.; Rumi, M.J.U.; Islam, M.T. Framework for evaluating risks in food supply chain: Implications in food wastage reduction. J. Clean. Prod. 2019 , 228 , 786–800. [ Google Scholar ]
• Govindan, K. Sustainable consumption and production in the food supply chain: A conceptual framework. Int. J. Prod. Econ. 2018 , 195 , 419–431. [ Google Scholar ] [ CrossRef ]
• de Oliveira, U.R.; Marins, F.A.S.; Rocha, H.M.; Salomon, V.A.P. The ISO 31000 standard in supply chain risk management. J. Clean. Prod. 2017 , 151 , 616–633. [ Google Scholar ] [ CrossRef ]
• Khan, O.; Burnes, B. Risk and supply chain management: Creating a research agenda. Int. J. Logist. Manag. 2007 , 18 , 197–216. [ Google Scholar ] [ CrossRef ]
• Mangla, S.K.; Kumar, P.; Barua, M.K. An integrated methodology of FTA and fuzzy AHP for risk assessment in green supply chain. Int. J. Oper. Res. 2016 , 25 , 77–99. [ Google Scholar ] [ CrossRef ]
• Bogataj, D.; Hudoklin, D.; Bogataj, M.; Dimovski, V.; Colnar, S. Risk mitigation in a meat supply chain with options of redirection. Sustainability 2020 , 12 , 8690. [ Google Scholar ] [ CrossRef ]
• Nguyen, A.H.T.; Singh, A.; Kumari, S.; Choudhary, S. Multi-agent architecture for waste minimisation in beef supply chain. Prod. Plan. Control 2023 , 34 , 1082–1096. [ Google Scholar ] [ CrossRef ]
• Deng, X.; Yang, X.; Zhang, Y.; Li, Y.; Lu, Z. Risk propagation mechanisms and risk management strategies for a sustainable perishable products supply chain. Comput. Ind. Eng. 2019 , 135 , 1175–1187. [ Google Scholar ] [ CrossRef ]
• Srivastava, S.K.; Chaudhuri, A.; Srivastava, R.K. Propagation of risks and their impact on performance in fresh food retail. Int. J. Logist. Manag. 2015 , 26 , 568–602. [ Google Scholar ] [ CrossRef ]
• Amani, M.A.; Sarkodie, S.A. Mitigating spread of contamination in meat supply chain management using deep learning. Sci. Rep. 2022 , 12 , 5037. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Klein, A.Z.; Da Costa, E.G.; Vieira, L.M.; Teixeira, R. The use of mobile technology in management and risk control in the supply chain: The case of a Brazilian beef chain. J. Glob. Inf. Manag. (JGIM) 2014 , 22 , 14–33. [ Google Scholar ] [ CrossRef ]
• Mohebi, E.; Marquez, L. Intelligent packaging in meat industry: An overview of existing solutions. J. Food Sci. Technol. 2015 , 52 , 3947–3964. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Martins, C.; Melo, M.; Pato, M. Redesigning a food bank supply chain network in a triple bottom line context. Int. J. Prod. Econ. 2019 , 214 , 234–247. [ Google Scholar ] [ CrossRef ]
• Kowalski, Z.; Kulczycka, J.; Makara, A.; Harazin, P. Quantification of material recovery from meat waste incineration–An approach to an updated food waste hierarchy. J. Hazard. Mater. 2021 , 416 , 126021. [ Google Scholar ] [ CrossRef ]
• Teigiserova, D.A.; Hamelin, L.; Thomsen, M. Towards transparent valorization of food surplus, waste and loss: Clarifying definitions, food waste hierarchy, and role in the circular economy. Sci. Total Environ. 2020 , 706 , 136033. [ Google Scholar ] [ CrossRef ]
• Beheshti, S.; Heydari, J.; Sazvar, Z. Food waste recycling closed loop supply chain optimization through renting waste recycling facilities. Sustain. Cities Soc. 2022 , 78 , 103644. [ Google Scholar ] [ CrossRef ]
• Aschemann-Witzel, J.; Jensen, J.H.; Jensen, M.H.; Kulikovskaja, V. Consumer behaviour towards price-reduced suboptimal foods in the supermarket and the relation to food waste in households. Appetite 2017 , 116 , 246–258. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Porpino, G. Household food waste behavior: Avenues for future research. J. Assoc. Consum. Res. 2016 , 1 , 41–51. [ Google Scholar ] [ CrossRef ]
• Gholami-Zanjani, S.M.; Jabalameli, M.S.; Pishvaee, M.S. A resilient-green model for multi-echelon meat supply chain planning. Comput. Ind. Eng. 2021 , 152 , 107018. [ Google Scholar ] [ CrossRef ]
• Rijpkema, W.A.; Rossi, R.; van der Vorst, J.G. Effective sourcing strategies for perishable product supply chains. Int. J. Phys. Distrib. Logist. Manag. 2014 , 44 , 494–510. [ Google Scholar ] [ CrossRef ]
• Albrecht, A.; Ibald, R.; Raab, V.; Reichstein, W.; Haarer, D.; Kreyenschmidt, J. Implementation of time temperature indicators to improve temperature monitoring and support dynamic shelf life in meat supply chains. J. Packag. Technol. Res. 2020 , 4 , 23–32. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ndraha, N.; Vlajic, J.; Chang, C.-C.; Hsiao, H.-I. Challenges with food waste management in the food cold chains. In Food Industry Wastes ; Elsevier: Amsterdam, The Netherlands, 2020; pp. 467–483. [ Google Scholar ]
• Thyberg, K.L.; Tonjes, D.J. Drivers of food waste and their implications for sustainable policy development. Resour. Conserv. Recycl. 2016 , 106 , 110–123. [ Google Scholar ] [ CrossRef ]
• Saeed, M.A.; Kersten, W. Drivers of sustainable supply chain management: Identification and classification. Sustainability 2019 , 11 , 1137. [ Google Scholar ] [ CrossRef ]
• Kumar, A.; Choudhary, S.; Garza-Reyes, J.A.; Kumar, V.; Rehman Khan, S.A.; Mishra, N. Analysis of critical success factors for implementing industry 4.0 integrated circular supply chain–Moving towards sustainable operations. Prod. Plan. Control 2023 , 34 , 984–998. [ Google Scholar ] [ CrossRef ]
• Chauhan, A.; Debnath, R.M.; Singh, S.P. Modelling the drivers for sustainable agri-food waste management. Benchmarking Int. J. 2018 , 25 , 981–993. [ Google Scholar ] [ CrossRef ]
• Jo, J.; Yi, S.; Lee, E.-k. Including the reefer chain into genuine beef cold chain architecture based on blockchain technology. J. Clean. Prod. 2022 , 363 , 132646. [ Google Scholar ] [ CrossRef ]
• Kler, R.; Gangurde, R.; Elmirzaev, S.; Hossain, M.S.; Vo, N.V.; Nguyen, T.V.; Kumar, P.N. Optimization of Meat and Poultry Farm Inventory Stock Using Data Analytics for Green Supply Chain Network. Discret. Dyn. Nat. Soc. 2022 , 2022 , 8970549. [ Google Scholar ] [ CrossRef ]
• Handfield, R.B.; Cousins, P.D.; Lawson, B.; Petersen, K.J. How can supply management really improve performance? A knowledge-based model of alignment capabilities. J. Supply Chain Manag. 2015 , 51 , 3–17. [ Google Scholar ] [ CrossRef ]
• Qi, L.; Xu, M.; Fu, Z.; Mira, T.; Zhang, X. C2SLDS: A WSN-based perishable food shelf-life prediction and LSFO strategy decision support system in cold chain logistics. Food Control 2014 , 38 , 19–29. [ Google Scholar ] [ CrossRef ]
• Matta, V.; Moberg, C. Defining the Antecedents for Adoption of RFID in the Supply Chain. Issues Inf. Syst. 2007 , 8 , 449–454. [ Google Scholar ]
• Nychas, G.-J.E.; Skandamis, P.N.; Tassou, C.C.; Koutsoumanis, K.P. Meat spoilage during distribution. Meat Sci. 2008 , 78 , 77–89. [ Google Scholar ] [ CrossRef ]
• Papargyropoulou, E.; Lozano, R.; Steinberger, J.K.; Wright, N.; bin Ujang, Z. The food waste hierarchy as a framework for the management of food surplus and food waste. J. Clean. Prod. 2014 , 76 , 106–115. [ Google Scholar ] [ CrossRef ]
• Joshi, R.; Banwet, D.; Shankar, R.; Gandhi, J. Performance improvement of cold chain in an emerging economy. Prod. Plan. Control 2012 , 23 , 817–836. [ Google Scholar ] [ CrossRef ]
• Sharma, S.; Pai, S.S. Analysis of operating effectiveness of a cold chain model using Bayesian networks. Bus. Process Manag. J. 2015 , 21 , 722–742. [ Google Scholar ] [ CrossRef ]
• Cousins, P.D.; Menguc, B. The implications of socialization and integration in supply chain management. J. Oper. Manag. 2006 , 24 , 604–620. [ Google Scholar ] [ CrossRef ]
• Beulens, A.J.; Broens, D.-F.; Folstar, P.; Hofstede, G.J. Food safety and transparency in food chains and networks Relationships and challenges. Food Control 2005 , 16 , 481–486. [ Google Scholar ] [ CrossRef ]
• Mangla, S.K.; Sharma, Y.K.; Patil, P.P.; Yadav, G.; Xu, J. Logistics and distribution challenges to managing operations for corporate sustainability: Study on leading Indian diary organizations. J. Clean. Prod. 2019 , 238 , 117620. [ Google Scholar ] [ CrossRef ]
• Balaji, M.; Arshinder, K. Modeling the causes of food wastage in Indian perishable food supply chain. Resour. Conserv. Recycl. 2016 , 114 , 153–167. [ Google Scholar ]
• Kuo, J.-C.; Chen, M.-C. Developing an advanced multi-temperature joint distribution system for the food cold chain. Food Control 2010 , 21 , 559–566. [ Google Scholar ] [ CrossRef ]
• Smigic, N.; Antic, D.; Blagojevic, B.; Tomasevic, I.; Djekic, I. The level of food safety knowledge among meat handlers. Br. Food J. 2016 , 118 , 9–25. [ Google Scholar ] [ CrossRef ]
• Sander, F.; Semeijn, J.; Mahr, D. The acceptance of blockchain technology in meat traceability and transparency. Br. Food J. 2018 , 120 , 2066–2079. [ Google Scholar ] [ CrossRef ]
• Patel, S.; Dora, M.; Hahladakis, J.N.; Iacovidou, E. Opportunities, challenges and trade-offs with decreasing avoidable food waste in the UK. Waste Manag. Res. 2021 , 39 , 473–488. [ Google Scholar ] [ CrossRef ]
• Raak, N.; Symmank, C.; Zahn, S.; Aschemann-Witzel, J.; Rohm, H. Processing-and product-related causes for food waste and implications for the food supply chain. Waste Manag. 2017 , 61 , 461–472. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wittstruck, D.; Teuteberg, F. Understanding the success factors of sustainable supply chain management: Empirical evidence from the electrics and electronics industry. Corp. Soc. Responsib. Environ. Manag. 2012 , 19 , 141–158. [ Google Scholar ] [ CrossRef ]
• Paul, A.; Shukla, N.; Paul, S.K.; Trianni, A. Sustainable supply chain management and multi-criteria decision-making methods: A systematic review. Sustainability 2021 , 13 , 7104. [ Google Scholar ] [ CrossRef ]
• Parfitt, J.; Barthel, M.; Macnaughton, S. Food waste within food supply chains: Quantification and potential for change to 2050. Philos. Trans. R. Soc. B Biol. Sci. 2010 , 365 , 3065–3081. [ Google Scholar ] [ CrossRef ]
• Pinto, J.; Boavida-Dias, R.; Matos, H.A.; Azevedo, J. Analysis of the food loss and waste valorisation of animal by-products from the retail sector. Sustainability 2022 , 14 , 2830. [ Google Scholar ] [ CrossRef ]
• Mota, B.; Gomes, M.I.; Carvalho, A.; Barbosa-Povoa, A.P. Towards supply chain sustainability: Economic, environmental and social design and planning. J. Clean. Prod. 2015 , 105 , 14–27. [ Google Scholar ] [ CrossRef ]
• Mohammed, A.; Wang, Q. The fuzzy multi-objective distribution planner for a green meat supply chain. Int. J. Prod. Econ. 2017 , 184 , 47–58. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Zhao, G.; Qi, Y.; Li, B. A robust fuzzy optimization model for closed-loop supply chain networks considering sustainability. Sustainability 2019 , 11 , 5726. [ Google Scholar ] [ CrossRef ]
• Ko, H.J.; Evans, G.W. A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Comput. Oper. Res. 2007 , 34 , 346–366. [ Google Scholar ] [ CrossRef ]
• Mosallanezhad, B.; Arjomandi, M.A.; Hashemi-Amiri, O.; Gholian-Jouybari, F.; Dibaj, M.; Akrami, M.; Hajiaghaei-Keshteli, M. Metaheuristic optimizers to solve multi-echelon sustainable fresh seafood supply chain network design problem: A case of shrimp products. Alex. Eng. J. 2023 , 68 , 491–515. [ Google Scholar ] [ CrossRef ]
• Accorsi, R.; Ferrari, E.; Gamberi, M.; Manzini, R.; Regattieri, A. A closed-loop traceability system to improve logistics decisions in food supply chains: A case study on dairy products. In Advances in Food Traceability Techniques and Technologies ; Elsevier: Amsterdam, The Netherlands, 2016; pp. 337–351. [ Google Scholar ]
• Alinezhad, M.; Mahdavi, I.; Hematian, M.; Tirkolaee, E.B. A fuzzy multi-objective optimization model for sustainable closed-loop supply chain network design in food industries. Environ. Dev. Sustain. 2022 , 24 , 8779–8806. [ Google Scholar ] [ CrossRef ]
• Sgarbossa, F.; Russo, I. A proactive model in sustainable food supply chain: Insight from a case study. Int. J. Prod. Econ. 2017 , 183 , 596–606. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Che, A.; Chu, F. Improved model and efficient method for bi-objective closed-loop food supply chain problem with returnable transport items. Int. J. Prod. Res. 2022 , 60 , 1051–1068. [ Google Scholar ] [ CrossRef ]
• Amin, S.H.; Zhang, G.; Eldali, M.N. A review of closed-loop supply chain models. J. Data Inf. Manag. 2020 , 2 , 279–307. [ Google Scholar ] [ CrossRef ]
• MahmoumGonbadi, A.; Genovese, A.; Sgalambro, A. Closed-loop supply chain design for the transition towards a circular economy: A systematic literature review of methods, applications and current gaps. J. Clean. Prod. 2021 , 323 , 129101. [ Google Scholar ] [ CrossRef ]
• Tavana, M.; Kian, H.; Nasr, A.K.; Govindan, K.; Mina, H. A comprehensive framework for sustainable closed-loop supply chain network design. J. Clean. Prod. 2022 , 332 , 129777. [ Google Scholar ] [ CrossRef ]
• Martindale, W.; Duong, L.; Swainson, M. Testing the data platforms required for the 21st century food system using an industry ecosystem approach. Sci. Total Environ. 2020 , 724 , 137871. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vlajic, J.V.; Mijailovic, R.; Bogdanova, M. Creating loops with value recovery: Empirical study of fresh food supply chains. Prod. Plan. Control 2018 , 29 , 522–538. [ Google Scholar ] [ CrossRef ]
• Liu, Z.; de Souza, T.S.; Holland, B.; Dunshea, F.; Barrow, C.; Suleria, H.A. Valorization of food waste to produce value-added products based on its bioactive compounds. Processes 2023 , 11 , 840. [ Google Scholar ] [ CrossRef ]
• Mangla, S.K.; Luthra, S.; Mishra, N.; Singh, A.; Rana, N.P.; Dora, M.; Dwivedi, Y. Barriers to effective circular supply chain management in a developing country context. Prod. Plan. Control 2018 , 29 , 551–569. [ Google Scholar ] [ CrossRef ]
• Sehnem, S.; Jabbour, C.J.C.; Pereira, S.C.F.; de Sousa Jabbour, A.B.L. Improving sustainable supply chains performance through operational excellence: Circular economy approach. Resour. Conserv. Recycl. 2019 , 149 , 236–248. [ Google Scholar ] [ CrossRef ]
• Wiskerke, J.S.; Roep, D. Constructing a sustainable pork supply chain: A case of techno-institutional innovation. J. Environ. Policy Plan. 2007 , 9 , 53–74. [ Google Scholar ] [ CrossRef ]
• Ilbery, B.; Maye, D. Alternative (shorter) food supply chains and specialist livestock products in the Scottish–English borders. Environ. Plan. A 2005 , 37 , 823–844. [ Google Scholar ] [ CrossRef ]
• Mundler, P.; Laughrea, S. The contributions of short food supply chains to territorial development: A study of three Quebec territories. J. Rural Stud. 2016 , 45 , 218–229. [ Google Scholar ] [ CrossRef ]
• Rucabado-Palomar, T.; Cuéllar-Padilla, M. Short food supply chains for local food: A difficult path. Renew. Agric. Food Syst. 2020 , 35 , 182–191. [ Google Scholar ] [ CrossRef ]
• Vittersø, G.; Torjusen, H.; Laitala, K.; Tocco, B.; Biasini, B.; Csillag, P.; de Labarre, M.D.; Lecoeur, J.-L.; Maj, A.; Majewski, E. Short food supply chains and their contributions to sustainability: Participants’ views and perceptions from 12 European cases. Sustainability 2019 , 11 , 4800. [ Google Scholar ] [ CrossRef ]
• Blanquart, C.; Gonçalves, A.; Vandenbossche, L.; Kebir, L.; Petit, C.; Traversac, J.-B. (Eds.) The logistic leverages of short food supply chains performance in terms of sustainability. In Proceedings of the 12th World Conference on Transport Research, Lisbonne, Portugal, 11–15 July 2010. 10p. [ Google Scholar ]
• Majewski, E.; Komerska, A.; Kwiatkowski, J.; Malak-Rawlikowska, A.; Wąs, A.; Sulewski, P.; Gołaś, M.; Pogodzińska, K.; Lecoeur, J.-L.; Tocco, B. Are short food supply chains more environmentally sustainable than long chains? A life cycle assessment (LCA) of the eco-efficiency of food chains in selected EU countries. Energies 2020 , 13 , 4853. [ Google Scholar ] [ CrossRef ]
• Collison, M.; Collison, T.; Myroniuk, I.; Boyko, N.; Pellegrini, G. Transformation trends in food logistics for short food supply chains-what is new? Stud. Agric. Econ. 2019 , 121 , 102–110. [ Google Scholar ] [ CrossRef ]
• De Bernardi, P.; Tirabeni, L. Alternative food networks: Sustainable business models for anti-consumption food cultures. Br. Food J. 2018 , 120 , 1776–1791. [ Google Scholar ] [ CrossRef ]
• Loiseau, E.; Colin, M.; Alaphilippe, A.; Coste, G.; Roux, P. To what extent are short food supply chains (SFSCs) environmentally friendly? Application to French apple distribution using Life Cycle Assessment. J. Clean. Prod. 2020 , 276 , 124166. [ Google Scholar ] [ CrossRef ]
• Chen, W.; Jafarzadeh, S.; Thakur, M.; Ólafsdóttir, G.; Mehta, S.; Bogason, S.; Holden, N.M. Environmental impacts of animal-based food supply chains with market characteristics. Sci. Total Environ. 2021 , 783 , 147077. [ Google Scholar ] [ CrossRef ]
• Hill, A. Whole of Meat Supply Chain Food Loss and Waste Mapping and Interventions-Phase 1–Final Report ; Meat and Livestock Australia (MLA): Sydney, NSW, Australia, 2023. [ Google Scholar ]
• Bonou, A.; Colley, T.A.; Hauschild, M.Z.; Olsen, S.I.; Birkved, M. Life cycle assessment of Danish pork exports using different cooling technologies and comparison of upstream supply chain efficiencies between Denmark, China and Australia. J. Clean. Prod. 2020 , 244 , 118816. [ Google Scholar ] [ CrossRef ]
• Chen, Q.; Qian, J.; Yang, H.; Wu, W. Sustainable food cold chain logistics: From microenvironmental monitoring to global impact. Compr. Rev. Food Sci. Food Saf. 2022 , 21 , 4189–4209. [ Google Scholar ] [ CrossRef ]
• Caldeira, C.; De Laurentiis, V.; Corrado, S.; van Holsteijn, F.; Sala, S. Quantification of food waste per product group along the food supply chain in the European Union: A mass flow analysis. Resour. Conserv. Recycl. 2019 , 149 , 479–488. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Betz, A.; Buchli, J.; Göbel, C.; Müller, C. Food waste in the Swiss food service industry–Magnitude and potential for reduction. Waste Manag. 2015 , 35 , 218–226. [ Google Scholar ] [ CrossRef ]
• Apaiah, R.K.; Linnemann, A.R.; Van Der Kooi, H.J. Exergy analysis: A tool to study the sustainability of food supply chains. Food Res. Int. 2006 , 39 , 1–11. [ Google Scholar ] [ CrossRef ]
• Peters, G.M.; Rowley, H.V.; Wiedemann, S.; Tucker, R.; Short, M.D.; Schulz, M. Red meat production in Australia: Life cycle assessment and comparison with overseas studies. Environ. Sci. Technol. 2010 , 44 , 1327–1332. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Asem-Hiablie, S.; Battagliese, T.; Stackhouse-Lawson, K.R.; Alan Rotz, C. A life cycle assessment of the environmental impacts of a beef system in the USA. Int. J. Life Cycle Assess. 2019 , 24 , 441–455. [ Google Scholar ] [ CrossRef ]
• Tsakiridis, A.; O’Donoghue, C.; Hynes, S.; Kilcline, K. A comparison of environmental and economic sustainability across seafood and livestock product value chains. Mar. Policy 2020 , 117 , 103968. [ Google Scholar ] [ CrossRef ]
• Gerbens-Leenes, P.W.; Mekonnen, M.M.; Hoekstra, A.Y. The water footprint of poultry, pork and beef: A comparative study in different countries and production systems. Water Resour. Ind. 2013 , 1 , 25–36. [ Google Scholar ] [ CrossRef ]
• Bottani, E.; Vignali, G.; Mosna, D.; Montanari, R. Economic and environmental assessment of different reverse logistics scenarios for food waste recovery. Sustain. Prod. Consum. 2019 , 20 , 289–303. [ Google Scholar ] [ CrossRef ]
• Omolayo, Y.; Feingold, B.J.; Neff, R.A.; Romeiko, X.X. Life cycle assessment of food loss and waste in the food supply chain. Resour. Conserv. Recycl. 2021 , 164 , 105119. [ Google Scholar ] [ CrossRef ]
• Lipińska, M.; Tomaszewska, M.; Kołożyn-Krajewska, D. Identifying factors associated with food losses during transportation: Potentials for social purposes. Sustainability 2019 , 11 , 2046. [ Google Scholar ] [ CrossRef ]
• León-Bravo, V.; Caniato, F.; Caridi, M.; Johnsen, T. Collaboration for sustainability in the food supply chain: A multi-stage study in Italy. Sustainability 2017 , 9 , 1253. [ Google Scholar ] [ CrossRef ]
• Accorsi, R.; Baruffaldi, G.; Manzini, R. A closed-loop packaging network design model to foster infinitely reusable and recyclable containers in food industry. Sustain. Prod. Consum. 2020 , 24 , 48–61. [ Google Scholar ] [ CrossRef ]
• Tonn, B.; Frymier, P.D.; Stiefel, D.; Skinner, L.S.; Suraweera, N.; Tuck, R. Toward an infinitely reusable, recyclable, and renewable industrial ecosystem. J. Clean. Prod. 2014 , 66 , 392–406. [ Google Scholar ] [ CrossRef ]
• Pålsson, H.; Hellström, D. Packaging logistics in supply chain practice–current state, trade-offs and improvement potential. Int. J. Logist. Res. Appl. 2016 , 19 , 351–368. [ Google Scholar ] [ CrossRef ]
• Mahmoudi, M.; Parviziomran, I. Reusable packaging in supply chains: A review of environmental and economic impacts, logistics system designs, and operations management. Int. J. Prod. Econ. 2020 , 228 , 107730. [ Google Scholar ] [ CrossRef ]
• Wikström, F.; Verghese, K.; Auras, R.; Olsson, A.; Williams, H.; Wever, R.; Grönman, K.; Kvalvåg Pettersen, M.; Møller, H.; Soukka, R. Packaging strategies that save food: A research agenda for 2030. J. Ind. Ecol. 2019 , 23 , 532–540. [ Google Scholar ] [ CrossRef ]
• Goossens, Y.; Berrens, P.; Custers, K.; Van Hemelryck, S.; Kellens, K.; Geeraerd, A. How origin, packaging and seasonality determine the environmental impact of apples, magnified by food waste and losses. Int. J. Life Cycle Assess. 2019 , 24 , 667–687. [ Google Scholar ] [ CrossRef ]
• McMillin, K.W. Advancements in meat packaging. Meat Sci. 2017 , 132 , 153–162. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Domínguez, R.; Barba, F.J.; Gómez, B.; Putnik, P.; Kovačević, D.B.; Pateiro, M.; Santos, E.M.; Lorenzo, J.M. Active packaging films with natural antioxidants to be used in meat industry: A review. Food Res. Int. 2018 , 113 , 93–101. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fang, Z.; Zhao, Y.; Warner, R.D.; Johnson, S.K. Active and intelligent packaging in meat industry. Trends Food Sci. Technol. 2017 , 61 , 60–71. [ Google Scholar ] [ CrossRef ]
• Chowdhury, E.; Morey, A. Intelligent packaging for poultry industry. J. Appl. Poult. Res. 2019 , 28 , 791–800. [ Google Scholar ] [ CrossRef ]
• Realini, C.E.; Marcos, B. Active and intelligent packaging systems for a modern society. Meat Sci. 2014 , 98 , 404–419. [ Google Scholar ] [ CrossRef ]
• Moustafa, H.; Youssef, A.M.; Darwish, N.A.; Abou-Kandil, A.I. Eco-friendly polymer composites for green packaging: Future vision and challenges. Compos. Part B Eng. 2019 , 172 , 16–25. [ Google Scholar ] [ CrossRef ]
• Ocampo, L.A.; Villegas, Z.V.A.; Carvajal, J.-a.T.; Apas, C.-A.A. Identifying significant drivers for sustainable practices in achieving sustainable food supply chain using modified fuzzy decision-making trial and evaluation laboratory approach. Int. J. Adv. Oper. Manag. 2018 , 10 , 51–89. [ Google Scholar ]
• Irani, Z.; Sharif, A.M. Sustainable food security futures: Perspectives on food waste and information across the food supply chain. J. Enterp. Inf. Manag. 2016 , 29 , 171–178. [ Google Scholar ] [ CrossRef ]
• Jeswani, H.K.; Figueroa-Torres, G.; Azapagic, A. The extent of food waste generation in the UK and its environmental impacts. Sustain. Prod. Consum. 2021 , 26 , 532–547. [ Google Scholar ] [ CrossRef ]
• Shafiee-Jood, M.; Cai, X. Reducing food loss and waste to enhance food security and environmental sustainability. Environ. Sci. Technol. 2016 , 50 , 8432–8443. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kamble, S.S.; Gunasekaran, A.; Parekh, H.; Joshi, S. Modeling the internet of things adoption barriers in food retail supply chains. J. Retail. Consum. Serv. 2019 , 48 , 154–168. [ Google Scholar ] [ CrossRef ]
• Rejeb, A. Halal meat supply chain traceability based on HACCP, blockchain and internet of things. Acta Tech. Jaurinensis 2018 , 11 , 218–247. [ Google Scholar ] [ CrossRef ]
• Harvey, J.; Smith, A.; Goulding, J.; Illodo, I.B. Food sharing, redistribution, and waste reduction via mobile applications: A social network analysis. Ind. Mark. Manag. 2020 , 88 , 437–448. [ Google Scholar ] [ CrossRef ]
• Mishra, N.; Singh, A. Use of twitter data for waste minimisation in beef supply chain. Ann. Oper. Res. 2018 , 270 , 337–359. [ Google Scholar ] [ CrossRef ]
• Biuki, M.; Kazemi, A.; Alinezhad, A. An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. J. Clean. Prod. 2020 , 260 , 120842. [ Google Scholar ] [ CrossRef ]
• Alkaabneh, F.; Diabat, A.; Gao, H.O. Benders decomposition for the inventory vehicle routing problem with perishable products and environmental costs. Comput. Oper. Res. 2020 , 113 , 104751. [ Google Scholar ] [ CrossRef ]
• Elhedhli, S.; Merrick, R. Green supply chain network design to reduce carbon emissions. Transp. Res. Part D Transp. Environ. 2012 , 17 , 370–379. [ Google Scholar ] [ CrossRef ]
• Daskin, M.S.; Coullard, C.R.; Shen, Z.-J.M. An inventory-location model: Formulation, solution algorithm and computational results. Ann. Oper. Res. 2002 , 110 , 83–106. [ Google Scholar ] [ CrossRef ]
• Liu, A.; Zhu, Q.; Xu, L.; Lu, Q.; Fan, Y. Sustainable supply chain management for perishable products in emerging markets: An integrated location-inventory-routing model. Transp. Res. Part E Logist. Transp. Rev. 2021 , 150 , 102319. [ Google Scholar ] [ CrossRef ]
• Diabat, A.; Dehghani, E.; Jabbarzadeh, A. Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times. J. Manuf. Syst. 2017 , 43 , 139–149. [ Google Scholar ] [ CrossRef ]
• Bravo, J.J.; Vidal, C.J. Freight transportation function in supply chain optimization models: A critical review of recent trends. Expert Syst. Appl. 2013 , 40 , 6742–6757. [ Google Scholar ] [ CrossRef ]
• Javid, A.A.; Azad, N. Incorporating location, routing and inventory decisions in supply chain network design. Transp. Res. Part E Logist. Transp. Rev. 2010 , 46 , 582–597. [ Google Scholar ] [ CrossRef ]
• Benders, J.F. Partitioning procedures for solving mixed-variables programming problems. Comput. Manag. Sci. 2005 , 2 , 3–19. [ Google Scholar ] [ CrossRef ]
• Savelsbergh, M. A branch-and-price algorithm for the generalized assignment problem. Oper. Res. 1997 , 45 , 831–841. [ Google Scholar ] [ CrossRef ]
• Talkhestani, H.R.A.; Jahromi, M.H.M.A.; Keshavarzfard, R. A Location-Inventory Model for Multi-Product Supply Chain with Perishable Products and Price-Dependent Demand. Adv. Ind. Eng. 2023 , 56 , 1–12. [ Google Scholar ]
• Mohammadi, Z.; Barzinpour, F.; Teimoury, E. A location-inventory model for the sustainable supply chain of perishable products based on pricing and replenishment decisions: A case study. PLoS ONE 2023 , 18 , e0288915. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Tarantilis, C.; Kiranoudis, C. Distribution of fresh meat. J. Food Eng. 2002 , 51 , 85–91. [ Google Scholar ] [ CrossRef ]
• Zhang, G.; Habenicht, W.; Spieß, W.E.L. Improving the structure of deep frozen and chilled food chain with tabu search procedure. J. Food Eng. 2003 , 60 , 67–79. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Yu, L.y. (Eds.) Optimization model of refrigerated food transportation. In Proceedings of the ICSSSM12, Shanghai, China, 2–4 July 2012; IEEE: New York, NY, USA, 2012; pp. 220–224. [ Google Scholar ]
• Neves-Moreira, F.; Almada-Lobo, B.; Cordeau, J.-F.; Guimarães, L.; Jans, R. Solving a large multi-product production-routing problem with delivery time windows. Omega 2019 , 86 , 154–172. [ Google Scholar ] [ CrossRef ]
• Govindan, K.; Jafarian, A.; Khodaverdi, R.; Devika, K. Two-echelon multiple-vehicle location–routing problem with time windows for optimization of sustainable supply chain network of perishable food. Int. J. Prod. Econ. 2014 , 152 , 9–28. [ Google Scholar ] [ CrossRef ]
• Mohammed, A.; Wang, Q. Developing a meat supply chain network design using a multi-objective possibilistic programming approach. Br. Food J. 2017 , 119 , 690–706. [ Google Scholar ] [ CrossRef ]
• Moreno, S.; Pereira, J.; Yushimito, W. A hybrid K-means and integer programming method for commercial territory design: A case study in meat distribution. Ann. Oper. Res. 2020 , 286 , 87–117. [ Google Scholar ] [ CrossRef ]
• Rahbari, M.; Hajiagha, S.H.R.; Dehaghi, M.R.; Moallem, M.; Dorcheh, F.R. Modeling and solving a five-echelon location–inventory–routing problem for red meat supply chain: Case study in Iran. Kybernetes 2020 , 50 , 66–99. [ Google Scholar ] [ CrossRef ]
• Qu, S.; Zhou, Y.; Ji, Y.; Dai, Z.; Wang, Z. Robust maximum expert consensus modeling with dynamic feedback mechanism under uncertain environments. J. Ind. Manag. Optim. 2024 . [ Google Scholar ] [ CrossRef ]
• Soysal, M.; Bloemhof-Ruwaard, J.M.; Van Der Vorst, J.G. Modelling food logistics networks with emission considerations: The case of an international beef supply chain. Int. J. Prod. Econ. 2014 , 152 , 57–70. [ Google Scholar ] [ CrossRef ]
• Mohebalizadehgashti, F.; Zolfagharinia, H.; Amin, S.H. Designing a green meat supply chain network: A multi-objective approach. Int. J. Prod. Econ. 2020 , 219 , 312–327. [ Google Scholar ] [ CrossRef ]
• Aung, M.M.; Chang, Y.S. Traceability in a food supply chain: Safety and quality perspectives. Food Control 2014 , 39 , 172–184. [ Google Scholar ] [ CrossRef ]
• Ketzenberg, M.; Bloemhof, J.; Gaukler, G. Managing perishables with time and temperature history. Prod. Oper. Manag. 2015 , 24 , 54–70. [ Google Scholar ] [ CrossRef ]
• Bottani, E.; Casella, G.; Nobili, M.; Tebaldi, L. An analytic model for estimating the economic and environmental impact of food cold supply chain. Sustainability 2022 , 14 , 4771. [ Google Scholar ] [ CrossRef ]
• Vrat, P.; Gupta, R.; Bhatnagar, A.; Pathak, D.K.; Fulzele, V. Literature review analytics (LRA) on sustainable cold-chain for perishable food products: Research trends and future directions. Opsearch 2018 , 55 , 601–627. [ Google Scholar ] [ CrossRef ]
• De Keizer, M.; Haijema, R.; Bloemhof, J.M.; Van Der Vorst, J.G. Hybrid optimization and simulation to design a logistics network for distributing perishable products. Comput. Ind. Eng. 2015 , 88 , 26–38. [ Google Scholar ] [ CrossRef ]
• Coelho, L.C.; Laporte, G. Optimal joint replenishment, delivery and inventory management policies for perishable products. Comput. Oper. Res. 2014 , 47 , 42–52. [ Google Scholar ] [ CrossRef ]
• Hasani, A.; Zegordi, S.H.; Nikbakhsh, E. Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. Int. J. Prod. Res. 2012 , 50 , 4649–4669. [ Google Scholar ] [ CrossRef ]
• Song, B.D.; Ko, Y.D. A vehicle routing problem of both refrigerated-and general-type vehicles for perishable food products delivery. J. Food Eng. 2016 , 169 , 61–71. [ Google Scholar ] [ CrossRef ]
• de Keizer, M.; Akkerman, R.; Grunow, M.; Bloemhof, J.M.; Haijema, R.; van der Vorst, J.G. Logistics network design for perishable products with heterogeneous quality decay. Eur. J. Oper. Res. 2017 , 262 , 535–549. [ Google Scholar ] [ CrossRef ]
• Mohammed, A.; Govindan, K.; Zubairu, N.; Pratabaraj, J.; Abideen, A.Z. Multi-tier supply chain network design: A key towards sustainability and resilience. Comput. Ind. Eng. 2023 , 182 , 109396. [ Google Scholar ] [ CrossRef ]
• Tavakkoli Moghaddam, S.; Javadi, M.; Hadji Molana, S.M. A reverse logistics chain mathematical model for a sustainable production system of perishable goods based on demand optimization. J. Ind. Eng. Int. 2019 , 15 , 709–721. [ Google Scholar ] [ CrossRef ]
• Mohammed, A.; Wang, Q.; Li, X. A cost-effective decision-making algorithm for an RFID-enabled HMSC network design: A multi-objective approach. Ind. Manag. Data Syst. 2017 , 117 , 1782–1799. [ Google Scholar ] [ CrossRef ]
• Alfian, G.; Syafrudin, M.; Farooq, U.; Ma’arif, M.R.; Syaekhoni, M.A.; Fitriyani, N.L.; Lee, J.; Rhee, J. Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control 2020 , 110 , 107016. [ Google Scholar ] [ CrossRef ]
• Verghese, K.; Lewis, H.; Lockrey, S.; Williams, H. The Role of Packaging in Minimising Food Waste in the Supply Chain of the Future: Prepared for: CHEP Australia ; RMIT University Report: Melbourne, VIC, Australia, 2013. [ Google Scholar ]
• Singh, A.; Kumari, S.; Malekpoor, H.; Mishra, N. Big data cloud computing framework for low carbon supplier selection in the beef supply chain. J. Clean. Prod. 2018 , 202 , 139–149. [ Google Scholar ] [ CrossRef ]
• Thakur, M.; Forås, E. EPCIS based online temperature monitoring and traceability in a cold meat chain. Comput. Electron. Agric. 2015 , 117 , 22–30. [ Google Scholar ] [ CrossRef ]
• Kittipanya-Ngam, P.; Tan, K.H. A framework for food supply chain digitalization: Lessons from Thailand. Prod. Plan. Control 2020 , 31 , 158–172. [ Google Scholar ] [ CrossRef ]
• Luo, H.; Zhu, M.; Ye, S.; Hou, H.; Chen, Y.; Bulysheva, L. An intelligent tracking system based on internet of things for the cold chain. Internet Res. 2016 , 26 , 435–445. [ Google Scholar ] [ CrossRef ]
• Rodrigues, V.S.; Demir, E.; Wang, X.; Sarkis, J. Measurement, mitigation and prevention of food waste in supply chains: An online shopping perspective. Ind. Mark. Manag. 2021 , 93 , 545–562. [ Google Scholar ] [ CrossRef ]
• Fung, F.; Wang, H.-S.; Menon, S. Food safety in the 21st century. Biomed. J. 2018 , 41 , 88–95. [ Google Scholar ] [ CrossRef ]
• Siems, E.; Land, A.; Seuring, S. Dynamic capabilities in sustainable supply chain management: An inter-temporal comparison of the food and automotive industries. Int. J. Prod. Econ. 2021 , 236 , 108128. [ Google Scholar ] [ CrossRef ]
• Gholami-Zanjani, S.M.; Klibi, W.; Jabalameli, M.S.; Pishvaee, M.S. The design of resilient food supply chain networks prone to epidemic disruptions. Int. J. Prod. Econ. 2021 , 233 , 108001. [ Google Scholar ] [ CrossRef ]
• Wang, J.; Yue, H. Food safety pre-warning system based on data mining for a sustainable food supply chain. Food Control 2017 , 73 , 223–229. [ Google Scholar ] [ CrossRef ]
• Esmaeilian, B.; Sarkis, J.; Lewis, K.; Behdad, S. Blockchain for the future of sustainable supply chain management in Industry 4.0. Resour. Conserv. Recycl. 2020 , 163 , 105064. [ Google Scholar ] [ CrossRef ]
• McKinna, D.; Wall, C. Commercial Application of Supply Chain Integrity and Shelf Life Systems ; Technical Report; Meat and Livestock Australia Limited: Sydney, NSW, Australia, 2020. [ Google Scholar ]
• Jia, L.; Evans, S. Improving food allergen management in food manufacturing: An incentive-based approach. Food Control 2021 , 129 , 108246. [ Google Scholar ] [ CrossRef ]
• Nyarugwe, S.P.; Linnemann, A.R.; Luning, P.A. Prevailing food safety culture in companies operating in a transition economy-Does product riskiness matter? Food Control 2020 , 107 , 106803. [ Google Scholar ] [ CrossRef ]
• Sestino, A.; Prete, M.I.; Piper, L.; Guido, G. Internet of Things and Big Data as enablers for business digitalization strategies. Technovation 2020 , 98 , 102173. [ Google Scholar ] [ CrossRef ]
• Astill, J.; Dara, R.A.; Campbell, M.; Farber, J.M.; Fraser, E.D.; Sharif, S.; Yada, R.Y. Transparency in food supply chains: A review of enabling technology solutions. Trends Food Sci. Technol. 2019 , 91 , 240–247. [ Google Scholar ] [ CrossRef ]
• Singh, M.; Corradini, M.G. Big data and its role in mitigating food spoilage and quality deterioration along the supply chain. In Harnessing Big Data in Food Safety ; Springer: Berlin/Heidelberg, Germany, 2022; pp. 93–112. [ Google Scholar ]
• Tiwari, S.; Wee, H.-M.; Daryanto, Y. Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Comput. Ind. Eng. 2018 , 115 , 319–330. [ Google Scholar ] [ CrossRef ]
• Wang, G.; Gunasekaran, A.; Ngai, E.W.; Papadopoulos, T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016 , 176 , 98–110. [ Google Scholar ] [ CrossRef ]
• Addo-Tenkorang, R.; Helo, P.T. Big data applications in operations/supply-chain management: A literature review. Comput. Ind. Eng. 2016 , 101 , 528–543. [ Google Scholar ] [ CrossRef ]
• Feng, H.; Wang, X.; Duan, Y.; Zhang, J.; Zhang, X. Applying blockchain technology to improve agri-food traceability: A review of development methods, benefits and challenges. J. Clean. Prod. 2020 , 260 , 121031. [ Google Scholar ] [ CrossRef ]
• Sanka, A.I.; Irfan, M.; Huang, I.; Cheung, R.C. A survey of breakthrough in blockchain technology: Adoptions, applications, challenges and future research. Comput. Commun. 2021 , 169 , 179–201. [ Google Scholar ] [ CrossRef ]
• Azzi, R.; Chamoun, R.K.; Sokhn, M. The power of a blockchain-based supply chain. Comput. Ind. Eng. 2019 , 135 , 582–592. [ Google Scholar ] [ CrossRef ]
• Powell, W.; Foth, M.; Cao, S.; Natanelov, V. Garbage in garbage out: The precarious link between IoT and blockchain in food supply chains. J. Ind. Inf. Integr. 2022 , 25 , 100261. [ Google Scholar ] [ CrossRef ]
• Verboven, P.; Defraeye, T.; Datta, A.K.; Nicolai, B. Digital twins of food process operations: The next step for food process models? Curr. Opin. Food Sci. 2020 , 35 , 79–87. [ Google Scholar ] [ CrossRef ]
• Kuffi, K.D.; Defraeye, T.; Nicolai, B.M.; De Smet, S.; Geeraerd, A.; Verboven, P. CFD modeling of industrial cooling of large beef carcasses. Int. J. Refrig. 2016 , 69 , 324–339. [ Google Scholar ] [ CrossRef ]
• Wu, W.; Beretta, C.; Cronje, P.; Hellweg, S.; Defraeye, T. Environmental trade-offs in fresh-fruit cold chains by combining virtual cold chains with life cycle assessment. Appl. Energy 2019 , 254 , 113586. [ Google Scholar ] [ CrossRef ]
• Agalianos, K.; Ponis, S.; Aretoulaki, E.; Plakas, G.; Efthymiou, O. Discrete event simulation and digital twins: Review and challenges for logistics. Procedia Manuf. 2020 , 51 , 1636–1641. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Ma, X.; Liu, M.; Gong, K.; Liu, Y.; Xu, M.; Wang, Y. Cooperation and profit allocation in two-echelon logistics joint distribution network optimization. Appl. Soft Comput. 2017 , 56 , 143–157. [ Google Scholar ] [ CrossRef ]
• Zhu, Z.; Bai, Y.; Dai, W.; Liu, D.; Hu, Y. Quality of e-commerce agricultural products and the safety of the ecological environment of the origin based on 5G Internet of Things technology. Environ. Technol. Innov. 2021 , 22 , 101462. [ Google Scholar ] [ CrossRef ]
• Osman, S.A.; Xu, C.; Akuful, M.; Paul, E.R. Perishable Food Supply Chain Management: Challenges and the Way Forward. Open J. Soc. Sci. 2023 , 11 , 349–364. [ Google Scholar ] [ CrossRef ]
• Verhoef, P.C.; Lemon, K.N. Successful customer value management: Key lessons and emerging trends. Eur. Manag. J. 2013 , 31 , 1–15. [ Google Scholar ] [ CrossRef ]
• Tseng, M.-L. Modeling sustainable production indicators with linguistic preferences. J. Clean. Prod. 2013 , 40 , 46–56. [ Google Scholar ] [ CrossRef ]
• Derqui, B.; Fayos, T.; Fernandez, V. Towards a more sustainable food supply chain: Opening up invisible waste in food service. Sustainability 2016 , 8 , 693. [ Google Scholar ] [ CrossRef ]
• Heising, J.K.; Claassen, G.; Dekker, M. Options for reducing food waste by quality-controlled logistics using intelligent packaging along the supply chain. Food Addit. Contam. Part A 2017 , 34 , 1672–1680. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ravindran, R.; Jaiswal, A.K. Exploitation of food industry waste for high-value products. Trends Biotechnol. 2016 , 34 , 58–69. [ Google Scholar ] [ CrossRef ]
• Lin, C.S.K.; Pfaltzgraff, L.A.; Herrero-Davila, L.; Mubofu, E.B.; Abderrahim, S.; Clark, J.H.; Koutinas, A.A.; Kopsahelis, N.; Stamatelatou, K.; Dickson, F. Food waste as a valuable resource for the production of chemicals, materials and fuels. Current situation and global perspective. Energy Environ. Sci. 2013 , 6 , 426–464. [ Google Scholar ] [ CrossRef ]
• Krishnan, R.; Arshinder, K.; Agarwal, R. Robust optimization of sustainable food supply chain network considering food waste valorization and supply uncertainty. Comput. Ind. Eng. 2022 , 171 , 108499. [ Google Scholar ] [ CrossRef ]
• Cristóbal, J.; Castellani, V.; Manfredi, S.; Sala, S. Prioritizing and optimizing sustainable measures for food waste prevention and management. Waste Manag. 2018 , 72 , 3–16. [ Google Scholar ] [ CrossRef ]
• Karwowska, M.; Łaba, S.; Szczepański, K. Food loss and waste in meat sector—Why the consumption stage generates the most losses? Sustainability 2021 , 13 , 6227. [ Google Scholar ] [ CrossRef ]
• Dani, S.; Deep, A. Fragile food supply chains: Reacting to risks. Int. J. Logist. Res. Appl. 2010 , 13 , 395–410. [ Google Scholar ] [ CrossRef ]
• Corrêa, H.L.; Xavier, L.H. Concepts, design and implementation of Reverse Logistics Systems for sustainable supply chains in Brazil. J. Oper. Supply Chain Manag. 2013 , 6 , 1. [ Google Scholar ] [ CrossRef ]
• Pagell, M.; Shevchenko, A. Why research in sustainable supply chain management should have no future. J. Supply Chain Manag. 2014 , 50 , 44–55. [ Google Scholar ] [ CrossRef ]
• Difrancesco, R.M.; Huchzermeier, A. Closed-loop supply chains: A guide to theory and practice. Int. J. Logist. Res. Appl. 2016 , 19 , 443–464. [ Google Scholar ] [ CrossRef ]
• Battini, D.; Bogataj, M.; Choudhary, A. Closed Loop Supply Chain (CLSC): Economics, Modelling, Management and Control ; Elsevier: Amsterdam, The Netherlands, 2017; Volume 183, pp. 319–321. [ Google Scholar ]
• Meneghetti, A.; Monti, L. Greening the food supply chain: An optimisation model for sustainable design of refrigerated automated warehouses. Int. J. Prod. Res. 2015 , 53 , 6567–6587. [ Google Scholar ] [ CrossRef ]
• Adekomaya, O.; Jamiru, T.; Sadiku, R.; Huan, Z. Sustaining the shelf life of fresh food in cold chain–A burden on the environment. Alex. Eng. J. 2016 , 55 , 1359–1365. [ Google Scholar ] [ CrossRef ]
• Benn, S.; Edwards, M.; Williams, T. Organizational Change for Corporate Sustainability ; Routledge: London, UK, 2014. [ Google Scholar ]
• Chopra, S.; Meindl, P. Supply Chain Management. Strategy, Planning & Operation ; Springer: Berlin/Heidelberg, Germany, 2007. [ Google Scholar ]
• Li, Q.; Liu, A. Big data driven supply chain management. Procedia CIRP 2019 , 81 , 1089–1094. [ Google Scholar ] [ CrossRef ]
• Nagy, G.; Salhi, S. Location-routing: Issues, models and methods. Eur. J. Oper. Res. 2007 , 177 , 649–672. [ Google Scholar ] [ CrossRef ]
• Kamariotou, M.; Kitsios, F.; Charatsari, C.; Lioutas, E.D.; Talias, M.A. Digital strategy decision support systems: Agrifood supply chain management in smes. Sensors 2021 , 22 , 274. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Trstenjak, M.; Opetuk, T.; Đukić, G.; Cajner, H. Logistics 5.0 Implementation Model Based on Decision Support Systems. Sustainability 2022 , 14 , 6514. [ Google Scholar ] [ CrossRef ]
• Cai, L.; Li, W.; Luo, Y.; He, L. Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment. Int. J. Prod. Res. 2022 , 61 , 1373–1393. [ Google Scholar ] [ CrossRef ]
• Stijn, E.v.; Phuaphanthong, T.; Keretho, S.; Pikart, M.; Hofman, W.; Tan, Y.-H. Implementation Framework for e-solutions for Trade Facilitation. In Accelerating Global Supply Chains with IT-Innovation ; Springer: Berlin/Heidelberg, Germany, 2011; pp. 285–317. [ Google Scholar ]
• Yavas, V.; Ozkan-Ozen, Y.D. Logistics centers in the new industrial era: A proposed framework for logistics center 4.0. Transp. Res. Part E Logist. Transp. Rev. 2020 , 135 , 101864. [ Google Scholar ] [ CrossRef ]
• Zokaei, A.K.; Simons, D.W. Value chain analysis in consumer focus improvement: A case study of the UK red meat industry. Int. J. Logist. Manag. 2006 , 17 , 141–162. [ Google Scholar ] [ CrossRef ]
• Cox, A.; Chicksand, D.; Yang, T. The proactive alignment of sourcing with marketing and branding strategies: A food service case. Supply Chain Manag. Int. J. 2007 , 12 , 321–333. [ Google Scholar ] [ CrossRef ]
• Jie, F.; Gengatharen, D. Australian food retail supply chain analysis. Bus. Process Manag. J. 2019 , 25 , 271–287. [ Google Scholar ] [ CrossRef ]
• Knoll, S.; Marques, C.S.S.; Liu, J.; Zhong, F.; Padula, A.D.; Barcellos, J.O.J. The Sino-Brazilian beef supply chain: Mapping and risk detection. Br. Food J. 2017 , 119 , 164–180. [ Google Scholar ] [ CrossRef ]
• Schilling-Vacaflor, A. Integrating human rights and the environment in supply chain regulations. Sustainability 2021 , 13 , 9666. [ Google Scholar ] [ CrossRef ]
• Knoll, S.; Padula, A.D.; dos Santos, M.C.; Pumi, G.; Zhou, S.; Zhong, F.; Barcellos, J.O.J. Information flow in the Sino-Brazilian beef trade. Int. Food Agribus. Manag. Rev. 2018 , 21 , 17–38. [ Google Scholar ] [ CrossRef ]
• E-Fatima, K.; Khandan, R.; Hosseinian-Far, A.; Sarwar, D.; Ahmed, H.F. Adoption and Influence of Robotic Process Automation in Beef Supply Chains. Logistics 2022 , 6 , 48. [ Google Scholar ] [ CrossRef ]
• Storer, M.; Hyland, P.; Ferrer, M.; Santa, R.; Griffiths, A. Strategic supply chain management factors influencing agribusiness innovation utilization. Int. J. Logist. Manag. 2014 , 25 , 487–521. [ Google Scholar ] [ CrossRef ]
• Mangla, S.K.; Börühan, G.; Ersoy, P.; Kazancoglu, Y.; Song, M. Impact of information hiding on circular food supply chains in business-to-business context. J. Bus. Res. 2021 , 135 , 1–18. [ Google Scholar ] [ CrossRef ]
• Faisal, M.N. A study of inhibitors to transparency in red meat supply chains in Gulf cooperation council (GCC) countries. Bus. Process Manag. J. 2015 , 21 , 1299–1318. [ Google Scholar ] [ CrossRef ]
• Shanoyan, A.; Schiavi Bankuti, S.M.; Colares-Santos, L. Analysis of incentive structures at producer–processor interface of beef supply chain in Brazil. J. Agribus. Dev. Emerg. Econ. 2019 , 9 , 159–174. [ Google Scholar ] [ CrossRef ]
• Nakandala, D.; Lau, H.; Zhang, J. Cost-optimization modelling for fresh food quality and transportation. Ind. Manag. Data Syst. 2016 , 116 , 564–583. [ Google Scholar ] [ CrossRef ]
• Ge, H.; Gómez, M.; Peters, C. Modeling and optimizing the beef supply chain in the Northeastern US. Agric. Econ. 2022 , 53 , 702–718. [ Google Scholar ] [ CrossRef ]
• Hsiao, Y.-H.; Chen, M.-C.; Chin, C.-L. Distribution planning for perishable foods in cold chains with quality concerns: Formulation and solution procedure. Trends Food Sci. Technol. 2017 , 61 , 80–93. [ Google Scholar ] [ CrossRef ]
• Meksavang, P.; Shi, H.; Lin, S.-M.; Liu, H.-C. An extended picture fuzzy VIKOR approach for sustainable supplier management and its application in the beef industry. Symmetry 2019 , 11 , 468. [ Google Scholar ] [ CrossRef ]
• Taylor, D.H. Strategic considerations in the development of lean agri-food supply chains: A case study of the UK pork sector. Supply Chain Manag. Int. J. 2006 , 11 , 271–280. [ Google Scholar ] [ CrossRef ]
• Erol, E.; Saghaian, S.H. The COVID-19 pandemic and dynamics of price adjustment in the US beef sector. Sustainability 2022 , 14 , 4391. [ Google Scholar ] [ CrossRef ]
• Galuchi, T.P.D.; Rosales, F.P.; Batalha, M.O. Management of socioenvironmental factors of reputational risk in the beef supply chain in the Brazilian Amazon region. Int. Food Agribus. Manag. Rev. 2019 , 22 , 155–171. [ Google Scholar ] [ CrossRef ]
• Silvestre, B.S.; Monteiro, M.S.; Viana, F.L.E.; de Sousa-Filho, J.M. Challenges for sustainable supply chain management: When stakeholder collaboration becomes conducive to corruption. J. Clean. Prod. 2018 , 194 , 766–776. [ Google Scholar ] [ CrossRef ]
• Eriksson, M.; Strid, I.; Hansson, P.-A. Waste of organic and conventional meat and dairy products—A case study from Swedish retail. Resour. Conserv. Recycl. 2014 , 83 , 44–52. [ Google Scholar ] [ CrossRef ]
• Accorsi, R.; Bortolini, M.; Gallo, A. Modeling by-products and waste management in the meat industry. In Sustainable Food Supply Chains ; Elsevier: Amsterdam, The Netherlands, 2019; pp. 339–349. [ Google Scholar ]
• Ersoy, P.; Börühan, G.; Kumar Mangla, S.; Hormazabal, J.H.; Kazancoglu, Y.; Lafcı, Ç. Impact of information technology and knowledge sharing on circular food supply chains for green business growth. Bus. Strategy Environ. 2022 , 31 , 1875–1904. [ Google Scholar ] [ CrossRef ]
• Mahbubi, A.; Uchiyama, T. Assessing the sustainability of the Indonesian halal beef supply chain. Int. J. Food Syst. Dyn. 2020 , 11 , 468–481. [ Google Scholar ]
• Bragaglio, A.; Napolitano, F.; Pacelli, C.; Pirlo, G.; Sabia, E.; Serrapica, F.; Serrapica, M.; Braghieri, A. Environmental impacts of Italian beef production: A comparison between different systems. J. Clean. Prod. 2018 , 172 , 4033–4043. [ Google Scholar ] [ CrossRef ]
• Zeidan, R.; Van Holt, T.; Whelan, T. Existence inductive theory building to study coordination failures in sustainable beef production. J. Clean. Prod. 2020 , 267 , 122137. [ Google Scholar ] [ CrossRef ]
• Santos, A.B.; Costa, M.H. Do large slaughterhouses promote sustainable intensification of cattle ranching in Amazonia and the Cerrado? Sustainability 2018 , 10 , 3266. [ Google Scholar ] [ CrossRef ]
• E-Fatima, K.; Khandan, R.; Hosseinian-Far, A.; Sarwar, D. The Adoption of Robotic Process Automation Considering Financial Aspects in Beef Supply Chains: An Approach towards Sustainability. Sustainability 2023 , 15 , 7236. [ Google Scholar ] [ CrossRef ]
• Huerta, A.R.; Güereca, L.P.; Lozano, M.d.l.S.R. Environmental impact of beef production in Mexico through life cycle assessment. Resour. Conserv. Recycl. 2016 , 109 , 44–53. [ Google Scholar ] [ CrossRef ]
• Cox, A.; Chicksand, D.; Palmer, M. Stairways to heaven or treadmills to oblivion? Creating sustainable strategies in red meat supply chains. Br. Food J. 2007 , 109 , 689–720. [ Google Scholar ] [ CrossRef ]
• Teresa, H.; Áine, M.-W.; Olive, M.; Carol, P.; Maeve, H. Co-operation among Irish beef farmers: Current perspectives and future prospects in the context of new producer organisation (PO) legislation. Sustainability 2018 , 10 , 4085. [ Google Scholar ] [ CrossRef ]
• Kyayesimira, J.; Wangalwa, R.; Kagoro Rugunda, G.; Lejju, J.B.; Matofari, J.W.; Andama, M. Causes of losses and the economic loss estimates at post-harvest handling points along the beef value chain in Uganda. J. Agric. Ext. Rural. Dev. 2019 , 11 , 176–183. [ Google Scholar ]
• Ranaei, V.; Pilevar, Z.; Esfandiari, C.; Khaneghah, A.M.; Dhakal, R.; Vargas-Bello-Pérez, E.; Hosseini, H. Meat value chain losses in Iran. Food Sci. Anim. Resour. 2021 , 41 , 16–33. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wiedemann, S.; McGahan, E.; Murphy, C.; Yan, M.-J.; Henry, B.; Thoma, G.; Ledgard, S. Environmental impacts and resource use of Australian beef and lamb exported to the USA determined using life cycle assessment. J. Clean. Prod. 2015 , 94 , 67–75. [ Google Scholar ] [ CrossRef ]
• Martínez, C.I.P.; Poveda, A.C. Characterization of cooling equipment in the food industry: Case study of the Colombian meat, dairy, and fruit and vegetable sectors. Environ. Dev. 2022 , 41 , 100693. [ Google Scholar ] [ CrossRef ]
• Fattahi, F.; Nookabadi, A.S.; Kadivar, M. A model for measuring the performance of the meat supply chain. Br. Food J. 2013 , 115 , 1090–1111. [ Google Scholar ] [ CrossRef ]
• Florindo, T.; Florindo, G.d.M.; Talamini, E.; da Costa, J.; de Léis, C.; Tang, W.; Schultz, G.; Kulay, L.; Pinto, A.; Ruviaro, C.F. Application of the multiple criteria decision-making (MCDM) approach in the identification of Carbon Footprint reduction actions in the Brazilian beef production chain. J. Clean. Prod. 2018 , 196 , 1379–1389. [ Google Scholar ] [ CrossRef ]
• Diaz, F.; Vignati, J.A.; Marchi, B.; Paoli, R.; Zanoni, S.; Romagnoli, F. Effects of energy efficiency measures in the beef cold chain: A life cycle-based study. Environ. Clim. Technol. 2021 , 25 , 343–355. [ Google Scholar ] [ CrossRef ]
• Schmidt, B.V.; Moreno, M.S. Traceability optimization in the meat supply chain with economic and environmental considerations. Comput. Ind. Eng. 2022 , 169 , 108271. [ Google Scholar ] [ CrossRef ]
• Domingues Zucchi, J.; Zeng, A.Z.; Caixeta-Filho, J.V. Optimum location for export-oriented slaughterhouses in Mato Grosso, Brazil: A dynamic mathematical model. Int. J. Logist. Res. Appl. 2011 , 14 , 135–148. [ Google Scholar ] [ CrossRef ]
• Dorcheh, F.R.; Rahbari, M. Greenhouse gas emissions optimization for distribution and vehicle routing problem in a poultry meat supply chain in two phases: A case study in Iran. Process Integr. Optim. Sustain. 2023 , 7 , 1289–1317. [ Google Scholar ] [ CrossRef ]
• Javanmard, S.; Vahdani, B.; Tavakkoli-Moghaddam, R. Solving a multi-product distribution planning problem in cross docking networks: An imperialist competitive algorithm. Int. J. Adv. Manuf. Technol. 2014 , 70 , 1709–1720. [ Google Scholar ] [ CrossRef ]
• Dai, Z.; Aqlan, F.; Zheng, X.; Gao, K. A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Comput. Ind. Eng. 2018 , 119 , 338–352. [ Google Scholar ] [ CrossRef ]
• Saragih, N.I.; Bahagia, N.; Syabri, I. A heuristic method for location-inventory-routing problem in a three-echelon supply chain system. Comput. Ind. Eng. 2019 , 127 , 875–886. [ Google Scholar ] [ CrossRef ]
• Hiassat, A.; Diabat, A.; Rahwan, I. A genetic algorithm approach for location-inventory-routing problem with perishable products. J. Manuf. Syst. 2017 , 42 , 93–103. [ Google Scholar ] [ CrossRef ]
• Le, T.; Diabat, A.; Richard, J.-P.; Yih, Y. A column generation-based heuristic algorithm for an inventory routing problem with perishable goods. Optim. Lett. 2013 , 7 , 1481–1502. [ Google Scholar ] [ CrossRef ]
• Wang, X.; Wang, M.; Ruan, J.; Zhan, H. The multi-objective optimization for perishable food distribution route considering temporal-spatial distance. Procedia Comput. Sci. 2016 , 96 , 1211–1220. [ Google Scholar ] [ CrossRef ]
• Rafie-Majd, Z.; Pasandideh, S.H.R.; Naderi, B. Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Comput. Chem. Eng. 2018 , 109 , 9–22. [ Google Scholar ] [ CrossRef ]
• Singh, A.; Mishra, N.; Ali, S.I.; Shukla, N.; Shankar, R. Cloud computing technology: Reducing carbon footprint in beef supply chain. Int. J. Prod. Econ. 2015 , 164 , 462–471. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Baker, D.; Griffith, G. Product quality information in supply chains: A performance-linked conceptual framework applied to the Australian red meat industry. Int. J. Logist. Manag. 2020 , 31 , 697–723. [ Google Scholar ] [ CrossRef ]
• Cao, S.; Powell, W.; Foth, M.; Natanelov, V.; Miller, T.; Dulleck, U. Strengthening consumer trust in beef supply chain traceability with a blockchain-based human-machine reconcile mechanism. Comput. Electron. Agric. 2021 , 180 , 105886. [ Google Scholar ] [ CrossRef ]
• Kassahun, A.; Hartog, R.J.; Tekinerdogan, B. Realizing chain-wide transparency in meat supply chains based on global standards and a reference architecture. Comput. Electron. Agric. 2016 , 123 , 275–291. [ Google Scholar ] [ CrossRef ]
• Ribeiro, P.C.C.; Scavarda, A.J.; Batalha, M.O. The application of RFID in brazilian harvest facilities: Two case studies. Int. J. Eng. Bus. Manag. 2011 , 3 , 1–63. [ Google Scholar ] [ CrossRef ]
• Cao, S.; Foth, M.; Powell, W.; Miller, T.; Li, M. A blockchain-based multisignature approach for supply chain governance: A use case from the Australian beef industry. Blockchain Res. Appl. 2022 , 3 , 100091. [ Google Scholar ] [ CrossRef ]
• Wu, J.-Y.; Hsiao, H.-I. Food quality and safety risk diagnosis in the food cold chain through failure mode and effect analysis. Food Control 2021 , 120 , 107501. [ Google Scholar ] [ CrossRef ]

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Davoudi, S.; Stasinopoulos, P.; Shiwakoti, N. Two Decades of Advancements in Cold Supply Chain Logistics for Reducing Food Waste: A Review with Focus on the Meat Industry. Sustainability 2024 , 16 , 6986. https://doi.org/10.3390/su16166986

Davoudi S, Stasinopoulos P, Shiwakoti N. Two Decades of Advancements in Cold Supply Chain Logistics for Reducing Food Waste: A Review with Focus on the Meat Industry. Sustainability . 2024; 16(16):6986. https://doi.org/10.3390/su16166986

Davoudi, Sina, Peter Stasinopoulos, and Nirajan Shiwakoti. 2024. "Two Decades of Advancements in Cold Supply Chain Logistics for Reducing Food Waste: A Review with Focus on the Meat Industry" Sustainability 16, no. 16: 6986. https://doi.org/10.3390/su16166986

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Please note you do not have access to teaching notes, enhancing halal food traceability: a model for rebuilding trust and integrity in muslim countries.

Journal of Islamic Marketing

ISSN : 1759-0833

Article publication date: 16 August 2024

Due to increasing scandals involving non-halal foods, contamination and fraudulent practices within the halal food supply chain, this paper aims to identify pivotal factors closely tied to halal food traceability and subsequently proposes a comprehensive halal food traceability model rooted in these factors.

Design/methodology/approach

The approach involved conducting a content analysis to meticulously gather data from existing scholarly works. Subsequently, the authors analysed this data using a thematic approach.

The extensive literature review yielded the identification of eight pivotal factors for the adoption and implementation of effective halal food traceability systems. These factors encompass consensus on halal food standards, government support, meeting consumer demands, ensuring the authenticity of halal food integrity, leveraging technological advancements, adherence to halal standards and certification systems, fostering stakeholder collaboration and promoting research and educational initiatives. Building upon these factors, this study presents a halal food traceability factorial model that can serve as a foundation for constructing a robust and readily-adoptable traceability system within Muslim countries.

Practical implications

The proposed halal food traceability model offers invaluable insights to stakeholders within both private enterprises and governmental bodies. By taking into account the identified factors, these stakeholders can significantly enhance their prospects for the successful adoption and implementation of traceability systems. Additionally, the paper expounds upon practical recommendations for practitioners and highlights avenues for future research aimed at establishing a robust halal traceability system across Muslim countries.

Originality/value

This paper stands as a significant contribution within the limited body of research addressing the development of an effective and readily-adoptable traceability model, thereby bolstering the integrity and safety of halal food. The outcomes of this paper are expected to catalyse improvements in the adoption and implementation of halal food traceability practices across Muslim nations.

• Traceability system
• Localised factors
• Stakeholders

Dashti, L.A.H.F. , Jackson, T. , West, A. and Jackson, L. (2024), "Enhancing halal food traceability: a model for rebuilding trust and integrity in Muslim countries", Journal of Islamic Marketing , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JIMA-06-2023-0167

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