industry 4.0 in management studies a systematic literature review

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Industry 4.0 in Management Studies: A Systematic Literature Review

Sustainability

Recent developments in production processes and their automation have led to the definition of the Fourth Industrial Revolution, commonly known as “Industry 4.0”. Industry 4.0 is a very broad domain that includes: production processes, efficiency, data management, relationship with consumers, competitiveness, and much more. At the same time, obviously, Industry 4.0 has become a new theme for management scholars and business economics disciplines and a number of contributions covering various issues and aspects have been published. However, a systematic formulation of all these contributions is still lacking in management literature. Therefore, the aim of the paper is to analyze and classify the main contributions published on the topic of Industry 4.0 in management literature, seeking to give it a unique definition, discover the gaps still remaining in literature and outline future avenues of research in this domain. A systematic review of the literature of the major academic and re...

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This study provides an exploratory bibliometric analysis of the emerging literature on Industry 5.0, which is a new visionary concept on the future of industry. Industry 5.0 has in recent years begun to attract the interest of both practitioners and academics, but this new field can still be considered embryonic and not well documented. Therefore, this study aims to map the field and provide a preliminary picture of the emergence and status of the scientific literature on Industry 5.0. Bibliometric data covering the period from 2015 to 2021 were extracted from the Scopus database. Bibliometric analyses of overall publication volume and growth trajectory, influential documents, authors, sources and countries are performed. The exploratory analysis provides a preliminary overview of the birth and emergence of this new research area. The results are discussed in relation to theories on the emergence and evolution of new management concepts. The article closes with some speculations abo...

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Industry 4.0 (I4.0) technologies have been highlighted in recent literature as enablers of servitisation. Simultaneously, businesses are advised to implement a circular economy (CE) to bring new opportunities. However, it is pertinent to mention that little attention has been given to assess the role of I4.0 in adopting the CE and servitisation in a fully integrated manner. This research fills this gap by developing a conceptual framework through a systematic literature review of 139 studies investigating the relationship between the I4.0, CE, and servitisation. This study identifies the impact of these variables on a firm’s operational and financial performance (revenue stream, growth, and profitability). Our research findings advocate that adopting I4.0 technologies to the business and manufacturing model enables sustainability, energy and resource efficiency while enhancing performance and offering innovative products through smart services. Thus, firms must systematically adopt ...

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Administrative Sciences (ISSN 2076-3387) (ESCI & Scopus indexing)

The Industry 4.0 (I4.0) concept is concerned with the fourth industrial revolution in manufacturing, in which technological trends such as digitalization, automation and artificial intelligence are transforming production processes. Since the concept's introduction at the Hannover Fair in Germany in 2011, I4.0 has enjoyed a meteoric rise in popularity and is currently high on the agenda of governments, politicians and business elites. In light of these observations, some commentators have asked the question of whether I4.0 is a concept that is hyped up and possibly just the latest in a long line of fashionable management concepts introduced over the course of the last few decades. Therefore, the aim of this paper is to provide a critical outside-in look at the emergence and rise of I4.0. Theoretically, these processes are viewed through the lens of management fashion, a theoretical perspective well suited to examinations of evolutionary trajectories of management concepts and ideas. The findings indicate that the I4.0 concept has quickly become highly popular and is dominating much of the popular management discourse. The concept has migrated out of the specialized manufacturing discourse to become a more general concept with mainstream appeal and applicability, evidenced by a multitude of neologisms such as Work 4.0 and Innovation 4.0. The numbers 4.0 have spread in a meme-like fashion, evidenced by the fact that the combination of a noun and the numbers 4.0 are used to signal and usher in discussions about the future of business and society. While there is much evidence that clearly shows that the concept has had a wide-ranging impact at the discursive level, the currently available research is less clear about what impact the concept has had so far on industries and organizations worldwide.

Veljko Mijušković

Industry 4.0 has been a major force framing the societal, economic and technological environment after 2010. Exposed to ongoing digital transformation, companies are able to exploit opportunities offered by Industry 4.0, and are forced to manage immanent risks and barriers. However, studies on opportunities and challenges relevant for the implementation of Industry 4.0 for companies are scarce. In response to this literature gap, the aim of this exploratory research is to provide a deeper analysis of the level of digital transformation of companies in Serbia based on a digital maturity model, and examine their managers’ opinions on the most important driving forces and implementation barriers. The paper uses exploratory research design based on a survey responded to by 122 high-level managers within the Serbian manufacturing sector. Findings show that, contrary to expectations, digitally transforming enterprises do not see human resources as a driving force, but rather as an obstacl...

maria grazia saporito

The present review retraces the steps of the industrial and agriculture revolution that have taken place up to the present day, giving ideas and considerations for the future. This paper analyses the specific challenges facing agriculture along the farming supply chain to permit the operative implementation of Industry 4.0 guidelines. The subsequent scientific value is an investigation of how Industry 4.0 approaches can be improved and be pertinent to the agricultural sector. However, industry is progressing at a much faster rate than agriculture. In fact, already today experts talk about Industry 5.0. On the other hand, the 4.0 revolution in agriculture is still limited to a few innovative firms. For this reason, this work deals with how technological development affects different sectors (industry and agriculture) in different ways. In this innovative background, despite the advantages of industry or agriculture 4.0 for large enterprises, small- and medium-sized enterprises (SMEs)...

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Industry 4.0 maturity and readiness models: a systematic literature review and future framework.

• Pinosh Kumar Hajoary

Department of Management Studies, Indian Institute of Science, Bangalore, India

E-mail Address: [email protected]

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Over the last five years, Industry 4.0 (I4.0) has gained a lot of attention from industry leaders, policymakers, and government officials worldwide. In an era where new concepts and techniques are introduced continuously, there is a lack of systematic literature review (SLR) on identifying main dimensions, levels, methods to assess the maturity and readiness level toward I4.0. To address this gap, we have chosen our primary objective to provide a critical review of existing literature on dimensions, methods, levels, and current trends to evaluate the I4.0 maturity and readiness models. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) methodology was adopted to make sure that there is no replication and to maintain complete transparency in the review process. A total of 53 papers were deemed relevant for thematic analysis. From the literature, we have found and proposed 10 main dimensions — Strategy and Organization, Manufacturing and Operations, Supply Chain, Business Model, IT, People, Customers, Product, Services, and Culture, to assess the I4.0 maturity and readiness levels of an organization. Further, a conceptual framework was proposed for the same. This study contributes theoretically to the development of I4.0 maturity and readiness models. So far, this is the first review paper on dimensions of I4.0 maturity and readiness models and is expected to give future researchers and practitioners a holistic guideline to design and develop I4.0 maturity and readiness models.

• Literature review
• Industry 4.0
• Industry 4.0 maturity model
• Industry 4.0 readiness model
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Received 10 March 2020 Revised 11 November 2020 Accepted 11 November 2020 Published: 9 January 2021

Lean supply chain management and Industry 4.0: a systematic literature review

International Journal of Lean Six Sigma

ISSN : 2040-4166

Article publication date: 22 August 2022

Issue publication date: 28 February 2023

Even though the integration of Lean Supply Chain Management (LSCM) and Industry 4.0 (I4.0) technologies is relatively recent, it has been receiving a lot of attention. Partly because it is a recent field of practise and research and partly because the number of works developed in this field has grown rapidly, it is important to frequently update the perspectives on this field of investigation. Thus, this study aims to review the integration between LSCM and I4.0 analysing relationship at operative, tactical and strategic levels.

Design/methodology/approach

Systematic literature review was conducted to identify and explain the integration of LSCM and I4.0 from scientific sources that were published before March 2021.

The analysis of the literature revealed the level of integration of LSCM and I4.0 is present at different managerial levels. Moreover, when the integration is detailed at different managerial levels, it appears that LSCM paves the way for the adoption of I4.0 at a strategic level, while I4.0 technologies promise to enhance LSCM practices at the operational level.

Research limitations/implications

The main contribution of this study is the framework which shows that LSCM paves the way for the adoption of I4.0 at a strategic level, while I4.0 technologies promise to enhance LSCM practices at the operational level.

Originality/value

This study develops a new perspective of the articles published under the thematic of LSCM and I4.0. Additionally, it proposes a framework of analysis that can be used by future researchers. Finally, it shows the most recent implementations of LSCM and I4.0, exposing the current trends, improvements and also the main gaps.

• Supply chain management
• Lean production
• Industry 4.0
• Digitalization
• Lean supply chain

Rossini, M. , Powell, D.J. and Kundu, K. (2023), "Lean supply chain management and Industry 4.0: a systematic literature review", International Journal of Lean Six Sigma , Vol. 14 No. 2, pp. 253-276. https://doi.org/10.1108/IJLSS-05-2021-0092

Emerald Publishing Limited

Copyright © 2022, Matteo Rossini, Daryl John Powell and Kaustav Kundu

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Since the past decades, the Lean Supply Chain Management (LSCM) practices and principles have been successfully adopted across different sectors ( Chakrabarty and Wang, 2020 ). Such increasing interest is because LSCM implementation can bring several benefits in the form of cost reduction, throughput time shortening, quality and other aspects (Moyano-Fuentes et al. , 2020; Vanichchinchai, 2019).

The advent of Industry 4.0 (I4.0) has re-opened the debate of the introduction of novel technologies in already established managerial system led by human-centred philosophy such as lean management ( Ghadge et al. , 2020 ). The modern information and communication technologies (ICT) of I4.0 make it possible to establish connection among the products, machines and processes. In this sense, many authors have spotlighted the potential benefits of integrating technologies such as Big Data analytics, Internet of Things and Cloud Computing in lean systems, generating great expectations and enthusiasm ( Buer et al. , 2020 ; Tortorella et al. , 2019a , 2019b ).

Literature presents papers discussing interplay between LSCM and I4.0, but the topic is still in the novelty phase ( Núñez-Merino et al. , 2020 ).

Moreover, the literature is presenting different perspective of analysis, which does not support the structure of such relationship, thereby creating chaos on the value of the interplay.

Is there any interrelationship between Lean Supply Chain Management and Industry 4.0?

To answer the research question, this study provides a systematic literature review (SLR).

The article is structured as follows: Section 2 introduces and defines the domain of LSCM and I4.0, while Section 3 describes the applied methodology for the SLR. Section 4 outlines the findings of the study and presents the conceptual framework of the interplay between LSCM and I4.0. Section 5 concludes the article, indicating its limitations and future research opportunities.

2. Theoretical background

2.1 lean supply chain management.

Given the current situation where there is high turbulence in the customer demand and the competitive landscape is rapidly changing, firms grapple with the challenge of satisfying the desired needs of the customers (Vanpoucke et al. , 2014).

According to Krajewski et al. (2015), the most successful companies are those that take into consideration the external customers and suppliers into their internal improvement processes. Thus, integration with suppliers and customers acts as an external aid to improve competitiveness and efficiency (Flynn et al. , 2010). One approach that can help supply chains to reduce waste and achieve sustainability is Lean Production (LP), based on the Toyota Production System ( Rossini et al. , 2019 ). As such, in an environment where the companies are competing for shorter lead times and better quality with cost reduction, LP practices can be implemented in the spirit of a supply chain integrative approach (Guilherme Luz Tortorella et al. , 2017 ).

LSCM is the management of different organizations integrating both upstream and downstream flows of different entities that can increase value and reduce cost and waste by responding promptly to satisfy the demand of each customer ( Anand and Kodali, 2008 ; Saxby et al. , 2020). Thus, the aim of LSCM is to ensure that value is created and transferred efficiently to the downstream. The concept of LSCM is not only confined in automotive, construction or manufacturing companies but also extended to service firms such as hospitality, health care and retail ( Borges et al. , 2019 ). However, several studies on the topic have focused only on individual aspects of LSCM. In particular, many studies have concentrated only on analysing “upstream” lean practices, while scarcely any attention has been given on their application in the “downstream” (Reichhart and Holweg, 2007; Reitsma et al. , 2020 ). The gradual adoption of LP practices “downstream” in the flow could be justified by the production levelling (heijunka) concept, as there is a requirement of production to cope up with fluctuations with high market demand variability (Mason-Jones et al. , 2000). Nevertheless, it is found out that it is not possible to achieve complete sustainable results by applying LP principles only at upstream actors in the supply chain.

Hines et al. (2004) indicated that the evolution of LP principles and practices over the years from the shop floor to every actor across the supply chain. They can be applied, from developing products and services, through placing orders with suppliers, to transporting the products to the customers ( Bittencourt et al. , 2021 ). Its aim is to continuously improve all the activities which will be beneficial for the customers. In this sense, LSCM highlights the importance of using LP practices effectively to create an efficient production and logistics system that meets the customers’ expectations ( Chu et al. , 2021 ).

As stated by Agarwal et al. (2006) , LSCM requires a different business model, which should consider a strategic relationship with different supply chain actors and eliminate waste through cooperative and systematic approach. In this sense, the advent of new technologies opens many new options for LSCM, facilitating coordination and collaboration among supply chain partners and fostering integration to more competitive levels (Tortorella G., Miorando R., 2019).

2.2 Industry 4.0

The term I4.0 was first coined out in 2011 by the German Industry–Science Research Alliance ( Buhr, 2017 ). Nowadays, I4.0 or simply the digital transformation creates many challenges for manufacturing companies from different points of view. The I4.0 can be seen as an expansion of ICT horizontally (Lee et al. , 2018). In fact, ICT is now used more extensively across all spheres, including business, government and everyday life. The digitalization of I4.0 helps to connect different entities across the whole supply chain through real time information ( Chiarini et al. , 2020 ). Because of this interconnection, different entities will be able to cope with the fluctuations in the environmental factors thanks to artificial intelligence ( Hecklau et al. , 2016 ). Mario et al. (2017) described I4.0 as “[…] a collective term for technologies and concepts of value chain organization”.

Digitization is affecting all sectors, either replacing traditional products with digital counterparts or enhancing those products with new digital features (Prem, 2015). However, the digital transformation that results from a shift towards I4.0 goes beyond the improvement at the product and process levels. Though it has created revolution in production and business models, it left the companies with challenges ( Bleicher and Stanley, 2018 ). In particular, the ultimate goal of digitalization of manufacturing is to establish connection among all the actors in manufacturing value chains. In other words, digitalization enhances not only the physical products but also the business and the overall strategy of the organizational structure (Matt et al. , 2015). According to Meier (2016) , for the full digital transformation, the companies should first analyse and identify what customers want; then, they should perform the changes within the organization according to the customer needs. But the challenge for business remains on the speed and depth that the companies can adopt digital transformation ( Rossini et al. , 2021 ).

It has been proved a strict link between the implementation of I4.0 technologies and lean systems (G.L. Tortorella et al. , 2019b ), but the research is focused on the single plant implementation and still misses a broader perspective of relationship outside the walls of the single plant, looking at the supply chain.

Given that LSCM and I4.0 represent two relevant opportunities for business but their relationship is still unstructured, this paper aims to explore and discuss the potential synergy for firms adopting both approaches.

3. Methodology

To explore the landscape of combining LSCM with I4.0, we adopted SLR. According to Fink (2005), a SLR is:

[…] a systematic, explicit, comprehensive and reproducible method for identifying, evaluating, and synthesizing the existing body of completed and recorded work produced by researchers, scholars, and practitioners.

In this section, the methodology adopted to conduct the literature review is described. According to the authors, it can be said to be systematic to a fair degree, considering the strict procedure that has been used.

Following the guidance of Xiao and Watson (2017), for a review to be successful, it should involve three phases: Planning, Conducting and Reporting.

Figure 1 shows the reviews in eight different steps.

In the following paragraphs, all the steps are detailly discussed and an explanation of how they have been put in practice is provided.

Step 1: Formulating the problem

The starting point for conducting a SLR is the formulation of the problem. The driving forces of the SLR process are the research questions ( Kitchenham and Charters, 2007 ), which are the aim of the formulation of the problem.

A too broad research question could invalidate the SLR because of a huge amount of identified data to work with ( Cronin et al. , 2008 ). For this reason, the adequate research question could be identified with an iterative process.

The foundation of the SLR is the relationship between the environment of lean supply chain and the one of I4.0. Therefore, the starting aim was to study how the two paradigms interact with each other. Gradually, conducting the initial research, a more structured and refined question has been necessary. Indeed, different relationships between lean supply chain and I4.0 could be identified in relation with different business sectors. Moreover, these relationships may have already been thoroughly studied in some sectors, while in other sectors, this research has just begun. This iterative process has been instrumental in identifying the boundaries of the research question of the SLR.

Step 2: Developing and validating the review protocol

The review protocol is “a preset plan that specifies the methods utilized in conducting the review” (Xiao and Watson, 2017), and it is absolutely crucial for rigorous systematic reviews. It should significantly reduce the bias during the analysis of the selected data (Kitchenham and Charters 2007).

Step 3: Searching the literature

The systematic search follows five steps. First, the channels for the literature search must be defined by researchers. As there are infinitely many channels available, researchers should choose a subgroup of channels. Second, the research question must be translated in keywords that could be used for the research. Third, researchers should identify the sampling strategy. According to the review requirement, the research can be either exhaustive or selective (Suri and Clarke, 2009; Bayliss and Beyer, 2015 ). Fourth, researchers should refine results with additional restrictions because there could be further practical criteria to exclude some papers from the SRL. Finally, it is important to identify a stopping rule that helps researcher to understand when the studies could be considered finished.

Regarding the channels for literature review, among the countless available sources, Scopus has been chosen as the only database for this SLR. This choice is because this electronic storage delivers “a comprehensive overview of the world’s research output in the fields of science, technology, medicine, social sciences, and arts and humanities”.

The following step is the identification of the keyword used for the search. For what concerns this study, as the aim is to study the relationship between LSCM and I4.0, the first keywords are “Lean”, “Supply Chain” and “Industry 4.0”. Given the need to have a comprehensive research environment, the search must be extended using synonymous, alternative spellings, abbreviations and related terms. Specifically, one synonymous of “Lean” is “JIT”, while the synonymous of “Industry 4.0” are “Digital” and “Smart”. Moreover, there is also an abbreviation for “Industry 4.0” that is “I4.0” and the alternative German spelling of “Industry 4.0” that is “Industrie 4.0”. To create the search strings, “AND” is used to join the main terms, while “OR” is used to include synonymous and abbreviations. Therefore, the final search string was {[(“JIT” OR “Lean”) AND (“Supply Chain”)] AND (“Industry 4.0” OR “I 4.0” OR “Digital” OR “Smart” OR “Industrie 4.0”)}. Table 1 synthesizes the final set of keywords, divided into the two themes addressed by this literature review.

For what concerns the sampling strategy, the search can be either exhaustive or selected (Suri and Clarke, 2009; Bayliss and Beyer, 2015 ). This SLR is more selected and representative. For this reason, “grey literature”, such as conferences, was not included in the search papers. This decision stems from the fact that grey literature is considered of lower quality than peer-reviewed papers (Xiao and Watson, 2017). Moreover, “grey literature” could be affected by more biases than peer-reviewed papers.

To further refine the results, additional restrictions have been applied including date range of publication and publication language. For what concerns the time horizon, documents published before 2011 were excluded from the research, as the term “Industry 4.0” came up for the first time during the Hannover Fair of that year. On the other side, the ending date of the research horizon corresponds to the end of 2020. As regards the publication language, the research has been confined to English language.

Finally, according to Levy and Ellis (2006), a rule of thumb is to stop the search in case of obtaining the same references with no new results. In this case, the stopping rule has been applied to the research keywords. In particular, it has been tested that adding the new keywords “I4.0” and “Industrie 4.0” to identify I4.0 leads to the same results. For this reason, no other keywords besides those presented in Table 1 were included.

Step 4: Screen for inclusion

Once the search of articles has come to the end, a list of paper has been generated in the channel selected for literature search. From this list, researchers have to screen each article to identify the possibility of including it for data extraction and analysis. Starting from the articles list generated in previous steps, the authors should screen this list by reading the abstract of each article according to inclusion and exclusion criteria. This procedure should be done by each researcher to select the studies based on the inclusion and exclusion criteria ( Gomersall et al. , 2015 ). These criteria, that are based on the research question, should match with the practical situations and appropriate enough to classify the research. Moreover, they can be trustworthy and should result in a manageable source of literature (Xiao and Watson, 2017). Only when the screen of articles is completed by each researcher, they should compare the two respective outputs of this procedure. In the case of conflicting opinions, the paper should be included.

Not peer-reviewed ( NR ): Articles found with the keywords previously described and that are not-peer-review should be discarded. Specifically, conferences articles are considered “grey literature”. For this reason, according to the sampling strategy adopted, these papers should be rejected, while only article and review types should be considered by researchers;

Completely unrelated ( CU ): Papers which are not related to the relationship between lean supply chain and I4.0 should not be included in the literature review. Indeed, because of some keywords used, such as “Smart” and “Digital”, articles that have in their abstract these terms have appeared in the search list. However, these articles sometimes are not related to the relationship between lean supply chain and I4.0, and for this reason, it should be rejected.

Vaguely related ( VR ): Articles which are related only to lean supply chain or I4.0 but not to the relationship between these two should be excluded. Moreover, papers that are just focused on one of these two areas and just briefly mention the other ones were discarded from the literature review.

Duplicates ( D ): As it has been decided for the literature to use only one database, Scopus , there are no overlapping results that could be obtained by using different databases. By the way, in the same database, there could be duplicates that must be deducted from the total number of articles reviewed.

No access ( NA ): It is possible that in the list of the literature review, there are some papers that could be accessed in terms of title and abstract, but that do not allow to access to the full text. For this reason, these papers should be rejected from the final list of papers.

Backward related ( BR ): Some articles that are cited in the articles reviewed should be included in the literature review to deepen the knowledge about the two paradigms under analysis.

Forward related ( FR ): Researchers should include publications by the key authors of the two paradigms analysed using the same database, Scopus.

Outdated ( O ): Articles published before 2011 should be excluded from the literature review because the paradigm of “Industry 4.0” came up the first time during the Hannover Fair of 2011.

Language ( L ): Papers written in a language that differs from English must be discarded for the literature review.

A summary of the exclusion and inclusion process with the related numbers is provided in Figure 2 .

After the application of the inclusion and exclusion criteria presented above, a final list of papers for the literature review could be identified, first by each researcher independently, before all researchers were able to discuss the list and reach consensus for inclusion in the study. We ensured that researchers had a balance of expertise, for example, one of us has expertise in lean, another in I4.0 and the other in both lean and I4.0. To guarantee future readers to be able to replicate the same search, a record of excluded papers has been kept for their reference (Kitchenham and Charters, 2007).

Step 5: Assessing quality

According to Whittemore and Knafl (2005), quality standards will differ from one review to another. On one hand, quality assessment is of vital importance for testing reviews, whose aim is generalization. On the other hand, this phase is not important for certain reviews such as scoping reviews, whose objective is to discover the breadth of studies, not the quality. Similar to the inclusion screening process, the authors have performed the quality assessment parallelly, as recommended by Noordzij et al. (2009). Any disagreement has been resolved through discussion and consultation.

As it has already been discussed that this literature review can be classified as a scoping review, the type of quality assessment applied has not excluded papers from the pool. This decision is aligned with both the desire to have as much input as possible and the novelty of the topic.

Therefore, quality assessment has been an important means for learning the overall quality and distribution of the selected studies.

The specifications objects of this assessment are the methodology used to conduct the study and the ranking of the publishing journal. The list of 45 papers including their title, authors, publication year, journal name, ranking and methodology is reported in Table 2 .

From Figure 3 , it is possible to notice an increasing trend in the interest for this topic. In particular, it is worth to point out that the majority of papers has been published during past three years, 2018–2020.

Looking at the methodologies used for the research ( Figure 4 ), it is immediately clear that 31% of studies have used case study, typically applying this methodology to specific fields.

For instance, Ramirez-Peña et al. (2020) focused on shipbuilding, a complex manufacturing industry that needs 4.0 guidelines to improve supply chain efficiency, while Ge and Jackson (2014) investigated usability of Big Data technologies as tools for Six Sigma process to achieve cost reduction in automotive industry.

Second, significant number of studies developed a framework connecting the two paradigms of lean supply chain and I4.0. As an example, Bevilacqua et al. (2019) formulated a procedure based on lean principles to minimize the number as well as the duration of picking processes in a warehouse equipped with an automated storage and retrieval system.

Then, three studies of the sample used interviews as a way to gather non-structured information to derive insightful findings.

Third, 4% of studies exploited the power of simulation to conduct some what-if analysis. Through this technique, Vazquez-Martinez et al. (2018) developed a model that is able to connect different customers, partners and organizations and perform the stages of digital product lifecycle across them.

Next, 16% of studies have used the literature review methodology with the aim to describe the state of the art of the literature regarding the interaction and concurrent application of lean supply chain and I4.0. In particular, the purpose of De Giovanni and Cariola (2020) is to investigate how a process innovation strategy that firms implement through I4.0 technologies affect the performance of lean practices and green supply chains.

Finally, the survey methodology has been applied by Rossini et al. (2019) among others (22%) to evaluate how the adoption of I4.0 technologies along with LP practices impact the operational performance of European manufacturers.

Step 6: Extracting data

Generally, the data extraction process often involves coding. As recommended by Gomersall et al. (2015), the researchers have coded the studies independently by reviewing the entire papers, not simply relying on the main interpretation. In this way, it is possible to avoid the distortion of the original paper. The articles were coded to identify the characteristics of each study (e.g. research method) and variables that might be used to explain emergent themes or differences in the works (e.g. interplay between both lean and I4.0 approaches).

Step 7: Analysing and synthesizing data

Once the data extraction process is completed, the reviewers have to sort out the data according to the previously defined review type (Xiao and Watson, 2017). Each qualitative and quantitative literature identified with the search set through the above steps was again analysed by each researcher independently. Subsequently, reviewers discussed together to translate the quantitative results proposed in the papers to a qualitative output. The approach adopted is the integrated design which analyses and synthesizes quantitative and qualitative research together. Results were synthesized into three emergent groups (again by reaching consensus on the emergent similarities and differences from the literature) – lean favouring I4.0, mutual support of lean and I4.0 and I4.0 supporting lean (see analysis of literature in the following section).

Step 8: Report findings

In the end, the findings from literature search should be reported after screening and quality assessment in a visual diagram. New insights should be highlighted. In addition, report findings should include also opportunities and direction for future research (Okoli and Schabram, 2010).

4. Results and discussion

In this section, the literature on the interplay between I4.0 and LSCM is analysed. Through literature analysis, 45 articles were identified that directly and indirectly satisfied our criteria and helped us to establish the interrelationships between I4.0 and LSCM. First, an analysis of the literature is presented showing the trend of articles. Then the findings in each of the identified lines are presented followed by discussion.

4.1 Analysis of literature

We can divide the papers into three main categories: Lean favouring I4.0; Mutual support of lean and I4.0; and I4.0 supporting lean. Table 3 shows the papers based on these categories.

Because of the low number of articles and the novelty of the topic, authors defined two research lines that clearly define different approaches to this field of research: strategic and operative levels.

The former research line includes all the articles that investigate interplay of the two paradigms focusing on a system perspective, looking at a path of implementation, with long-term period perspective. The latter research line includes all articles that analyse the interplay of the two paradigms at operative level, with a single implementation point of view and short-term perspective linked with the specific practice/technology implementation in a specific context.

The details are discussed in the next sections.

4.2 Strategic level

With the automation provided by I4.0, questions have risen about the interoperability with the lean approach. Lean environment creates a culture more receptive to new technologies, especially the ones that reduce waste ( Bittencourt et al. , 2019 , Torri et al. , 2021). In this sense, LSCM and I4.0, despite different perspectives, should be considered together, as they have the same goal of reducing the costs and increasing the productivity for companies. The authors conducted reviewed this interaction, focusing on the role that lean could play in the ongoing fourth Industrial Revolution. They derived that lean could facilitate to implement I4.0 in the companies, which is also mentioned by Kolberg and Zühlke (2015) . Therefore, the maturity level of lean within a system is an important metric which determines the association with I4.0 (Saxby et al. , 2020). However, the authors stressed the importance of understanding which LSCM elements provide more support to I4.0 introduction. This is necessary to avoid huge investments, both in terms of money and time, for re-inventing operations systems, while a simply update of some element effectively supports I4.0 technologies implementations (Saxby et al. , 2020).

The linkage between ICT and LSCM has been confirmed also by Jasti and Kodali (2015) who included Information Technology Management among the pillars of their LSCM framework already presented in the literature review on lean supply chain. Moreover, according to Tan et al. (2002), the information technology is monitoring the information flow within the supply chain, and it is also crucial to maintain and control the multi-organization networks in the present scenario (White and Pearson, 2001). Adamides et al. (2008) highlighted the relevance of ICTs as facilitator for LSCM development if greater integration with customers and suppliers is allowed.

Many studies presented I4.0 and LSCM as mutually supportive, where LSCM items are believed to be drivers of I4.0, and I4.0 is thought to strengthen lean (Uriarte et al. , 2018). This insight is also confirmed by Tortorella and Fettermann (2018) that presented a survey study demonstrating that companies who implemented both lean and I4.0 had achieved higher levels of performance improvement than other companies.

Chiarini et al. (2020) in their exploratory research on I4.0 technological developments have highlighted the support that I4.0 can provide to LSCM, confirming the results of previous scholars (Kolberg and Zühle, 2015). In particular, they highlight the impact of I4.0 on quality management practices – particularly the emergence of Quality 4.0 concept. However, they also showed some concern about the implementation of I4.0 technologies in an agile and fast-changing lean environment. They suggested that LSCM should be used to remove the waste, which will be a pre-requisite for I4.0 technologies and thereby prevent the automation of waste.

The issue of waste is addressed also by Alieva and von Haartman (2020) who focused on the negative impact of inefficient usage of data in the decision-making process on manufacturing performance. In fact, these authors claimed that technological solutions created to gain advantages through data analytics also produce a new form of waste, the digital waste. According to them, digital waste should be considered as a new type of Muda and should encourage to give extra attention to data analytics.

According to Ashrafian et al. (2019) , digital lean manufacturing means the application of digital technology in the forms of e-Kanbans or kaizen in digital collaborative environments in an environment where lean is already implemented. It can enhance the lean principles in the form of digitalization that creates less waste.

In addition, digitalization in communication is very important for all the partners in the supply chain ( Ashrafian et al. , 2019 ). In fact, strategic supplier development is trying to encourage and enable the suppliers to develop their lean capabilities. It will help them to improve their performance using lean practices (Sako, 2004). Furthermore, the challenges of competitive rivalry have been considered in the development of lean supplier networks.

The merits of digital technologies for transforming performance are now widely recognized, and a few contributions can also be seen for connection of digital transformation with LSCM principles (Pinho and Mendes, 2017). However, how digitalization can facilitate or hinder the way in which manufacturing firms use and encourage the development of lean practices with their suppliers is still challenging.

Although, despite being complementary initiatives in concept, information technology of I4.0 and LM are not always compatible, companies should try to adopt both in such a way so that sustainable competitiveness can be achieved. The work of Powell (2013) presented ways in which ERP could be used to support LP and pull production, particularly in small- and medium-sized enterprises. In similar fashion, Spenhoff et al. (2021) present challenges and opportunities for lean and I4.0 integration from a technical perspective. As such, we suggest that IT should be adopted to solve real, organizational problems – rather than blindly adopting “sexy” new tech. If firms begin by finding and framing problems, then the solution space for lean techniques, and indeed, digital technologies can be much better explored. In this way, the firms can strengthen the benefits obtained with LM and become more digital and sustainable over time (Mo, 2009).

Kolberg and Zühlke (2015) discussed a potential contradiction between lean approach and I4.0 paradigm. The latter stressed the contradiction between lean’s focus on people and I4.0’s focus on the machine technologies. Also, Ruttimann and Stockli (2016) investigated the different role of employee for the two approaches: while lean has historically valued the human resources for their knowledge, I4.0 considers them as a source of variability and potential trouble-shooters in their work interaction with the work carried out by machines. Instead, few authors emphasized total different perspectives and highlighted the potential job enrichment which I4.0 could lead employees, removing physical activities monotony and more intellectual stimulation ( Lagorio et al. , 2021 ).

Many authors stressed the relevance of ICTs as necessary elements for managing LSCM operations by not only simply exchanging information with different actors of the supply chain but also establishing an integration with the external partners (Martínez-Jurado and José Moyano-Fuentes, 2014), such as the identification and optimization of value flows.

However, Ward and Zhou (2006) found that the information technology implementations used for lean functioning along the supply chains resulted in failures because of the poor infrastructure and communication. Also, different types of information technology have different impact on the lean supply chain. Therefore, Hong et al. (2010) pointed out the importance of categorization of information technology for the supply chain performance.

Based on the assumption that the existence of companies totally depends on the long-term competitiveness, Ante et al. (2018) highlighted the relevance of a robust performance indicators system to obtain excellent results. In this context, I4.0 represents a major opportunity, as on one hand it makes easier to analyse machine data, thereby enhancing quality; on the other hand, it helps to avoid faults in the production process. However, the digital revolution has lot of disadvantages for different industries. In fact, I4.0 requires a high level of system control to provide greater flexibility and competitiveness ( Digiesi et al. , 2015 ; Lu, 2017). Then, Ante et al. (2018) designed a key performance indicators tree to connect the key performance indicators with the improvement measures in an efficient manner. The KPI tree is divided in a five-level hierarchical structure: the Value Contribution, the Key Performance Result (KPR), the Value stream KPRs, the Monitoring KPRs and the Improvement KPRs. These structures help to monitor I4.0 projects as well as to drive the lean continuous improvement process.

4.3 Operative level

In a different branch of literature about interplay of LSCM and I4.0, authors have focused on specific I4.0 technologies and applications and their links with specific LSCM items.

According to the study of Ramirez-Peña et al. (2020), to properly implement I4.0, the supply chain must be lean. In fact, I4.0 technologies can play an important role for different actors of the supply chain.

Simulation is one of them, as highlighted by Rossini and Staudacher (2016) that conducted a study through simulation to understand how lean techniques can improve the performance of supply chain planning. Artificial intelligence has proven to be connected to lean supply chain by Haq and Boddu (2015) , who presented a fuzzy logic model incrementing significantly the efficiency of the system.

According to Ahmed et al. (2018) , Big Data impacts the supply chain management from the environmental point of view. Ramirez-Peña et al. (2020) investigated and found out that Autonomous Robots, Additive Manufacturing, Cybersecurity, Cloud Computing and Augmented Reality are the most relevant to improve Green LSCM items.

With a similar perspective, De Giovanni and Cariola (2020) investigated the impact of I4.0 technologies implementation on lean and green practices in supply chains. The authors stated that process innovation via I4.0 technologies does not lead to relevant effect on green performances, while it makes lean practices more effective for improving operational and economic performance.

Roy and Roy (2019) previously described in the literature review on I4.0 about developing their Smart Management System (SMgS). The new technology-based SMgS will help the industries to become more efficient, sustainable, lean, safer and cost effective. In particular, “Lean and Efficient” shows up between the benefits of the system, meaning that SMgS can be used faster with less difficulty compared to lean philosophy but leads to the same results. In fact, the system integration will make the operation lean and efficient by removing wastes ( Heikkilä, 2002 ).

Instead, recently, few authors investigated the evolution of a traditional LSCM elements, such as Kanban, with the introduction of I4.0 technology. In particular, they focused on the implementation of a CPS and on the consequent benefits on the Kanban aiming to automate with limited or no human interaction. The “e-Kanban” is based on a virtual system; the traditional card is evolved in a digital card and is transmitted electronically. The e-Kanban replaces traditional Kanban cards with barcodes; it exploits the technology to improve the movement of material. The e-Kanban reduces human effort for managing the card, and it provides real-time availability of all relevant Kanban information, directly linked online with the Cloud.

Bevilacqua et al. (2019) stated that, with the current manufacturing environment moving towards a 4.0 perspective, there is a growing focus on Big Data Analytics techniques. Indeed, data understanding represents a key aspect for extracting useful knowledge and new information with the aim of taking advantage from them. These authors claimed that like lean manufacturing, manufacturing automation also has the goal of satisfying the customers at the possible minimum cost. Manufacturing automation addresses the removal of nonvalue-added activities and produces predictable quality to achieve these goals. In this scenario, Big Data Analytics methods help to communicate between automation and LP approaches ( Bevilacqua et al. , 2019 ).

Vazquez-Martinez et al. (2018) presented a new distribution model with the name CloudChain, inspired by LSCM principles and useful for digital products supply chain. In the model, I4.0 technologies create secured containers and operations network configuration that supports transportation activities.

Managers can configure CloudChain as a traditional packing and logistic service but exploiting I4.0 potentials. This distribution model aligns applications of multiple partners involved in the supply chain, both upstream and downstream, and supports the design of value chains with continuous information flows through different cloud storage resources (Vazquez-Martinez et al. , 2018).

Earlier Blockchain, the leading technology layer, is used only for financial applications. But since the past few years, the research trend for Blockchain technologies is shifting to other domains; in particular, Perboli et al. (2018) have investigated the application of Blockchain in supply chain and logistics.

Ensuring data immutability and public accessibility of data streams make the Blockchain a disruptive innovation. Moreover, the decentralization structure of the Blockchain overcomes the issues that occurred in the centralization structure, including trust issues. However, since its inception, Blockchain has some inherent defects, which need to be addressed before its deployment to other sectors.

Anyhow, the adoption of the Blockchain technology can represent the backbone of a new digital supply chain. Together with the other technological aspects, such as Business Analytics and artificial intelligence, it has contributed to the rapid evolution of logistics in the past decade. Blockchain can be used to overcome the security challenges in Internet of Things. As, as highlighted, Blockchain can break down some barriers of I4.0 technologies along the supply chain, it can favour the integration of the lean philosophy and the I4.0 in the supply chain.

Ge and Jackson (2014) focused on the newest adoption of Big Data to minimize cost in the automotive sector. Authors identified Big Data application beneficial proactively minimizing cost and reactiveness. Moreover, Big Data technologies can enable automotive companies to develop circular economy. Some of the benefits provided by Big Data are also supportive of other existing methodologies used in the automotive engineering domain such as Continuous Product Improvement methodologies. Based on Plan-do-check-act, DMAIC is one such Continuous Product Improvement methodology. The successful usage of Big Data technologies for the DMAIC process suggests that they might work more efficiently in aggregating data, in performing preliminary analysis and increasing the support that DMAIC methodology could give high-level decisions. Therefore, there is room for the complementary usage of lean techniques and Big Data in terms of both cost reduction and optimization of high-level strategies.

thorough understanding of the customer demand;

rapid exchange of the demand data throughout the complex supply chain;

faster smart factories production with less waste;

much quicker one-piece flow of customized products;

potential to radically reduce inventories throughout the supply chain;

real-time information sharing through coordination in the entire supply chain; and

radically improvement form of instant just-in-time pull production reducing/eliminating overproduction (Netland, 2015).

Rossini et al. (2019) examined the impact of the association between lean adoption and I4.0. Authors highlighted that, of the 16 main I4.0 technologies found in the literature, “big data” and “augmented reality” were the most common. On the other hand, “Collaboration with suppliers/customers through real-time data sharing” seems to be scarcely investigated in the I4.0 literature. This justifies the fact that a lower emphasis is given on the studies related to I4.0 for customers/suppliers relationships ( Rossini et al. , 2019 ).

This insight is notably important for the purpose of this analysis. In fact, this study provides some answers to the interrelationships between I4.0 and lean at the operation level of supply chain context.

Haddud and Khare (2019) investigated the impact of digitalization in supply chains and suggest that they may provide benefits in a lean environment. More specifically, the study examined the potential impacts of seven enabling digital technologies ( Meier, 2016 ) on five selected lean operations practices that are JIT, Visual Management, Total Productive Maintenance, Continuous Improvement and Autonomation (failure prevention) and Poka-Yoke (mistake-proofing). The digitalization in supply chains was shown to have an enormous impact on all the five lean operations practices mentioned above.

4.4 Discussion

The SLR clearly shows that LSCM and I4.0 support each other in a synergistical perspective. As I4.0 technologies enhance LSCM practices with the digitalization of traditional LSCM items, LSCM practices work as enablers for the introduction of I4.0 technologies in the supply chain system. This mutual support is perfectly aligned with the most part of recent research about interplay between lean system and I4.0 ( Buer et al. , 2018 ; Núñez-Merino et al. , 2020 ).

However, some more insights clearly emerge. In fact, if it is out of discussion that LSCM and I4.0 mutually support each other, then this slightly changes while we split analysis from strategical and operative level.

The operative stream of research mainly focuses on presenting the positive impact of LSCM practices digitalization, or the support that the introduction of I4.0 technologies gives to traditional, and sometime old, LSCM practices. On the other face of the coin, the strategic stream of research mainly focuses on proposing LSCM paradigm as necessary base for the introduction of I4.0 technologies, because of the goodness of the operations supply chain system and the guideline principles for the correct choice of investments.

With these perspectives, there is a clear trend of what supports and what is supported, as shown in the framework presented Figure 5 : at strategic level, LSCM supports I4.0 because it drives I4.0 introduction, but at operative level, I4.0 supports LSCM because it enhances LSCM practices.

This framework adds a piece of knowledge of the literature of the interplay between LSCM and I4.0, but at the same time, it could support in understanding more in general the interplay between lean and I4.0. It is coherent with all the research works that presented a positive correlation between lean and I4.0 ( Rossini et al. , 2019 ; Tortorella et al. , 2021 ). It supports the literature of synergic perspective of the two paradigms ( Buer et al. , 2020 ), given a clearer positioning of the role of the paradigms. This fact is also supported by the recent literature where specific I4.0 technologies are adapted with lean techniques from the simple 5S to the more sophisticated SPC and Total Productive Maintenance systems (Chiarini and Kumar, 2020; Bittencourt et al. , 2021 ; Ciano et al. , 2021 ; Raji et al. , 2021; Spenhoff et al. , 2021 ).

At strategic level, lean supply chain is driving I4.0 by streamlining different entities of supply chain to reduce waste. This is particularly true with regard to the greater horizontal and vertical integration enabled by ICT in I4.0, for example, the Industrial Internet of Things, Digital Twin and advanced IT infrastructures which build on more traditional ERP systems. According to lean, once the value is defined for the customers, the situation is mapped within and across organizations, to find the nonvalue-adding activities that have to be eliminated. The value stream mapping is also supported by I4.0, but importance will be given to digital environment rather than the physical environment.

At the operational level, continuous flow is one of the key lean elements. The production process must maintain the takt time agreed with the customers. Instead of physical entities, I4.0 aims to make the data stream flow according to the real time. Again, we see that digital technologies such as Big Data Analytics and digital twins, as well as tools that support the worker such as augmented reality, virtual reality and smart wearables, will in fact contribute to enhanced lean practices.

The final objective of lean is to produce only according to the time and specifications provided by the customers. In addition to the products, I4.0 makes the customers to pull the essential services related to the product. This will help to improve the customer satisfaction. Therefore, the smartification of products using Radio Frequency Identification will provide an evolutionary impact of I4.0 in favour of lean principles.

In the following paragraphs, the theoretical and practical implications of the study are going to be discussed.

4.5 Theoretical implications

To the best of our knowledge, this paper represents the first structured analysis regarding the interrelation between LSCM and I4.0 in the literature. Indeed, currently, in literature, there are several papers focused on this argument, but they are not totally comprehensive. Therefore, this study represents the fundamental starting point for those academicians that want to develop further advanced researches in the field of LSCM and I4.0 environments.

The outcomes achieved through the analysis developed in this paper confirm the role of I4.0 practices in a LSCM environment. At the strategic level, LSCM influences I4.0, whereas I4.0 in the LSCM enhances integration and information flow with customers and suppliers, especially horizontal and vertical integration, CloudChain, Big Data, etc. Additionally, this paper attaches an active role of I4.0 regarding continuous improvement, which is a salient feature of lean. Thus, this confirms the knowledge direction supported by the previous studies by Ward and Zhou (2006) and Adamides et al. (2008) . Hence, in this paper, the academicians can find the reasoning behind the school of thought in the literature that sustain the existence of a mutual beneficial partnership between LSCM and I4.0. Moreover, each relationship that exists between these two paradigms and the detailed explanation of these connections can by analysed by academicians.

4.6 Practical implications

From a managerial point of view, this study investigates the interactions between LSCM and I4.0 providing a twofold and complete point of view. On one hand, it offers suggestions on the action plan to follow if a firm is already implementing the lean paradigm along the supply chain and wants to begin a new project involving the I4.0; on the other hand, it represents a guideline for companies already adopting I4.0 and seeking to begin to implement also some lean concepts.

The understanding of the implications of I4.0 and its expansion to the supply chain level will help companies to properly adopt the LSCM and the I4.0 paradigms concurrently. The lack of knowledge about the potential benefits or uncertain outcomes of investments has many times prevented many companies to implement both LSCM and I4.0. But the findings from this research are suggesting to concentrate more to develop LSCM given its high importance in driving the improvement in I4.0 paradigm, not only fostering the adoption of other practices but also reducing the barriers of I4.0. Moreover, the framework can act as a decision-making tool for the companies to obtain maximum benefits while implementing LSCM and I4.0 together.

Another practical implication that is worth to point out regards the I4.0 practices and in particular the characteristics of the most relevant practices according to the influence that they can exert on other practices or on the barriers.

Finally, to make I4.0 a success in LSCM internally and externally, the government or the public sector should come forward and take into consideration the findings of this research as a reference to promote a roadmap transition towards the adoption of I4.0 and LSCM.

5. Conclusion

For many years, the philosophy of lean thinking and its principles offered a way for practitioners to build an efficient and competitive production system. Success by success, lean management is slowly becoming relevant for improvement in supply chain, that is, LSCM. On the other hand, I4.0 appeared more recently as a revolution, offering potentials of improvement in quality, flexibility and productivity exploiting the digital technologies. Thus, it comes into doubt about the relationship between the two paradigms, whether they are coherent or they are opposite, they are alternative or complementary. In this context, we provide a framework which suggests the interrelationship and interplay between the two paradigms.

The starting point of the research has been to collect the information about the two paradigms under analysis, LSCM and I4.0 that were available in the current literature. Then, the authors have investigated through a SLR the relationships between the two paradigms.

It is observed from the literature that there is an increasing trend in papers which are discussing about both importance of lean and I4.0. From the literature analysis, it is possible to categorize the interrelationship between LSCM and I4.0 in two directions: strategic and operative levels. At the strategic level, LSCM is driving I4.0 to build sustainability and diverse demand of the customers is thriving for Digital LSCM. At the operative level, I4.0 is supporting LSCM in terms of advanced tools and techniques, like Big Data, Augmented reality, Digital products, CloudChain, Blockchain and additive manufacturing.

The main contributions of this paper are the identification of the research lines developed from the literature analysis; an understanding of the interrelationship between LSCM and I4.0 and an exploration of the most relevant areas where the greatest effort has to be made.

An overview of the limitations of the research and some suggestions about the direction of further studies are presented below.

5.1 Limitations

Considering the interrelations between LSCM and I4.0, the discussed results have contributed to enhance the knowledge in this field both in terms of theoretical and practical implications, as mentioned above. Nevertheless, this paper is not exempt from limitations, as any other research paper.

Starting from the SLR performed, the authors have included only one database, Scopus. According to this, there might be additional knowledge about the relationship between lean supply chain and I4.0. Therefore, considering different databases, such as Google Scholar and Web of Science, or a combination of more than a single database could bring different information.

Furthermore, it has been difficult to detect practitioners that are applying the two industrial paradigms in the boundaries of their firms or, at least, that are experts of both LSCM and I4.0. This aspect demonstrates the novelty of the argument covered with this paper.

5.2 Future scope

Given the novelty of the paper, it could be considered as a preliminary analysis for further advanced researches regarding the relationships between LSCM and I4.0, as highlighted in the theoretical implications. Therefore, the authors have identified several further improvements for future possible complementary studies.

To increase the knowledge in the literature, relevant results could be obtained considering only firms that are currently applying LSCM and want to start the journey with I4.0 or vice versa. In this scenario, a case study could be developed to understand the practical implications of the simultaneous application of both paradigms and to validate the theoretical results obtained.

A possible future step can involve the application of the framework of analysis developed in this paper on specific situations, that is, considering a specific industry, company size or geographical location. Indeed, considering a small- and medium-sized enterprise or a large enterprise could bring different outcomes in the simultaneous adoption of LSCM and I4.0.

Finally, because of the novelty of the argument and the significant importance that this argument is achieving nowadays, these types of analysis would need to be replicated after few years.

Literature review methodology

Literature review process

Yearly distribution of the papers considered

Methodology distribution of the papers considered

Relationship between lean supply chain management and Industry 4.0

Research keywords used

Selected papers for systematic literature review

Literature details

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• DOI: 10.55927/ajma.v3i3.10378
• Corpus ID: 271834205

Mapping the Digital Leadership Research Landscape on Industri 4.0: A Bibliometric Analysis

• Mardika Prawestri , Meika Kurnia Puji Rahayu , I. N. Qamari
• Published in Asian Journal of Management… 2 August 2024
• Computer Science, Business
• Asian Journal of Management Analytics

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Implementation of industry 4.0 in construction industry: a review

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• Published: 02 August 2024

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• Ankur Tayal   ORCID: orcid.org/0000-0001-9727-4363 1 ,
• Saurabh Agrawal 1 &
• Rajan Yadav 1  

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The article aims to study the literature on Industry-4.0 technologies and “Triple Bottom Line” (social, economical and environmental) parameters in the construction industry. The study focuses on analyzing the gaps in various researches conducted till now and suggests possible information that can be used to improve business processes. Preferred Reporting Items for Systematic Reviews and Meta-Analysis Method is adopted to select the articles. One hundred fifty-six published articles from 2015 to 2023 are examined to understand various theoretical frameworks. Content-based analysis is used for the categorization of five significant categories: (1) Industry 4.0 Enablers; (2) Barriers in Industry 4.0 Adoption; (3) Challenges in Construction Industry; (4) Opportunities for the principle Industry 4.0 Technology; (5) Impact of “Industry 4.0” Technologies. Based on categorization, rewards or incentives, management involvement, employers training, Building Information Modeling, Big Data, Cloud computing, etc., are major enablers of Industry 4.0 in the construction industry. Implementation cost, lack of knowledge, and poor long-term planning are analyzed as common barriers. Numerous challenges and opportunities related to Industry 4.0 technologies have been identified.

Moreover, the Triple Bottom Line impacts of Industry 4.0 technologies, such as waste management, cost reduction, health and security, and resource planning, are also analyzed. The study also revealed that there are numerous research gaps in the integrated application of technology and sustainability because of information inadequacy and unawareness of the stakeholders. The study’s findings will help uncover detailed information in a systematical manner for developing an integrated sustainable business environment in the construction industry. The study considering the specific period and inclusion/exclusion criteria can possibly develop limitations of missing a few relevant articles and information in this context.

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Tayal, A., Agrawal, S. & Yadav, R. Implementation of industry 4.0 in construction industry: a review. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02432-6

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Jusmita Weriza

The ability to design and implement microcontroller-based systems is one of the skills and soft skills needed in Industry 4.0. Many researchers have identified various problems and have made solutions related to the learning process using microcontrollers, even though the names of the courses taught in the study programs at their respective universities are different. So a map of microcontroller learning problems and solutions that have been carried out by previous researchers is needed, then summarize and analyze them so that the core problems and solutions related to microcontroller learning in the Industry 4.0 era can be known. So in this study, we conducted a systematic literature review and meta-analysis of problems and solutions in microcontroller learning from research works on the Scopus database from 2019 to 2023, using the Watase UAKE application in which there is Prisma. The first stage carried out is the identification of studies on the Scopus database using several keyword variables that have been determined in advance, resulting in 173 articles. Then the first stage screening resulted in 78 articles, and continued with the second stage screening resulting in 36 articles. Retrieve articles that have DOI obtained 29 articles to be uploaded into the Watase database. Based on the results of data extraction and reading the contents of the article carefully, 20 articles were obtained that discussed the industry in the article, consisting of 13 qualitative research articles and 7 quantitative research articles. Only 7 quantitative research articles can proceed to the classification process stage of the meta-analysis. The results showed that the most popular learning method related to the learning process using microcontrollers in the Industry 4.0 era is the Project Based Learning (PBL) learning method.

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Impact of neonatal sepsis on neurocognitive outcomes: a systematic review and meta-analysis

• Wei Jie Ong   ORCID: orcid.org/0000-0001-8244-2977 1   na1 ,
• Jun Jie Benjamin Seng   ORCID: orcid.org/0000-0002-3039-3816 1 , 2 , 3   na1 ,
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BMC Pediatrics volume  24 , Article number:  505 ( 2024 ) Cite this article

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Introduction

Sepsis is associated with neurocognitive impairment among preterm neonates but less is known about term neonates with sepsis. This systematic review and meta-analysis aims to provide an update of neurocognitive outcomes including cognitive delay, visual impairment, auditory impairment, and cerebral palsy, among neonates with sepsis.

We performed a systematic review of PubMed, Embase, CENTRAL and Web of Science for eligible studies published between January 2011 and March 2023. We included case–control, cohort studies and cross-sectional studies. Case reports and articles not in English language were excluded. Using the adjusted estimates, we performed random effects model meta-analysis to evaluate the risk of developing neurocognitive impairment among neonates with sepsis.

Of 7,909 studies, 24 studies ( n  = 121,645) were included. Majority of studies were conducted in the United States ( n  = 7, 29.2%), and all studies were performed among neonates. 17 (70.8%) studies provided follow-up till 30 months. Sepsis was associated with increased risk of cognitive delay [adjusted odds ratio, aOR 1.14 (95% CI: 1.01—1.28)], visual impairment [aOR 2.57 (95%CI: 1.14- 5.82)], hearing impairment [aOR 1.70 (95% CI: 1.02–2.81)] and cerebral palsy [aOR 2.48 (95% CI: 1.03–5.99)].

Neonates surviving sepsis are at a higher risk of poorer neurodevelopment. Current evidence is limited by significant heterogeneity across studies, lack of data related to long-term neurodevelopmental outcomes and term infants.

Peer Review reports

Sepsis is a major cause of mortality and morbidity among neonates [ 1 , 2 , 3 , 4 ]. Young infants especially neonates, defined by age < 28 days old, have a relatively immature immune system and are susceptible to sepsis [ 5 , 6 ]. Annually, there are an estimated 1.3 to 3.9 million cases of infantile sepsis worldwide and up to 700,000 deaths [ 7 ]. Low-income and middle-income countries bear a disproportionate burden of neonatal sepsis cases and deaths [ 7 , 8 ]. While advances in medical care over the past decade have reduced mortality, neonates who survive sepsis are at risk of developing neurocognitive complications, which affect the quality of life for these children and their caregivers [ 9 ].

Previous reviews evaluating neurocognitive outcomes in neonates with infections or sepsis have focused on specific types of pathogens (e.g., Group B streptococcus or nosocomial infections [ 10 ]), or are limited to specific populations such as very low birth weight or very preterm neonates [ 11 ], and there remains paucity of data regarding neurocognitive outcomes among term and post-term neonates. There remains a gap for an updated comprehensive review which is not limited by type of pathogen or gestation. In this systematic review, we aim to provide a comprehensive update to the current literature on the association between sepsis and the following adverse neurocognitive outcomes (1) mental and psychomotor delay (cognitive delay (CD)), (2) visual impairment, (3) auditory impairment and (4) cerebral palsy (CP) among neonates [ 11 ].

We performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines [ 12 ]. This study protocol was registered with Open Science Framework ( https://doi.org/10.17605/OSF.IO/B54SE ).

Eligibility criteria

We identified studies which evaluated neurocognitive outcomes in neonates less than 90 days old (regardless of gestational age) with sepsis. While the neonatal period is traditionally defined to be either the first 28 days postnatally for term and post-term infants, or 27 days after the expected date of delivery for preterm infants [ 13 ], serious late onset infections in the young infant population can present beyond the neonatal period [ 14 ], hence we defined the upper age limit as 90 days old to obtain a more complete picture of the burden of young infantile sepsis [ 15 ]. Post-term neonates was defined as a neonate delivered at >  = 42 weeks of gestational age in this study [ 16 ]. We included studies that either follow international sepsis definitions such as Surviving Sepsis Campaign guidelines definitions [ 17 ], or if they fulfilled clinical, microbiological and/or biochemical criteria for sepsis as defined by study authors. The primary outcome of interest was impaired neurocognitive outcome defined by the following domains of neurodevelopmental impairment (NDI) [ 11 ]: (1) CD, (2) visual impairment, (3) auditory impairment and (4) CP. We selected these domains because they were highlighted as key neurocognitive sequelae after intrauterine insults in a landmark review by Mwaniki et al. [ 18 ]. The authors’ definitions of these outcomes and their assessment tools were captured, including the use of common validated instruments (e.g., a common scale used for CD is the Bayley Scales of Infant Development (BSID) [ 19 ] while a common instrument used for CP was the Gross Motor Function Classification System (GMFCS) [ 20 ]. Specifically for BSID, its two summative indices score – Mental Development Index (MDI) and Psychomotor Development Index (PDI) were collected. The MDI assesses both the non-verbal cognitive and language skills, while PDI assess the combination of fine and gross motor skills. The cut-off points for mild, moderate and severe delay for MDI and PDI were < 85 or < 80, < 70 and < 55 respectively [ 21 ]. There were no restrictions on duration of follow-up or time of assessment of neurocognitive outcomes to allow capturing of both short- and long-term neurocognitive outcomes.

Case–control, cohort studies and cross-sectional studies published between January 2011 and March 2023 were included. Because the definition and management of sepsis has evolved over the years [ 22 ], we chose to include studies published from 2011 onwards. Case reports, animal studies, laboratory studies and publications that were not in English language were excluded. Hand-searching of previous systematic reviews were performed to ensure all relevant articles were included. To avoid small study effects, we also excluded studies with a sample size of less than 50 [ 23 ].

Information sources and search strategy

Four databases (PubMed, Cochrane Central, Embase and Web of Science) were used to identify eligible studies. The search strategy was developed in consultation with a research librarian. The first search was conducted on 4 December 2021 and an updated search was conducted on 3 April 2023. The detailed search strategy can be found in Supplementary Tables 1A and B.

Study selection process

Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) [ 24 ] was utilized during this review. Five reviewers (WJO, BJY, NM, CLN and GH) independently conducted the database search and screened the title and abstracts for relevance. Following training on inclusion and exclusion eligibility, 4 reviewers (WJO, NM, CLN and GH) subsequently assessed the full text of shortlisted articles for eligibility. All full texts were independently assessed by at least 2 reviewers. Any conflict related to study eligibility were resolved in discussion with the senior author (S-LC). We recorded the reason(s) for exclusion of each non-eligible article.

Data collection process and data items

Four reviewers (WJO, NM, CLN and GH) independently carried out the data extraction using a standardized data collection form, and any conflict was resolved by discussion, or with input from the senior author (S-LC). A pilot search was performed for the first 200 citations to evaluate concordance among reviewers and showed good concordance among reviewers of 94%. For studies with missing data required for data collection or meta-analyses, we contacted the corresponding authors of articles to seek related information. If there was no reply from the authors, the data were labelled as missing.

Study risk of bias assessment

Three reviewers (BJY, GH and WJO) independently carried out the assessment of risk of bias using the Newcastle–Ottawa Scale (NOS) for all observational studies [ 25 ]. Studies were graded based on three domains namely, selection, comparability and outcomes. Studies were assigned as low, moderate and high risk of bias if they were rated 0–2 points, 3–5 points and 6–9 points respectively. Any conflict was resolved by discussion or with input from the senior author (S-LC).

Statistical analysis

All outcomes (i.e. CD, visual impairment, auditory impairment and CP) were analysed as categorical data. Analyses were done for each NDI domain separately. To ensure comparability across scales, results from different studies were only pooled if the same measurement tools were used to assess the outcomes and hence sub-group analyses were based on different scales and/or different definitions of neurocognitive outcomes used by authors. Both unadjusted and adjusted odds ratios (aOR) and/or relative risk (RR) for each NDI domain were recorded. Where source data were present, we calculated the unadjusted OR if the authors did not report one, together with the 95% confidence interval (CI). For adjusted odds ratio, these were extracted from individual studies and variables used for adjustment were determined at the individual study level.

Meta-analysis was conducted for all outcomes that were reported by at least 2 independent studies or cohorts. Studies were included in the meta-analysis only if they reported outcomes for individual NDI domains within 30 months from sepsis occurrence. For each domain, all selected studies were pooled using DerSimonian-Laird random effects model due to expected heterogeneity. Studies were pooled based on adjusted and unadjusted analyses. Case–control and cohort studies were pooled separately. The pooled results were expressed as unadjusted odds ratio (OR) or adjusted odds ratio (aOR) with corresponding 95% confidence interval (95% CI). If there was more than 1 study that utilized the same population, we only analysed data from the most recent publication or from the larger sample size, to avoid double counting. Standard error (SE) from studies with multiple arms with same control group were adjusted using SE = √(K/2), where K refers to number of treatment arms including control [ 26 ]. Heterogeneity across studies was evaluated using the I^2 statistic, for which ≥ 50% is indicative of significant heterogeneity. With regards to publication bias, this was performed using Egger’s test and funnel plots only if the number of studies pooled were 10 or more for each outcome.

For neurocognitive related outcomes, subgroup analyses were performed based on the severity of the NDI domain outcomes and distinct, non-overlapping populations of septic infants (such as late onset vs early onset sepsis, culture positive sepsis vs clinically diagnosed sepsis, term and post term patients).

All analyses were done using ‘meta’ library from R software (version 4.2.2) [ 27 ]. The statistical significance threshold was a two tailed P- value < 0.05.

Certainty of evidence

The certainty of evidence for outcomes in this review was performed during the GRADE criteria [ 28 ] which is centred on the study design, risk of bias, inconsistency, indirectness, imprecision, and other considerations.

Study selection

From 7,909 studies identified, a total of 24 articles were included (Fig.  1 ) [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. A total of 101,657 and 19,988 preterm and term infants were included in this review.

PRISMA flowchart of the study selection process for search

Study characteristics

There were 2 case–control studies and 22 cohort studies, with a total of 121,645 infants (Table  1 ). Studies were conducted in 16 different countries (Fig.  2 ), with the most studies conducted in the United States of America (USA) (7 studies, n  = 92,358 patients) [ 30 , 33 , 37 , 41 , 42 , 47 , 52 ]. There were no studies that were conducted solely on term infants. 5 studies reported data specifically on ELBW infants (27,078 infants) and 6 studies on VLBW infants (3,322 infants). All studies were performed among neonates.

World map depicting distribution of studies that evaluate neurocognitive outcomes in infantile and neonatal sepsis

Risk of bias 

Overall, all 24 studies were classified as low risk (Supplementary Table 2). 5 papers scored high risk for outcome bias for having greater than 10% of initial population being lost to follow-up [ 29 , 32 , 40 , 41 , 42 ].

Outcome measures reported by domain

As the number of studies pooled for each outcome was less than 10, publication bias was not analysed in the meta-analyses.

Cognitive delay (CD)

Among 24 studies that assessed for CD, 16 studies reported either the incidence of CD among young infants with sepsis compared to those without, and/or the odds ratio (adjusted and/or unadjusted) comparing the two populations [ 29 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 48 , 49 ]. The scales used, authors’ definition of CD, incidence of CD among those with sepsis and those without are described in Table  2 . The most common tools used for assessment of CD were the Bayley Scales of Infant Development (BSID) ( n  = 13) and Denver Development Screening Test II ( n  = 2).

Infantile sepsis was associated with increased risk of overall CD delays [aOR 1.14 (95%CI: 1.01, 1.28)], overall PDI delay (aOR 1.73 (95%CI: 1.16, 2.58)) and moderate PDI delay [aOR 1.85 (95%CI: 1.01, 3.36)]. Conversely, infantile sepsis was not associated with increased risk for severe PDI delay nor overall MDI delay [aOR 1.30 (95%CI: 0.99, 1.71)] or its subgroups. There were no significant differences in outcomes between different subgroups of infections as well as culture-proven or clinically defined sepsis for either MDI or PDI (Table  8 , Fig.  3 A and B).

A Forest plot on adjusted odds ratios for neurocognitive outcomes related to MDI, PDI, visual impairment, hearing impairment and cerebral palsy. B Forest plot on unadjusted odds ratios for neurocognitive outcomes related to MDI, PDI, visual impairment, hearing impairment and cerebral palsy. Legend: MDI: Mental Developmental Index; PDI: Psychomotor Developmental Index. Foot note: Mild MDI or PDI: < 85 or < 80; Moderate MDI or PDI < 70; Severe MDI or PDI < 55

Visual impairment

Seven studies reported data on visual impairment (Table  3 ) [ 31 , 33 , 41 , 42 , 47 , 49 ]. The most common definition of visual impairment utilized was “visual acuity of < 20/200” ( n  = 4, 66.7%).

In the meta-analysis, infantile sepsis was associated with significantly increased risk of visual impairment [aOR 2.57 (95%CI: 1.14, 5.82)] but there were no statistically significant differences in visual impairment between subgroups of early or late onset sepsis, and blood culture negative conditions as compared to the non-septic population (Table  8 , Fig.  3 A and B).

Hearing impairment

Seven studies reported data on hearing impairment (Table  4 ) [ 31 , 33 , 41 , 42 , 47 , 49 ]. Two studies defined hearing impairment as permanent hearing loss affecting communication with or without amplification [ 42 , 47 ]. Other definitions included “sensorineural hearing loss requiring amplification” ( n  = 1), “bilateral hearing impairment with no functional hearing (with or without amplification)” ( n  = 1), “clinical hearing loss” ( n  = 1).

In the meta-analysis, sepsis was associated with increased risk of hearing impairment [aOR 1.70 (95% CI: 1.02–2.81)]. However, in the subgroup analyses, there were no differences in risk of hearing impairment between patients with late onset sepsis as compared to the non-septic population (Table  8 , Fig.  3 A and B).

Cerebral palsy

Nine studies [ 29 , 32 , 33 , 41 , 42 , 47 , 48 , 49 , 50 ] reported data on CP (Table  5 ), of which 5 studies [ 41 , 42 , 45 , 49 , 50 ] used the GMFCS scale. In the meta-analysis, infantile sepsis was associated with significantly increased risk of CP [aOR 2.48 (95%CI: 1.03; 5.99)]. There was no difference in rates of CP among patients with proven or suspected sepsis, as compared with infants with no sepsis (Table  8 , Fig.  3 A and B).

Differences in neurocognitive outcomes between neonates with culture-proven or clinically diagnosed sepsis as well as early or late onset sepsis

Tables 6 and 7 showed data related to differences in neurocognitive outcomes between neonates with culture-proven or clinically diagnosed sepsis as well as early or late onset sepsis. Meta-analyses were not be performed due to significant heterogeneity in definitions of sepsis, time of assessment of outcomes.

Differences in neurocognitive outcomes between term and post-term neonates

There were no studies which evaluated neurocognitive outcomes between term and post-term neonates and infants.

We found that the certainty of evidence to be very low to low for the four main neurocognitive outcomes selected. (Supplementary File 3).

In this review involving more than 121,000 infants, we provide an update to the literature regarding young infant sepsis and neurocognitive impairment. Current collective evidence demonstrate that young infant sepsis was associated with increased risk of developing neurocognitive impairment in all domains of CD, visual impairment, auditory impairment and cerebral palsy.

Cognitive delay

In this review, higher rates of cognitive delay were noted among infants with sepsis [ 29 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 45 , 46 , 48 , 49 , 52 ]. We found that infants with sepsis reported lower PDI scores (Table  8 ), which measures mainly neuromotor development. On the other hand, young infant sepsis was not associated with lower MDI scores (Table  8 ), which assesses cognitive and language development. The pathophysiological mechanism of young infant sepsis and its preferential impact on PDI remains unclear. Postulated mechanisms include development of white matter lesions which may arise from the susceptibility of oligodendrocyte precursors to inflammatory processes such as hypoxia and ischemia [ 53 ]. Future studies should look into evaluating the causes of the above findings. A majority of included studies focused on early CD outcomes while no studies evaluated long-term outcomes into adulthood. CD is known to involve complex genetic and experiential interactions [ 54 ] and may evolve overtime with brain maturation. Delays in speech and language, intellectual delay and borderline intellectual functioning are shown to be associated with poorer academic or employment outcomes in adulthood [ 55 , 56 ], and early assessment of CD may not fully reveal the extent of delays. The only study with follow-up to the adolescent phase showed a progressive increase in NDI rate as the participants aged, which provides evidence of incremental long-term negative outcomes associated with infantile sepsis [ 44 ]. Moving forward, studies with longer follow-up may allow for further examination of the long-term effects of neonatal sepsis on CD.

There were different versions of the BSID instrument (BSID-II and BSID-III) [ 19 , 57 , 58 ]. BSID-II lacked subscales in PDI and MDI scores, leading to the development of BSID-III with the segregation of PDI into fine and gross motor scales and MDI into cognitive, receptive language, and expressive language scales [ 59 ]. Although we pooled results of both BSID-II and BSID-III in our study, we recognize that comparisons between BSID-II and BSID-III are technically challenging due to differences in standardised scores [ 59 , 60 ]. In addition, the BSID-IV was created in 2019 which has fewer items, However, none of our studies utilized this instrument. Future studies should consider this instrument, as well as standardising the timepoints for assessment of CD.

Young infant sepsis was associated with increased risk of developing visual impairment. This was similar to results noted by a previous systematic review published in 2014 [ 61 ] and 2019 [ 62 ] which showed that neonatal sepsis was associated with twofold risk of developing retinopathy of prematurity in preterm infants. Specifically, meningitis was associated with a greater risk of visual impairment compared to just sepsis alone [ 47 ]. The mechanism of visual impairment has not been fully described although various theories have been suggested, including sepsis mediated vascular endothelial damage, increased body oxidative stress response as well as involvement of inflammatory cytokines and mediators [ 63 , 64 ].

Our meta-analysis showed an increased risk of hearing impairment for young infants with young infants with sepsis. This is consistent with a previous report that found an association between neonatal meningitis and sensorineural hearing loss [ 65 ]. One potential confounder which we were unable to account for may have been the use of ototoxic antimicrobial agents such as aminoglycosides. Additional confounders include very low birth weight, patient’s clinical states (e.g. hyperbilirubinemia requiring exchange transfusion) and use of mechanical ventilation or extracorporeal membrane support. To allow for meaningful comparisons of results across different study populations, it is imperative that a standardised definition of hearing impairment post neonatal sepsis be established for future studies.

Our meta-analysis found an association between neonatal sepsis and an increased risk of developing CP. This is also consistent with previous systematic reviews which had found a significant association of sepsis and CP in VLBW and early preterm infants [ 11 ]. One study found that infants born at full term and who experienced neonatal infections were at a higher risk of developing a spastic triplegia or quadriplegia phenotype of CP [ 66 ]. The pathophysiology and mechanism of injury to white matter resulting in increased motor dysfunction remains unclear and more research is required in this area.

Limitations and recommendations for future research

The main limitation of this review lies in the heterogeneity in the definitions of sepsis, exposures and assessment of outcomes across studies. This is likely attributed to the varying definition of sepsis used in different countries as well as lack of gold standard definitions or instruments for assessment of each component of NDI. A recent review of RCTs [ 67 ] also reported similar limitations where 128 different varying definitions of neonatal sepsis were used in literature. Notably, there is a critical need for developing international standardized guidelines for defining neonatal sepsis as well as assessment of NDI such as hearing and visual impairment. Another important limitation relates to the inability to assess quality of neonatal care delivered as well as temporal changes in medical practices which could have affected neurocognitive outcomes for neonates with sepsis. Improving quality of neonatal care has been shown to significantly reduce mortality risk among neonates with sepsis, especially in resource-poor countries [ 68 ]. We performed a comprehensive search strategy (PubMed, Embase, Web of Science and CENTRAL) coupled with hand searching of references within included systematic reviews, but did not evaluate grey literature. Future studies should include additional literature databases and grey literature. Another area of research gap lies in the paucity of data related to differences in neurocognitive outcomes between term and post-term neonates with sepsis and future research is required to bridge this area of research gap. Likewise, there are few studies which evaluated differences in neurocognitive outcomes between early or late onset sepsis and outcomes assessed were significantly heterogenous which limits meaningful meta-analyses. Similarly, there was significant heterogeneity in study outcomes, causative organisms and severity of disease.

We found a lack of long-term outcomes and recommend that future prospective cohorts include a longer follow-up duration as part of the study design. This is important given the implication of NDI on development into adulthood. Most data were reported for preterm infants with low birth weight, and there was a paucity of data for term infants in our literature review. Since prematurity itself is a significant cause of NDI [ 69 ], future studies should consider how gestational age and/or birth weight can be adequately adjusted for in the analysis.

Apart from the domains of NDI we chose to focus on in this review, there are other cognitive domains classified by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) [ 70 ] and/or recommended by the Common Data Elements (CDE) workgroup [ 71 ]. Future studies may wish to look into the implications of sepsis on other neuro-cognitive domains related to executive function, complex attention and societal cognition which are studied for other types of acquired brain injury [ 71 , 72 ].

Our systematic review and meta-analysis found that neonates surviving sepsis are at a higher risk of poorer neurodevelopment. However, the evidence is limited by significant heterogeneity and selection bias due to differing definitions used for NDI and for sepsis. There is also a lack of long-term follow-up data, as well as data specific for term and post-term infants. Future prospective studies should be conducted with long-term follow-up to assess the impact of neurodevelopmental impairment among all populations of neonates with sepsis.

Availability of data and materials

All data generated or analyzed in the study are found in the tables and supplementary materials.

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Acknowledgements

We would like to thank Ms. Wong Suei Nee, senior librarian from the National University of Singapore for helping us with the search strategy. We will also like to thank Dr Ming Ying Gan, Dr Shu Ting Tammie Seethor, Dr Jen Heng Pek, Dr Rachel Greenberg, Dr Christoph Hornik and Dr Bobby Tan, for their inputs in the initial design of this study.

Conflict of interest

No financial or non-financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article.

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Wei Jie Ong and Jun Jie Benjamin Seng are co-first authors.

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MOH Holdings, Singapore, 1 Maritime Square, Singapore, 099253, Singapore

Wei Jie Ong, Jun Jie Benjamin Seng, Beijun Yap & Chen Lin Ng

SingHealth Regional Health System PULSES Centre, Singapore Health Services, Outram Rd, Singapore, 169608, Singapore

Jun Jie Benjamin Seng

SingHealth Duke-NUS Family Medicine Academic Clinical Programme, Singapore, Singapore

Yong Loo Lin School of Medicine, 10 Medical Dr, Yong Loo Lin School of Medicine, Singapore, Singapore

George He & Nooriyah Aliasgar Moochhala

Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore, Singapore

Rehena Ganguly

Children’s Intensive Care Unit, KK Women’s and Children’s Hospital, SingHealth Paediatrics Academic Clinical Programme, 100 Bukit Timah Rd, Singapore, 229899, Singapore

Jan Hau Lee

Department of Emergency Medicine, KK Women’s and Children’s Hospital, SingHealth Paediatrics Academic Clinical Programme, SingHealth Emergency Medicine Academic Clinical Programme, 100 Bukit Timah Rd, Singapore, 229899, Singapore

Shu-Ling Chong

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Contributions

SLC and JHL were the study’s principal investigators and were responsible for the conception and design of the study. WJO, JJBS, BY, GE, NAM and CLN were the co-investigators. WJO, JJBS, BY, GE, NAM and CLN were responsible for the screening and inclusion of articles and data extraction. All authors contributed to the data analyses and interpretation of data. WJO, JJBS, BY, GE, NAM and CLN prepared the initial draft of the manuscript. All authors revised the draft critically for important intellectual content and agreed to the final submission. All authors had access to all study data, revised the draft critically for important intellectual content and agreed to the final submission.

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Ong, W.J., Seng, J.J.B., Yap, B. et al. Impact of neonatal sepsis on neurocognitive outcomes: a systematic review and meta-analysis. BMC Pediatr 24 , 505 (2024). https://doi.org/10.1186/s12887-024-04977-8

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Knowledge management for improved digital transformation in insurance companies: systematic review and perspectives.

1. Introduction

2. driving innovation: the digital transformation landscape in the insurance industry, 3. knowledge management for insurance companies.

• Socialization: Involves the transmission of tacit knowledge through experiences such as verbal exchanges, observation, imitation, and practice.
• Externalization: Entails converting tacit knowledge into explicit form, such as concepts, models, or hypotheses, often through activities like writing a presentation or reaching a consensus on problem-solving.
• Internalization: Involves the assimilation of explicit knowledge for use in different or specific contexts, which can occur through activities like reading documents or participating in team meetings.
• Combination: Refers to the amalgamation of explicit knowledge from various sources to create new explicit knowledge, exemplified by actions like disseminating a pivot table or drafting a meeting report.
• How does KM influence the operations and performance of insurance companies?
• What impact does KM have on the relationship between insurance companies and their customers?
• What processes are necessary to leverage KM effectively?
• What is the relationship between deploying a knowledge management system in insurance and managing innovation for successful digital transformation?

4. Methodology

• Cultural and Regulatory Differences: Different countries and regions have unique cultural norms, regulatory environments, and business practices that influence knowledge management (KM) strategies and practices in the insurance industry. By analyzing publications per country or region, researchers can identify trends, challenges, and best practices that are specific to each context.
• Tailored Solutions: Understanding the context-specific challenges and opportunities in different countries or regions allows for the development of tailored KM solutions. What works well in one country may not be as effective in another due to varying cultural norms, legal frameworks, or market dynamics.
• Comparative Analysis: Analyzing publications per country or region enables researchers to conduct comparative studies, which can yield valuable insights into the relative effectiveness of different KM approaches. By comparing practices across countries or regions, researchers can identify factors that contribute to success or failure and extract lessons that can inform future KM initiatives.
• Global Perspective: Studying publications from diverse countries or regions provides a more comprehensive and global perspective on KM in the insurance industry. This broader view helps researchers identify emerging trends, innovative practices, and universal challenges that transcend geographical boundaries.

5.1. Organizational Level

5.2. technological level, 5.3. detailed results, 5.3.1. impact of knowledge management in the insurance industry, 5.3.2. global evaluation of km in the insurance industry.

• Ensure knowledge alignment with business objectives;
• Define efficient business roadmaps;
• Study KM implementation at various sub-organizations;
• Evaluate the initiative in terms of its business impact: knowledge submission, retrieval, application, and reuse.
• Create logical segmentation of files into centralized folders;
• Manage project versions;
• Implement a tag-based search feature;
• Provide role-based secure access;
• Store metadata and comments (wikis, blogs, discussion threads, calendar, and links).

6.3. Taiwan

6.4. indonesia, malaysia and thailand, 6.5. china and hong kong, 6.6. turkey, 6.7. arab countries, 7.1. norway and germany, 7.2. france and greece, 8. america (usa), 9.1. south africa, 9.2. nigeria, 10. australia, 11. discussion.

• Facilitate knowledge recording and sharing within teams, fostering a culture of experimentation and innovation;
• Ensure managerial involvement in KM initiatives, aligning organizational and knowledge strategies;
• Strategically align knowledge management with business objectives, maintaining a balanced intellectual capital portfolio.
• Design a robust technological framework for KM, investing in effective digital KM infrastructure;
• Utilize question/answer systems, wikis, blogs, and social apps for enhanced customer and employee engagement;
• Incorporate emerging technologies such as IoT, AI, and BI into the KM system structure for improved efficiency and effectiveness;
• Prioritize data update, security, and sharing practices as essential components of e-insurance KM;
• Implement IT governance best practices to manage KM media effectively and maximize results.
• What are the technological and organizational challenges specific to e-insurance KM?
• How can personalized customer expectations be effectively matched with insurance company knowledge in the digital era?
• What strategies can sustain balanced corporate performance and business growth through KM in the insurance context?
• How can the digital transformation from traditional insurance to e-insurance be effectively managed from a knowledge management perspective?
• What are the optimal technological choices for developing an effective insurance KM system?

12. Conclusions and Perspectives

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Elgargouh, Y.; Chbihi Louhdi, M.R.; Zemmouri, E.M.; Behja, H. Knowledge Management for Improved Digital Transformation in Insurance Companies: Systematic Review and Perspectives. Informatics 2024 , 11 , 60. https://doi.org/10.3390/informatics11030060

Elgargouh Y, Chbihi Louhdi MR, Zemmouri EM, Behja H. Knowledge Management for Improved Digital Transformation in Insurance Companies: Systematic Review and Perspectives. Informatics . 2024; 11(3):60. https://doi.org/10.3390/informatics11030060

Elgargouh, Younes, Mohammed Reda Chbihi Louhdi, El Moukhtar Zemmouri, and Hicham Behja. 2024. "Knowledge Management for Improved Digital Transformation in Insurance Companies: Systematic Review and Perspectives" Informatics 11, no. 3: 60. https://doi.org/10.3390/informatics11030060

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