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Professor Jose Cruz is an Associate Professor of Operations and Information Management and Associate Dean for Graduate Programs. Dr. Cruz’s teaching interests are in Business Analytics, Project Management, Operations Management, and Statistics. His research is multidisciplinary and combines his background and interests in management, engineering, computer systems, and applied mathematics. His general area is complex decision-making on network systems with a specific focus on global issues. He is especially interested in supply chain management, corporate social responsibility, sustainability, social relationships, and risk management. The methodological tools that he utilizes are variational inequalities, dynamical systems, network theory, multicriteria decision-making, and optimization. Dr. Cruz has published his research in European Journal of Operational Research, Naval Research Logistics, Decision Support Systems, International Journal of Production Research, International Journal of Production Economics, Environment & Planning B, Journal of Cleaner Production, Mathematical and Computer Modelling, Computational Management Science, and Quantitative Finance. He has received the School of Business Ackerman Scholarship Award (two times), Outstanding Graduate Teaching Award, Outstanding Undergraduate Teaching Award, and Distinguished Service Award. He is the recipient of the 2019 UConn-AAUP Excellence Award: Service Excellence .
Professor Moustapha Diaby’s research interests are in the areas of Operations Management, Logistics and Distribution Management, Manufacturing Systems Modeling and Analysis, and Mathematical Programming. His focus in recent years has been on the modeling of Cellular Manufacturing Systems for Flexibility, Loyalty Reward Programs, Reverse Logistics Manufacturing Systems, and the formulation of hard Combinatorial Optimization Problems as Linear Programs (which has to do with the theoretical foundations of methods for solving Scheduling and Sequencing Problems efficiently). His publications have appeared in journals such as European Journal of Operational Research, Information Systems Frontiers, INFORMS J. on Computing, Int. J. of Math. in Operational Research, Int. J. of Operational Research, Int. J. of Production Economics, Int. J. Production Research, Int. Transactions in Operational Research, J. of the Operational Research Society, Management Science, Operations Research , and others.
Professor Cuihong Li ’s research interests lie on the interfaces between operations, economics, and technology, in particular supply chain management and online markets. Her research applies quantitative analysis tools and information economic models in an operations context. Her research papers have appeared in or been accepted to Management Science, Manufacturing and Service Operations Management, Production and Operations Management, Naval Research Logistics , and numerous other academic journals.
Professor Tao Lu’s research interests lie in supply chain management, transport logistics, socially responsible operations, operations under new business models. The methodologies he often use in research include stochastic optimization, analytical modeling and game theory. His research has been published in journals such as Manufacturing & Service Operations Management, Operations Research, Production and Operations Management, Transportation Science and others.
Professor Lakshman Thakur’s current research focuses on supply chains, production scheduling, product design, computation and applications of spline functions, facility location problems, and Advanced Manufacturing Techniques adoption process in India (with Professors VK Jain, IIT Kanpur, Ravi Shankar, IIT Delhi, Dinesh Kumar IIM Bangalore). His research on production scheduling and Internet based implementation (both with Dr. Peter B. Luh) has been supported by National Science Foundation and Connecticut state Yankee Ingenuity Initiative grants. He is a full member of Informs (Operations Research Society of America & The Institute of Management Sciences) and Mathematical Programming Society. He has published in Management Science, Mathematics of Operations Research, SIAM Journal on Applied Mathematics, SIAM Journal on Optimization and Control, Journal of Mathematical Analysis and Applications, Naval Research Logistics, and other journals.
Professor Fasheng Xu’s general research interests lie in the interface of operations, finance, and economics. In particular, he is interested in studying the economic and social implications of emerging technologies and identifying effective designs and policies for innovative markets and platforms. His recent research has been focused on the following areas: blockchain, fintech, supply chain finance, digital platforms, and economics of data and privacy. His research has been published in leading academic journals such as Management Science and Manufacturing & Service Operations Management. His research has also won multiple awards, including 2022 Guttag Junior Faculty Award at Syracuse University, Finalist in 2022 MSOM iFORM SIG Best Paper Award Competition and Finalist in 2021 INFORMS Junior Faculty Interest Group (JFIG) Paper Competition.
The Operations and Decision Technologies area studies how business processes function, and how to employ advanced analytics to make optimal or near-optimal decisions in a variety of business contexts. As our world becomes increasingly digital, the technologies that automate business processes and link them to human systems generate more data – bigger data – than ever before. We use this data to uncover the fundamental business relationships that drive operational excellence, build analytical models that communicate core system dynamics, illustrate consequences to actions that may not be immediately obvious, and develop computational methods that assist decision-makers by suggesting one or more near-optimal solutions. Courses offered by the Operations and Decision Technologies area draw on cutting-edge theory and research. Methodological topics include optimization, simulation, statistics, predictive analytics, decision theory, collaborative and non-collaborative game theory, networks, queueing theory, stochastic modeling and dynamic control. Application areas include supply chain management, logistics, inventory management, revenue management and pricing, sustainability and reverse supply chains.
Thomas Eppel Senior Continuing Lecturer Research Interests: Decision Analysis, Behavioral Economics
Zuguang Gao Assistant Professor Research Interests: Sustainable operations, Electricity market and energy systems, Control, optimization, and game theory, Reinforcement learning, algorithms design and analysis
Luyi Gui Associate Professor Research Interests: Sustainable Operations, Environmental and Public Policy, Operations in Developing Economies, Supply Chain Management, Optimization and Algorithm Design, Network Economics, Game Theory, Mechanism Design
Robin Keller Professor Emerita Research Interests: Creative Problem Structuring, Cross-Cultural Decision Making, Fairness in Decision Making, Decision Analysis Theory and Applications, Medical Decision Making, Multiple Attribute Decision Making, Probability Judgments, Ambiguity of Probabilities or Outcomes, Risk Analysis for Terrorism, Environmental, Health, and Safety Risks, Time Preferences and Discounting, Utility Models, Models of Risk
Ken Murphy Assistant Professor of Teaching
Carlton Scott Professor Emeritus Research Interests: Decision Making Problems, Optimization
Rick So Professor Emeritus Research Interests: Supply Chain Management, Production and Inventory Management, Manufacturing and Service System Design, Time-Based Management, Operations Research
John Turner Associate Professor Research Interests: Media planning / advertising allocation, Applied & large-scale optimization, Revenue management, Health care management
Shuya Yin Professor Research Interests: Supply chain management, Operations management, Cooperative and non-cooperative game theory in supply chains, Interface of operations management and marketing
U ndergraduate business classes may be found through the UCI course catalogue .
MGMT HC 201B. Operations Analytics for Healthcare Executives This course introduces you to quantitative methods and their application to management problems. The practice of operations analytics relies heavily on digital technologies, using sophisticated algorithms to find optimal or near-optimal solutions to management problems, such as evaluating the cost-effectiveness of medical treatments, assigning hospital staff, scheduling operating rooms, routing equipment deliveries, locating trauma centers, and setting contract terms for revenue management in hospitals. Our focus will not be on understanding the inner workings of these algorithms, but rather on formulating problems so the computer knows how to solve them.
The main topics include Monte Carlo simulation, optimization via linear and integer programming models, and decision analysis (multiple objective decisions under certainty and decision-making under risk using decision trees). We will apply these models and ways of thinking to several functional areas of business, as well as to decisions about health and personal lives, using software such as Excel for multiple attribute decisions under certainty and optimization and the TreeAge package for decision tree analysis and simulation for decisions under risk.
Operations analytics (also called management science) should be viewed as a toolkit that will empower you to enact meaningful improvements in the organizations that you will be part of. Effective use of the right tools frequently saves businesses millions of dollars, and this course will teach you how to properly use some of the most effective tools in the box. Moreover, you will learn the vocabulary of operations analytics and learn to think like a management scientist, so that you may direct strategic implementations of digital technology tools in the organizations you work with in the future.
MBA 208. Operations Management Operations Management is concerned with analyzing business processes involved in production and delivery of goods/services to meet customers’ demands. We discuss the core concepts in the development and management of these processes and apply them to businesses that are operated in today’s digitally-driven business environment. For example, we study how digital technologies affect the applications of these core concepts, such as how Starbucks’ business process is influenced by mobile orders, how robots change the efficiency in e-commerce fulfillment, and how data analysis on a firm’s inventory shapes the firm’s future potential value. Through critical analysis of business processes, the students will gain a good understanding of a number of major issues in successfully managing both manufacturing and service operations in an inter-connected world.
This course provides a blend of qualitative and quantitative treatment for understanding process performance and operations issues. A combination of lectures, cases, videos and in-class mini cases/exercises will be used.
201B. Management Science
Organizations routinely seek to minimize costs or maximize profits, through the efficient use of resources and by effective planning and execution. Regardless of how much data a firm has, and how accurate its forecasts are, a firm nevertheless needs methods to transform predictions about the future into actionable plans and decisions. This course introduces students to prescriptive analytics and its application to management problems. The main topics are the management science core methodologies of optimization (problem formulation, solution methods, and sensitivity analysis), and simulation. We will apply these core methodologies to several functional areas of business, including operations, marketing, and finance. Effective use of management science techniques frequently saves businesses millions of dollars. The practice of management science relies heavily on computers, which use sophisticated algorithms to find optimal or near-optimal solutions to management problems. This course blends application with just the right amount of theory so that students always have a conceptual understanding of how to make good modeling decisions and to choose the right algorithm for the task at hand. We aim for students to become advanced users of optimization and simulation software, or managers with a keen eye for detail and an ability to manage technical staff implementing a management science project.
Effective use of management science techniques frequently saves businesses millions of dollars. The practice of management science relies heavily on computers, which use sophisticated algorithms to find optimal or near-optimal solutions to management problems. This course blends application with just the right amount of theory so that students always have a conceptual understanding of how to make good modeling decisions and to choose the right algorithm for the task at hand. We aim for students to become advanced users of optimization and simulation software, or managers with a keen eye for detail and an ability to manage technical staff implementing a management science project.
281. Analytical Decision-Making Models in a Digital World This course will introduce you to prescriptive analytics and its application to management problems. Prescriptive analytics is a collection of skillsets and methodologies which are strategically important to businesses in today’s digital world. Students will learn how to identify the key aspects of real-world logistical problems, build models to quantify the effects of anticipated outcomes and suggested courses of action, use software to find optimal or near-optimal solutions, and simulate the performance of suggested policies to estimate how our decisions may unfold in the real world. Examples include optimizing the advertising mix, multi-period inventory & production planning, portfolio optimization, online advertising, and trauma care system design. The course empowers students to apply prescriptive analytics to several functional areas of business, including operations, marketing, and finance.
282. Revenue Management Revenue Management studies how a firm should set and update pricing and product availability decisions across its selling channels to maximize profitability. It is the science of selling the right product to the right customer at the right time for the right price, and can be viewed as the demand-side complement to traditional supply-side inventory management. Enabled by digital technologies, revenue management is now pervasive across a broad range of industries. Using mathematical models and advanced analytics, students will study how airlines decide how many seats to reserve for high-paying business customers, how hotels determine when to discount their rooms, and how rental car companies determine how many reservations to overbook. Additionally, students will learn how auctions are used to price and sell online advertising, and discuss how revenue management is being used by the health care, retail, and entertainment industries.
283. Decision Analysis Facing many important and far-reaching decision situations in your professional and personal life, this class will provide you with the digital technology tools and thought processes to approach such situations with clarity and confidence and improve your decision making skills. This course will teach the use of decision analysis digital technologies for multiple objective decisions under certainty, decision-making under risk using decision trees, fitting probability distributions to judgments or data, and Monte Carlo simulation, applied to business, government, not-for-profit, and personal decisions.
285. Supply Chain Management This course introduces students to the tools and strategies to successfully manage uncertainty, meeting customer needs in the most timely and cost effective manner, and driving business disruptions through supply chain innovations. The use of advanced analytics and data-driven methods will be emphasized. Based on case studies, simulations, group discussions and guest lectures from practitioners, the course prepares students for managing supply chain challenges in practice such as the digital transformation, complex organizational network, globalization, and environmental and social responsibility concerns.
287. Project Management In this digital era characterized by the storms of technology changes, software upgrades, and communication system alterations, managers need to learn to manage the non-routine tasks related to and resulting from such rapid changes. Additionally, as companies constantly devise new products and services to stay competitive, the resultant tasks do not fit into the mold of business-as-usual. Organizing such tasks into projects affords managers with the ability to meet timelines, budget, performance goals, and expectations of many dissimilar stakeholders. This course equips students with tools and techniques to effectively manage projects in a rapidly changing environment. Using a project management framework and a computer software package, students will learn about the issues, problems, and solutions to carry out a team project from initiation to termination.
288. Predictive Analytics This course deals with predicting entities such as the demand for a product or service (commonly called forecasting) and predicting membership of known groups (commonly called classification). As such it is a blending of methodologies of forecasting and data mining. In particular we focus on multiple regression, logistic regression, neural nets, ARIMA, discriminate analysis and k-nearest neighbors. Although very technical and mathematical concepts lie behind these methodologies, our focus is more on the application of these methods to managerial problems and decision making.
In many examples, we will work with large data sets which will be split into training and validation sets in order to develop usable models. This approach facilitates model comparison with cross validation .
290. Fundamentals of Business Analytics With data fueling the digital transformation of enterprises, Fundamentals of Business Analytics will teach concepts on how to recognize and use meaningful data. This course will focus on the business understanding, the process of business analytics, and teaching a framework to understand what information forms the key drivers that could be fed into a mathematical model. Moreover, the course emphasizes how to make use of this information to drive digital change within organizations through analytics models that propose data-driven decision-making. In addition, this course will leverage case studies involving the digital transformation in automotive, retail, healthcare, entertainment, and other select industries to showcase how the analytics framework can be used to create new markets as well as products and services.
290. Redefining Operations in the Digital World This course will develop a process excellence driven approach for digital operations. While conventional Operations management processes leverage lean six sigma approach to excellence based on manufacturing practices, similar measures of excellence for digital operations will be necessary to minimize “defects” so that digital productivity could be defined, measured, analyzed, improved, and controlled.
Examples of digital operations in the industry where these processes are studied are in the internet and app driven consumer world, Robotic Process Automation (RPA), as well as digital manufacturing. Consumers are very demanding, and competition is fierce. To succeed in the “on-demand economy” a company needs to stand out from the crowd. Companies like Google, Amazon, Facebook, Netflix and Airbnb have developed ways of working that allow them to respond faster to consumer demands than their rivals (and are reaping the rewards). They have proven that even the largest companies can be as fleet-footed as a start-up. Case studies with these companies would be studied in this course.
291-OD1 Stochastic Models in Operations and Decisions (2 units) This doctoral seminar covers some fundamental concepts in queueing systems and dynamic programming. We also apply these models to analyze the optimal decisions in a number of stochastic operations.
291-OD2 Research Seminars in Supply Chain Management (2 units) This doctoral seminar provides some basic knowledge in several key research issues in supply chain management. We discuss a number of current research topics and challenges in supply chain management research.
291-OD3 Optimization Modeling and Methodology Part 1: Nonlinear Programming (2 units) An overview of the different classes of nonlinear optimization problems with applications to management. Includes convexity and duality.
291-OD4 Optimization Modeling and Methodology Part 2: Integer and Network Programming (2 units) Types of network optimization problems. Binary integer and mixed integer programs. Application to management.
291-OD5 Game Theory and Its Applications in Supply Chain Management (4 units) This Ph.D. seminar course introduces some fundamental concepts and methodologies in cooperative and non-cooperative game theory and their applications in supply chain models. Each class is a combination of lectures and class discussions.
291-OD6 Large Scale Optimization (4 units) This doctoral course explores various computational techniques that are useful for solving optimization problems with a large number of variables and/or constraints. We will study general techniques for computing optimal solutions to large problems using iterative methods, as well as ways to aggregate the solution space of some types of problems to yield near-optimal solutions. We will study Lagrangian relaxation, column generation, Dantzig-Wolfe decomposition, and Bender’s decomposition, from both a theoretical and practical perspective. Students will learn to formulate and solve large-scale problems using the modeling language AMPL, and learn how to exploit these techniques for their own research.
291-OD7 Network Models and Application (4 units, anticipated for 2014-2015, Gui) The course introduces students to the optimization and game theoretic tools to study network systems. We also survey applications of network models in transportation, supply chain, Internet, e-commerce, and social networks. The course involves a mixture of lectures and discussion seminars.
291-OD8 Stochastic Programming (2 units, anticipated for 2014-2015 Lejeune) This course will focus on Stochastic Modeling and Programming. Stochastic Programming is a discipline intersecting with probability theory and statistics on one hand and with mathematical programming on the other hand. It is a framework for modeling optimization problems that involve uncertainty. While deterministic optimization problems are formulated with known parameters, real world problems almost invariably include some unknown ones; their eventual outcome depends on the future realization of random events. Stochastic Programming relies upon the fact that probability distributions governing the data are known or can be estimated. The goal here is to find some policy that is feasible for (almost) all possible realizations and optimizes a function of the decision and the random variables. More generally, such models are formulated, analytically or numerically solved, and studied in order to provide useful information to the decision-maker.
291-OD9 Convex Math Programming: Optimization & Decomposition (4 units, Turner) This doctoral course introduces students to the field of mathematical programming through the lens of convex optimization. We will study the theory of convex optimization, and learn how to identify, formulate, transform, and solve convex optimization problems.
Convex programs are an important class of mathematical programs because (1) many problems can be formulated as convex programs, and (2) we have efficient techniques to find globally optimal solutions to convex programs. However, translating and formulating a given problem as a convex program is not always easy; in fact, it can require a high level of expertise to verify that a math program is indeed convex. In this class, we will introduce a methodology called disciplined convex programming (DCP) , which defines a set of rules derived from convex analysis If a math program is formulated following the DCP rules, it is guaranteed to be convex, eliminating the need to verify its convexity post-construction.
We will also study how classical decomposition techniques (e.g., column generation, Dantzig-Wolfe decomposition, Benders decomposition, and Lagrangian relaxation) can be helpful when solving large-scale convex optimization problems.
Students will choose a project which can be modeled as a convex optimization problem, and put to practice what they have learned using the modeling languages AMPL, MATLAB, CVX, and CVXPY. The techniques we will cover are applicable to a wide variety of business and engineering applications, and students are encouraged to choose a course project that is in line with their current research interests.
There are no formal prerequisites for this course beyond having a level of mathematical maturity which is expected of a PhD student at the Paul Merage School of Business. For example, it is expected that you know matrix algebra and multivariable calculus. Given that students may come from different backgrounds, I do not assume that students have a working knowledge of optimization theory. To get everyone up to speed, I will cover some background material in the first two lectures. But most importantly, if at some point during the course I start using terminology that you are unfamiliar with, please point this out so I can summarize any concepts which are unclear.
291-O10 Nonlinear Optimization (2 units, Scott) Modelling nonlinear optimization problems, properties, geometric programming, convex programming, signomial programming, nonconvex programming, entropy optimization, applications to operations management, transportation planning, location, statistics and data mining.
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Position: Doctoral Researcher
Supervisor: Prof. Siavash Khajavi
Department: Industrial Engineering and Management
Location: Aalto University, Espoo, Finland
Application Deadline: As soon as a qualified candidate is found
Starting Date: September 1st, 2024 or based on the agreement with the candidate
Expected Completion Period: 4 years (2+2)
About Aalto University
Aalto University is a multidisciplinary community where science and art meet technology and business. We are committed to identifying and solving grand societal challenges and building an innovative future. Our campus is located in Espoo, part of the greater Helsinki metropolitan area, which is known for its high quality of life, vibrant culture, and strong tech industry presence.
Department Overview
The Department of Industrial Engineering and Management at Aalto University is at the forefront of education and research in the fields of industrial engineering, operations management, and technology-based entrepreneurship. More information about our department can be found on our website: Department of Industrial Engineering and Management.
About the Position
We are seeking a highly motivated and talented individual to join our team as a PhD candidate. The successful applicant will work under the supervision of Prof. Siavash Khajavi, an expert in the field of digital transformation of operations management. The PhD project will focus on the digital transformation of operations and supply chain management, which has significant implications for the economic prosperity of nations and production efficiency and sustainability.
The research will explore the digital transition in the context of industrial operations and manufacturing, focusing on novel technologies. This multidisciplinary approach will cover topics such as the impact evaluation of industrial additive manufacturing, digital twins, blockchain-enabled technologies, and large language & vision models (LVLM). The aim is to understand their combined impact on the future of operations and supply chain management and industrial operations.
Responsibilities
Qualifications
Application Instructions
To apply for the position, please submit your application including the attachments mentioned below as one single PDF document through our online recruitment system Workday by using the link "Apply now!”.
For further information, please contact Dr. Siavash Khajavi at [email protected] .
Join us at Aalto University and contribute to groundbreaking research that shapes the future of industrial engineering and management!
More about Aalto University:
Aalto.fi youtube.com/user/aaltouniversity linkedin.com/school/aalto-university/ www.facebook.com/aaltouniversity instagram.com/aaltouniversity twitter.com/aaltouniversity
COMMENTS
Supply chain (SC) is a mechanism that con cerns the preparation, a rrangement, and management of. products, parts, and finished goods from supp lier to consumer. SC also deals with the flow of ...
The Journal of Operations Management (JOM) is one of the leading journals in the ISI Operations Research and Management Science category. JOM's mission is to publish original, empirical, operations and supply chain management research that demonstrates both academic and practical relevance.
by Matt Lowe, G V Nadhanael, and Benjamin N. Roth. Policy makers in the developing world face important tradeoffs in reacting to a pandemic. The quick and complete recovery of India's food supply chain suggests that strict lockdown measures at the onset of pandemics need not cause long-term economic damage.
Operations Management Research focuses on rapidly publishing high-quality, peer-reviewed research that enhances the theory and practice of operations management across a wide range of topics and research paradigms.. Presents research that advances both theory and practice of operations management. Includes all aspects of operations management, from manufacturing and supply chain to health care ...
Research in the supply chain systems explores the effective and efficient production and flow of goods and services in supply chains. Supply chain management is one of the core and influential areas within industrial engineering and operations research, and UC Berkeley IEOR faculty members are some of the world's leading supply chain management experts.
Description. Operations research widely applies existing scientific and technological knowledge and mathematical methods to solve specific problems in supply chain management and provides a basis for decision-makers to choose the best decision. The basic methods of operations research include mathematical methods, statistical methods, computer ...
1. Introduction. Field research, understood as the systematic study of original data - qualitative or quantitative - gathered from real settings (Edmondson and McManus, 2007) is critical to the development of scientific knowledge within operations and supply chain management.Such research is usually characterized by a detailed understanding of the practice of operations in a particular ...
We reviewed the manuscripts focused on Supply Chain Management that had been published in Production and Operations Management (POM) over roughly 15 years (1992 to 2006).The manuscripts covered dealt with topics including supply chain design, uncertainty and the bullwhip effect, contracts and supply chain coordination, capacity and sourcing decisions, applications and practice, and teaching ...
His research has been published in top-tier journals and conferences in operations research (OR), machine learning (ML) and artificial intelligence (AI). He leads research and industrial initiatives which integrate predictive and prescriptive analytics in various supply chain applications in manufacturing, retail, omni-channel and transportation.
He has authored or co-authored several articles, book chapters, and four books on operations and supply chain management. His research appeared in International Journal of Operations and Production Management, International Journal of Production Economics, Industrial Marketing Management and Production Planning & Control.
As incoming editors a year ago, our 2019 editorial described 14 topic areas deserving of research and professional practical attention in operations and supply chain management (O/SCM), that are still important and deserving of scholarly and practical work (Samson and Kalchschmidt, 2019).While the topics such as supply chain risk, sustainability and new technology have remained relevant, the ...
SCM therefore encompasses a wide range of strategic, financial, and operational issues. With the emergence of supply chain management as a new discipline, the role of operations research (OR) models in effective SCM has become significant. From 1985 onwards the performance, design, and analysis of the supply chain has received increased attention.
The aim of this paper is to map the state of empirical research with respect to the dyadic relationship of SCM practices with supply chain performance (SCP), published in literature in recent past (2018-2022). The importance of empirical studies has been emphasized by various authors [11]. Hence this study aims to synthesize the findings of ...
The Master of Science in Operations Research (MSOR) is a 30-credit STEM program for students to concentrate in areas such as mathematical programming, stochastic models, and simulation, through domain specific courses in logistics, supply chain management, revenue management, financial engineering, risk management, entrepreneurship, and general ...
Operations Research for Supply chain management - An Overview of Issue and Contributions, S Amulu Priya, V Maheswari, V. Balaji. ... [24] Kouvelis P, Chambers C and Wang H 2006 Supply Chain Management Research and Product and Operations Management: Review, Trends, and Opportunities Production and Operations Management 15 449-469.
Container terminal operations and management: ... such as designing mechanisms for supply chains under demand uncertainties (Kwon et al., 2007), supply chain risk management ... and logistics risk management are likely to be improved with AI due to both their applicative potential and the lack of research in this field. In supply chain, the ...
1. Introduction. Operations and supply chain management (OSCM) encompasses the internal and external activities of a firm, such as the supply of raw material and the assembly and delivery of finished goods (Mentzer, Stank, and Esper Citation 2008).In the past decade, OSCM activities have become more networked, resulting in the generation of a huge volume of real-time data, referred to as ...
Supply chain management is the design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measuring performance globally. ... Operations Research and Information Engineering ...
Within operations research (OR), operations management (OM), supply chain management (SCM) and related fields, DEI can provide a deeper understanding of the research enterprise: what research questions are asked, how the questions are answered through research design and analytic methods, and how the knowledge gained can influence scholarship ...
Professor Tao Lu's research interests lie in supply chain management, transport logistics, socially responsible operations, operations under new business models. The methodologies he often use in research include stochastic optimization, analytical modeling and game theory. His research has been published in journals such as Manufacturing ...
Abstract. Purpose The purpose of this paper is to provide an overview of the evolution of service supply chain management from a behavioural operations perspective, pointing out future research directions for scholars. Design/methodology/approach This study searched five databases for relevant literature published.
We also apply these models to analyze the optimal decisions in a number of stochastic operations. 291-OD2 Research Seminars in Supply Chain Management (2 units) This doctoral seminar provides some basic knowledge in several key research issues in supply chain management. We discuss a number of current research topics and challenges in supply ...
The Journal of Business Logistics provides an academic forum for original thought, research, and best practices across logistics and supply chain management. The article titled "Defining Supply Chain Management" published in 2001 in the Journal of Business Logistics has been cited over 4,900 times in the last 17 years.
Explain the term supply chain, describe its flows, and the organizations that participate in a typical supply chain. Identify types of inventory in the supply chain and reasons for carrying inventory. Define the term logistics and give advantages and disadvantages to various forms of transportation.
The purpose of this study is to investigate the integration of information sharing and supply chain practice in supply chain management. Data from 125 North American manufacturing firms were ...
Business process management. Future research should explore how blockchain enables digital trust, demand supply management, confidentiality in customer order process, and interorganizational business process through smart contracts and distributed ledger technology. ... Supply chain operations managers should understand and do in-depth research ...
The APICS Certified Supply Chain Professional (CSCP) program is recognized worldwide as the premier supply chain management educational and certification program. The APICS CSCP program takes a broad view of operations, extending beyond internal operations to encompass the entire end-to-end supply chain— from supplier, through the company, to ...
Green supply chain management. The practices of GSCM have become the backbone for many organisations as competition increases in the marketplace and consumers are becoming more and more environmentally conscious (Liu et al., Citation 2020).GSCM is fundamentally growing as a key component of sustainability within the SCM in that its adoption and implementation improve the competitiveness of the ...
Green finance policies, financing constraints and corporate ESG performance: insights from supply chain management 6 August 2024 | Operations Management Research, Vol. 28 Sourcing from supplier in the presence of financial service providers' information asymmetry and quit probabilities
The aim is to understand their combined impact on the future of operations and supply chain management and industrial operations. Responsibilities. Conducting high-quality research in the field of industrial engineering and management. Publishing research findings in reputable scientific journals and presenting at international conferences.