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Challenges and opportunities for empirical research on data analytics in supply chain management

Guest editors:

  • Prof Diego Pacheco, Aarhus University, Denmark

  • Prof Deepika Koundal, University of Petroleum and Energy Studies, Dehradun, India

  • Prof Angappa Gunasekaran, The Pennsylvania State University, USA

  • Prof Mohsen Attaran, California State University, Bakersfield, USA

  • Prof Fabio Sgarbossa, NTNU, Norway

  • Prof Arunachalam (Chalam) Narayanan, University of North Texas, USA

  • Prof Suhaiza Zailani, University Malaya, Malaysia


Submission deadline: May 3rd, 2024

About the issue:

Today, most global value chains operate in volatile and turbulent environments affecting supply, production, distribution and sales integration. Recently, different disruptive events have added more complexity to the management, integration and operational decision-making in different industrial sectors. Examples of such disruptive events include the consequences of the post-COVID-19 pandemic in the ‘new normal’ context, the Suez Canal blockage, geopolitics conflicts such as the war in Ukraine and the microchip and semiconductor shortage worldwide. As a consequence of this context, taking appropriate decisions to address supply chain issues has become a more complex managerial task in several industries.

To deal with these conditions, the scientific community have developed solutions based on data analytics science (Riahi et a., 2021; Kumar et al., 2023). In this regard, there have been recent extended literature reviews regarding the use of data analytics in supply chain management (Wang et al., 2016; Nguyen et al., 2018; Manikas et al., 2020; Maheshwari et al., 2021; Ritchi et al., 2023), and they all call for additional research in this growing area. A recent literature review (Manikas et al., 2020) examined 21,053 articles, including publications from six leading operations management journals since their inception, and found 18 leading operations and supply chain themes. Among them, they found areas of increasing relevance and trend include (i) supply chain design (e.g., issues in global supply chains, supply chain integration, supply chain strategy, political and cultural factors in supply chains), (ii) supply chain management (e.g., newsvendor models, two-echelon supply chain models, game theory-based methods, Vendor Managed Inventory, performance measurement in supply chains, inventory transportation decisions) and (iii) service operations (e.g., buyer-seller relationship, queuing theory, service quality, service design, technology in services, health care services, retail, globalization of services). Another review study on the role of disruptive technologies in supply chains concluded that blockchain enhances supply chain transparency by addressing various risks such as fraud, data loss, and operational vulnerabilities through its tamper-resistant ledger, promoting trust and traceability (Alkhudary et al., 2022). In another article, Queiroz and Telles (2018) examined the impact of big data analytics projects in supply chains among industries and found that not all are successful, and they provide a framework for implementing these projects.

In parallel to these advances, the interplay between supply chain digitalization, supply chain integration, and firm performance is another research area that has received increasing relevance and attention (Liu & Chiu et al., 2021). In a literature review, Kache and Seuring (2017) identify several opportunities and challenges linked to the emergence of big data analytics from a corporate and supply chain perspective; they include supply chain visibility and transparency, cybersecurity, operational efficiency, IT capability and infrastructure and finally the need for more integration and collaboration. For example, blockchain research has shown that it assists in the development and execution of smart contracts, fostering trading-partner visibility and more efficient collaboration. Its peer-to-peer transactions remove the need for intermediaries, thereby reducing transaction costs (Koonce, 2016). Administrative tasks in logistics processes may be significantly reduced or eliminated due to increased transaction visibility and the potential to bypass non-value-adding functions. This, in turn, enhances supply chain efficiency and simplifies the system (Adams et al., 2017; Bridgers, 2017; Seidel, 2018; Shermin, 2017; Watson and Mishler, 2017). Blockchain use in supply chain problems can also streamline processes such as inventory management, asset tracking, and verifying goods’ authenticity, leading to improved accuracy, reduced errors, and increased trust among supply chain participants. However, insufficient emphasis in the extant literature on empirical studies examining robust models and data analytics techniques.


Collectively, we have observed more and more the increasing relevance of data analytics methods and quantitative models to cope with supply chain problems of industrial relevance in different organizational and contexts (Kumar et al., 2023). Nevertheless, while research has been carried out to comprehend the role of different Industry 4.0 technologies on supply chains performance (Rad et al., 2022; Arji et al., 2023), more research efforts are still required to understand how these technologies can be empirically orchestrated to help companies and society to cope with the sustainable development challenges (Taddei et al., 2022; Shet et al., 2022; Kumar et al., 2023). Moreover, the growing unpredictability and hazards, along with numerous feedback loops and changes, represent difficulties for modern production and logistics systems, supply chains, and Industry 4.0 networks. As a result, supply chains have been challenged today by increased uncertainty and risks (Ivanov et al., 2018).

Therefore, to move the empirically grounded research on data analytics in SCM, this Special Issue (SI) aims to explore the trends and potentials of data analytics to deal with empirical supply chain problems to improve the accuracy of the decision-making process in different industries. In line with the scope of the SCFIJ, this SI is interested in receiving studies applying diagnostic, descriptive, predictive and prescriptive analytics in the context of supply chain issues. Regarding the research approach, this SI welcomes empirical and analytical studies considering real-world applications in supply chain management issues.

Submitted research must show a clear link between real supply chain problems and the data set used to develop the proposed solutions. To improve research transparency and enable continuation and replication studies, the data sets used should be available for readers - when possible - and the research should be replicable. Data can be anonymized, considering confidentiality agreements with the cases involved in the studies. The papers must clearly state managerial insights, practical implications, and theoretical contributions.


This SI also welcomes real-world applications and pragmatic aspects of supply chain management. It encompasses contemporary ideation, scholarly investigation, and nascent paradigms about supply chain management. Additionally, it encompasses instances that elucidate global implementation challenges through comprehensive case studies. This call will examine empirical solutions of practical relevance to solve SCM problems related to the following themes (but not limited to):

  • Supply chain design and performance

  • Machine Learning and Supply Chain 4.0

  • Digitalization of supply chain and digitalization for supply chain visibility

  • Sustainable supply chains and supply chain decarbonization

  • Sustainable Develop Goals and supply chains

  • Supply chain risk management approaches

  • Service supply chains

  • Trafficking, slavery and corruption in supply chains

  • Healthcare supply chains and operations

  • Humanitarian and disaster supply chains and operations

  • Climate and natural hazards impact supply chains and operations

  • Performance measures and metrics involving data analytics and supply chains

We expected that this SI would contribute to extending the current empirical research on data analytics in supply chain and operations problems and offer readers a platform to understand better the latest developments, new ideas and research trends in these areas.

Author can send questions about the SI and paper submissions to any or all of the SI guest editors at any time.


  • Adams, R., Parry, G., Godsiff, P. and Ward, P. (2017). The future of money and further applications of the blockchain, Strategic Change, Vol. 26, No. 5, pp. 417–422.

  • Alkhudary, R., Queiroz, M. M. & Féniès, P. (2022). Mitigating the risk of specific supply chain disruptions through blockchain technology, Supply Chain Forum: An International Journal, DOI: 10.1080/16258312.2022.2090273

  • Arji, G., Ahmadi, H., Avazpoor, P., & Hemmat, M. (2023). Identifying resilience strategies for disruption management in the healthcare supply chain during COVID-19 by digital innovations: A systematic literature review. Informatics in Medicine Unlocked, 101199.

  • Bridgers, A. (2017). Will workplaces be going off the rails on the blockchain? Journal of Internet Law, Vol. 20, No. 11, pp.3–6.

  • Ivanov, D., Sethi, S., Dolgui, A., & Sokolov, B. (2018). A survey on control theory applications to operational systems, supply chain management, and Industry 4.0. Annual Reviews in Control, Vol. 46, pp. 134-147.

  • Kache, F. and Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management, International Journal of Operations & Production Management, Vol. 37 No. 1, pp. 10-36.

  • Koonce, L. (2016). The wild, distributed world: get ready for radical infrastructure changes, from blockchains to the interplanetary file system to the internet of things, Intellectual Property & Technology Law Journal, Vol. 28, No. 10, pp. 3–5.

  • Kumar, D., Singh, R. K., Mishra, R., & Vlachos, I. (2023). Big data analytics in supply chain decarbonization: a systematic literature review and future research directions. International Journal of Production Research, 1-21.

  • Liu, K.P., Chiu, W. (2021). Supply Chain 4.0: the impact of supply chain digitalization and integration on firm performance. Asian J Bus Ethics, Vol. 10, pp. 371–389.

  • Maheshwari, S., Gautam, P., & Jaggi, C. K. (2021). Role of Big Data Analytics in supply chain management: current trends and future perspectives. International Journal of Production Research, Vol. 59 No. 6, pp. 1875-1900.

  • Manikas et al. (2020). A review of operations management literature: a data-driven approach. International Journal of Production Research, Vol. 58 No. 5, pp. 1442-1461.

  • Nguyen, T., Li, Z. H. O. U., Spiegler, V., Ieromonachou, P., & Lin, Y. (2018). Big data analytics in supply chain management: A state-of-the-art literature review. Computers & Operations Research, Vol. 98, pp. 254-264.

  • Queiroz, M.M. and Telles, R. (2018). Big data analytics in supply chain and logistics: an empirical approach, The International Journal of Logistics Management, Vol. 29 No. 2, pp. 767-783.

  • Rad, F. F., Oghazi, P., Palmié, M., Chirumalla, K., Pashkevich, N., Patel, P. C., & Sattari, S. (2022). Industry 4.0 and supply chain performance: A systematic literature review of the benefits, challenges, and critical success factors of 11 core technologies. Industrial Marketing Management, Vol.  105, pp. 268-293.

  • Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, Vol. 173, 114702.

  • Ritchi, H. (2023). Reviving the information veracity in healthcare supply chain with blockchain: a systematic review, Supply Chain Forum: An International Journal, DOI: 10.1080/16258312.2023.2199904.

  • Seidel, M.L. (2018). Questioning centralized organizations in a time of distributed trust, Journal of Management Inquiry, Vol. 27, No. 1, pp. 40–44.

  • Shermin, V. (2017). Disrupting governance with blockchains and smart contracts, Strategic Change, Vol. 26, No. 5, pp. 499–509.

  • Shet, S. V., & Pereira, V. (2021). Proposed managerial competencies for Industry 4.0–Implications for social sustainability. Technological Forecasting and Social Change, Vol. 173, 121080.

  • Taddei, E., Sassanelli, C., Rosa, P., & Terzi, S. (2022). Circular supply chains in the era of Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 108268.

  • Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, Vol. 176, pp. 98-110.

  • Watson, L. and Mishler, C. (2017). Get ready for blockchain, Strategic Finance, pp. 62–63.



Guest Editors - Short Biography

Prof. Diego Pacheco -

Diego Pacheco is an Associate Professor at the Department of Business and Technology (Aarhus Business School) at the Aarhus University, DK. He is a researcher, educator and an experienced senior school leader with 20+ years of combined experience in the industry and academic activities, passionate about solving real-world problems of enterprises and society, thereby bridging the gaps between theory and practice. In the academy, he has worked in research, teaching, visiting professor in Postgraduation programs and leadership duties as Head of Departments and Dean. As a result of the research projects, he has published over 200+ peer-reviewed papers in journals and conferences, including the Journal of Business Research, International Journal of Production Economics, Production Planning & Control, International Journal of Product Lifecycle Management, Journal of Engineering and Technology Management, Journal of Business & Industrial Marketing, Industrial Marketing and Management among others. He served as a guest editor in Computers & Industrial Engineering, Sustainability and Frontiers in Manufacturing Technology. He also serves as Associate Editor in the Supply Chain Analytics Journal and Decision Analytics Journal. He is a researcher in Operations and Supply Chain Management, Industry 4.0 and Green Transition.

Prof. Deepika Koundal -  

Deepika Koundal is currently associated with the University of Petroleum and Energy Studies, Dehradun. She received recognition and honorary membership from the Neutrosophic Science Association from the University of Mexico, USA. She is also selected as a young scientist in 6th BRICS Conclave in 2021. She received the Master's and PhD degrees in computer science & engineering from Panjab University, Chandigarh, in 2015. She has published over 100 research articles in reputed SCI and Scopus-indexed journals, conferences and three books. She served as a guest editor in Computers & Electrical Engineering, Internet of Things Journals and IEEE Transaction of Industrial Informatics, Computational and Mathematical Methods in Medicine. She also serves as Associate Editor in IEEE Transactions in Artificial Intelligence, Healthcare Analytics, Supply Chain Management and International Journal of Computer Applications. She has also served as a reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi, Wiley and Sage. Her Areas of Interest are Artificial Intelligence, Biomedical Imaging and Signals, Image Processing, Soft Computing, Machine Learning/Deep Learning.

Prof. Angappa Gunasekaran -

Angappa Gunasekaran is the director of the School of Business Administration at The Pennsylvania State University. He has a PhD in Industrial Engineering and Operations Research from the Indian Institute of Technology (Bombay). Dr. Gunasekaran has held academic positions at Brunel University (UK), Monash University (Australia), the University of Vaasa (Finland), the University of Madras (India) and the University of Toronto, Laval University, and Concordia University (Canada). He has over 200 articles published/forthcoming in 40 different peer-reviewed journals. Dr Gunasekaran is on the Editorial Board of over 20 peer-reviewed journals, which include some prestigious journals such as the Journal of Operations Management, International Journal of Production Economics, International Journal of Computer-Integrated Manufacturing, International Journal of Production Planning and Control, International Journal of Operations & Production Management, Technovation, and Computers in Industry: An International Journal. Dr Gunasekaran is involved with several national and international collaborative projects that private and government agencies fund. Dr. Gunasekaran has edited a couple of books that include Knowledge and Information Technology Management: Human and Social Perspectives, (Idea Group Publishing) and Agile Manufacturing: The 21st Century Competitive Strategy (Elsevier). Dr Gunasekaran is the Editor of Benchmarking: An International Journal, the North American Editor of the International Journal of Enterprise Network Management and other several journals. He has edited special issues for several highly reputed journals. Dr. Gunasekaran is currently interested in researching benchmarking, agile manufacturing, management information systems, e-procurement and competitiveness of SMEs, information technology/ systems evaluation, performance measures and metrics in the new economy, technology management, logistics, and supply chain management. He actively serves on several university committees.

Prof. Mohsen Attaran -

Mohsen Attaran is the 2004-05 Millie Ablin Outstanding Professor of Management at California State University, Bakersfield. He obtained his PhD in Systems Science with specializations in Operations Management and Business Forecasting from Portland State University. In 2019 he was honored with Emeritus Award for his significant contributions to the university, community, discipline, and profession. He is the author/co-author of four books, four book chapters, over 120 peer-reviewed research papers, and ten commercial software packages. He has published articles in the top-ranked journals in his discipline, including the Journal of Business Research, Information & Management, Industrial Management & Data Systems, Supply Chain Management: An International Journal, Business Horizons, and HBR.

Prof. Fabio Sgarbossa -

Fabio Sgarbossa has been a Full Professor of Industrial Logistics at the Department of Mechanical and Industrial Engineering (MTP) at NTNU (Norway) since October 2018. He was an Associate Professor at the University of Padova (Italy), where he also received his PhD in Industrial Engineering in 2010. He is currently the leader of the Production Management Group at MTP and is responsible for the logistics 4.0 Lab at NTNU. He has been and is involved in several European and National Projects. He is the author and co-author of about 150 publications in relevant international journals about industrial logistics, material handling, materials management, and supply chain. He is a member of the Organizing and Scientific Committees of several International Conferences, and he is a member of editorial boards and associate editor in relevant International Journals, such as the International Journal of Production Research.

Prof. Arunachalam (Chalam) Narayanan, Ph.D. -   

Arunachalam (Chalam) Narayanan, PhD, is an Associate Professor of Analytics at the Information Technology and Decision Sciences Department at the University of North Texas. He earned a PhD in Operations Management and MS in Industrial Engineering from Texas A&M University. Prior to his appointment at the University of North Texas, he was a faculty member in the Industrial Distribution Program in the College of Engineering at Texas A&M University (tenured in 2012) and a faculty member in the Bauer College of Business at the University of Houston. He has consulted with several firms in O&G, retail, logistics and manufacturing sectors, apart from working with non-profits such as food banks and Salvation Army. His dissertation was recognized by DSI and CSCMP in 2007 and has been published in journals including POMS, JOM, MSOM, DSJ, EJOR, Omega, IJPE, IJPR, among others. He has also created a popular version of an online beer game, which will soon be available on the UNT servers.

Prof. Suhaiza Zailani -

Suhaiza Zailani received her PhD in Management Science from Lancaster University, England, UK. She is a Professor of Supply Chain at the Faculty of Business and Economics, University Malaya. She was the Head of the Training Section, Deputy Dean of Postgraduates, and a Director at the University of Malaya Entrepreneurship Center (UMEC). Her field of expertise is in Supply Chain Management with a focus on the issues of sustainability. She is currently an editor of the Decision Analytics Journal. She has published more than 200 papers in various citation-indexed journals on Operations Management, particularly in ISI-based and Scopus journals. Her current ISI H-Index is 36 with 4,648 total times cited, Scopus H-Index is 41 with 6,523 citations, and GS H-Index is 60 with 14.100 citations. She was awarded the UM Distinguished Researcher in 2016, Emerald Literati Network Awards for Excellence in 2016 and 2019, and in 2021 and 2022, she was listed as one of the recipients of the Top 80 UM Researchers listed amongst the World’s Top 2% of scientists by Stanford University and was the only recipient from the arts discipline.

Special Issue information:

Submissions will be subject to a double-blind peer review process. Online submissions to Supply Chain Forum: an International Journal are made using ScholarOne Manuscripts, the online submission and peer review system.

Registration and access is available at  

Information concerning formatting is available at:

Publication schedule

Deadline for submissions of full papers: May 3rd, 2024

Notification of final acceptance for publication: March, 1st, 2025

The special issue is expected to appear in June 2025 (vol 26-2 2025)

Questions should be addressed to the guest editors:

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