AHP/ANP in supply chain resilience in the era of digital enterprise
We invite original research contributions to the practice of Analytic Hierarchy/Network Process (AHP/ANP) in Supply Chain Resilience in the Era of Digital Enterprise. “Supply Chain Disruption” has become a common term across the world over the past five years with predictable and unpredictable implications of UK’s departure from the EU (Brexit), US-China Trade War, the COVID-19 pandemic, and the Ukraine-Russia War. The impact of the pandemic on supply chain flows was extremely pronounced owing to lean supply chain structures that saved operational and storage costs and implemented mostly a “Just-In-Time” ordering policy where inventory levels are kept at minimum (Abdallah, 2021; Riezebos et al., 2009). While extreme leanness in supply chains saves money in the short run, it has proved to be catastrophic in the wake of the pandemic, and it seems that the difficult situation may remain well into the near future (Cuvero et al., 2021). Supply chain disruptions are expected to continue globally in 2023 and beyond, as 60-70% of manufacturing and shipping deals between America and Asia are made as short-term agreements (Akalin et al., 2022). To make matters worse, experts predict that climate change will disrupt supply chains far more than COVID-19 has as we start to see dramatic increases in wildfires, droughts, floods, hurricanes, and other extreme weather events (Sodhi & Tang, 2021). Given this information, it is critical that we improve our understanding of supply chain disruption risks, learn how to mitigate them, and learn how to properly respond to disruptions to minimize their long-term damage to resilience. Future disruptions are inevitable and will likely occur on a greater scale than we have ever seen; so, we must better be prepared (Akalin et al.,2022).
Multicriteria approaches to Supply Chain Resilience problems are few though this research area is a multiple criteria decision-making problem. It is not surprising that among the multiple criteria approaches, AHP/ANP and extensions are used most (Zhang, et al. 2021) since AHP/ANP is commonly used in practical applications in many different areas of decision-making. As a matter of fact, the AHP is one of, if not the most widely used multiple criteria decision-making analysis methodologies worldwide (Mu, 2022). AHP-entropy method is a recent extension, that combines subjective (AHP) and objective (entropy) information to reach criteria weights (Chen et al., (2022); Jin et al., 2022; Xiaofei, 2022).
Firms are lacking in many ways when it comes to supply chain risk management. Very few multi-objective supply chain networks include supply chain risk as an objective (Bilici et al., 2017). Moreover, most of the previous research only focuses on post-disruption strategy, what is done after a disruption has occurred, rather than preparation/risk-mitigation (Chen et al., 2019).
As the world continues to move into the digital era, new technological tools have the potential to significantly enhance firms’ supply chain operations by increasing the visibility and faster response in supply chains (Hou & Su, 2006; Lai et al., 2006). For example, the use of Artificial Intelligence (Baryannis et al., 2019), cyber-resilience strategies (Boyes, 2015), blockchain (de Boissieu et al., 2021 Gao et al., 2018; Manupati et al., 2020), and Big Data Analytics (BDA) can help better manage supply chain risks (Dubey et al., 2021; Singh N.P & Singh, S., 2019). The Internet of Things (IoT) and BDA are predicted to have the most significant impact on supply chains (Hopkins, 2021). Appropriate adoption of Artificial Intelligence (AI)/Machine Learning (ML) will support the formation of the new generation of intelligent manufacturing (Cioffi et al., 2020). The most significant benefits of AI and ML in supply chain management comprise more significant innovation, process, and resource optimization, and improved quality. The integration of operational objectives with key performance indicators like resilience, stability, and robustness can help ensure that a company’s long-term goals are aligned with performance indicators that go beyond short-term profit and extend to a supply chain’s ability to withstand disruptions (Ivanov et al., 2017).
We welcome high quality papers that address any type of decision problem in the theme of the special issue that describe important applications of AHP/ANP by itself or with other MCDA methodologies in practice. Qualitative approaches, literature review papers, applications implemented, affected managerial decisions are highly sought after. Also, this special issue is connected – but not restricted – to the International Symposium on the Analytic Hierarchy Process (ISAHP) conference. The participants of the 17th ISAHP are invited to submit their papers on supply chain resilience supported by digital transformation. Contributions arising from papers given at a conference should be substantially extended and should cite the conference paper where appropriate.
List of topics
All researchers worldwide working on the topics indicated above are also invited to contribute. Such studies might be focused on, but not limited to, the following areas of research and related topics. In all research areas AHP/ANP in Supply Chain Resilience in the Era of Digital Enterprise is a tying theme. In addition, innovative applied papers in model building, in managerial implications are highly sought:
- Machine Learning, Artificial Intelligence and Digitalization, Blockchain, Industry 4.0
- Supply Chain Management
- Role of Risk Analysis and Disaster Management in resilient Supply Chain
- Supply Chain Operations Reference (SCOR) processes
- UN’s Sustainable Consumption and Production patterns
- Supply Chain Analytics
- Big Data Applications in SCM
- Ethics, Social Responsibility and Sustainability
- Circular Economy, Circular Supply Chains, Closed-Loop Supply Chains
- Circular Supply Chain Governance
- Industrial and Manufacturing Engineering
- Extensions of Fuzzy Sets and Fuzzy Decision Making
- Integrated Business Management
- Business Applications, Business Analytics
- Healthcare Supply Chain Resilience
Papers targeting the special issue should be submitted through the JEIM submission system and will undergo the same review process as regularly submitted papers.
Author guidelines must be strictly followed. Please see the journal webpage.
Authors should select (from the drop-down menu) the special issue title AHP/ANP in supply chain resilience in the era of digital enterprise at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Authors should submit their high-quality manuscripts via JEIM web-based system, by 30 June 2023. Manuscripts submitted after the deadline may not be considered for the special issue and may be transferred to a regular issue.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere. The submitted manuscripts will be sent to reviewers as they arrive, but the editors will have the last word in accepting or rejecting any manuscript.
When the page proofs are finalized, the fully typeset and proofed version of the record is published online. This is referred to as the EarlyCite version. While an EarlyCite article has yet to be assigned to a volume or issue, it does have a digital object identifier (DOI) and is fully citable. It will be compiled into an issue according to the journal’s issue schedule, with papers being added by chronological date of publication.
Tentative time scale:
- Submission deadline: 30 June 2023
- Final decision: 30 November 2023.
Further inquiries should be sent to the Guest Editors:
- Birsen Karpak, Youngstown State University, USA, [email protected]
- Emel Aktas, Cranfield University, UK, [email protected]
- Ilker Topcu, Istanbul Technical University, Turkey, [email protected]
Guest Editors’ Bios
Birsen Karpak is distinguished professor emeritus, Youngstown State University, USA, (Twice, first on April 2015, second on April 2021. (Title of Distinguished Professor, given professors at Youngstown State University who recognized as a distinguished professor in teaching, research and service, the first distinguished professor at Williamson College of Business Administration (WCBA), she earned this title second time in 2021, again first faculty who recognized with this title second time in WCBA.)
Dr. Karpak is a co-chair of the International Symposium on the Analytic Hierarchy Process (ISAHP) ISAHP 2022 web conference, December 15-18, 2022. ISAHP takes place every two years. It brings together researchers, educators, and practitioners (users) of AHP and ANP to share their research and experiences in decision making. She is guest editor for a special issue for the Journal of Multi-Criteria Decision Analysis (JMCDA).
In the past, Karpak has been a guest editor for the International Journal of Production Economics (IJPE) for the 2014 ISAHP conference. Karpak has been also an associate editor for the International Journal of Analytic Hierarchy Process (IJAHP) for more than 9 years.
She has published in a variety of journals including International Journal of Production Economics, Transportation, European Journal of Purchasing & Supply Management and Journal of Multi-Criteria Decision Analysis – Wiley.
Emel Aktas, Chair of Supply Chain Analytics, Cranfield School of Management, Cranfield University, has B.Sc., M.Sc. and Ph.D. degrees in industrial engineering from Istanbul Technical University, Turkey. She began her career at Istanbul Technical University as a research and teaching assistant. She worked as a visiting researcher at Wayne State University, USA and as a lecturer at Dogus University, Istanbul, Turkey during her PhD studies.
Dr. Aktas was a guest editor, Journal of Enterprise Information Management, 2020; Volume 33, Issue 5; Special Issue: Multiple Criteria Decision Making; Guest Editors: Emel Aktas, Ilker Topcu, Berk Kucukaltan.
Dr. Aktas took part as a researcher in public and private funded projects on location selection, shift scheduling, and transportation master plan strategy. Her refereed articles have appeared in a variety of journals including European Journal of Operational Research, Interfaces, Supply Chain Management: An International Journal, Socio-Economic Planning Sciences and Transportation Research Part A: Policy and Practice. Before joining Cranfield, Emel was the course director of MSc Global Supply Chain Management program at Brunel University Business School.
Ilker Topcu is a Professor of Decision Sciences at the Industrial Engineering Department of Istanbul Technical University (ITU), Turkey. He visited Leeds University Business School, UK, from 1998 through 1999 as a visiting researcher and received his PhD degree in Engineering Management from ITU in 2000. He held a visiting professor position at the Katz Business School, University of Pittsburgh, USA, from 2018 through 2019.
Dr. Topcu has been ranked in the list of the World's Most Influential Scientists "career-long impact", created by researchers from Stanford University, recently published by Elsevier. His research interests include multiple criteria decision making, decision analysis, and operations research/management science. He has published several journal papers, book chapters, and conference proceedings. He edited three books for Springer entitled "OR Applications in Health Care Management" in 2018, "Multiple Criteria Decision Making – Beyond the Information Age" in 2021, and "New Perspectives in Operations Research and Management Science" in 2022.
Dr. Topcu was a guest editor, Journal of Enterprise Information Management, 2020; Volume 33, Issue 5; Special Issue: Multiple Criteria Decision Making; Guest Editors: Emel Aktas, Ilker Topcu, Berk Kucukaltan.
Dr. Topcu was an executive committee member of the International Society of Multiple Criteria Decision Making from 2015 through 2022. He was the general chair of the 25th International Conference on Multiple Criteria Decision Making (MCDM 2019), which took place in İstanbul, Turkey, from June 16 to June 21, 2019.
Abdallah Ali, A. (2021). How can lean manufacturing lead the manufacturing sector during health pandemics such as COVID 19: a multi response optimization framework. Computers, Materials, & Continua, 1397-1410. https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/pt/covidwho-955046
Akalin, G., Corredor, F., Karpak, B., & Illia, A. (2022). Impacts and Implications of Digital Technology Use in Supply Chain Preparedness and Response During the COVID-19 Pandemic. Journal Of Information Systems And Technology Management, 19. Retrieved from http://www.jistem.tecsi.org/index.php/jistem/article/view/3240
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202. https://doi.org/10.1080/00207543.2018.1530476
Bilici, C., Ekici, S.O., & Fusun, U. (2017). An integrated multi-objective supply chain network and competitive facility location model. Computers & Industrial Engineering, 108, 136-148. https://doi.org/10.1016/j.cie.2017.04.020
de Boissieu, E., Kondrateva, G., Baudier, P. and Ammi, C. (2021), "The use of blockchain in the luxury industry: supply chains and the traceability of goods", Journal of Enterprise Information Management, Vol. 34 No. 5, pp. 1318-1338. https://doi.org/10.1108/JEIM-11-2020-0471
Boyes, H. (2015). Cybersecurity and cyber-resilient supply chains. Technology Innovation Management Review, 5(4), 28. https://www.timreview.ca/sites/default/files/Issue_PDF/TIMReview_April2015.pdf#page=28
Chen, H. Y., Das, A., & Ivanov, D. (2019). Building resilience and managing post-disruption supply chain recovery: Lessons from the information and communication technology industry. International Journal of Information Management, 49, 330-342. https://doi.org/10.1016/j.ijinfomgt.2019.06.002
Chen, L., Duan, D., Mishra, A. R., & Alrasheedi, M. (2022). Sustainable third-party reverse logistics provider selection to promote circular economy using new uncertain interval-valued intuitionistic fuzzy-projection model. Journal of Enterprise Information Management, 35(4/5), 955-987.
Cioffi, R.,Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions. Sustainability, 12 (2), 492. https://doi.org/10.3390/su12020492
Cuvero, M., Pilkington, A., & Barnes, D. (2021, December). Supply Chain Management and Resilience During Disruption. Evaluation of the Covid-19 Pandemic on the Supply of Personal Protective Equipment. In 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 233-237). IEEE. https://ieeexplore.ieee.org/abstract/document/9672913
Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., Roubaud, D., & Foropon, C. (2021). Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59(1), 110-128. https://doi.org/10.1080/00207543.2019.1582820
Gao, Z., Xu, L., Chen, L., Zhao, X., Lu, Y., & Shi, W. (2018). CoC: A unified distributed-ledger-based supply chain management system. Journal of Computer Science and Technology, 33(2), 237-248. https://doi.org/10.1007/s11390-018-1816-5
Hopkins, J. L. (2021). An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia. Computers in Industry, 125, 103323. https://doi.org/10.1016/j.compind.2020.103323
Hou, J., & Su, D. (2006). Integration of web services technology with business models within the total product design process for supplier selection. Computers in Industry, 57(8-9), 797-808. https://doi.org/10.1016/j.compind.2006.04.008
Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158-6174. https://doi.org/10.1080/00207543.2017.1330572
Jin, L., Liu, W., Chen, C., Wang, W., & Long, H. (2022). Study on risk index system and prevention mechanism under information disclosure in China. Journal of Enterprise Information Management, 35(2), 358-375.
Lai, K. H., Wong, C. W., & Cheng, T. E. (2006). Institutional isomorphism and the adoption of information technology for supply chain management. Computers in Industry, 57(1), 93-98. https://doi.org/10.1016/j.compind.2005.05.002
Manupati, V. K., Schoenherr, T., Ramkumar, M., Wagner, S. M., Pabba, S. K., & Inder Raj Singh, R. (2020). A blockchain-based approach for a multi-echelon sustainable supply chain. International Journal of Production Research, 58(7), 2222-2241. https://doi.org/10.1080/00207543.2019.1683248
Mu, E. (2022). Reporting Public Multicriteria Decision-Making Applications: A Journal Editor’s Perspective. International Journal of the Analytic Hierarchy Process, 14(2). https://doi.org/10.13033/ijahp.v14i2.1025
Riezebos, J., Klingenberg, W., & Hicks, C. (2009). Lean production and information technology: connection or contradiction? Computers in industry, 60(4), 237-247. https://doi.org/10.1016/j.compind.2009.01.004
Singh, N. P., & Singh, S. (2019). Building supply chain risk resilience: Role of big data analytics in supply chain disruption mitigation. Benchmarking: An International Journal. https://doi.org/10.1108/BIJ-10-2018-0346
Sodhi, M. S., & Tang, C. S. (2021). Supply chain management for extreme conditions: research opportunities. Journal of Supply Chain Management, 57(1), 7-16. https://doi.org/10.1111/jscm.12255
Xiaofei, Y. (2022). Research on the action mechanism of circular economy development and green finance based on entropy method and big data. Journal of Enterprise Information Management, 35(4/5), 988-1010.
Zhang, Z.(J)., Srivastava, P.R., Eachempati, P. and Yu, Y. (2021), "An intelligent framework for analyzing supply chain resilience of firms in China: a hybrid multicriteria approach", The International Journal of Logistics Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJLM-11-2020-0452