Prioritization of Supply Chain Digital Transformation Strategies Using Multi-Expert Fermatean Fuzzy Analytic Hierarchy Process

Alkan, Nursah; Kahraman, Cengiz

doi:10.15388/22-INFOR493

Informatica

Prioritization of Supply Chain Digital Transformation Strategies Using Multi-Expert Fermatean Fuzzy Analytic Hierarchy Process

Volume 34, Issue 1 (2023), pp. 1–33

Nursah Alkan Cengiz Kahraman

https://doi.org/10.15388/22-INFOR493

Pub. online: 22 July 2022 Type: Research Article

Open Access

Received
1 February 2022

Accepted
1 July 2022

Published
22 July 2022

Abstract

Innovations in technology emerged with digitalization affect all sectors, including supply chain and logistics. The term “digital supply chain” has arisen as a relatively new concept in the manufacturing and service sectors. Organizations planning to utilize the benefits of digitalization, especially in the supply chain area, have uncertainties on how to adapt digitalization, which criteria they will evaluate, what kind of strategies should be developed, and which should be given more importance. Multi-criteria decision making (MCDM) approaches can be addressed to determine the best strategy under various criteria in digital transformation. Because of the need to capture this uncertainty, fermatean fuzzy sets (FFSs) have been preferred in the study to widen the definition domain of uncertainty parameters. Interval-valued fermatean fuzzy sets (IVFFSs) are one of the most often used fuzzy set extensions to cope with uncertainty. Therefore, a new interval-valued fermatean fuzzy analytic hierarchy process (IVFF-AHP) method has been developed. After determining the main criteria and sub-criteria, the IVFF-AHP method has been used for calculating the criteria weights and ranking the alternatives. By determining the most important strategy and criteria, the study provides a comprehensive framework of digital transformation in the supply chain.

References

Abdel-Basset, M., Mohamed, M., Zhou, Y., Hezam, I. (2017). Multi-criteria group decision making based on neutrosophic analytic hierarchy process. Journal of Intelligent and Fuzzy Systems, 333(6), 4055–4066.

Agrawal P., Narain, R. (2018). Digital supply chain management: an overview. In: IOP Conference Series: Materials Science and Engineering, Vol. 455.

Alicke, K., Rachor, J., Seyfert, A. (2016). Supply Chain 4.0 – the next-generation digital supply chain, McKinsey&Company.

Alkan, N. (2021). Risk analysis for digitalization oriented sustainable supply chain using interval-valued Pythagorean fuzzy AHP. Advances in Intelligent Systems and Computing, 1197, 1373–1381.

Alkan, N., Kahraman, C. (2021). Extensions of fuzzy sets in big data applications: a literature review. Advances in Intelligent Systems and Computing, 1197, 884–893.

Atannasov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.

Atannasov, K. (1999). Intuitionistic Fuzzy Sets, Theory and Applications. Physica-Verlag, Heidelberg.

Bailey, G., Moss, C., Kurz, D. (2017). Digital Supply Chain Transformation Guide: Essential Guide. The Center for Global Enterprise.

Bhargava, B., Ranchal, R., Othmane, L. (2013). Secure information sharing in digital supply chains. In: Proceedings of the 2013 3rd IEEE International Advance Computing Conference, IACC 2013.

Bienhaus, F., Haddud, A. (2018). Procurement 4.0: factors influencing the digitisation of procurement and supply chains. Business Process Management Journal, 24(4), 965–984.

Bolturk, E., Kahraman, C. (2018). A novel interval-valued neutrosophic AHP with cosine similarity measure. Soft Computing, 22(15), 4941–4958.

Buckley, J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems 17(3), 233–247.

Büyüközkan, G., Göçer, F. (2017). An extension of MOORA approach for group decision making based on interval valued intuitionistic fuzzy numbers in digital supply chain. In: IFSA-SCIS 2017 – Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems, Japan.

Büyüközkan, G., Göçer, F. (2018a). Digital supply chain: literature review and a proposed framework. Computers in Industry, 97, 157–177.

Büyüközkan, G., Göçer, F. (2018b). An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain. Applied Soft Computing, 69, 634–654.

Büyüközkan, G., Göçer, F. (2019). A novel approach integrating AHP and COPRAS under Pythagorean fuzzy sets for digital supply Chain partner selection. IEEE Transactions On Engineering Management.

Chang, D. (1986). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655.

Dougados, M., Felgendreher, B. (2016). The Current and Future State of Digital Supply Chain Transformation. Infor, Capgemini Consulting and NT Nexus.

Duan, W., Gulistan, M., Abbasi, F., Khurshid, A., Al-Shamiri, M. (2021). q-Rung double hierarchy linguistic term set fuzzy AHP; applications in the security system threats features of social media platforms. International Journal of Intelligent Systems.

Farahani, P., Meier, C., Wilke, J. (2016a). A Vision on Digital Supply Chain Management in 2020, Shaping the Digital Enterprise: Trends and Use Cases in Digital Innovation and Transformation. Springer Nature, Switzerland, pp. 157–172.

Farahani, P., Meier, C., Wilke, J. (2016b). Digital Supply Chain Management Agenda for the Automotive Supplier Industry, Shaping the Digital Enterprise: Trends and Use Cases in Digital Innovation and Transformation. Springer International Publishing, Switzerland, pp. 157–172.

Farahani, P., Meiner, C., Wilke, J. (2020). Digital Supply Chain Management 2020 Vision, SAP.

Garg, H., Ali, Z., Mahmood, T. (2021). Algorithms for complex interval-valued q-rung orthopair fuzzy sets in decision making based on aggregation operators, AHP, and TOPSIS. Expert Systems, 38(1).

Gezgin, E., Huang, X., Samal, P., Silva, I. (2017). Digital Transformation: Raising Supply Chain Performance the New Levels. McKinsey&Company.

Gul, M. (2018). Application of Pythagorean fuzzy AHP and VIKOR methods in occupational health and safety risk assessment: the case of a gun and rifle barrel external surface oxidation and colouring unit. International Journal of Occupational Safety and Ergonomics, 26(4), 705–718.

Ivanov, D., Dolgui, A., Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829–846.

Jeevaraj, S. (2021). Ordering of interval-valued Fermatean fuzzy sets and its applications. Expert Systems with Applications, 185, 115613.

Kahraman, C., Oztaysi, B., Sari, I., Turanoğlu, E. (2016). Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowl Based System, 59, 48–57.

Kahraman, C., Onar, S.Ş., Öztayşi, B., Şeker, Ş., Karaşan, A. ((1-2) (2020)). Integration of fuzzy AHP with other fuzzy multicriteria methods: a state of the art survey. Journal of Multiple-Valued Logic and Soft Computing, 35, 61–92.

Karasan, A., Ilbahar, E., Kahraman, C. (2019). A novel pythagorean fuzzy AHP and its application to landfill site selection problem. Soft Computing, 23(21), 10953–10968.

Kearney, A.T., (2015). Digital Supply Chains: Increasingly Critical for Competitive Edge. WHU Otto Beisheim School of Management.

Korpela, K., Mikkonen, K., Hallikas, J., Pynnönen, M. (2016). Digital business ecosystem transformation: toward cloud integration. In: Proceedings of the Annual Hawaii International Conference on System Sciences, United States.

Kutlu Gündoğdu, F., Duleba, S., Moslem, S., Aydın, S. (2021). Evaluating public transport service quality using picture fuzzy analytic hierarchy process and linear assignment model. Applied Soft Computing, 100, 106920.

Luthra, S., Mangla, S. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179.

Mathew, M., Chakrabortty, R., Ryan, M. (2020). A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Engineering Applications of Artificial Intelligence, 96, 103988.

Otay, I., Oztaysi, B., Onar, S., Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90–106.

Öztaysi, B., Onar, S., Bolturk, E., Kahraman, C. (2015). Hesitant fuzzy analytic hierarchy process. In: 2015 IEEE international conference fuzzy systems (FUZZ-IEEE), PP. 1–7.

Pundir, A., Jagannath, J., Chakraborty, M. (2019). Technology integration for improved performance: a case study in digitization of supply chain with integration of internet of things and blockchain technology. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019, United States.

PwC Sweden (2018). Digital Supply Chain: Making the Supply Chains More Efficient, Agile, and Customer-Focused.

Raab M., Griffin-Cryan, B. (2011). Digital Transformation of Supply Chains. Capgemini Consulting.

Saaty, T.L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.

Sadiq, R., Tesfamariam, S. (2009). Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process. Stochastic Environmental Research and Risk Assessment, 23(1), 75–91.

Schrauf, S., Berttram, P. (2016). How Digitization Makes the Supply Chain More Efficient, Agile, and Customer-Focused. PwC Strategy& Germany.

Scuotto, V., Caputo, F., Villasalero, Del Giudice M, M. (2017). A multiple buyer – supplier relationship in the context of SMEs’ digital supply chain management. Production Planning & Control, 28(16), 1378–1388.

Senapati, T., Yager, R. (2019a). Some new operations over fermatean fuzzy numbers and application of fermatean fuzzy WPM in multiple criteria decision making. Informatica, 30(2), 391–412.

Senapati, T., Yager, R. (2019b). Fermatean fuzzy weighted averaging/geometric operators and its application in multi-criteria decision-making methods. Engineering Applications of Artificial Intelligence, 85, 112–121.

Senapati, T., Yager, R. (2020). Fermatean fuzzy sets. Journal of Ambient Intelligence and Humanized Computing, 11(2), 663–674.

The Center for Global Enterprise (2015). Digital Supply Chains: A Frontside Flip.

Tjahjono, B., Esplugues, C., Ares, E., Pelaez, G. (2017). What does Industry 4.0 mean to Supply Chain? In: Manufacturing Engineering Society International Conference 2017, MESIC 2017, Vigo, Spain.

Torra, V. (2010). Hesitant fuzzy sets. International Journal of Intelligent Systems, 25(6), 529–539.

Uhl, A., Gollenia, A. (2016). Digital Enterprise Transformation: A Business-Driven Approach to Leveraging Innovative IT. Routledge.

Van Laarhoven, P., Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst, 11(1–3), 229–241.

WTO, IDE-JETRO, OECD, UIBE, World Bank Group (2019). Technological, Innovation Supply Chain Trade, and Workers in a Globalized World. World Trade Organization, Switzerland.

Wu, J., Huang, H., Cao, Q. (2013). Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multicriteria decision making problems. Applied Mathematical Modelling, 37(24), 9898–9906.

Xu, J. (2014). Managing Digital Enterprise Ten Essential Topics. Atlantis Press, Paris.

Yager, R. (2013). Pythagorean fuzzy subsets. In: Proceedings of the 2013 Joint IFSA World Congress and NAFIPS Annual Meeting, IFSA/NAFIPS 2013, pp. 57–61.

Yager, R. (2017). Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems, 25(5), 1222–1230.

Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

Zadeh, L. (1975). The concept of a linguistic variable and its application. Information Sciences, 8(3), 199–249.

Zhang, X., Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with pythagorean fuzzy sets. International Journal of Intelligent Systems, 29(12), 1061–1078.

Biographies

Alkan Nursah

nalkan@itu.edu.tr

N. Alkan is a PhD candidate and a research assistant in the Department of Industrial Engineering at Istanbul Technical University since 2019. She received the MSc degree in Department of Industrial Engineering from Yildiz Technical University, Turkey, in 2019. Her research interests include fuzzy sets and their extensions, multi-criteria/objective decision making, and data analysis. She published some journal papers, conference papers and book chapters in mentioned areas.

Kahraman Cengiz

kahramanc@itu.edu.tr

C. Kahraman is a full professor at Istanbul Technical University. His research areas are engineering economics, quality management, statistical decision making, multicriteria decision making, and fuzzy decision making. He published about 300 international journal papers and about 200 conference papers. He became the guest editor of many international journals and the editor of many international books from Springer. He is a member of editorial boards of 20 international journals. He is the chair of INFUS International Conferences on fuzzy and intelligent systems ZS, Yager RR, some geometric aggregation operators based on intuitionistic fuzzy sets.

Full article Related articles Cited by

Open access article under the CC BY license.

Keywords

digital transformation supply chain fermatean fuzzy sets MCDM AHP

Metrics (since January 2020)

870 Article info views	672 Full article views
647 PDF downloads	153 XML downloads

RSS

Authors

Abstract

References

Biographies

Export citation

Copy and paste formatted citation

Download citation in file