EVALUATION OF SUPPLY CHAIN ANALYTICS WITH AN INTEGRATED FUZZY MCDM APPROACH

Yıl 2019, , 136 - 147, 01.10.2019

Gülçin Büyüközkan Merve Güler Esin Mukul Fethullah Göçer

https://doi.org/10.14514/byk.m.26515393.2019.sp/136-147

Öz

Recently, the popularity of big data and business analytics has increased with advanced
technological developments. Supply chain analytics (SCA) notion was born with the
implementation of these technologies in supply chains that become more global, more complex,
more extended, and more connected each day. SCA aims to find meaningful patterns in supply
chain processes with the application of statistics, mathematics, machine-learning techniques,
and predictive modeling. In this context, companies try to find ways to create business value for
their supply chains by leveraging SCA. However, the selection of the most appropriate SCA
tool is a complicated process that contains many influencing factors. For instance, the graphical
and intuitive features, the data extraction method and real-time operability can be the
influencing factors for such a selection. Therefore, in this study, it is aimed to provide an
integrated technique for prioritizing SCA success factors and for evaluating SCA tools. For
addressing these problems, fuzzy logic and multi-criteria decision making (MCDM) techniques
are used. An integrated fuzzy simple additive weighting (SAW) - a technique for order
preference by similarity to ideal solution (TOPSIS) approach is applied. The weights of the
success factors are calculated by using fuzzy SAW technique, and the SCA tools are evaluated
by using fuzzy TOPSIS technique. The success factors and the SCA tool alternatives are
determined by reviewing the literature and industry reports, and by collecting experts' opinions.
An application is given to illustrate the potential of the proposed approach. At the end of the
study, the suggestions for future studies are presented.

Anahtar Kelimeler

fuzzy logic, MCDM, SAW, success factors, supply chain analytics, TOPSIS

Kaynakça

• Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges, and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.
• Arya, V., Sharma, P., Singh, A., & De Silva, P. T. M. (2017). An exploratory study on supply chain analytics applied to spare parts supply chain. Benchmarking: An International Journal, 24(6), 1571-1580.
• Barbosa, M. W., Ladeira, M. B., & de la Calle Vicente, A. (2017). An analysis of international coauthorship networks in the supply chain analytics research area. Scientometrics, 111(3), 1703-1731.
• Barbosa, M. W., Ladeira, M. B., & de la Calle Vicente, A. (2017). An analysis of international coauthorship networks in the supply chain analytics research area. Scientometrics, 111(3), 1703-1731.
• Barnaghi, P., Sheth, A., & Henson, C. (2013). From data to actionable knowledge: big data challenges in the web of things. IEEE Intelligent Systems, (6), 6-11.
• Biswas, S., & Sen, J. (2017). A proposed architecture for big data driven supply chain analytics. arXiv preprint arXiv:1705.04958.
• Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011.
• Chae, B., Olson, D., & Sheu, C. (2014). The impact of supply chain analytics on operational performance: a resource-based view. International Journal of Production Research, 52(16), 4695-4710.
• Chen, J. K., & Chen, I. S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981-1990.
• Chou, S. Y., Chang, Y. H., & Shen, C. Y. (2008). A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1), 132-145.
• Engel, T., Meier, N., & Möller, T. (2017). Proposing A Supply Chain Analytics Reference Model As Performance Enabler.
• Gupta, S., Drave, V. A., Bag, S., & Luo, Z. (2019). Leveraging smart supply chain and information system agility for supply chain flexibility. Information Systems Frontiers, 21(3), 547-564.
• Hoehle, H., Aloysius, J. A., Chan, F., & Venkatesh, V. (2018). Customers’ tolerance for validation in omnichannel retail stores: Enabling logistics and supply chain analytics. The International Journal of Logistics Management, 29(2), 704-722.
• 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.
• Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2019). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics.
• Kumar, R., Singh, R. K., & Shankar, R. (2015). Critical success factors for implementation of supply chain management in Indian small and medium enterprises and their impact on performance. IIMB Management review, 27(2), 92-104.
• Lamba, K., & Singh, S. P. (2018). Modeling big data enablers for operations and supply chain management. The International Journal of Logistics Management, 29(2), 629-658.
• Ngai, E. W. T., Cheng, T. C. E., & Ho, S. S. M. (2004). Critical success factors of web-based supply-chain management systems: an exploratory study. Production Planning & Control, 15(6), 622-630.
• Pontius, N. Top Supply Chain Analytics: 50 Useful Software Solutions and Data Analysis Tools to Gain Valuable Supply Chain Insights, <https://www.camcode.com/asset-tags/topsupply-chain-analytics/>, Accessed in.: July 30, 2019.
• Rozados, I. V., & Tjahjono, B. (2014, December). Big data analytics in supply chain management: Trends and related research. In 6th International Conference on Operations and Supply Chain Management, Bali.
• Souza, G. C. (2014). Supply chain analytics. Business Horizons, 57(5), 595-605. Taghikhah, F., Daniel, J., & Mooney, G. (2017, January). Sustainable Supply Chain Analytics: Grand Challenges and Future Opportunities. In PACIS (p. 44).
• Tiwari, S., Wee, H. M., & Daryanto, Y. (2018). Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115, 319-330.
• Trkman, P., McCormack, K., De Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318-327.
• Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626-639.
• 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, 176, 98-110.
• Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.