AI Summary & Key Takeaways
AI InsightAt the end of the study, the suggestions for future studies are presented.
📌 3 Key Takeaways
Özet
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.