EVALUATION OF SUPPLY CHAIN ANALYTICS WITH AN INTEGRATED FUZZY MCDM APPROACH

Year 2019, BEYKOZ AKADEMİ 2019 ÖZEL SAYI, 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

Abstract

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.

Keywords

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

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