Technology Selection for Logistics and Supply Chain Management by the Extended Intuitionistic Fuzzy TOPSIS

Abstract

Logistics and Supply Chains Management have yet to leverage the power of digitalization the way the other industries do. Satisfying the needs of nowadays’ ever-more-demanding consumers requires a more responsive, active, and visible logistics and supply chain that performs a quick exchange of data by novel technologies, e.g., cloud computing, big data, and internet of things. Digital transformation in logistics and supply chains is a novel phenomenon to define consumer-centric thinking to capture and maximize the utilization of real-time data in order to have optimized performance. Utilization of digital technology enablers (e.g., Big data (BD), Internet of Things (IoT), Cloud Computing (CC), etc.) can assist in generating better planning strategies by gathering, verifying, and analyzing real-time data for real-world problems. As opposed to the linear supply chains, digitalization can now take advantage of technologies to make sense of complex information in a connected world with shared pools of configurable system resources. Digital technology enablers can now collect, analyze, and convert such data into understandable reports that can provide logistics and supply chains with valuable insights, which in turn reduce costs and drives profits. In this study, the best advanced analytical software for logistics and supply chain management in the current market are explored. Their features and functionalities are discussed in detail, and the best candidate is selected by an MCDM approach based on The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) under Intuitionistic fuzzy (IF) environment. That is, a technology selection system is considered where the assessment of software is performed in a Group Decision Making (GDM) setting. A practical study is presented to demonstrate the potential of the methodology and validate the outcome.