Investigating the Internet-of-Things (IoT) risks for supply chain management using q-rung orthopair fuzzy-SWARA-ARAS framework

Abstract

Modern “Supply Chains (SCs)” have recently been introduced as value networks of high complexity, and firms have focused on its efficiency as an important support for staying competitive in the market. Firms are currently capable of observing, tracking, and monitoring their products, activities, and processes throughout their value chain networks using new technologies, namely the “Internet of Things (IoT)”. Though, the influencing factors of IoT are highly complex and diverse, which result in the information-intensiveness of the SCs processes. This, in turn, leads to lots of barriers to SCs. In this paper, we evaluate and rank the IoT risks for “Supply Chain Management (SCM)” by utilizing “Stepwise Weight Assessment Ratio Analysis (SWARA)” and “Additive Ratio Assessment (ARAS)” under “q-Rung Orthopair Fuzzy Sets (q-ROFSs)”. A case study is presented for investigating the IoT risks for SCM in the q-ROFSs setting. Moreover, the obtained results were compared to those of some methods currently used in the literature. The outcomes of the study show that the security and privacy risks with a weight value of 0.0572 is the main IoT risk factor for the SCM and the organization-I with the utility degree 0.8208 is the best option with diverse IoT risks for SCM. First published online 25 April 2022

Keywords

• Internet of Things (IoT)
• supply chain
• q-rung orthopair fuzzy sets
• SWARA
• multi-criteria decision-making
• ARAS