Using DEMATEL, clustering, and fuzzy logic for supply chain evaluation of electric vehicles: A SCOR model

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Abstract

The transportation sector is considered among the major sources of greenhouse gas emissions. Given advancements in transportation technology, customers’ willingness to reduce carbon footprints, as well as policy incentives, Electric Vehicles (EVs) are becoming an increasingly important part of the passenger vehicle industry. Evaluation of Supply Chain (SC) performance in the EV industry seems to contribute significantly to the enhancement of the operational consequences across the supply chain tiers. The SCOR (Supply Chain Operations Reference) model was designed to help businesses optimize their supply chain operations, reduce costs, and improve customer satisfaction. Although many performance measurement models have been developed in the context of SC, there is no performance measurement model in relation to the EV supply chain based on indicators of customer perceived value (Reliability, Responsiveness and Agility) in the SCOR model. Therefore, we aimed to develop a new method to evaluate the performance of the EV supply chain using a set of critical SC performance evaluation indicators. Multi-criteria decision-making along with machine learning was used in order to develop a new method for evaluating SC performance. We used k-means clustering and fuzzy logic approaches in the development of the new method. An assessment of indicators’ importance level was performed using the fuzzy logic approach. The results of the method evaluation show that the proposed method is capable of predicting the performance of the EV supply chain accurately. According to the results, by optimizing their supply chain, companies can improve their ability to deliver products and services that meet or exceed customer expectations, resulting in higher customer perceived value and customer satisfaction.

Keywords

fuzzy logic / DEMATEL / electric vehicles / supply chain performance / SCOR metrics

CHANGE HISTORY

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