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

Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Hossein Ahmadi; Mesfer Alrizq; Hamad Abosaq; Abdullah Alghamdi; Murtaza Farooque; Syed Salman Mahmood; Mehrbakhsh Nilashi; Rabab Ali Abumalloh; Hossein Ahmadi; Mesfer Alrizq; Hamad Abosaq; Abdullah Alghamdi; Murtaza Farooque; Syed Salman Mahmood

doi:10.3934/environsci.2024008

AIMS Environmental Science

2024, Volume 11, Issue 2: 129-156. doi: 10.3934/environsci.2024008

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Research article

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

1.
UCSI Graduate Business School, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia
2.
Centre for Global Sustainability Studies (CGSS), Universiti Sains Malaysia, 11800 Penang, Malaysia
3.
Department of Computer Science and Engineering, Qatar University, Doha 2713, Qatar
4.
Centre for Health Technology, Faculty of Health, University of Plymouth, Plymouth PL4 8AA, UK
5.
Information Systems Dept., College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
6.
Scientific and Engineering Research Center (SERC), Najran University, Najran, Saudi Arabia
7.
Computer Science Dept. College of Computer Science and Information Systems, Najran University, Najran, Saudi Arabia
8.
Information Systems Dept., College of Computer Science and Information Systems, Najran Univesity, Najran, Saudi Arabia
9.
Department of MIS, Dhofar University, Salalah, Oman
10.
Department of Mathematics & Statistics, Colleges of Arts & Sciences, Abu Dhabi University, United Arab Emirates

Received: 28 October 2023 Revised: 19 December 2023 Accepted: 07 February 2024 Published: 29 March 2024

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.
- fuzzy logic,
- DEMATEL,
- electric vehicles,
- supply chain performance,
- SCOR metrics
Citation: Mehrbakhsh Nilashi, Rabab Ali Abumalloh, Hossein Ahmadi, Mesfer Alrizq, Hamad Abosaq, Abdullah Alghamdi, Murtaza Farooque, Syed Salman Mahmood. Using DEMATEL, clustering, and fuzzy logic for supply chain evaluation of electric vehicles: A SCOR model[J]. AIMS Environmental Science, 2024, 11(2): 129-156. doi: 10.3934/environsci.2024008

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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.

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