Identifying and Ranking the Enablers in the Supply Chain Using Multi-Criteria Decision-Making Tools

Subject Areas : Urban Transportation

Omid Mehri namakavarani ¹ , Hossein Kazemi ² , Farzin Rezaei ³ , reza Ehtesham rasi ⁴

1 - PhD student, accounting department, Qazvin branch, Islamic Azad University, Qazvin, Iran
2 - Assistant Professor, Accounting Department, Qazvin Branch, Islamic Azad University, Qazvin, Iran. (responsible for correspondence)
3 - Associate Professor, Department of Accounting, Qazvin Branch, Islamic Azad University, Qazvin, Iran
4 - Assistant Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

Received: 2023-02-26 Accepted : 2023-06-11 Published : 2023-08-23

Keywords: Supply Chain, enablers, Weight Production Model, Weight Sum Model,

Abstract :

In the present industrial environment of the country (Iran), the issue of supply chain is among the discussed topics. However, the supply chain in a company will only have a competitive power when having strong and competitive components and an ideal stability as well. The present research is mainly aimed at identifying and ranking the enablers in the smart supply chain through using multi-criteria decision-making tools. This research’s case study includes four Petrochemical companies, Nouri, Jam, Khark, and Pardis during the solar year 1400 (2021-2022). The importance of each of the aforementioned enablers in the smart supply chain was investigated according to the relevant questionnaires; given the ranking of the enablers’ importance in the smart supply chain and in accordance with the experts’ opinions, seven factors were chosen as the key enablers of the smart supply chain, including big data skills and knowledge, appropriate and feasibility studies to help choose and use big data techniques, product tracking and localization, transparency and visibility, electronic supply chain management, data transfer improvement, and effective and cost-effective development, as well as the weight of data integrity technology. At the next stage, these options’ performance in the realization of supply chain enablers was assessed. WASPAS method was the proposed technique to this end, based on which, Khark Petrochemical Company, Nouri Petrochemical Company, Pardis Petrochemical Company, and Jam Petrochemical Company were respectively introduced as the final ranking based on the relevant enabler indicators.