Green supplier selection for the steel industry using BWM and fuzzy Topsis

A case study of Khouzestan steel company

Bibliographic Data

ID13135186
AuthorsMahsa Oroojeni Mohammad Javad (0000-0003-1843-3763, American University, corresponding author), Maryam Darvishi (Islamic Azad University, Ahvaz Branch), Arash Oroojeni Mohammad Javad (Islamic Azad University, Ahvaz Branch)
Year2020
Volume2
Pages100012-100012
Publication date2020-01-01
Open AccessYes
TypeARTICLE
VenueSustainable Futures (JOURNAL)
Journal identifiersISSN: 2666-1888
PublisherElsevier BV (PUBLISHER)
DOI10.1016/j.sftr.2020.100012
OpenAlexW3005902236
LanguageEN
Citations received9
References cited40

Green supply chain management increasingly has emerged as an essential approach for many business enterprises and organizations to become environmentally sustainable. Suppliers play an important role in creating a sustainable supply chain. This study aims at selecting suppliers of Khouzestan Steel Company (KSC) based on their green innovation ability. In this research, the Company's alternative suppliers are identified and the most effective criteria for supplier selection based on the supplier's green innovation abilities is determined. The Best-Worst Method is used to rank the various criteria of green supplier selection in the multi-criteria decision-making problem. Then, the Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is employed to rank the various suppliers based on weighted criteria for selecting the most effective suppliers among set of alternative suppliers. This study contributes to finding the key factors of selecting green suppliers for KSC. Analyzing the KSC supplier selection's key factors indicate that the green innovations criteria should be given more attention by KSC in the green supplier selection. The outcome of this research is helpful to rank the suppliers consequently based on their green innovation abilities. The organizations can replicate the proposed supplier selection framework for other suppliers such as spare parts, consumable materials and technical, design, and development services suppliers. Sensitivity analysis is also performed in order to check the robustness of the framework and eliminate the effect of biasness. Limitations of the study along with future research directions are also presented

Business · Industrial organization · Operations research · Order (exchange · Selection (genetic algorithm · Supplier evaluation · Supplier relationship management · Supply chain · Supply chain management · TOPSIS · Computer Science · Engineering · Multi-Criteria Decision Making · Sustainable Supply Chain Management · Marketing

Tools

Open DOISci-HubOpen Access