Strategic taxonomy of supply chain sustainability in manufacturing companies: evidence from Iran

Hossein Alaee Kakhki, Amir Mohammad Fakoor Saghih and Alireza Pooya

International Journal of Logistics Systems and Management, 2026, vol. 53, issue 1, 69-92

Abstract: The present study was conducted with the aim of clustering manufacturing companies based on the indicators affecting the supply chain sustainability. For this purpose, 496 companies active in the North Eastern Iran were clustered using support vector machine, artificial neural network and electromagnetic methods considering the indicators affecting the supply chain sustainability, and the best clustering method was determined. Next, the discriminant function was extracted and the results were analysed in order to discriminate between the dominant groups. Based on the obtained results, the studied manufacturing companies can be grouped into two dominant strategic categories of sustainable and unsustainable, so that 65% of the studied population is classified in the unsustainable group, with a score of 2.78 in the environmental dimension that puts them in an unfavourable situation. Finally, the results of this study, in addition to evaluating the company's performance in the field of sustainability, help managers in formulating appropriate strategies to improve the level of supply chain sustainability.

Keywords: supply chain sustainability; taxonomy; support vector machine; SVM; artificial neural network; electromagnetic; multilayer perceptron neural network. (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijlsma:v:53:y:2026:i:1:p:69-92

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