article · Results in Engineering
Global automotive supply chains experience disruptions from geopolitical instability, intense competition, and increasing environmental and regulatory pressures. Addressing these challenges requires integrating sustainability and resilience directly into how organisations evaluate and choose suppliers. A structured methodology has been developed to improve supplier selection by combining multi-criteria decision-making with machine learning tools. Following a comprehensive literature review, key selection criteria were validated through a survey of automotive industry experts. The resulting framework applies a Subjective Weighting Method based on survey findings to rank prospective suppliers. It then compares these outcomes against results from machine learning techniques, specifically K-means clustering and RankNet. This integrated, data-driven methodology provides automotive companies with a replicable decision-making process adaptable to diverse commodity categories.
Modern vehicle manufacturing depends heavily on reliable, environmentally sound sourcing. Disruptions or unsustainable vendor practices can halt production lines and breach regulatory standards. By offering an integrated framework to evaluate suppliers on both resilience and sustainability metrics, this approach helps automotive companies make balanced, objective procurement choices that mitigate operational risks.
This methodology targets procurement teams and supply chain managers within the automotive industry seeking to standardise sustainable sourcing. As an analytical framework tested on expert survey data and algorithmic models, it represents applied research rather than an off-the-shelf software tool. Practical adoption would require integrating these multi-criteria and machine learning algorithms into enterprise resource planning or vendor management systems.
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The supplier selection field is a crucial part of the automotive supply chain. Despite its importance, it faces several challenges, such as geopolitical instability, intense competition among organizations, and regulatory and environmental pressures. In this context, it becomes essential to integrate sustainability and resilience considerations into the supplier selection phase. This integration is primarily achieved through sourcing activities, which directly influence overall supply chain performance. In this article, we present a clear methodology that can be used by companies for sustainable supplier selection. This methodology is based on a comprehensive literature review of supplier selection criteria and methods. Building on this theoretical foundation, a survey was conducted with experts from the automotive industry to validate the identified criteria. Based on this theoretical and practical groundwork, we collected data and developed a framework for supplier selection within the automotive supply chain. Supplier selection is recognized as a Multi-Criteria Decision-Making problem. To address this, we apply Subjective Weighting Method SWM with Survey results to rank suppliers. To enhance our analysis, we compare the results obtained from SWM with those generated by Machine Learning techniques, specifically K-means clustering and RankNet. The primary objective of this research is to propose and validate an integrated decision-making framework that enhances sustainable and resilient supplier selection in the automotive sector. Secondary objectives include identifying the most critical supplier selection criteria through expert validation, assessing the consistency and complementarity of SWM and Machine Learning outputs, and providing practitioners with a replicable, data-driven methodology adaptable to various automotive commodities.
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DOI: 10.1016/j.rineng.2026.112588
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