Journal of Quality Engineering and Management

Abstract Purpose: In today's competitive environment, selecting suitable suppliers plays a pivotal role in enhancing the efficiency and sustainability of supply chains. The LARG supply chain model, as a comprehensive approach, integrates various dimensions in supplier management. However, despite its critical influence on interaction success and supplier selection, the cultural dimension has received limited attention. This study aims to evaluate suppliers within the LARG framework while incorporating the cultural dimension to improve supply chain performance and achieve sustainable competitive advantage.
Methodology: This research developed a data-driven and forward-looking decision-making model for supplier evaluation and selection. First, key criteria were identified through literature review and expert consultation, and weighted using the Fuzzy Best–Worst Method (FBWM). Then, suppliers' efficiency was assessed and ranked using Fuzzy Data Envelopment Analysis (FDEA). Subsequently, the Random Forest algorithm was employed to predict future supplier performance, yielding highly accurate results.
Findings: The initial results highlighted the significance of sub-criteria such as greenhouse gas emission reduction, risk management, quality, and delivery speed in supplier evaluation. In the second phase, supplier efficiency was analyzed under various α-cuts and classified into three performance groups: high, medium, and low. The Random Forest model demonstrated high accuracy in forecasting supplier performance. Moreover, the paired t-test results revealed that incorporating the cultural dimension significantly improves the supplier selection process.
Originality/Value: The proposed model contributes to strategic decision-making by identifying key performance factors, enabling predictive evaluation, and employing robust analytical tools. This approach not only reduces risks and costs but also serves as a practical model for improving supply chain performance in similar industries.

Abstract Purpose: In light of the growing challenges in today's supply chains, including market fluctuations, increasing environmental and social pressures, and the need to enhance resilience against foreseeable crises such as the COVID-19 pandemic and economic disruptions, the strategic importance of selecting suppliers that simultaneously meet sustainability and resilience criteria has become more prominent. Accordingly, the main objective of this study is to present a comprehensive, data-driven, and forward-looking decision-making model for evaluating and selecting suppliers within the supply chain, accounting for multiple dimensions of sustainability and resilience simultaneously.
Methodology: In the proposed model, the weights of the defined criteria and sub-criteria were initially determined using the Stochastic Best-Worst Method (SBWM). Supplier performance was then evaluated using the Stochastic VIKOR Multi-Criteria Decision-Making (MCDM) method. In the final stage, the Random Forest regression algorithm was applied to predict future supplier performance. The model was tested through a case study conducted at SAIPA Kashan Automotive Company using expert input collected via structured questionnaires.
Findings: Sustainability and resilience criteria play a central role in supplier selection in the automotive industry. Among the sub-criteria, "greenhouse gas emissions" and "energy consumption reduction" were most influential due to environmental regulations. At the same time, "cost" and "safety stock level" had the greatest impact due to their direct effect on economic performance and operational continuity. Furthermore, the Random Forest algorithm achieved high predictive accuracy (RMSE = 0.0976), confirming the model's ability to generate reliable, data-driven forecasts.
Originality/Value: Although each of the methods used in this research (Random Best-Worst Method, Random VIKOR, and Random Forest algorithm) has been employed individually in previous studies, the main innovation of this study lies in presenting an integrated framework that combines all three approaches. In fact, this research is the first to merge MCDM methods with a machine learning algorithm, offering a comprehensive, data-driven decision-making model. This model not only assesses the current performance of suppliers but also enables prediction of their future performance. Such a combination has not previously been introduced in the supplier selection literature with a simultaneous focus on supply chain sustainability and resilience in the automotive industry, marking a clear methodological innovation.