Developing a probabilistic decision-making model for reinforced sustainable supplier selection

Abstract

The competitive environment and recent regulations require corporations to implement sustainable and rein-forced solutions in their business operations and, thereby, sustainable supplier selection (SSS) has become a critical concern of companies. This study introduces a neoteric approach by extending the SSS framework containing the three widespread indicators, i.e., economic, social, and environmental sustainability dimensions (S), with additional three genuine aspects such as innovation (I), lean principles (L), and knowledge management (K), namely the S-ILK framework. To deal with probabilistic uncertainty, a novel Monte Carlo (MC) aided hybrid multi-criteria decision analysis model was constructed. MC simulation with Beta-PERT distribution was inte-grated with the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to identify criteria weights and perform supplier evaluations, respectively. Hence, criteria weights and supplier evaluation scores were illustrated as probability density plots instead of crisp values with MC aided decision-making model. The findings emphasized the role of economic sustainability and knowledge management capabilities of suppliers, which require a diligent investigation of life cycle cost of production and quality of knowledge management systems of suppliers. This study contributes to theory by highlighting inter-personal uncertainty through MC simulation and to practice by informing industry professionals about urgent needs for focusing on the innovation, knowledge management, and lean capabilities of suppliers. The proposed S-ILK framework can be regarded as a roadmap for companies to enhance their sustainability performance with innovative solutions, increased data quality, and continuous improvement with lean principles.