Sustainability-Driven Supplier Selection: Insights from Supplier Life Value and Z-Numbers

Abstract

In recent years, the strategic selection of the most suitable supplier within the supply chain has garnered increasing attention. Incorporating vital criteria like sustainable development further complicates this decision-making process. Companies and manufacturing facilities recognize the pivotal role of suppliers in their overall success and aim for mutually advantageous partnerships. Establishing long-term relationships with suppliers can yield benefits for both parties. However, supplier selection is intricate, often transpiring within an environment of limited information. Consequently, evaluating and selecting organizational suppliers necessitate methodologies yielding more dependable and pragmatic results due to the uncertainties inherent in expert judgments. This study introduces Supplier Life Cycle Value (SLV) criteria for extended partnerships with suppliers and sustainability metrics for selecting “industrial equipment suppliers”. The Hierarchical Best-Worst Method (HBWM) is then applied to determine Sustainable Supplier Life Value (SSLV) criteria weights. Subsequently, employing the PROMETHEE-GAIA approach, suppliers are systematically ranked and comprehensively analyzed. To account for the inherent uncertainty in expert judgments, this study incorporates fuzzy numbers enriched with probability and reliability parameters (Z-Numbers) by introducing novel verbal spectra for supplier evaluation. This facilitates more effective decision making in supplier management. The findings underscore the significance of considering the supplier’s longevity beyond economic metrics, emphasizing the importance of sustained supplier participation. Moreover, the varying outcomes across definite and fuzzy scenarios, accounting for reliability (Z-Numbers), underscore the impact of data uncertainty on decision making. Given that fuzzy numbers incorporating reliability (Z-Numbers) encompass the confidence probability within the unclear number, they offer a more robust and realistic representation of real-world scenarios.