Managing risks in supplier selection and order allocation

Abstract

Purpose Supplier Selection (SS) and Order Allocation (OA) are strategic procurement processes crucial for mitigating supply chain uncertainties and potentially becoming a competitive advantage for companies in the mitigation strategies. Most of the previous studies dealing with SS and OA focused on straight rebuy situations, while there is a limited number of studies focusing on modified rebuy and new task situations, where uncertainty is higher, and comparison between historical and new suppliers is needed in a world, where the demand for new, technologically advanced products and services keeps increasing, pushing companies to continuously search for new suppliers. Design/methodology/approach Considering this gap, this paper aims to propose a Multiple-Criteria Decision-Making (MCDM) model to compare new and historical suppliers, with limited knowledge about the new suppliers, using measurable and forecastable decision criteria through a scenario planning approach that considers decision-makers’ different risk attitudes in evaluating suppliers’ performance. The proposed model adopts the Best-Worst Method and a two-stage Linear Programming model. The effectiveness of the model has been tested in a real industrial setting. Findings This model would support companies in their decision-making process to anticipate and address potential risks inherent in SS and OA decisions, thus enhancing supply chain resilience and agility in dynamic market environments. Originality/value The proposed model, requiring minimal computational resources, is accessible to a broad range of companies. It fills a literature gap by enabling comparison between new and historical suppliers in modified rebuy and new task situations, where uncertainty is higher, thereby enhancing supply chain decision-making.