In today's global environment, supplier selection is one of the critical strategic decisions made by supply chain management. The supplier selection process involves the evaluation of suppliers based on several criteria, including their core capabilities, price offerings, lead times, geographical proximity, data collection sensor networks, and associated risks. The ubiquitous presence of internet of things (IoT) sensors at different levels of supply chains can result in risks that cascade to the upstream end of the supply chain, making it imperative to implement a systematic supplier selection methodology. This research proposes a combinatorial approach for risk assessment in supplier selection using the failure mode effect analysis (FMEA) with hybrid analytic hierarchy process (AHP) and the preference ranking organization method for enrichment evaluation (PROMETHEE). The FMEA is used to identify the failure modes based on a set of supplier criteria. The AHP is implemented to determine the global weights for each criterion, and PROMETHEE is used to prioritize the optimal supplier based on the lowest supply chain risk. The integration of multicriteria decision making (MCDM) methods overcomes the shortcomings of the traditional FMEA and enhances the precision of prioritizing the risk priority numbers (RPN). A case study is presented to validate the combinatorial model. The outcomes indicate that suppliers were evaluated more effectively based on company chosen criteria to select a low-risk supplier over the traditional FMEA approach. This research establishes a foundation for the application of multicriteria decision-making methodology for unbiased prioritization of critical supplier selection criteria and evaluation of different supply chain suppliers.