Background: The supplier selection process in high-risk industries is extremely important to ensure suppliers are chosen to fulfil the requirements of safety and compliance, and to mitigate any potential reputational damage. Poor supplier selection can lead to just such an occurrence, as demonstrated by some of the previous high-profile cases we examine. Supplier evaluation in high-risk industries such as aviation, oil & gas, and construction is challenging due to the inherent complexity and uncertainty associated with each supplier, and therefore requires a methodical and transparent approach in providing decision support for the selection of suppliers to do business with.
Methods: In this study a systematic review of the existing literature, along with a comparative analytical methodology, is used to evaluate the current use of multi-criteria decision-making techniques used for supplier selection in these three industries. The study includes an analysis of common established supplier evaluation techniques (e.g. Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIKOR, ELECTRE, PROMETHEE and hybrid and fuzzy-based extensions), all of which focus on addressing uncertainty and incomplete data. We then conclude the study by developing a methodology to evaluate carefully selected supplier evaluation criteria, weighting those criteria based on the criteria identified, and developing an integrated conceptual framework (including sensitivity analysis) that demonstrates the effects of varying the evaluation criteria on the outcome of the evaluation process.
Results: The results show that there is no one MCDM method that works best for choosing suppliers in high-risk industrial settings. The appropriateness of a method depends on how complicated the decision is, how much data is available, how uncertain things are, and the goals of the organization. When choosing aviation suppliers, regulatory certification, safety compliance, and traceability are the most important factors. When choosing oil and gas suppliers, operational risk management, environmental compliance, and reliability are the most important factors. When choosing construction suppliers, cost efficiency, schedule adherence, and delivery performance are the most important factors. Hybrid and fuzzy MCDM models show that they are better at dealing with qualitative criteria and decision-making situations that are not clear.
Conclusion: The study finds that MCDM frameworks that are specific to a situation and can change are necessary for choosing suppliers in high-risk industries. The suggested integrated framework makes decisions more clear and strong, which helps to ensure fair procurement results. Future research ought to investigate the amalgamation of machine learning, real-time data analytics, and digital twin technologies to enhance MCDM-based supplier selection systems.