Managing Supplier Selection: Strategic And Collaborative Alignment To Engineering Requirements From A Global Perspective

ABSTRACTMANAGING SUPPLIER SELECTION: STRATEGIC AND COLLABORATIVE ALIGNMENT TO ENGINEERING REQUIREMENTS FROM A GLOBAL PERSPECTIVE by PASCHAL AGUWA May 2022 Advisor: Dr. Leslie MonplaisirMajor: Industrial Engineering Degree: Doctor of Philosophy Most global manufacturing companies are faced with increasing competitiveness and dynamic market challenges, inadvertently putting strain on the overall product development process. One of the strained processes is the supplier selection process where there exist inadequate strategic and collaborative partnerships. The resultant effect of these challenges is the non-compliance to the product requirements leading to sub-standard products, and high warranty cost. In this dissertation, we investigated the real-life practical factors that enhanced the robust alignment of the product to the engineering requirements, identified unaccounted product complexity impacts, cultural impediments, and incorporated the practitioner’s engagement in the final decisions during the supplier selection process. The research was based on a qualitative research method that is structured into “Knowledge acquisition” and the “Interpretive analysis/criteria formula” phases to obtain meaningful input, identify patterns, network links to establish relationships between the codes, and formulation of the relevant assessment criteria. Combined multi-criteria decision method (MCDM) comprising of Analytical Hierarchical method (AHP), and the Technique for Order of Preference by similarity to ideal solution (TOPSIS) were deployed to capture the relative comparison on criteria weight, and the determination of performance score of the preferred supplier. The findings supported the key propositions that multi-practitioner engagement is positively related to a successful supplier outcome and the supplier’s technical and cultural awareness positively impact the robust alignment with engineering requirements. Further, the proposed framework reduces the effect of bias and uncertainty in the supplier selection process and provide quantifiable data to support management decision