Design and Implementation of a Strategic Supplier Segmentation Framework with Evaluation

Thesis Abstract

The study addresses the persistent inefficiencies in supplier management within manufacturing procurement by developing a strategic supplier segmentation framework that aligns supplier categories with organizational value creation, risk exposure, and performance potential. Despite widespread use of supplier segmentation in practice, there is limited empirical evidence on a comprehensive framework that integrates multi-criteria segmentation, dynamic prioritization, and an evaluative mechanism to monitor ongoing performance. The aim is to design, implement, and evaluate a practical framework that enables procurement teams to classify suppliers into strategic, preferred, transactional, and bottleneck segments, and to calibrate sourcing strategies, risk controls, and supplier development programs accordingly. The specific objectives are (1) to identify critical segmentation criteria across cost, quality, delivery, innovation, and risk dimensions through a mixed-methods approach; (2) to develop a composite scoring model and governance process that weightings reflect organizational strategy and product portfolio complexity; (3) to implement the framework in a mid-size manufacturing firm with 120 suppliers over 12 months and track segment transitions; (4) to evaluate the framework’s effectiveness using pre- and post-implementation performance indicators including cost savings, lead time, defect rate, supplier on-time delivery, and risk exposure; and (5) to derive robust recommendations for scaling the framework across similar industrial contexts. The methodology combines design science and empirical evaluation. A mixed-methods research design is employed, beginning with a qualitative phase using 25 semi-structured interviews with procurement managers, category leads, and supply risk analysts, complemented by 8 focus groups with supplier development teams, to identify segmentation criteria and functional requirements. This is followed by a quantitative phase that constructs a weighted multi-criteria decision analysis (MCDA) model, validated through expert elicitation and back-testing on historical data from 180 active suppliers. The population comprises all suppliers of a regional manufacturing group in the automotive components sector, with a purposive sample of 60 key suppliers for the implementation pilot. Data collection instruments include a structured survey instrument for quantitative scoring, procurement performance databases for historical and current metrics, and a process log for implementation activities. Validity and reliability are addressed through instrument triangulation, Cronbach’s alpha testing for internal consistency (targeting ? ? 0.80), and pilot testing with three procurement teams prior to full deployment. Data analysis employs factor analysis to verify segmentation criteria, analytic hierarchy process (AHP) to derive weights, and the MCDA method ELECTRE-like outranking to assign suppliers to segments. An interrupted time-series analysis assesses performance changes across 12 months pre- and post-implementation, while multiple regression models evaluate the relationship between segmentation quality and key outcomes such as total cost of ownership (TCO) and supplier delivery performance. Sensitivity analyses test the robustness of segment definitions under varying weight scenarios. Key expected findings include (i) a validated set of segmentation criteria that capture cost, quality, delivery reliability, innovation potential, and risk exposure; (ii) a defensible weighting schema revealing organizational priorities and product portfolio complexity; (iii) measurable improvements in TCO, reduction in average lead time, lower defect rates, and enhanced supplier on-time delivery within strategic and preferred segments; and (iv) a demonstrable reduction in supply risk exposure through targeted supplier development and contract risk mitigation measures. The study contributes to knowledge by operationalizing a holistic supplier segmentation framework that integrates MCDA-based segmentation with a governance process and an evaluative feedback loop, bridging gaps between theoretical models and practical procurement implementation. It advances theory by linking segmentation theory with supply network resilience and dynamic sourcing strategies, drawing on agency and transaction cost economics to explain segment-specific behaviors. The practical implications include a scalable framework for mid-market manufacturers seeking to optimize supplier portfolio, improve category strategy alignment, and institutionalize continuous improvement in supplier performance. The main conclusion is that a structured, data-driven supplier segmentation framework, implemented with rigorous weighting, governance, and continuous monitoring, yields sustained performance gains and resilience. Recommendations emphasize organizational alignment of procurement objectives, investment in data infrastructure and supplier development capabilities, periodic revalidation of segmentation criteria, and extension of the framework to multi-site operations and supplier co-innovation programs.