Design and Assessment of a Sustainable Supplier Evaluation Framework

Thesis Abstract

In the context of increasing global emphasis on sustainability and responsible supply chain management, traditional supplier evaluation frameworks have proven insufficient in capturing environmental, social, and economic dimensions essential for sustainable procurement practices. This study aims to design and empirically assess an integrated Sustainable Supplier Evaluation Framework (SSEF) that effectively incorporates key sustainability indicators to facilitate transparency and informed decision-making among supply chain stakeholders. The primary objectives are to identify relevant sustainability criteria, develop a composite evaluation model, and validate its effectiveness in real-world settings. The research adopts a mixed-methods approach, combining qualitative exploratory methods with quantitative validation. The qualitative phase involves semi-structured interviews with twenty-five procurement managers, sustainability officers, and industry experts from manufacturing firms operating in the metropolitan region, to gather insights on current evaluation practices and sustainability priorities. The quantitative phase entails a survey administered to 150 procurement professionals selected through stratified random sampling from a comprehensive database of manufacturing companies with annual procurement expenditures exceeding $10 million. Data collection instruments comprise a structured questionnaire based on an initial sustainability criteria framework and complemented by document analysis of corporate sustainability reports. The quantitative data are analyzed using exploratory factor analysis (EFA) to refine and validate the core sustainability criteria, while Confirmatory Factor Analysis (CFA) assesses the construct validity of the evaluation framework. Subsequently, the study employs multi-criteria decision-making techniques, such as Analytic Hierarchy Process (AHP), to weight the identified criteria and develop a composite scoring model. Regression analysis tests the model's predictive power concerning supplier performance outcomes, while cluster analysis categorizes suppliers into sustainability tiers. The theoretical underpinning draws on stakeholder theory and the triple bottom line framework, which collectively emphasize multi-stakeholder value and holistic sustainability performance. Expected findings suggest the identification of a set of critical sustainability indicators spanning environmental, social, and economic domains that significantly influence supplier selection decisions. The evaluation framework is anticipated to demonstrate enhanced accuracy and transparency in supplier assessments, facilitating better integration of sustainability priorities into procurement decisions. The model is also expected to reveal correlations between high sustainability scores and improved supplier performance metrics such as innovation, compliance, and social responsibility. This research contributes novel insights by operationalizing sustainability assessment within a comprehensive, empirically validated evaluation model tailored for manufacturing sectors. It advances existing procurement frameworks by integrating sustainability indicators explicitly and providing a practical decision-support tool validated through stakeholder engagement and statistical rigor. The study's findings will inform procurement policies and foster sustainable supplier relationships, addressing a critical gap in current supply chain management literature. The main conclusion underscores the importance of a structured, science-based approach to supplier sustainability assessment in promoting responsible sourcing. Recommendations include adopting the SSEF for routine supplier evaluations, integrating the framework into procurement software systems, and conducting periodic reviews to align with evolving sustainability standards. Furthermore, the study advocates for further research to adapt the framework across different industries and geographic contexts, enhancing its generalizability and impact on sustainable supply chain practices.