Optimizing supplier selection and procurement analytics for sustainable value creation in a multi-criteria decision-making framework

Project Abstract

This study develops an integrated framework for optimizing supplier selection and procurement analytics to create sustainable value through a multi-criteria decision-making (MCDM) approach that combines operational efficiency, environmental stewardship, and social responsibility. We address the growing need for procurement strategies that balance cost, risk, quality, lead time, supplier reliability, and sustainability outcomes in complex supply networks. The research advances a hybrid decision model that fuses quantitative analytics with qualitative assessments, leveraging techniques such as weighted multi-criteria optimization, analytic hierarchy process (AHP), ELECTRE, and data-driven supplier performance scoring. We further introduce a procurement analytics platform that ingests internal procurement data, supplier performance metrics, and external sustainability indicators (carbon footprint, resource usage, labor practices) to generate actionable insights for supplier selection, contract design, and risk mitigation. The methodology adopts a mixed-methods design anchored in a robust data collection plan comprising a systematic literature review, case studies across manufacturing and services sectors, and empirical validation using real-world procurement datasets from partner organizations. We build a comprehensive criteria framework that encompasses cost efficiency, total cost of ownership (TCO), quality and compliance, delivery and flexibility, innovation capability, and sustainability metrics such as emissions, circularity, and ethical governance. The optimization model integrates a multi-objective function to maximize overall value, subject to constraints on budget, capacity, lead times, and regulatory requirements. We implement scenario analysis and sensitivity testing to examine trade-offs among competing objectives under varying market conditions, supplier risk profiles, and policy changes. Key contributions include (1) a formalized, scalable MCDM-based optimization framework for supplier selection that embeds lifecycle value creation rather than isolated cost minimization, (2) a procurement analytics architecture enabling real-time performance monitoring, anomaly detection, and predictive insights for proactive supplier management, (3) a decision-support tool that supports negotiation and contract design with sustainability criteria embedded in supplier scoring and risk assessment, and (4) empirical evidence of improved value realization through sustainable procurement practices, demonstrated by reductions in total costs, lead-time variability, and environmental impact in pilot implementations. The study also investigates organizational enablers and barriers to adopting sustainable supplier selection, including data governance, cross-functional collaboration, supplier relationship management, and change management. Validation is conducted through comparative analysis against traditional procurement approaches, with performance metrics including value creation index, supplier reliability, sustainability scores, and risk-adjusted outcomes. The findings reveal that integrating procurement analytics with a rigorous MCDM framework enhances decision transparency, speeds up supplier selection cycles, and yields measurable improvements in environmental and social performance without compromising financial objectives. The research concludes with practical guidelines for practitioners and a roadmap for future enhancements, including integration with enterprise resource planning (ERP) and supply chain risk platforms, as well as potential extensions to dynamic, real-time optimization in volatile market environments.

Project Overview

What This Project Is About

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A straightforward study that looks at how companies choose suppliers and use data to buy things more efficiently and ethically. It explores how different factors like cost, quality, delivery speed, risk, and sustainability can be considered together to make better purchasing decisions.

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The Problem It Addresses

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Many organizations rely on quick, single-factor decisions (e.g., cheapest price) that can lead to risky supply chains or higher long-term costs. This project seeks to balance multiple needs to avoid supply disruptions, reduce waste, and promote responsible sourcing.

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Objectives of the Project

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1. Identify key criteria used in supplier selection beyond price (quality, reliability, sustainability).
2. Explore how procurement analytics can improve decision accuracy.
3. Develop a simple framework to compare suppliers using multiple factors.
4. Demonstrate how to incorporate risk and environmental considerations into choices.
5. Provide practical guidelines for implementing the framework in organizations.

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What You Will Do Step by Step

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1) Review basic supplier selection ideas; 2) Gather sample data or use a dataset; 3) Define criteria and weights in an easy-to-understand way; 4) Apply a simple multi-criteria method to rank suppliers; 5) Analyze results and discuss implications for sustainability; 6) Propose simple tools or dashboards to support decisions.

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Expected Outcome

Clear, practical guidelines for selecting suppliers using multiple factors, plus a small example showing how to apply the method. The project should help managers make more resilient, cost-effective, and environmentally friendly procurement choices.