Optimizing supplier selection and contract management through a data-driven multi-criteria decision framework for sustainable procurement in manufacturing firms

Project Abstract

In today’s manufacturing sector, the complexity of supply networks and the growing emphasis on sustainability demand advanced, data-driven approaches to supplier selection and contract management that balance cost, risk, quality, and environmental impact. This study presents a comprehensive framework that integrates multi-criteria decision analysis (MCDA) with real-time data analytics to optimize supplier choice and contract governance for sustainable procurement. We develop a methodology that combines weighted additive models with machine learning techniques to quantify supplier capabilities, reliability, and ESG performance, while incorporating contract variables such as pricing volatility, service levels, and termination risk. A novel scoring mechanism is introduced to harmonize qualitative and quantitative criteria, enabling decision-makers to compare potential suppliers across a unified, transparent index. The framework leverages a modular data architecture that ingests structured and unstructured data from internal procurement systems, supplier questionnaires, third-party sustainability audits, and external market signals. We implement a hybrid decision-making process that includes (i) a criteria elicitation phase with stakeholder workshops to capture strategic goals and risk tolerances, (ii) a dynamic weighting scheme that adapts to changing market conditions and corporate sustainability targets, (iii) a robust supplier ranking procedure using techniques such as TOPSIS, ELECTRE, and multi-objective optimization to identify Pareto-optimal supplier sets, and (iv) a contract optimization module that negotiates terms, volume commitments, and performance-based incentives aligned with sustainability KPIs. To ensure resilience, the model incorporates scenario analysis and sensitivity testing to explore the impact of disruptions, supplier failures, and regulatory shifts on procurement outcomes. The empirical validation employs a case study in a mid-to-large scale manufacturing firm, engaging multiple tiers of suppliers across components with varying ESG footprints. Data from procurement records, supplier audits, and emissions inventories are preprocessed to address missing values, biases, and temporal alignment. Model performance is evaluated through accuracy of supplier rankings, stability of selections under perturbations, and realized gains in total cost of ownership (TCO) linked to sustainability improvements. The results demonstrate that the data-driven MCDA framework yields more sustainable and cost-effective supplier portfolios than traditional lowest-cost strategies, with notable improvements in supplier reliability, ESG compliance, and contract performance. Sensitivity analyses reveal that integrating ESG scoring and dynamic risk weighting significantly shifts supplier rankings toward more resilient and ethically aligned partners, while the contract optimization component reduces variability in total spend and enhances value delivery through performance-based incentives. The framework supports decision-makers with an auditable, explainable process that facilitates governance and compliance across procurement activities. Management implications highlight the importance of data governance, cross-functional collaboration, and continuous improvement loops. The study contributes to the procurement literature by offering a replicable, adaptable model for sustainable supplier selection and contract management, and provides practitioners with actionable guidance for implementing data-driven, multi-criteria decision frameworks in manufacturing environments.

Project Overview

What This Project Is About

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A plain-language overview of the topic and what the project investigates.

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The Problem It Addresses

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What problem or gap this project tackles and why it matters to the field or society.

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Objectives of the Project

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1. Identify key factors that influence supplier choice and contract terms.
2. Develop a simple decision framework that combines different criteria into one recommendation.
3. Assess how sustainability goals can be integrated into supplier selection.
4. Provide practical steps for improved contract management in manufacturing settings.

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What You Will Do Step by Step

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1. Review current supplier selection and contract practices in a chosen manufacturing firm.
2. List criteria that matter for cost, quality, delivery, risk, and sustainability.
3. Collect data from company records or interviews about suppliers and contracts.
4. Build a simple, user-friendly framework (no heavy math) to score suppliers on criteria.
5. Test the framework with a real or sample set of suppliers to see which ones come out preferred.
6. Analyze how sustainability goals are reflected in the results and propose improvements.
7. Summarize findings and provide actionable recommendations for procurement teams.

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Expected Outcome

Expect a practical, easy-to-use guide that helps buyers choose suppliers and manage contracts more effectively while aligning with sustainability goals.