Optimizing supplier selection and contract management through blockchain-based provenance and AI-driven risk scoring in a global manufacturing supply chain

Project Abstract

Optimizing supplier selection and contract management through blockchain-based provenance and AI-driven risk scoring in a global manufacturing supply chain presents a comprehensive framework that integrates distributed ledger technology, advanced analytics, and procurement best practices to enhance transparency, resilience, and value creation across complex supplier ecosystems. This research investigates how immutable provenance records facilitated by a permissioned blockchain can capture end-to-end material histories, including origin, certifications, sustainability attributes, transit conditions, and quality checks, enabling verifiable compliance and anti-counterfeiting assurances in industries with stringent regulatory requirements. By coupling provenance data with AI-driven risk scoring, the study develops a dynamic, multi-criteria supplier evaluation model that continuously aggregates internal procurement metrics (cost, quality, delivery performance), external signals (geopolitical risk, supplier financial health, macroeconomic indicators), and operational data (inventory levels, lead times, lot traceability) to produce real-time risk-adjusted ranking and contract optimization recommendations. The methodology employs a mixed-methods approach, combining design science research to prototype an integrated platform and empirical testing through a multi-scenario simulation based on historical procurement episodes and live pilot data from select global suppliers. Key components include (i) a secure data model for provenance where hash-linked records ensure tamper-evidence and auditable traceability, (ii) smart contract primitives that automate award decisions, performance-based payments, and renewal terms while enforcing compliance checks, (iii) AI models for anomaly detection, supplier clustering, and predictive risk scoring using ensemble methods and explainable AI techniques to maintain procurement transparency, and (iv) an optimization engine that harmonizes cost, risk, ESG, and contractual risk transfer to generate optimal supplier portfolios and contract structures. The research also examines governance, data privacy, and interoperability challenges across heterogeneous ERP systems, with emphasis on standardization of data schemas and interoperability protocols to enable scalable rollouts. A comparative analysis evaluates traditional supplier selection methods against the proposed blockchain-enabled, AI-augmented approach under scenarios such as supplier default, supply shocks, and regulatory changes. The study measures impact on procurement cycle time, total cost of ownership, supplier collaboration levels, incident response effectiveness, and sustainability metrics. Anticipated contributions include a validated architectural blueprint for an integrated provenance and risk-scoring platform, methodological guidance for implementing smart contracts in procurement, and empirical evidence on performance gains, risk mitigation, and governance improvements in global manufacturing supply chains. The work concludes with recommendations for practitioners on deployment strategies, change management, and metrics for ongoing ROI assessment, as well as avenues for future research in cross-domain data fusion, ethics of AI in procurement decision-making, and resilience-oriented supply network design.

Project Overview

What This Project Is About

---

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

---

---

The Problem It Addresses

---

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

---

---

Objectives of the Project

---

1. Identify how supplier choices affect cost, quality, and risk in a global supply chain.
2. Explore how blockchain can provide transparent provenance of goods from supplier to buyer.
3. Understand how AI-driven risk scoring can flag potential supplier issues early.
4. Design a decision framework that combines provenance data and risk scores for supplier selection and contract management.
5. Prototype a simple, user-friendly model or dashboard to support procurement decisions.

---

---

What You Will Do Step by Step

---

1. Review basic concepts in purchasing, contracts, and supply chain risk.
2. Collect or simulate data on suppliers, contracts, and performance metrics.
3. Study blockchain basics and how it records provenance data.
4. Learn a straightforward AI approach to scoring supplier risk (without heavy math).
5. Build a simple data model linking provenance with risk scores.
6. Create a basic prototype tool or dashboard for supplier selection and contract decisions.

---

---

Expected Outcome

A clear, easy-to-use framework and a small prototype showing how provenance and risk scoring can improve supplier decisions and contract management.