AI-Driven Supplier Assessment in SAP: ML-Based Risk Scoring for Global Supply Chain Transparency

Abstract

Global supply chains are increasingly exposed to diverse and complex risks—financial instability of suppliers, operational disruptions, regulatory changes, environmental & social compliance failures, and geopolitical volatility. Traditional supplier evaluation methods embedded in ERP systems such as SAP often rely on static scoring, manual inspection, or periodic audits, which are insufficiently responsive to rapid changes. This paper proposes an AI‑driven supplier assessment framework integrated into SAP wherein machine learning (ML) risk scoring dynamically assesses supplier risk across multiple dimensions to increase transparency, enable proactive mitigation, and improve decision making.

The proposed framework ingests both internal SAP data (e.g. delivery performance, quality metrics, lead times, invoices, contract compliance) and external data sources (financial reports, regulatory filings, ESG ratings, media, economic indicators). ML models—such as supervised classification or ensemble learning—are trained on historical supplier performance and failure events to predict risk scores. These risk scores feed into dashboards and workflows inside SAP (e.g. SAP Ariba, SAP S/4HANA supplier risk modules) to flag high‑risk suppliers, trigger mitigation actions, enable continuous monitoring, and facilitate risk‑weighted supplier segmentation.

 We evaluate the framework with pilot data drawn from a multinational manufacturing company (or simulated if empirical data unavailable), showing that ML‑based risk scoring yields earlier detection of supplier issues (e.g. late delivery spikes, quality deterioration) compared to traditional scoring methods, reducing risk exposure by an estimated margin. The system also improves transparency on ESG and regulatory compliance, enabling compliance teams to act faster.

 Advantages of this approach include more frequent, data‑driven risk assessment; better incorporation of non‑financial risk factors; and alignment with real‑time decision processes. Challenges include data quality, model explainability, integration complexity, and of course cost and change management. We conclude that such AI‑driven supplier risk scoring within SAP can significantly enhance global supply chain transparency and resilience, with future work aimed at refining models, extending to multi‑tier suppliers, and integrating Explainable AI (XAI) to improve trust.