Cambridge
Academy
In London

Introduction

Sustainable sourcing requires strong visibility into supplier practices, ESG risks, and compliance obligations—often across complex, global supply networks. AI can accelerate supplier ESG scoring, detect emerging risks early, and improve compliance monitoring through smarter data use and automation. This practical program equips procurement and supply chain professionals with AI-enabled methods to assess suppliers, monitor ESG and regulatory requirements, and drive responsible sourcing decisions with governance and controls.

Course Objectives

By the end of this course, participants will be able to:

·        Identify high-value AI use cases for sustainable sourcing and supplier governance

·        Design supplier ESG scoring models, KPIs, and evidence requirements

·        Use AI to sense supplier risk signals and prioritize due diligence actions

·        Strengthen compliance monitoring for sustainability standards and reporting needs

·        Establish governance, data quality controls, and bias mitigation for ESG analytics

·        Build a 90-day pilot plan and 12-month roadmap for AI-enabled sustainable sourcing

Target Audience

This course is designed for:

·        Procurement and sourcing managers and category leaders

·        Supplier management and third-party risk professionals

·        Sustainability/ESG coordinators working with supply chain and procurement

·        Compliance, audit, and reporting officers supporting supplier governance

·        Data/analytics professionals supporting procurement insights and dashboards

Course Outlines

Day 1: AI Foundations for Sustainable Sourcing & Use-Case Discovery

·        Sustainable sourcing overview: ESG expectations, supplier accountability, and value creation

·        Where AI fits: supplier scoring, risk sensing, document review, and monitoring dashboards

·        AI capabilities and limits: data gaps, false signals, explainability, and human review

·        Data readiness: supplier master data, audit findings, certifications, and external signals

·        Activity: Build a sustainable sourcing AI use-case backlog + value/feasibility prioritization matrix 

Day 2: Supplier ESG Scoring Models & KPI Design

·        ESG scoring structure: pillars, indicators, weighting, and thresholds

·        Evidence-based scoring: audits, certifications, policies, and performance data

·        Normalizing data: supplier size, geography, sector, and maturity considerations

·        Designing scorecards: comparability, transparency, and decision usability

·        Workshop: Build a supplier ESG scorecard (KPIs + weights + evidence checklist + rating rules)

Day 3: AI-Driven Risk Sensing & Due Diligence Prioritization

·        Risk sensing sources: audit trends, delivery quality, incident logs, and external signals

·        AI for theme detection: identifying repeated issues and emerging risk patterns

·        Risk prioritization: impact/likelihood, criticality, and supply dependency considerations

·        Due diligence workflows: triggers, tiers, escalation, and remediation tracking

·        Practical activity: Create a risk sensing dashboard + due diligence playbook for high-risk suppliers

Day 4: Compliance Monitoring, Audits & Corrective Actions

·        Compliance obligations: sustainability standards, contractual clauses, and reporting requirements

·        AI-assisted document review: policy checks, certification validation, and exceptions (with verification)

·        Monitoring routines: periodic reviews, continuous monitoring concepts, and supplier QBR governance

·        CAPA management: corrective/preventive actions, timelines, verification, and closure discipline

·        Case study: Supplier non-compliance scenario (investigation, escalation, CAPA, and communications)

Day 5: Governance, Data Quality, Bias Mitigation & Implementation Roadmap

·        Responsible AI governance: roles, approvals, accountability, and escalation paths

·        Data quality controls: completeness, accuracy, consistency, and audit trails

·        Bias and fairness risks: avoiding unfair scoring and ensuring transparent decision rules

·        Reporting and executive communication: supplier ESG dashboards and decision narratives