Program Overview
This intensive course equips professionals with the analytical frameworks, models, and tools needed to manage suppliers and circular supply chains using data-driven insights. The program blends conceptual clarity with real-world case studies, predictive analytics techniques, ESG-linked supplier scoring, reverse logistics analytics, material flow modelling, and digital technologies such as AI, IoT, and blockchain. Participants learn how to evaluate supplier risks, optimise circular material loops, forecast disruptions, and design analytics-ready dashboards that support sustainable, cost-efficient, and resilient supply chain decisions. Designed for corporates, this program helps teams solve real operational challenges and accelerate their transition toward future-ready, circular supply networks.
Features
- Use analytics to evaluate supplier performance, resilience, ESG risk and compliance
- Map, analyse and optimise material flows for circularity, recovery, waste reduction and cost efficiency
- Apply predictive and prescriptive models to forecast disruptions and improve supply chain decisions
- Build data-driven dashboards and reporting frameworks supporting circular supply chain performance
Target audiences
- Supply Chain, Procurement & Supplier Management professionals
- Sustainability, ESG, Circularity & Operations teams
- Quality, Projects, Engineering & Manufacturing teams
- Risk, Compliance & Strategy/Business Excellence professionals
- Data Analytics & IT teams supporting supply chain and sustainability functions
Curriculum
- 6 Sections
- 52 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- Foundations of Data-Driven Supplier & Circular Supply Chains8
- 1.1Linear vs. Circular Supply Chain: Cradle-to-grave – Cradle-to-cradle
- 1.2Key Concepts: reverse logistics, recovery loops, refurbish/reman, material passports, traceability, EPR, Scope 3 emissions, supplier ESG scoring
- 1.3Analytics maturity model: descriptive – diagnostic – predictive – prescriptive
- 1.4Situational Awareness: Current supply chain challenges: demand volatility, sustainability pressures, ESG mandates, raw material scarcity
- 1.5Situational Awareness: Why data-driven models outperform intuition-based procurement decisions
- 1.6Example: How leading companies (FMCG, automotive, pharma, electronics) use analytics to redesign circular value chains
- 1.7Example: Data-driven supplier rationalization & risk minimization
- 1.8Exercise: Identify top waste, loss, and inefficiency hotspots in your supply chain
- Supplier Analytics & ESG Performance Measurement8
- 2.1Supplier performance dashboards (cost, delivery, quality, innovation, ESG)
- 2.2Data constructs: supplier master data, part-level traceability, lot genealogy, digital twins
- 2.3Risk classification models: operational, sustainability, geolocation, financial
- 2.4Situational Awareness: Data gaps in supplier ecosystems: inconsistent reporting, missing transparency, lack of supplier digital literacy
- 2.5Situational Awareness: Emerging legislations driving analytics: CSRD, SEC Climate Rule, EU Due Diligence Act
- 2.6Examples: Supplier scorecards used by top OEMs
- 2.7Examples: Predicting supplier disruptions using weather, political, ESG, and logistics data
- 2.8Live Framework: Build Supplier Risk Heatmap
- Circularity Data Models & Material Flow Analytics11
- 3.1Material flow analysis (MFA)
- 3.2Lifecycle assessment (LCA) datasets
- 3.3Circular data stack: recycling rates, recovery yields, scrap mapping, waste intensity analytics
- 3.4Design-for-circularity metrics: modularity, reusability, recoverability
- 3.5Situational Awareness: How circular supply chains fail due to data blind spots
- 3.6Difficulty of Scope 3 visibility in multi-tier networks
- 3.7Criticality of BOM-level and process-level data
- 3.8Example: Closed-loop systems in automotive & electronics
- 3.9Example: Predicting returns, repairs, reusable packaging cycles
- 3.10Example: Forecasting end-of-life material availability
- 3.11Exercise: Map a Product’s Material Loop from procurement – use – recovery – back to supplier
- Predictive & Prescriptive Analytics for Supplier & Circular Ecosystems8
- 4.1Predictive models: Regression, ARIMA, clustering, random forest, survival analysis for supplier failure
- 4.2Prescriptive analytics: optimization engines, network simulation, cost-to-serve models
- 4.3Digital twins for supply chains
- 4.4Situational Awareness: Shifting from reactive firefighting to proactive risk planning
- 4.5Predicting: Supplier disruptions, Recycled material availability, Reverse logistics flows, Environmental compliance violations
- 4.6Examples: Digital twin models used by automotive OEMs
- 4.7Examples: Optimization of circular packaging loops (plastics, pallets, bins)
- 4.8Exercise: Predict supplier risk score for hypothetical suppliers
- Technology Enablers – AI, IoT & Blockchain for Circular Supply Chains10
- 5.1IoT for real-time tracking & condition monitoring
- 5.2Blockchain for provenance & traceability
- 5.3AI/ML for demand–return forecasting
- 5.4Cloud-based supplier collaboration platforms
- 5.5Situational Awareness: Integration challenges: multi-ERP, fragmented suppliers, poor data cleanliness
- 5.6Understanding cost-benefit & ROI of tech adoption
- 5.7Examples: Tokenized recycling credits
- 5.8Example: IoT-based waste segregation & tracking
- 5.9Example: Blockchain-enabled provenance for metals, plastics, apparel
- 5.10Exercise: Design a Digital Data Flow Map for their supply chain
- Governance, Compliance & Circularity KPI Reporting7
- 6.1Circular supply chain metrics: Recycled content, reverse logistics efficiency, recovery rate, carbon intensity, reuse ratio
- 6.2Governance frameworks: ISO 14001, ISO 20400, GRI, EPR compliance, Responsible Sourcing Standards
- 6.3Situational Awareness: How global companies report circularity KPIs to regulators, customers, and investors
- 6.4Risks: greenwashing, poor data confidence, non-verifiable claims
- 6.5Examples: Circularity dashboards of large FMCG, automotive, and retail companies
- 6.6EPR compliance tracking in plastics and electronics.
- 6.7Review a sample Circularity KPI Report – identify red flags
Instructor
