Curriculum
- 6 Sections
- 52 Lessons
- 1 Day
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- 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