§ 40 — Use Cases

Use Cases

Pattern-level analyses of what works and what fails when supply-chain AI is applied to specific functions and techniques (demand forecasting, inventory optimization, procurement automation, warehouse computer vision), synthesized from the Post-Mortems and corroborated by cited external research (e.g., MIT CTL, SupplyChainBrain, peer-reviewed studies) rather than recycled unsourced industry statistics. Every claim carries a named source and date. Distinct from Post-Mortems (single-deployment accounts) and Readiness (task checklists): this section answers whether a category of AI application actually delivers, and under what conditions it fails, at a pattern level.

o9 Solutions Deployment in Automotive Supply Chain Planning: A Case Record

o9 Solutions Demand Planning Module: AI Forecasting, Touchless Planning, and Data Readiness for Enterprise Practitioners

Probabilistic Demand Forecasting for Seasonal CPG Supply Chains

Probabilistic Forecasting vs Statistical Forecasting in Supply Chain

Red Sea Shipping Disruptions 2024: Impact on Lead Time AI Models and Safety Stock Assumptions

How AI Accelerates Recovery After Storm Power Outages

· digital twin, agentic AI, predictive analytics

AI Slotting Optimization in WMS: Use-Case Reference for Warehouse Operations

What AI Deployments in African Solar Supply Chains Cost

demand-forecasting· forecasting

Failure pattern: data quality

Source: Atlas AI / Engie Energy Access

Why AI Supply Chain Compliance Needs Audit-Traceability

supply chain compliance· generative AI

Failure pattern: audit-traceability gap

Source: Watershed 2026 analysis, IntegrityNext 2026 report

Four organizational gaps behind the supply chain AI backlash

cross-functional· forecasting

Failure pattern: organizational-gap

Source: Loadstar/Raft 2026 survey

Why 94% of Supply Chains Plan AI Bots but Few Deliver

supply chain planning· generative AI

Failure pattern: unclear business value

Source: AI Agent Adoption 2026: Enterprise Data Points (Digital Applied, 2026)

Real Outcomes from AI in City Waste Management

collection logistics· route optimization

Failure pattern: single-city pilot scalability

Source: PMC 2023 review on AI in solid waste management

Can AI climate attribution actually inform procurement decisions?

procurement· forecasting

Failure pattern: last-mile action gap

Source: Jiménez-Esteve et al. (arXiv 2024) and CDP (May 2026)

Can AI Risk Management Reduce Cross-Contamination Recalls?

quality· predictive environmental monitoring

Failure pattern: inadequate early detection of contamination signals

Source: Lin and Hertig 2023 fuzzy DEMATEL study

What AI Supply Chain Disruption Planning Really Achieves

disruption management· agentic AI

Failure pattern: data governance gap

Source: arXiv:2601.09680 (AlMahri et al., 2025)

What AI for Food Supply Chain Recall Management Has Delivered

recall management· traceability AI

Failure pattern: overstated vendor claims

Source: Food Processing 2026, LF Decentralized Trust, Mergen AI 2026, Consumer Goods 2025, Lumafield

How AI traceability cut food-safety outbreak response times

traceability· blockchain

Failure pattern: fragmented lot-level data

Source: Food Logistics (Sep 2024)

The State of AI for Last-Mile Delivery Heat Safety

last-mile delivery· generative-ai

Failure pattern: subcontractor coverage gap

Source: Amazon 2026 disclosure, UPS ORION, SlateSafety/Benchmark Gensuite

Can AI FX Hedging Protect Nigerian Import Supply Chains?

procurement· forecasting

Failure pattern: infrastructure gap

Source: Citi/Ant International pilot (July 2025), Capital A deployment (October 2025), Coronation Merchant Bank (2023), Vanguard NG (March 2026), Tech in Africa (2026)

Which AI Vendor Has Real Proof for Geopolitical Disruption Planning?

scenario planning· scenario modeling

Failure pattern: training data mismatch under shock conditions

Source: o9, Kinaxis, RELEX, Anaplan, Blue Yonder public evidence (2025-2026), Xeneta risk report, KPMG/Gartner, MIT Sloan framework