§ 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.
Failure pattern: quantitative-reasoning-failure
Source: Blue Yonder (2024), Omnifold (2025)
Failure pattern: integration and organizational readiness gap
Source: INFORMS (Han et al. 2025), Skillings, Reuters
Failure pattern: llm-numerical-forecasting-limitation
Source: Anthropic Introducing Claude Opus 5
Failure pattern: hazard-forecast-only approach
Source: Early warning of complex climate risk with integrated artificial intelligence, Nature Communications, March 2025
Failure pattern: procurement governance gap
Source: Al Jazeera (June 2026); EDUCAUSE Review (March 2025)
Failure pattern: capability boundary misunderstanding
Source: Google Workspace Blog, November 2024
Failure pattern: supply chain data silos
Source: CDC HAN Notice (July 2026)
Failure pattern: traceability gap
Source: Consumer Reports (July 16, 2026)
Failure pattern: power-stability workflow disruption
Source: Belfer Center report, Reuters, HyperFrame Research
Failure pattern: data silos
Source: FDA Investigation (July 2026)
Failure pattern: coupled constraint neglect
Source: Reuters, July 2026
Failure pattern: inability to isolate contaminated lots
Source: FDA recall notice (Midwest Poultry Services, July 2026)
Failure pattern: single-basin planning failure
Source: NOAA El Niño declaration (June 2026), Atlantic Niña analysis by Severe Weather Europe (July 2026)
Source: NOAA, CSU, Risk Management Magazine, Yale Climate Connections, AccuWeather, House Homeland Security Committee, Baker Donelson
Failure pattern: traceability gap
Source: Safety Research & Strategies (2021); NHTSA (2020)
Source: Gartner, June 2026
Failure pattern: evacuation behavior data integration gap
Source: ASU supply chain analysis of LA fires; FLARE evacuation AI research (JHU/UF)
Source: Scaffold Digital (June 18, 2026)
Failure pattern: domain-expertise-gap
Source: Yahoo Finance/Moneywise, Car and Driver, NHTSA, Supply Chain Management Review, Worldmetrics
Failure pattern: incomplete ROI disclosure
Source: Four deployment case studies (Advanta Seeds, Unilever, electronics, UNICEF)