§ 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.
Source: Georgetown CSET
Failure pattern: lifecycle boundary omission
Source: Impakter (Mar 2026) and Carbon Trust (May 2026)
Failure pattern: investment misalignment
Source: Dataintelo April 2026 report
Failure pattern: weather-to-planning integration gap
Source: CargoAi 2025, Spire Global Izzy, Everstream Ian
Failure pattern: workforce capability gap
Source: Gartner, Deloitte, Accenture
Failure pattern: intensity forecasting bias
Source: Rice University study (Gori, Weng, et al., JGR Atmospheres, March 2026)
Source: Contrary Research, Octus/Reorg, company filings
Failure pattern: data-leakage
Source: Cyberhaven Q4 2025
Failure pattern: manual_inspection_gaps
Source: iFactory case study, ACM AI QC implementation research
Failure pattern: last-mile-connectivity-blind-spot
Source: Tidewater Telecom outage coverage (LCNME, StateScoop, NewsCenter Maine, DysruptionHub)
Failure pattern: talent shortage
Source: White House OSTP report July 21, 2026
Source: Reed Smith legal analysis
Failure pattern: autonomous freight scaling failure
Source: Waymo Via shutdown, NACFE 2024 autonomous trucking review
Failure pattern: data inaccuracy and trust gap
Source: Unframe AI (Apr 2026) and RELEX Solutions (Jun 2026)
Failure pattern: perimeter-centric security gap
Source: BBC report (2026)
Failure pattern: productization gap
Source: Dataintelo (2026), Ma et al. (2022), Local News Matters (2026)