§ 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.

May 2024 G5 Storm Tested AI Grid Models – Here's What Worked

demand-forecasting· mixture-of-experts

Failure pattern: validation gap

Source: Nature Scientific Reports (October 2025)

AI hedging for naira risk: saving Nigerian manufacturers billions

treasury· forecasting

Source: Global Finance Magazine, Veriv Africa

Can AI Justify Its Cost for Hurricane Air Travel Disruptions?

airline disruption management· forecasting and optimization

Failure pattern: overestimated savings scope

Source: MarketIntelo Airline Disruption Management AI Market Research Report 2034, Jun 2026

Which AI Capabilities Address Specific Hurricane Disruption Patterns?

demand forecasting· forecasting

Failure pattern: cascade prediction gap

Source: ClimateAi, ORMS Today, Everstream Analytics

Comparing AI Supply Chain Platforms for Hurricane Disruptions

disruption planning· optimization

Source: Forbes (June 2026)

How AI Addresses Nuclear Supply Chain Bottlenecks Post-Moratorium

fuel procurement· machine learning

Failure pattern: Overclaiming AI readiness for new-build supply chain

Source: U.S. Department of Energy (2024) – Artificial intelligence is helping optimize nuclear reactor operations

Which AI Tools Actually Assess Pharma Tariff Exposure

procurement· multi-tier-supplier-mapping

Failure pattern: tier-1-only-visibility

Source: Brookings March 2025, Logistics Viewpoints April 2026

How AI Planning Platforms Fared During the 2026 Oil Shock

demand-forecasting· forecasting

Failure pattern: batch reforecasting

Source: BCG, 2026

AI Prompt Techniques That Actually Work for Supply Chain Forecasting

demand-forecasting· generative-ai

Failure pattern: prompt-only forecast failure

Source: Argon & Co IRIS, JD.com SCPA, BCG X, European consumer goods case

What AI Planning Platforms Deliver for Texas Solar Procurement

procurement· forecasting

Source: First Solar Kinaxis 2013-2014 case study

Who Pays When AI Supply Chain Planning Causes Real Harm?

supply-chain-planning· forecasting

Failure pattern: contractual-liability-caps

Source: Jones Walker analysis (2025)

Mapping AI Supply Chain Optimization's Impact on EV Affordability

procurement, manufacturing, logistics· forecasting, optimization, deep learning

Source: McKinsey (2018), Caresoft Global (2026), Star.global/Toyota (2025), arXiv/Ford (2024)

AI Traceability Gaps in Foodborne Outbreak Investigations

traceability· predictive modeling

Failure pattern: data fragmentation

Source: FDA Cyclospora outbreak investigation (July 2026)

How AI Wildfire Detection Reshapes Supply Chain Risk Response

risk management· computer vision

Failure pattern: inaccurate outputs

Source: Brown & Brown (2025), GAO (2025)

How the AWS Outage Impacted Logistics Supply-Chain AI

Logistics Operations· Real-time data pipeline AI

Failure pattern: Lack of graceful degradation

Source: Logistics Viewpoints (Oct 2025)

Four AI detection modalities screen banana cargo for cocaine

cargo screening· machine learning

Failure pattern: insufficient standalone reliability

Source: Windward MIOC, DHS S&T, arXiv 2505.18851

Four years of Black Sea grain disruption expose resilience model blind spots

logistics· forecasting

Failure pattern: substitution-overestimation

Source: Rabobank/RaboResearch, IFCHOR Galbraiths, Miller Magazine

Greg Brockman's Philanthropy Flags Vendor Risk for Supply Chain AI

procurement· generative-ai

Failure pattern: model-concentration-risk

Source: SignalFire 2025 State of Talent Report

Parts Shortages, Not Defects, Drive Ford Bronco Recall Delays

inventory-optimization· optimization

Failure pattern: supplier-visibility-gap

Source: Hearn Law Firm Ford Recall Statistics, AMS/ABB Automotive Manufacturing Outlook Survey 2025

What the Cetirizine Recall Reveals About AI ROI in Pharma

recall management· planning optimization

Failure pattern: ROI overestimation

Source: VE3 (LogiPharma 2024 AI Report)