Managing $35M Inventory Correction
- Sector: Retail / FMCG
- Friction: 4 previous failed attempts to predict just sales because they looked at the wrong data.
- Intervention: A simple switch, when we stopped forecasting based on “Delivery Date” (when it arrives) and switched to “Order Date” (when they buy). Bridging gap between Supply Chain and Marketing.
Recovering £11M via Predictive Decay Modeling
- Sector: Telecom
- Friction: Inefficient risk pricing requiring massive labor over-provisioning for statistically rare network faults.
- Intervention: Shifting from “Binary Classification” to “Probabilistic Decay,” forecasting critical outages 8–16 hours in advance to optimize workforce allocation.
Pricing Cost of Claims to 15% Margin Expansion
- Sector: Perishable Products
- Friction: Unpredictable “Claims” accounting for 20% of yield. Inability to forecast P&L due to biological variance.
- Intervention: Applied Survival Analysis (Time-to-Failure), typically used in medical research and product warranties, to perishable fruit inventory.
Auditing 100% Inspection at Line Speed
- Sector: Automotive
- Friction: High-latency fault detection in manufacturing lines.
- Intervention: Adapting biomedical “ECG” algorithms to process mechanical test curves data in <5 seconds and finding ~100 “Black Swan” defects annually
Behind the Architecture
Every intervention follows the 70/30 Rule of Enterprise AI: 70% of the value is extracted from deterministic logic and old-school engineering; 30% is the interface.
I use the O.L.C.D. Framework to ensure every deployment is:
- Objective-Led: Driven by the P&L, not the tech.
- Learned-On: Built on verified data adequacy and model applicability.
- Controlled-With: Architected for safety and operational synchrony.
- Diagnosed-By: Monitored via board-ready ROI metrics.
Ready to Audit Your Architecture?
If your current AI roadmap is stuck in “Pilot Purgatory,” it’s time for a logic audit.