Track
What this track is about
- Moving from spreadsheet-based forecasting to AI-enhanced models that learn from history, market signals, and external factors.
- Right-sizing inventory and safety stocks with probabilistic models instead of fixed rules.
- Using AI to monitor supplier performance and risk events early.
- Optimizing logistics: routing, load building, and network flows.
What we work
What we work on inside the track
Segmentation of SKUs by predictability and value, then matching them with appropriate forecasting models.sciencedirect+1 Incorporating promotions, macro indicators, and seasonality into forecasts.
Multi-echelon inventory optimization (plant, DC, regional hubs) with service level constraints.sciencedirect+1 Using simulations to explain trade-offs to finance and sales.
Building risk scores from delivery performance, quality incidents, and external news signals. Structuring data to prioritize which suppliers truly threaten continuity.
Route optimization and shipment consolidation using AI to reduce cost-to-serve. Evaluating “what if” network changes (new DC, pooling, regionalization) before committing capex.
our services
Typical problems members bring
- Forecast accuracy is poor, so everyone adds their own buffer.
- We either have the wrong stock or stock in the wrong place.
- Supplier disruptions keep derailing our plans; we’re always reacting.
- We have data, but planners still ‘feel’ their way through the plan.
What you get
What you get by joining
