Supply Chain Optimization

AI Demand Prediction for a Canadian Manufacturer

The Challenge

The client was planning production and purchasing using spreadsheets and “tribal knowledge.” That worked—until it didn’t. Demand spikes around holidays and regional events were causing two expensive problems at the same time:

• Stockouts on fast-moving SKUs during peak weeks (lost sales + customer frustration)
• Overproduction and excess inventory in slower periods (cash tied up + storage costs)
  
  They needed a forecasting system that could predict demand at a weekly SKU level, explain why demand changed, and plug into real planning workflows.

Our Approach

InvenTide built a demand prediction application that combined historical sales patterns with real-world drivers that were previously ignored.

We started by consolidating and cleaning data sources:

• Past sales orders & shipments (by SKU, region, channel)
• Lead time and backorder history
• Pricing and promotions calendar
• Stockout flags (to avoid training the model on “false low demand”)
  
  Then we engineered features that reflect what actually drives purchase behavior:

Key demand features used

• Festival & holiday effects: Canadian statutory holidays, long weekends, Christmas/New Year, and region-specific holiday patterns
• Weather influence (where relevant): temperature swings and heavy snowfall affecting demand and delivery cadence
• Construction/industry activity proxy (where relevant): local project seasonality and month-end procurement behavior
• Promotion and price elasticity: discounts, bundle offers, and promo lag effects (demand lift often appears 1–3 weeks later)
• Customer ordering behavior: recurring accounts, reorder intervals, and bulk-buy patterns
• Calendar effects: pay-cycle timing, fiscal month-end, and quarter-end surges

The Solution Delivered

We delivered a production-grade Demand Prediction Application with:

• SKU x Region weekly forecasts (with confidence intervals)
• Scenario planning: “What if we run a promo?” / “What if lead time increases?”
• Driver explainability: top reasons behind forecast changes (so planners trust it)
• Exception alerts: sudden shifts, anomaly detection, and “forecast vs actual” monitoring
• Planner workflow integration: export-ready outputs for purchasing and production planning
  
  The application was designed for daily use—fast, clean UI, and no ML expertise required to operate it.

Outcomes (Significant ROI)

Within the first planning cycles, the client moved from reactive firefighting to proactive planning:

• Fewer stockouts during peak weeks due to earlier procurement and smarter safety stock
• Reduced excess inventory by avoiding overproduction in post-peak periods
• Improved planning confidence because forecasts were explainable and measurable
• Faster decision-making with scenario planning and exception alerts
  
  Result: the forecasting system paid for itself by improving availability, reducing waste, and preventing costly last-minute expedites—delivering clear ROI within the planning horizon.