Retail Demand Forecasting with Inventory Recommendations
Forecast product level demand and turn the forecast into reorder recommendations that account for lead time, holding cost and the cost of running out. The forecast is the easy half; converting uncertainty into a decision is the interesting one.
Intermediate
A small team, or one strong student willing to learn something new.1 semester, 2 students
5 to ship
3 optional extrasSuggested stack
What you should ship
- Cleaning pipeline handling promotions, stockouts and returns, since a stockout is censored demand rather than zero demand
- Baseline forecasts including seasonal naive, compared against a machine learning model
- Forecast intervals rather than point estimates, with calibration checked against actuals
- Reorder recommendations combining the forecast with lead time, holding cost and stockout cost
- Backtest reporting forecast error and simulated inventory cost against the actual historical policy
If you have time left
- Hierarchical forecasting reconciled across product, category and store levels
- New product forecasting from similar items with no sales history
- Explicit treatment of promotional uplift and cannibalisation between products
The problem
Retailers over order and hold dead stock, or under order and lose sales. Both are expensive and both come from forecasting by intuition or by last year plus ten percent.
What you build
A cleaning pipeline, forecasts with uncertainty intervals, and a reorder policy that uses those intervals to make a decision.
The data trap that catches everyone
Stockouts. When an item was unavailable, sales were zero and demand was not, and training on that teaches the model to predict low demand for exactly the products that sell out. Detecting and handling censored demand is the single most important step and the one most student projects miss.
Why intervals matter more than the point forecast
The reorder decision depends on the spread, not the middle. If stocking out is expensive and holding is cheap, you order against the upper end of the interval. A point forecast throws away the information the decision actually needs, so evaluate calibration, not just error.
The evaluation that counts
Backtest the whole policy, not just the forecast. Simulate what your recommendations would have ordered and compare total cost against what actually happened. A statement such as fourteen percent lower inventory cost with the same service level is a result; a lower error metric alone is not.
Scope warning
Use a public retail dataset. Forecast and reorder policy only, no supplier integration or warehouse management.
Ideas and guidance, not finished projects
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