Plan replenishment with a clearer view of demand.
Use sales history, promotions, and stock availability to forecast demand for a selected product range. Compare predictions with your current method and help planners review where replenishment may need to change.
When this is useful.
This is useful for a retail, distribution, or inventory planning manager when replenishment is inconsistent, stockouts are frequent, or too much inventory sits in the wrong place. The workflow is a fit when you want a forecast you can compare with the method you already use, and planners still decide what to order.
How the workflow operates.
- 01
Prepare
Sales history, promotions, and availability are checked for use.
- 02
Backtest
The method is tested on unseen periods at the planning horizon.
- 03
Forecast
Demand is projected for the selected product range.
- 04
Review
Planners compare the view with the method they use today.
What we would build.
- A data readiness review of the history you can actually use
- A comparison with your current forecasting method
- Historical backtesting at the planning horizon you buy to
- Forecasts for the selected product range
- A planner review dashboard that shows where replenishment may need to change
What we need to start.
- Dated product sales history
- Stock availability over the same period
- Promotion calendars where they affect demand
- Lead times for the selected range
- Existing forecasts, if you already produce them
Where your team stays in control.
Planners still approve replenishment decisions. A clearer forecast is an input to that review. It does not place orders, and a lower forecast error does not automatically improve inventory on its own.
A focused first pilot.
A first pilot covers a selected product range with enough usable history to backtest honestly. The range should be large enough to learn from, and small enough that planners can review it.
How success is measured.
Offline accuracy is evaluated on unseen periods, at the horizon you actually plan to. We compare against your current method and a simple seasonal baseline. Stockouts are measured separately during a live pilot, because a stockout can hide demand that never appeared in sales. Those two views are not treated as the same result.
Forecast error
Error on unseen time periods, at the horizon planners actually buy to, compared with the current method and a simple seasonal baseline.
Forecast bias
Whether forecasts systematically run high or low over the review window.
Stockout rate
Stockout outcomes during a live pilot, measured separately from offline forecast accuracy.
Related services
From the journal
Compare a forecast with the method you use today.
Tell us which product range you plan, how you forecast today, and what history you can share.