Demand signals for inventory planning
How historical sales data could inform reorder decisions by product and location.
This is an illustrative workflow, not a client deployment. It shows how BillianceAI would approach this kind of problem. It contains no client data and makes no claims about results.
The problem
Stock decisions are often based on rules of thumb. Ordering too much ties up cash; ordering too little loses sales. The data to do better usually exists but is spread across systems.
The approach
Consolidate sales and inventory data, assess whether its history is reliable enough to model, then produce demand estimates that sit alongside — not replace — buyers’ judgement.
The engineering involved
- 01
Data foundation
Sales and stock history brought together and cleaned.
- 02
Feasibility
Data quality and seasonality assessed before modelling.
- 03
Forecasting
Demand estimated by product and location.
- 04
Decision support
Estimates surfaced where buyers already work.
Outcomes
Not applicable — this is an illustrative workflow, so there are no results to report.