Demand forecasting for purchasing
Shared patterns across products were used alongside individual histories to forecast monthly demand, including products with limited sales history.
- Industry
- Consumer products / Purchasing
- Decision family
- Inventory control
- Evaluation scope
- Historical forecast evaluation
Customer identity and identifying operational details are withheld.
The operating decision
Volatile sales and short histories make purchasing and stock preparation difficult, particularly for newer products.
- Inputs
- Monthly sales history by product · Product attributes · Purchase ratios · Actual monthly demand for evaluation
- Decision
- Next-month demand estimate per product · Evidence for purchasing and stock review
- Constraints
- Only information available before each forecast month · Sparse histories · Seasonal variation
How it is approached
Learn across product histories
Combine patterns from products with ample data with the attributes and individual histories of products with less data.
Forecast the next month
Produce a demand estimate per product for the planner’s purchasing and stock review.
Move the evaluation forward
Advance the training point month by month and compare each new forecast with the existing forecast, using only the past available at that point.
From product history to a purchasing forecast.
Sales history per product
actual monthly demand
product attributes · purchase ratio
Shared and individual patterns
similar products' attributes used
demand estimated per product
Purchasing and stock review
how much to prepare
used in the planner's plan
Historical forecast evaluation
Validation & scope
This is a rolling forecast-accuracy evaluation across a seasonal cycle. It does not validate order-quantity optimization, order execution or stock-cost savings; those require separate evaluation.
Related decisions
All applications
Bring the decision, its data and its constraints.