Demand forecasts and supply constraints
When demand rises, can supply and capacity still support inventory targets?
Explore case and packageProjects & operations · Synthetic business case
Next-day bakery production planning
How much should each store bake tomorrow, and can the forecast evidence and production approval be reviewed together?
Train and evaluate the portable random forest on the instance’s synthetic sales history, inspect store-SKU forecasts and review production/distribution actions.
For · Bakery-chain operations, store planning and central production teams
The task starts here
Perishable products can sell out when production is too low and generate waste when it is too high. Planners need to know which history supports a forecast and how it becomes a production task.
- How it moves forward
- The portable policy trains on instance history using sales windows, calendar and store-manager features, preserving temporal holdout evaluation, predictions and evidence records.
- Check before deciding
- Planners review evaluation results and business rules before approval. Approval, dispatch and closure are recorded separately; synthetic-data metrics do not establish customer production accuracy.
Your path after importing
Follow the process. Check each step.
This is a walkthrough for the product. The page describes the case; calculations and business actions run inside your DataFocus project.
- 01
Inspect the business records
Select STORE-MALL-018, SKU-CROISSANT-006 and the guide’s start date, then inspect sales history and future features.
- 02
Analyze and check the evidence
Run training and forecasting on current history, checking training-row counts, temporal holdout MAE/MAPE and source rules; these metrics describe the synthetic example only.
- 03
Change an input and recalculate
Edit the forecast-feature record specified in the guide, recalculate and compare current forecasts and production proposals rather than using old prefilled results.
- 04
Review actions and their records
After reviewing forecasts and production rules, approve, dispatch and close the current task as guided, then inspect model snapshots and receipts.
Business context
Bring data, knowledge and actions together.
Use these business objects to understand the case. After importing, inspect the fields, relationships and rules, then adapt them to your work.
- Stores and SKUs
- Sales history
- Future features
- Model evaluation
- Production and delivery tasks
View package resources64 resources
- Business panels
- 1
- Documents
- 6
- Execution definitions
- 1
- Forms
- 20
- Knowledge resources
- 1
- Ontologies
- 1
- Relationships
- 14
- Tables
- 20
From a demo to your business
Bring a concrete question to the next conversation.
Confirm these differences before choosing the data to connect and the rules to retain. Example data and objects do not automatically connect to your production systems.
- Prepare valid store-SKU sales history; the current policy requires at least 60 historical dates.
- Confirm store-manager inputs, pack sizes, production buffers and early-delivery proportions.
- Evaluate errors and business outcomes on your own data; allow time for importing the larger package.