Forecasts put to work.Demand informs each location’s plan.
Store-level forecasting, in production.
From manual planning to a production forecasting workflow across 480 locations. Store over-ordering fell by 14%.
An anonymized client engagement.
- Engagement
- 10 weeks
- Delivery team
- 1 ML engineer
with 1 data engineer
Take demand planning beyond a manual process.
The retailer needed a production forecasting workflow for store-level planning across 480 locations.
One ML engineer and one data engineer worked together over ten weeks, using the client’s existing Azure environment.
The assignment included taking the workflow into production.
From model development to store-level planning.
Two engineers delivered the data and ML work together, keeping the focus on forecasts the retailer could use in its planning process.
Build the forecasts
Store-level demand forecasts are implemented in Python and compared with the manual baseline.
Result Forecasts for every store in scope
Track every run
MLflow records each model run and its results, connecting development with the production workflow.
Result A record of every model run
Plan from the forecasts
Databricks runs the workflow in production on Azure across the 480-location scope, feeding store-level planning.
Result 14% less store over-ordering
Built in the existing cloud environment.
Forecast implementation, model tracking and production execution, each with a distinct role in the delivery.
Python
Forecast implementation. The implementation language for the store-level forecasting work.
MLflow
Model tracking. A record of model runs and their results, connecting development with the production workflow.
Databricks
Production execution. The environment for running the forecasting workflow in production.
Azure
Existing cloud environment. The cloud foundation for the Databricks-based workflow.
Store over-orderingBefore: Manual baselineAfter: 14% less
Less over-ordering across the store network.
The forecasting workflow reached production across the 480-location scope. Store over-ordering decreased by 14% compared with the manual baseline.
Results from the anonymized engagement described here.
How to read this result
The 14% compares store over-ordering after the rollout with the manual baseline: a stated before and after result, not a measured weekly trend.
The result concerns store over-ordering. It is not a claim of 14% better forecast accuracy or a 14% reduction in total inventory.
Where does your planning need better forecasts?
Tell us about the decision, the data behind it, and how planning works today. We’ll work out the next step together.