480 locations
The production workflow addressed store-level demand planning across the operating network.
Case 02 / anonymized engagement
Forecasting moved from manual judgment to production.
Engagement10 weeks
Team1 ML engineer + 1 data engineer
Scope480 locations
01 / The brief
Move store-level demand planning beyond a manual process.
The engagement focused on store-level demand forecasting across 480 locations and taking the workflow into production.
Operating scope480 locations
ConstraintBeyond the notebook
ProofProduction workflow
02 / Delivery
A production forecasting workflow in the existing cloud environment.
One ML engineer and one data engineer delivered the forecasting workflow using Databricks, MLflow, Python and Azure.
DatabricksMLflowPythonAzure
Production footprint
Forecasting across 480 locations, not a lab model.
Store network480
Locations running the production forecasting workflow.
Operating loop
A model promotion path with a visible production end state.
01
PlanStore-level demand scope
02
BuildPython workflow
03
EvaluateMLflow tracking
04
RunDatabricks on Azure
Engagement detail
A forecasting workflow designed for operating decisions, not an isolated model.
Databricks and MLflow
The delivery used Databricks, MLflow, Python and Azure in the existing cloud environment.
10 weeks, two specialists
One ML engineer and one data engineer took the forecasting workflow into production.
Technical + business value
What turned forecasting from a manual activity into a production workflow.
A traceable forecasting path across the store network.
The workflow connected store-level scope with a Python implementation, MLflow model tracking and production execution in Databricks on Azure. One ML engineer and one data engineer owned the ten-week delivery.
Scope480 locationsWorkflowPythonTrackMLflowRunDatabricks + Azure
- The model workflow was moved into production.
- The operating scope covered store-level planning across 480 locations.
A production decision system with a measurable planning result.
The engagement replaced a manual forecasting process with a production workflow and reduced store over-ordering by 14 percent across the stated scope.
-14%Store over-ordering
480Locations in the production scope
Starting point
Production
03 / Outcome
Store over-ordering decreased by 14 percent.
Manual-14%