Program overview
Machine Learning Forecasting
We organise forecasting transformation as insight-driven engagement with checkpoints for accuracy gains.
First week activities
- Executive alignment
- Current forecast audit
- Diagnostic agenda confirmation
120 min
Planning briefing
Key categories
Typically modelled
1 owner
Per model
Assess
Current patterns.
Design
Models with input.
Deploy
Review and transfer.
Roles That Gain Most Value
These leaders unlock growth through precision insights.
Supply Chain Leads
Optimise inventory dynamically.
Heads of Analytics
Achieve superior forecast accuracy.
Engagement Scope
Programs deliver ML forecasting, NPI planning and cannibalisation analytics tailored to retail chains and e-commerce.
- Data Diagnostics
Review of sales history, promotions and external signals. - Model Accuracy Assessment
Benchmarking current forecasts and gaps. - NPI Process Review
Evaluation of launch planning and analogy usage. - Cannibalisation Check
Analysis of historical item interactions.
Core Deliverables
Outputs enable accurate planning and independent operation.
- Demand Forecasting Platform
- NPI Scenario Toolkit
- Cannibalisation Analytics Module
Key Performance Signals
15-35% MAPE reduction
Across categories
Revenue uplift
From captured demand
We monitor accuracy gains and business impact.
Discuss Machine Learning Forecasting
Share context about current initiatives and we will suggest the right sprint format.
Talk to us