IBP: Demand Forecasting
ML model accuracy 88→95%, planning cycle 12→4 days, ₽263–550M/year impact
CTO / Technical Director
1 год · 6 чел
- Python
- ML Pipeline
- Anaplan
- SAP BW
- S&OP Framework
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What doesn't work
Freight transportation planning was manual: commercial directors manually determined volumes by clients and routes. Production plan accuracy by clients — 88%, by routes — 59%. Planning cycle — 12 days. Labor costs — ~180 person-days per year.
Architectural approach
Automated demand forecasting across the network (unconstrained demand) with ML models. Integration into S&OP process: scenario planning, freight base optimization. 3 MVP phases: forecast for PV → expansion to KR → all RPS regions. Corporate architect on the project team.
CTO / Technical Director
Corporate architect on the project. Designed ML forecast integration with Optimizer and Navigator. Defined the 3-phase MVP strategy. Justified the business case to the Board (₽263-550M/year impact, accuracy-to-revenue correlation = 0.798).