Forecast demand from patterns in your data, not opinions in a meeting.
Your demand planning process runs on last year’s sales adjusted by a gut-feel percentage. ML models trained on your actual order history, seasonal patterns, and market signals produce forecasts that are measurably more accurate — and they improve automatically as more data accumulates.
Demand Forecasts That Nobody Trusts
- →Annual forecasts built in a conference room and never updated as the year progresses
- →Sales team forecasts inflated or sandbagged depending on how quotas are set
- →No SKU-level or customer-level forecast granularity — just top-line revenue targets
- →Stockouts and excess inventory coexisting because the forecast doesn’t match actual demand patterns
Demand Forecasting Analytics
Historical Pattern Analysis
Analyze 2–5 years of order history to identify demand patterns by product, customer, channel, and geography. Detect seasonality, trends, and cyclical patterns automatically.
ML Forecast Models
Time-series and regression models trained on your data to produce SKU-level forecasts. Multiple models compared and the best-performing selected for each product segment.
Forecast Accuracy Measurement
Track MAPE, WMAPE, and bias metrics continuously. Compare ML forecasts against your current method so improvement is quantified, not assumed.
Collaborative Forecast Adjustment
Sales and operations teams can review and adjust ML forecasts with their market intelligence. Adjustments are tracked so you can measure whether human overrides improve or degrade accuracy over time.
ERP Planning Integration
Forecasts feed directly into Odoo's MRP and purchasing modules. No manual re-entry between the forecast and the plan.
Demand Sensing
Short-term forecast adjustments based on recent order velocity, leading indicators, and market signals. Catch demand shifts weeks before they show up in the monthly forecast.
The Engagement Process
Data Assessment
Evaluate order history depth, quality, and granularity. Identify supplementary data sources — pricing, promotions, market indices — that improve forecast accuracy.
Model Development
Build and validate forecast models against historical data. Benchmark ML accuracy against your current forecasting method for a direct comparison.
Integration & Workflow
Connect forecast outputs to ERP planning modules and establish the S&OP review workflow. Define roles for forecast review, adjustment, and sign-off.
Production & Improvement
Deploy with accuracy dashboards and continuous model retraining. Monthly accuracy reviews drive model tuning and feature engineering improvements.
Frequently Asked Questions
Data Analytics · Demand Forecasting
Every engagement starts with an assessment.
We scope work after we understand your operation — not before. The Launchpad assessment maps where you are, quantifies what it's costing you, and sequences what to do first.
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