AI demand forecasting that replaces spreadsheet guesswork
Build forecasting models that incorporate historical data, seasonal patterns, promotional calendars, market signals, and external variables. Granular, accurate, and adaptive to changing conditions.
Your demand forecasts are wrong and it is costing you
Traditional demand forecasting uses historical averages and Excel models that cannot account for dozens of variables that affect demand: seasonal shifts, promotional impacts, competitor actions, weather, economic indicators. Forecast errors of 30-50% are common, leading to overstock (capital tied up in excess inventory) or stockouts (lost sales at the worst possible time).
30-50%
Typical forecast error range for spreadsheet-based methods
30%
Improvement in accuracy achievable with AI forecasting
10-15%
Of revenue lost to stockouts from poor demand planning
20-30%
Of inventory is excess stock from over-forecasting
How Optivus builds AI demand forecasting models
We build ML-powered forecasting models trained on your historical data and enriched with external signals. The models produce granular predictions (SKU-level, store-level, daily/weekly) that adapt as conditions change, not just at quarterly review cycles.
Data audit
Analyze your historical sales data, promotional calendars, and available external signals. Identify patterns and data quality issues.
Model development
Build forecasting models incorporating your specific demand drivers: seasonality, promotions, events, weather, economic indicators.
Validate and tune
Backtest against historical data. Measure accuracy against your current forecasting method. Tune until accuracy meets targets.
Deploy and integrate
Deploy models into your planning workflow. Integrate with ERP/inventory systems for automated reorder calculations.
Key capabilities
Multi-variable forecasting
Models that incorporate historical sales, seasonal patterns, promotional calendars, weather, economic indicators, and competitor activity.
Granular predictions
Forecasts at SKU-level, store-level, or region-level with daily, weekly, or monthly granularity. Match the precision your planning needs.
Promotion impact modeling
Quantify the impact of promotions, events, and marketing campaigns on demand. Plan inventory around promotional lifts.
Scenario planning
Run 'what if' scenarios: what happens if we run a promotion, open a new store, or a competitor launches a product?
New product forecasting
Forecast demand for products with no sales history using analogous products, market signals, and category patterns.
ERP/inventory integration
Connect forecasts directly to your ERP and inventory systems. Automated reorder point calculation and safety stock optimization.
Results you can expect
30%
Improvement in forecast accuracy vs spreadsheet methods
20-30%
Reduction in excess inventory
10-15%
Recovery of revenue lost to stockouts
3-6 weeks
Typical implementation timeline
Our AI implementation process
Every engagement follows the same four-phase structure.
Scope
Map the workflow, define success criteria, lock deliverables.
Build
Weekly working demos. Direct channel with the build team.
Ship
Production deployment on your cloud with monitoring.
Scale
Optimize on real usage. Expand to adjacent workflows.
Frequently asked questions
Related solutions
Ready to get started?
Book a 25-minute call. Bring your workflow and we will show you exactly how we would approach it.