Solutions
AI Demand Forecasting
Forecasts built in spreadsheets lead to stock-outs, overstock and last-minute staffing changes.
In one paragraph
AI demand forecasting uses time-series and machine-learning models trained on your sales, seasonality, promotions, pricing and external signals to predict demand by product, location and channel with confidence ranges. Forecasts flow into purchasing, planning and staffing tools. AIYatch benchmarks against your current method, delivers models with monitoring, and retrains as patterns change.
What you get
- Lower forecast error than the current spreadsheet or rule-based method
- Fewer stock-outs and less excess inventory
- Forecasts at the granularity planners actually use
- Automatic retraining and drift alerts as behaviour changes
Services involved
Frequently asked
Two or more years of daily or weekly data captures seasonality well, but useful models can be built with less by pooling similar products and using external signals. New products borrow patterns from comparable items until they have their own history. Data quality issues, such as stock-outs masking true demand, are handled explicitly in preparation.
Through your existing tools: ERP or planning system integration, BI dashboards, scheduled files or an API. Planners see forecasts, confidence ranges and the drivers behind them, and can override with reasons that feed back into evaluation. Overrides and actual demand are compared over time so both the model and the process improve.
Related work
Outcomes, not demos.
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