Demand Forecasting & Supply Chain
Right stock, right place, less capital tied up.
Demand forecasting and inventory optimisation that predicts what will sell, where and when, then recommends what to buy, make and move.
01 · Overview
Spreadsheets miss what moves demand
Simple forecasts miss promotions, seasonality, Ramadan and holiday effects, and local events. The result is stockouts on what customers want and excess stock on what they don’t.
Built with
- Forecast ensembles
- Inventory optimisation
- Scenario planning
Deployment
On-premise, private cloud or in-region.
02 · How it works
Forecast, then optimise
- 1
Forecast at your level
SKU × location × day or week, with ensembles that pool learning across similar products.
- 2
Include what moves demand
Promotions, prices, religious and public holidays, weather and events, modelled as drivers for “what if” planning.
- 3
Optimise the response
Order quantities, safety stock and transfers that respect lead times, MOQs and storage limits.
- 4
Learn every cycle
Accuracy tracked by item and location, with automatic retraining.
- Sales & promotionsPlus calendar, weather
- Demand driversHolidays, prices, events
- Forecast ensembleSKU × location × day
- Inventory optimiserLead times, MOQs
- ERP planActuals ↺ retraining
03 · Capabilities
What it plans
- Store and warehouse replenishment
- Promotion and seasonal planning
- Raw-material procurement and production scheduling
- Multi-warehouse stock balancing
- Delivery route and capacity planning
04 · Outcomes
What we measure
- More accurate forecasts
- WAPE / MAPE vs. current method
- Fewer stockouts
- On-shelf availability / fill rate
- Less working capital
- Inventory days at equal service level
- Less waste
- Write-offs for perishable and expiring goods
Example scenarios
Illustrative- Retail chainStore-level replenishment across thousands of SKUs.
- ManufacturerRaw-material procurement aligned to demand.
- E-commerceStock balancing across fulfilment centres.
- Pharmacy networkAvailability of critical medicines with less expiry waste.
05 · Governance
Planner control & integration
Controls built in
- Plans pushed to your ERP
- Planner overrides captured and learned from
- Forecast accuracy dashboards
- Every forecast shows which drivers moved it
Technology
- Python
- Prophet
- statsmodels
- LightGBM
- PyTorch
- Gurobi
- Spark
- TimescaleDB
- Power BI
06 · FAQ
Common questions
Can planners still override the forecast?
Yes. Overrides are captured, and the system learns which ones improve accuracy.
How does it handle Ramadan and other moving holidays?
Religious and public calendars are modelled as drivers, so their effect moves with the date each year.
What about new products with no history?
Forecasts borrow from similar products and launch patterns until enough of the item’s own sales exist.
