Demand ForecastingSolutions
Forecasts of what will sell and when, with an honest range around the number, tested on your own past sales before you rely on them.
Demand Forecasting Solutions: what the work involves
Many businesses plan purchasing, staffing and production by feel and last year's spreadsheet. Ramadan, Eid, school seasons, sale events, weather and price changes all move demand, and a manager remembers some of them. The cost shows up as empty shelves on the best days and cash tied up in stock that sits for months.
We gather sales history at the level you plan, such as product by week by branch, and add known drivers: holidays, promotions, price, stock-outs, and where useful weather or events. After cleaning the stock-out gaps that fake low demand, we compare simple baselines with models such as gradient-boosted trees, exponential smoothing or Prophet. The choice is made by back-testing, which means forecasting a past period and comparing with what happened. Output is a forecast with a range, not a single number, delivered to a dashboard or spreadsheet each week. Planners can override any figure, and those overrides are recorded to see if human adjustments help.
Core features
Driver-aware forecasting
Holidays, promotions, price changes and season are included, so predictable swings are not treated as noise.
Stock-out correction
Periods when an item was unavailable are handled so they do not teach the model that demand was low.
Forecast ranges
Each prediction comes with a likely low and high, so planners can see risk and decide how much buffer to hold.
Back-tested model choice
Candidate methods are tried on your past periods and the simplest one that performs well is selected.
Planner overrides
Staff can adjust forecasts for things the data cannot know, and the adjustments are tracked and compared afterwards.
Weekly delivery
Forecasts arrive as a dashboard, spreadsheet or ERP update on a schedule, with a short note on what changed.
What we get right before launch
Short or messy history
New products and businesses with months of data cannot be forecast precisely. We use category-level patterns, flag low-confidence items, and avoid false precision.
Unforeseeable shocks
Strikes, currency swings and supply disruptions cannot be predicted from history. Forecasts are for normal conditions, and planners need a way to apply judgement quickly.
Model drift
Customer behaviour shifts over time. We monitor forecast error each week, retrain on a schedule, and alert when performance degrades beyond an agreed range.
Tools and technology
- Python
- XGBoost
- Prophet
- scikit-learn
- pandas
- FastAPI
- PostgreSQL
- Metabase
Common questions, answered
How much sales history do we need?
At least a year or two for seasonal patterns, and more is better. With less, we can still produce simple forecasts, though ranges will be wider and we will say clearly how far to trust them.
How accurate will the forecasts be?
That depends on how stable your demand is. We will not promise a number in advance. We back-test on your history, compare with your current method, and show the results before you commit.
Can it forecast at product and branch level?
Yes, though detailed levels are noisier. We often forecast at a higher level and distribute downward, or combine both, choosing whichever back-tests better for your data.
Do we need AI for this?
Not always. Sometimes a seasonal average does nearly as well. We start from simple baselines, and use machine learning only where it clearly beats them on your data.
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Ready to start your Demand Forecasting Solutions project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.