Forecasts you can plan around, with the error range shown

A forecast is only worth building if someone will act differently because of it. So we begin with the decision: how many units to order, which customers to call, which machine to inspect, and how far ahead the answer is needed. That fixes the forecast horizon, the level of detail and the cost of being wrong in each direction. Overstocking and stockouts rarely cost the same, and the model should know it.

Then we look at the data honestly. Most predictive projects stall on records, not algorithms: missing months, products renamed, sales entered late, sensors that were offline. We profile the history, document the gaps, and tell you whether the data can support the question. Sometimes the answer is to collect better data for three months first. We would rather say so than build a model that looks clever and fails quietly.

Every model is compared with a simple baseline, such as the same week last year or a moving average. If a complex model cannot beat that baseline by a clear margin, we recommend the simple one, because it is cheaper to run and easier to explain. We test by backtesting: pretending to forecast past periods using only what was known at the time, then comparing with what really happened.

We report error in the units you plan in, such as units, riyals or days, and show a range, not a single number. Seasonality matters in Saudi demand: Ramadan, Eid, Hajj and Umrah seasons, school terms and the national calendar all move sales and need to be built in explicitly. Models that ignore the Hijri calendar tend to misplace these peaks by days every year.

Customer-level predictions use personal data, so the Personal Data Protection Law applies. We use only the fields the prediction needs, avoid sensitive categories, and keep a record of what each model uses. SDAIA's AI ethics principles also call for fairness and explainability, so scores come with the main reasons behind them, and decisions that affect individuals stay with a person.

Most predictive workloads are light enough to run on modest infrastructure, which makes in-Kingdom hosting practical and affordable. Your data stays in your environment or a Saudi data center, and the model code is delivered with documentation. A first forecast usually takes six to twelve weeks. Extending it across several product lines or business units takes a further six to ten.