What is demand forecasting? What it does and what it needs
Demand forecasting estimates, from historical and current operating data, how much of a good will be needed in a future period, for example products, materials, spare parts or energy. It is the basis for purchasing, inventory and production planning and replaces rules of thumb with a transparent calculation that is updated continuously.
Image: Yaning Wu / prediction / Licenced by CC-BY 4.0
What is demand forecasting? The definition in detail
The term is deliberately broad. Demand forecasting always answers the same question, namely which quantity is needed and when, and differs only in the target variable. In the past it was usually done in a spreadsheet. Today forecasting models do the calculation. They process seasonal patterns, changes in the product range or machine conditions as explanatory variables and adapt when new data arrives. In German the method is called Bedarfsprognose.
How it differs from related terms
In practice, four terms are often mixed up. The table sorts them out.
| Term | What is predicted | Typical data | What the result is used for |
|---|---|---|---|
| Demand forecasting | quantity of a good in a future period | varies with the target variable | umbrella term for planning in purchasing, inventory and production |
| Sales forecasting | sales volume per product or product line | order history, framework contracts, seasonal patterns | production and purchasing planning |
| Material requirements planning | material required per order and date | bills of materials, open and expected orders, replenishment lead times | procurement and scheduling |
| Predictive maintenance | time of a failure or of wear | sensor data, control data, failure history | planned maintenance instead of downtime |
Sales forecasting therefore often provides the input for material requirements planning. Predictive maintenance, by contrast, does not predict a demand but an event. The two meet in spare parts demand: if you know when wear is likely, you can also order the right part in time.
What the industry report shows for planning and supply chain
In our industry report, demand forecasting is the core of use case field C, planning and supply chain. The typical effect is inventory reduced by 10 to 50 %, and maturity is rated as medium. Individual cases show the range: Schneider Electric reports inventory reduced by 10 % on a value of around EUR 100 million (company statement, 2026), and Unilever in Hefei reports forecast accuracy improved by 39 % (WEF Global Lighthouse Network 2026). Both are documented cases from industry, not projects of ours.
The prerequisite stands out. No other field is as unforgiving of poor ERP master data. You need order and sales history covering several years including outliers, realistic replenishment lead times instead of well kept wishful values, and exactly one target variable, such as service level or days of inventory. How Orpheon covers these forecasting tasks in production is shown on the page on AI demand forecasting for manufacturing.
Practical example: three scenarios with Orpheon
With Orpheon we develop and operate our own forecasting platform. It covers three recurring tasks: sales forecasting from order and sales data, energy demand forecasting from meter data and production plans, and spare parts demand forecasting from maintenance history, machine data and bills of materials. In multi-plant operations, Orpheon calculates separately for each plant or line because patterns differ between sites. A first pilot with real data is ready in 72 hours, and productive operation follows in 4 to 10 weeks. The technical setup is described on our page on demand forecasting with AI. For manufacturers with machine data and bills of materials there is a dedicated page on AI demand forecasting for manufacturing.
Frequently asked questions
Is an incompletely maintained ERP database good enough?
Often it is, although it limits accuracy. What matters is knowing the gaps before you start. The report recommends starting with one article group with a high inventory value, run in parallel with the existing process.
How do you measure the success of such a forecast?
Not by model accuracy alone. The reliable measure of success is the difference in tied up capital at the same service level, measured against a baseline that was defined before the start.
