Evaluate a stock demand forecast project: historical data, stockouts, seasonality and comparison with simple methods before hiring AI.
Index
Recorded sales do not represent all demand when there was a shortage of product. Training a model without recognizing stockouts may recommend less stock precisely for items that failed to sell. Hiring needs to start with the quality of the track record and the replacement decision that will be supported.
Decision this guide helps you make: Decide whether a forecast improves purchasing and replenishment under actual business conditions.
Determine the useful horizon
The forecast must consider purchase deadline, replacement frequency and required level of detail. Estimating per day may not help a purchase made monthly. New products, promotions and substitutions also require treatment. Define which families have sufficient history and which need rules or manual assessment.
Rebuild the sales context
Include availability, returns, campaigns, and catalog changes when the data exists. Differentiate between absence of sale and absence of registration. Ask the team to document gaps rather than padding the story with invisible assumptions. Preparation can represent a relevant part of the project and must appear in the budget.
Compare with a simple reference
Before accepting a complex model, compare its result with basic methods suitable for the business. Make an assessment in periods subsequent to those used in the adjustment, avoiding using future information. Look at errors by household and impact on purchasing, not just an average forecast metric.
Keep the purchasing decision governed
The recommendation must consider restrictions such as minimum lot, box and space, when these rules are in scope. Forecasting demand is not equivalent to optimizing the entire inventory policy. Start with revised suggestions and track differences between predicted and realized before automating orders.
- Horizon aligned with the replacement deadline.
- Ruptures and campaigns identified.
- Comparison with reference method.
A scenario to check out in the demo
Hypothetical example: an item was unavailable for part of the month and sold little. A model that uses only sales may interpret a drop in interest. The evaluation must mark the rupture and compare scenarios, recognizing the limitations of the data. If the history does not allow estimating demand with sufficient confidence for purchase, the outlet must maintain a review or a simple rule.
Briefing to request a proposal
- Sales history, availability and catalog changes.
- Replenishment deadline and decisions that the forecast should support.
- Current method used as a reference for comparison.
Evaluate a product family
Quantum9 can organize history and test the usefulness of predictions within a defined time frame. Take sales and availability data, in addition to the current purchasing rule. The pilot must demonstrate support for the decision without promising to eliminate commercial uncertainty.
Discover the scope of Data and BI for management and deepen the context in related guide.