Demand Forecasting
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Do you know how to measure the accuracy of your forecasts?
Schedule a meetingDiscover AI-Powered Sales ForecastingDemand forecasting involves predicting future sales volumes for a product, category, or retail location over a given time frame (day, week, season). It combines historical sales data, external variables (weather, calendar, competition), and, increasingly, machine learning. It forms the foundation of sales planning, purchasing, and pricing.
The Essentials in 6 Questions
A forecast of future demand, at various levels of detail.
Planning, purchasing, pricing, logistics.
Months in advance for procurement, every week for operations.
SKU × store × week, category × month, product family × quarter.
Optimize inventory, promotions, and profit margins.
Statistics, machine learning, and domain expertise, as measured by the MAPE.
Why Forecast Demand?
Because inventory levels, promotions, and prices are all determined based on an assumption about demand.
- Optimize inventory: Avoid stockouts (lost revenue) and excess inventory (tied-up capital, damage).
- Calibrating promotions: Understanding natural demand makes it possible to measure the incremental effect of a promotion.
- Managing margins: adjusting prices based on projected demand, using an approach similar to predictive pricing and yield management.
Real-life example: a T-shirt from the summer collection
A clothing retailer plans to sell 28,000 units of a T-shirt, with sales peaking in mid-June, and orders 32,000.
28,000 units planned, 32,000 ordered
Textile Sign · Demand Forecast for a T-Shirt from the Summer Collection
units projected for the season for this product, based on historical data, weather forecasts, and fashion trends (NLP analysis of social media).
units ordered from suppliers based on this forecast, with a 15% margin
Anticipated sales peak, used to plan logistics and promotional windows
Starting in January, the models combine five years of historical data, fashion trends analyzed on social media, regional weather forecasts, and the sales calendar. The forecast guides purchasing decisions (with a 15% safety margin), logistics, and promotional windows.
How can you build a robust demand forecast?
By combining several approaches and continuously measuring accuracy.
| Brick | Contents |
|---|---|
| Traditional Statistics | Moving averages, ARIMA, Holt-Winters. |
| Machine learning | Gradient boosting, recurrent neural networks. |
| Business Expertise | The category manager's judgment on specific cases. |
| Measurement | MAPE: 5 to 25 percent, depending on the product's stability. |
Modern tools weight these sources based on context. This is exactly what our AI-powered sales forecast does; when applied to the end of the season, it calibrates the markdowns for our markdown and clearance offerings. These forecasts also feed into pricing strategy simulations.
The 3 Common Mistakes in Demand Forecasting
Too few external variables, only one forecasting level, or no performance metrics.
- Underestimating external variables: without factorssuch asweather, holidays, or competing promotions, the model fails to account for key variations.
- Forecast at only one level: the forecast must be consistent across all reference codes, stores, and categories.
- Failure to measure performance: without tracking the MAPE or conducting back-testing, you won't see the model deteriorating.
Frequently Asked Questions
Short answers to the most frequently asked questions about demand forecasting.
What is demand forecasting?
Demand forecasting is the process of predicting future sales volumes for a product, product category, or retail location over a given time horizon. It relies on historical sales data, external variables, and, increasingly, machine learning. It forms the foundation of sales planning, purchasing, and pricing strategy.
What level of accuracy should we aim for?
Less than 10% MAPE for stable products; 20 to 30% is acceptable for new products or volatile seasonal items. Accuracy also depends on the time horizon.
What level of detail should be included?
SKU × store × week for operations; category × month for sales planning; product family × quarter for purchasing.
How do I add promotions?
Using an "uplift" model that separates natural demand (baseline) from the promotional effect to accurately predict the impact of a future promotion.
Key Takeaways
- Demand forecasting is the foundation of purchasing, promotions, and pricing.
- It combines statistics, machine learning, and domain expertise.
- Its performance is measured (MAPE) and monitored at several levels.
Would you like to forecast demand before setting your prices?
Booper combines demand forecasts with pricing scenarios to help you make informed decisions.
Let's talk about your demand forecast →Discover AI-Powered Sales ForecastingYou might also be interested in these articles
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