Too much stock ties up money on shelves; too little loses sales and customers. In between sits demand forecasting — the increasingly scientific art of anticipating how much you will sell so you buy and store just right.
The cost of guessing wrong
Without forecasting, logistics runs on instinct: you order "like last year" and react to stockouts when it is already too late. The result is warehouses full of what does not move and empty of what customers want. Both errors cost — one in idle capital, the other in lost sales.
What demand forecasting uses
- Sales history: the base — patterns by product, store and period.
- Seasonality: predictable peaks (Christmas, summer, back-to-school) that repeat.
- Trend: products growing or declining steadily.
- External factors: promotions, holidays, weather, events that shift normal demand.
From simple averages to predictive models
Many companies still forecast with an average of recent months — better than nothing, but blind to seasonality and trend. Analytical models learn those patterns and adjust: they separate the Christmas peak from the normal level, detect the declining product before you fill a warehouse with it. They need not be complex to be worthwhile.
The real gain
Good demand forecasting means less dead stock, fewer stockouts, fewer expensive rush deliveries and customers who find what they are looking for. It is one of those cases where data pays for itself in a few months.
In practice
Start with the products that weigh most in your inventory (the 80/20 rule usually applies) and improve the forecast for those first. You do not need to predict everything perfectly — you need to err less where it costs most. Which products would a better forecast save you the most money on?
