A company can improve its demand forecast accuracy and still find its warehouses holding just as much stock as before. Another business might achieve similar forecasting results while continuing to struggle with shortages. For supply chain managers expecting better forecasts to free up working capital, the connection can be surprisingly difficult to establish.
Forecasting plays an important role in inventory planning, but the amount of stock a company needs also depends on supplier lead times, ordering practices and the level of product availability it wants to maintain. Even the direction of forecasting errors matters. Two forecasts with identical accuracy scores can lead to different inventory levels and customer service outcomes.
What Demand Forecast Accuracy Tells Us
Demand forecast accuracy measures how closely predicted demand matches what customers actually want to buy. A common approach compares the total size of forecasting errors with actual demand. If those errors add up to 10% of demand, a company might report 90% forecast accuracy.
This calculation is useful for monitoring forecasting performance, although it has limitations. As forecasting researcher Rob Hyndman explains in his discussion of weighted absolute percentage error, even widely used accuracy measures require careful interpretation.
One important limitation is that conventional accuracy calculations often treat overestimating and underestimating demand equally. Predicting sales of 110 units when customers buy 100 produces the same absolute error as predicting 90 units. For inventory managers, however, the consequences can be different. Ordering too much may leave products sitting in storage, while ordering too little can result in missed sales or delayed deliveries.
A consistent tendency to overestimate or underestimate demand is known as forecast bias. It can be particularly important for inventory planning because repeated errors in the same direction may systematically influence purchasing and replenishment decisions.
How Two Equally Accurate Forecasts Can Produce Different Results
Consider a hypothetical distributor selling a product with stable weekly demand of 100 units. Two forecasting approaches are evaluated over four weeks.
| Week | Actual demand | Forecast A | Forecast B |
|---|---|---|---|
| 1 | 100 | 90 | 90 |
| 2 | 100 | 110 | 90 |
| 3 | 100 | 90 | 90 |
| 4 | 100 | 110 | 90 |
Both forecasts miss actual demand by ten units every week. Across the four weeks, each produces 40 units of absolute forecasting error against total demand of 400 units. Under the same commonly used calculation, both therefore achieve 90% forecast accuracy.
Their patterns are quite different. Forecast A alternates between predicting too little and too much demand, with the errors cancelling out over the four weeks. Forecast B consistently underestimates demand by ten units.
Now assume the distributor uses a simple replenishment rule. At the beginning of each week, it brings available stock up to the forecast quantity. Initially there is no stock, deliveries arrive immediately, and any remaining inventory carries over into the following week. There is no additional safety stock, and demand that cannot be satisfied is lost.
Under these assumptions, Forecast A allows the distributor to supply 380 of the 400 units requested. It runs short by ten units in weeks one and three, while finishing weeks two and four with ten units left in stock.
Forecast B produces shortages every week. The distributor supplies only 360 units, although it also avoids holding any leftover inventory.
The two approaches have identical forecast accuracy scores, but Forecast A satisfies 95% of demand, compared with 90% for Forecast B. It also holds more inventory.
This simplified example does not establish which approach is more profitable. That would depend on the costs of unsold stock and lost sales. It demonstrates why an accuracy percentage cannot, by itself, explain inventory performance or delivery reliability.
Why Replenishment Rules and Lead Times Matter
In practice, companies rarely receive additional stock immediately after placing an order. Suppliers may require several weeks to deliver, production runs may be scheduled in advance, and minimum order quantities can limit purchasing flexibility. The difficulties of aligning forecasts, delivery frequency and stock availability can also be seen in Starbucks’ store replenishment experience.
Inventory planners therefore hold additional stock to protect against unexpected demand or delivery delays. This reserve is commonly called safety stock. Its required size depends partly on how much uncertainty a company must cover and the service level it aims to achieve.
For example, a company relying on a supplier with a long replenishment lead time must consider demand across the period until new stock becomes available. A more accurate forecast for next week’s sales may offer limited benefits if the business must commit to purchases covering several weeks.
Research supports the importance of these relationships. In a 2017 study published in the European Journal of Operational Research, Dennis Prak, Ruud Teunter and Aris Syntetos demonstrated that conventional safety-stock calculations can underestimate inventory requirements when forecasting errors across the replenishment period are related.
The economic trade-offs are equally important. Holding excess inventory ties up capital and creates storage costs, with additional risks for products that become obsolete or expire. Insufficient inventory can mean lost revenue, production interruptions or expensive emergency deliveries.
A 2025 study based on the M5 retail forecasting competition examined how forecast accuracy relates to inventory costs under different operating conditions. The researchers found that accuracy was more relevant when inventory holding costs were similar to or higher than the costs of lost sales. Where lost sales were substantially more expensive, the most accurate forecasting approach was not necessarily the most economical.
These findings help explain why improved forecasting accuracy does not automatically translate into lower inventory costs. Businesses must consider how forecasts influence replenishment decisions and what those decisions ultimately cost.
When Better Forecasts Improve Inventory Performance
More reliable forecasts can certainly help companies reduce inventory while maintaining product availability. Their economic value becomes clearer when improvements address the forecasting errors that matter for replenishment and when purchasing decisions can respond accordingly.
A forecast that consistently underestimates demand might need correction even if doing so initially increases inventory. The additional stock could improve product availability and prevent lost sales. Conversely, reducing persistent overestimation may release working capital without compromising customer service, provided the company maintains sufficient protection against unexpected demand.
The importance of evaluating forecasts against operational objectives was demonstrated in a 2020 study by Nikolaos Kourentzes, Juan R. Trapero and Devon Barrow. Their research showed that selecting forecasting models according to inventory-related objectives could produce better operational results than relying on conventional accuracy measures alone.
For supply chain managers, this suggests a practical approach to evaluating forecasting improvements. Rather than examining accuracy scores in isolation, companies can compare what different forecasts would have meant for purchasing decisions, inventory held, shortages and overall costs. Such comparisons should use consistent assumptions about supplier lead times, order quantities and customer service requirements.
The underlying demand data also deserve attention. When a product is unavailable, recorded sales may fall even though customers still want to purchase it. Forecasts based only on those sales can underestimate demand, a problem examined in a 2024 study in the International Journal of Forecasting.
A company seeking to reduce inventory should therefore evaluate forecasting improvements alongside its replenishment policies and service requirements. A higher accuracy score can be a useful indication of progress, but meaningful economic gains depend on whether the resulting decisions improve the balance between stock availability, inventory investment and operating costs.
