Demand forecasting is a barebone of every retailer's business: it is essential for managing supply chain, planning sales, and shaping customer loyalty. Nobody would say that getting accurate and timely forecast is easy. It turns out, however, that assessing the accuracy of the forecast can be an equally challenging task. To summarize, here are a few principles to bear in mind when measuring forecast accuracy:
- MAPE and WAPE are most commonly used metrics to measure forecast accuracy. Which metric to use depends on the type of goods and their sales volumes, as well as retailers' business priorities.
- The most accurate forecast doesn't always mean the best forecast. MAPE and WAPE are symmetric: while calculating the percentage of error, these metrics don't distinguish between overstocks and out-of-stocks.
- Statistical metrics should be aligned with the business ones. Depending on the retailer's priorities, these metrics may include the number of write-offs, costs of markdowns, the number of out-of-stocks, lost sales, or food wastage.
- The combination of statistical and business metrics gives an opportunity to make better decisions on the optimal quantity of each product to be ordered. After all, it is not the percentage of error but the cost of error that makes a difference.