Our focus in this blog series has been to establish forecast accuracy targets. In very general terms, the goal should be to add value to the business through the forecasting process. We have however focused on the forecast value add and using that to create a minimum acceptable forecast accuracy target in the previous blog. Now we will take that a step further and talk of ways of improving it.
A company’s total inventory is built up of many different parts such as strategic stock, anticipation stock, safety stock, cycle stock, unplanned stock. The cycle stock is the one most connected to the demand forecast; it is expected to be sold as the forecast becomes real demand. Safety stock on the other hand is extra stock to deal with the variability of the demand or supply. As such, it is not always linked to forecasting accuracy.
Learn the best approach to setting forecast accuracy targets and how to set expectations for your management team.
Should you factor returns in your forecast error calculation? In this article, we’ll use a sample data set, to demonstrate if you should consider returns when calculating your forecast errors.
For most businesses that rely on demand forecasts for supply and capacity planning, improving demand forecast accuracy is critical. There are many methods to measure forecast bias and the accuracy of supply chain forecasts including using statistical methods like the Mean Absolute Percent Error or MAPE that we’ve discussed in our previous blogs.