Over the past few months, we’ve been running simulation tests on different demand forecasting methods: Winter’s additive & multiplicative, seasonal and robust seasonal. Then, we used MAPE to determine the forecast accuracy for each method. Here’s what we found.
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.
You have a favorite forecast accuracy metric(s) you’ve been practicing within the organization for a while, and now you think you are ready to bring it to the Sales and Operations Planning (S&OP) meeting as a Key Performance Indicator (KPI) of your demand planning process. But you are not sure exactly how to go about
In our line of work at Arkieva, when we ask this question of business folks: What is your forecast accuracy? Depending on who we ask in the same business, we can get a full range of answers from 50% (or lower) to 95% (or higher). How is this possible? Imagine a management team being given