Written by Greg Spira
When working with companies to improve their demand planning, I regularly get the question: “What is best-in-class forecast accuracy? What does good look like?”
It’s a tricky question to answer. Many considerations must be taken into account to establish an appropriate definition. Cumulative lead times and planning time fences guide us to when, and at what level of detail we take snapshots of the demand plan against which to measure accuracy. Companies often have different supply chains and go-to-market approaches, making these calculations almost always unique to them in some way.
The truth is that there is no such thing as a best-in-class forecast accuracy.
This then leads to the question: “How should we set our accuracy targets?”
Some would say that the best approach is to look at “forecastability”, which has us look at the underlying variability of actual demand. While this is better than looking for a benchmark, the problem is that it does not consider your current capabilities. Think of it this way: Olympic marathon runners complete a race in well under 3 hours. If you were a casual runner, would it make any sense to set a goal of running sub 3 hours? Probably not.
My answer then is to take your current accuracy, and plan to improve it. Note that I used the word plan. A plan to improve accuracy, just like the demand plan itself, should be built on assumptions. Are you implementing new tools? New processes? New inputs? Hiring more planners? Are there headwinds? We all know that forecast accuracy has been thrown out the window in many organizations given the impacts of COVID-19. Before resetting targets for 2021, companies will have to take a hard look at their process and assess whether they will be able to bounce back to pre-COVID levels of accuracy.
If you don’t have an idea of what you will do to improve accuracy, establishing targets significantly beyond where you are today will be an exercise in frustration.
Learn more about Forecast Accuracy by reading this white paper.