I've been walking around the last few days with what looks like a dollop of chocolate syrup or grape jelly on my chin. Alas, it is just a bruise from getting elbowed in the mouth at basketball last Thursday night. (Church leagues may be the only dirtier place to play than in the SAS gym.)
After picking myself up off the ground and determining I hadn't lost any teeth, I said to the guy "Are you serious, or was that some kind of a joke?" to which he replied, "Man, I'm serious." I said, "Well that's good, because I don't take too kindly to jokes like that."
Customer Question on Naive Models
The following was forwarded from a customer of Clayton Wooddy, one of the SAS account executives:
Are companies using a specific calculation for naïve or is it just assumed that it is a basic average calculation but could vary by how many months used to create that average? Or is it specific like always just the last month’s average?
The naïve model should be something simple to calculate that can be run automatically. It serves as a forecast you can generate “for free” – what you would use if you didn’t have forecasters, forecasting software, or a forecasting process.
The two traditional naïve models (referenced in the academic literature) are the random walk and the seasonal random walk:
• The random walk (aka Naïve Forecast 1 or NF1 in the classic text Forecasting Methods and Applications by Makridakis, Wheelwright and Hyndman) just uses your last observation as your future forecast. So if you sold 12 last week, your forecast becomes 12 for all future weeks. If you sell 6 this week, you change all future forecasts to 6, etc.
• An example of a seasonal random walk (aka NF2) would be to use the actual from a year ago as the forecast for the corresponding period this year. Thus, your forecast for April 2011 would be the actual from April 2010, etc.
One thing to note is that you would never want to use the random walk as your real-life forecasting system, because your forecasts could change radically every period as the new actuals come in. (E.g. If you sold 100, 10, and 1000 units in consecutive periods, all of your future forecasts would change from 100, to 10, to 1000, creating unmanageable swings in your supply planning and operations, as well as your revenue forecasts.)
A good choice is to use simple exponential smoothing or a moving average as the naïve. Forecasts will change with new each observation, but depending on the alpha factor of your exponential smoothing (e.g. alpha = .15 is more stable than alpha = .5), or the length of the moving average (e.g. 52 week average is more stable than a 3 week moving average), the change won’t be huge and will go up or down with recent performance.
Companies have also used a composite as their naïve model – for example taking the average of a seasonal random walk (to incorporate seasonality) along with simple exponential smoothing (to incorporate the general level and trend). I think this is a great approach.
To conclude – there is no fixed or required naïve model to use. However, it should follow the principle of being simple to calculate, you should be able to automate it, and I would suggest you would also want to be able to fall back and use it as your actual forecast if your existing system and process aren’t doing any better.
Naive models can be surprisingly difficult to beat. As a critical part of FVA analysis, they are a great way to identify waste and inefficiency in your forecasting process.