When you should use traditional extrapolative forecasting techniques.
With so much hype around new Machine Learning (ML) and probabilistic forecasting methods, the traditional “extrapolative” or “time series” statistical forecasting methods seem to be getting the cold shoulder. However, it is worth remembering that these traditional techniques (such as single and double exponential smoothing, linear and simple moving averaging, and Winters models for seasonal items) often work quite well for higher volume data. Every method is good for what it was designed to do. Just apply each appropriately, as in don’t bring a knife to a gunfight and don’t use a jackhammer when a simple hand hammer will do.
Extrapolative methods perform well when demand has high volume and is not too granular (i.e., demand is bucketed monthly or quarterly). They are also very fast and do not use as many computing resources as probabilistic and ML methods. This makes them very accessible.
Are the traditional methods as accurate as newer forecasting methods? Smart has found that extrapolative methods do very poorly when demand is intermittent. However, when demand is higher volume, they only do slightly worse than our new probabilistic methods when demand is bucketed monthly. Given their accessibility, speed, and the fact you are going to apply forecast overrides based on business knowledge, the baseline accuracy difference here will not be material.
The advantage of more advanced models like Smart’s GEN2 probabilistic methods is when you need to predict patterns using more granular buckets like daily (or even weekly) data. This is because probabilistic models can simulate day of the week, week of the month, and month of the year patterns that are going to be lost with simpler techniques. Have you ever tried to predict daily seasonality with a Winter’s model? Here is a hint: It’s not going to work and requires lots of engineering.
Probabilistic methods also provide value beyond the baseline forecast because they generate scenarios to use in stress-testing inventory control models. This makes them more appropriate for assessing, say, how a change in reorder point will impact stockout probabilities, fill rates, and other KPIs. By simulating thousands of possible demands over many lead times (which are themselves presented in scenario form), you’ll have a much better idea of how your current and proposed stocking policies will perform. You can make better decisions on where to make targeted stock increases and decreases.
So, don’t throw out the old for the new just yet. Just know when you need a hammer and when you need a jackhammer.
