Fitting a forecast
With so many planning decisions pending a forecast, many practitioners have been tempted to rely on their computer system to select, for them, the best prediction model from a wide range of techniques.
Compared to more scientific approaches, this might only be a little more advanced than the random movements of a laboratory rat seeking to exit a maze. The forecasting models in a pick best approach can produce wildly divergent results. Different samples of history will influence predictions, and performance is often negatively impacted by significant fluctuations in the data.
The technique usually creates endless hours of model tweaking and data manipulations to gain only slightly less confidence in the forecasting. Fitting a forecast based on how tightly it fits the past, does not guarantee a better alignment to the future. It’s very often the root-cause of knee-jerk planning.
A more rational approach to the problem is to include statistical recognition of different types of demand before precisely applying the right technique. Failure to recognise the differences in demand characteristics can result in the application of inappropriate planning techniques and create undesired fallout like excess inventory, wild swings in replenishment plans, late promotional deliveries, and more frequent stock-outs. Slimstock’s Slim4 Inventory Optimisation software automatically recognises what stage of life cycle a product is in. It systematically analyses demand history or replenishment challenges before scientifically applying the optimal planning strategy.