How to make spare parts forecasting work
Making predictions is hard, especially if they are about the future.
This is a variation of a quote variously attributed to the physicist Niels Bohr, baseball manager Yogi Berra, writer Mark Twain and others. While the origin of this quote may be in dispute, the issue it raises is not. What happened in the past is not necessarily an indicator of the future.
With spare parts inventory, the real problem is that essentially all spare parts inventory stocking problems are really forecasting problems. This is because the very essence of spare parts inventory management is determining the most appropriate level of inventory to hold, to service the expected future demand for that inventory, based on the expected supply constraints. Thus, all inventory management requires a forecast of both demand and supply in order to establish the buffer that needs to be held to match these two factors.
When most demand is based on (apparently) random failure events, as with MRO and spare parts, then demand is impossible to forecast. In many ways, it is a bit like throwing darts at a dart board. So, how do you make spare parts forecasting work?
All forecasting methods can be grouped into one of two classes: either extrapolation of historical data or causal and predictive models.
Extrapolation of historical data is a fancy way of saying that you take whatever happens in the past and assume it will happen again in the future. This is something we know to rarely be true.
The methods employed are typically quantitative. Because of this they can appear to be rigorous. The accuracy achieved is driven by both the validity of the fundamental premise and the quality of the data used in the analysis, not the sophistication of the modelling.
On the other hand, causal or predictive methods can be either quantitative or qualitative.
A quantitative approach might rely on forecasts of future planned activities (such as planned maintenance) and the expected usage of the part in each activity.
It is this aspect of causal or predictive methods that often makes people think that such approaches are less accurate than historic data-driven approaches. This is not the case because with spare parts inventory causal approaches and the use of forward-looking information are more appropriate for deciding future inventory holdings than relying on the extrapolation of history. The key is to ensure the basis of decision-making is more robust and consensus-driven than just “today’s opinion.”
By understanding that an inventory stocking problem is essentially a forecasting problem and that all forecasting methods have their limitations, it is easy to see good spare parts inventory management relies on an understanding of both these limitations and the application of the techniques in practical terms, not just in theory. This is why training is the most important task you can undertake to improve your spare parts inventory management outcomes. Without this, you may as well be throwing darts. NP