This month I am addressing the important issue of forecasting spare parts requirements.
In keeping with our policy of providing content of substance, this article is continued on the website (link is below) and members can access a white paper that explains more on forecasting and provides specific rules for deciding which techniques to use in different circumstances.
Keep on improving,
Phillip Slater
Founder, SparePartsKnowHow.com
Forecasting Spare Parts Requirements
Essentially all inventory stocking decisions can be resolved as a forecasting problem. This is because the essence of 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. This is no different with MRO and spare parts except that demand based on random failure events is, by definition, impossible to forecast. The following is a discussion of forecasting techniques and their applicability to MRO and spare parts inventory management.
Two Classes of Forecasting
All forecasting methods can be grouped into one of two classes:
1. Extrapolation of historical data
2. Causal or predictive models
Extrapolation of historical data can vary from simplistic to highly sophisticated but all historical data methods are based on the premise that the future can be predicted by looking at the past.
The methods are typically quantitative and can appear to be rigorous but the accuracy is driven by both the validity of the fundamental premise, that is, that the future can be predicted by looking at the past, and the quality of the data rather than the sophistication of the modelling.
Causal or predictive methods can be either quantitative or qualitative. A quantitative approach might rely on forecasts of future planned maintenance activities and the expected usage of parts for each activity. A qualitative approach might rely on the opinions of people involved in the parts usage and procurement.
For example, a maintenance team member wants to have a spare part stocked and their view of how many to stock is based solely on their opinion, on the day that the decision is made, of what is ‘safe’. Unfortunately, this happens too often with spare parts inventory and the natural conservatism of maintenance personnel results in significant overstocking.
This aspect of causal or predictive methods often makes people think that all such approaches are unscientific and less accurate than historic data driven approaches. However, this is not the case because causal approaches and the use of forward-looking information is more appropriate for deciding future inventory holdings than relying on the extrapolation of history.
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