Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context

Petros Boutselis and Ken McNaught

International Journal of Production Economics, 2019, vol. 209, issue C, 325-333

Abstract: A problem faced by some Logistic Support Organisations (LSOs) is that of forecasting the demand for spare parts, corresponding to equipment failures within the system. Here we are particularly concerned with a final phase of operations and the opportunity to place only a single order to cover demand during this phase. The problem is further complicated when the service logistics context can change during this final phase, e.g. as the number of systems supported or the LSO's resources change. Such a problem is typical of the final phase of many military operations.

Keywords: Bayesian networks; Failure rates; Spare parts forecasting; Changing demand context (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:209:y:2019:i:c:p:325-333

DOI: 10.1016/j.ijpe.2018.06.017

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