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

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. The LSO operates the recovery and repair loop for the equipment in question. By developing a simulation of the LSO, we can generate synthetic operational data regarding equipment breakdowns, etc. We then split that data into a training set and a test set in order to compare several approaches to forecasting demand in the final operational phase. We are particularly interested in the application of Bayesian network models for this type of forecasting since these offer a way of combining hard observational data with subjective expert opinion. Different LSO configurations were simulated to create a test dataset and the simulation results were compared with the various forecasts. The BN that learned from training data performed best, followed by a hybrid BN design combining expert elicitation and machine learning, and then a logistic regression model. An expert-adjusted exponential smoothing model was the poorest performer and these differences were statistically significant. The paper concludes with a discussion of the results, some implications for practice and suggestions for future work.

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

“…It is also consistent with the conclusion of literature review by Bacchetti and Saccani [7]. Hence, to achieve the purpose of this study, we need to narrow down to the military sector and investigate thoroughly prior works, related to spare parts demand forecasting for weapon systems [43,44]. The military sector also has suffered from spare parts supply problems, caused by inaccurate forecasts of spare parts demand.…”

A Genetic Algorithm-Based Model for Inventory Control in Intermittent Demands

“…With this method, the percentage of not finding spare parts in the relevant company has been reduced by approximately 4-5 percent during the year (Stip & Van Houtum, 2020). Boutselis and McNaught worked on the problem of forecasting spare parts demand related to equipment failures within some Logistics Support Organizations (LSOs) (Boutselis & McNaught, 2019). A detailed examination by Jiang et al showed that an adaptive univariate SVM (AUSVM) model was created to estimate the intermittent demand.…”

Modelling supply chain risks and their impacts on the performance of the sago starch agro-industry

“…Alternatively, the hybrid approach offers flexibility in developing the BN structure. It is used to obtain the best of previous approaches (Boutselis and McNaught, 2019). The main advantage of the expert-driven approach is its ability to derive an understandable nature of BN.…”

“…Commonly, the BN structure is developed by either knowledge elicitation of experts or learning of dataset. The expert-driven BN model can derive causal relations even if lack of historical data (Boutselis and McNaught, 2019; Zwirglmaier and Straub, 2017). However, its drawbacks primarily considerable efforts are needed to develop the structure and assign the conditional probability values (Qazi et al , 2017).…”