Bayesian degradation modelling for spare parts inventory management

Decision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

Automated summary

Decision makers in various sectors such as manufacturing and transportation aim to minimize downtime costs by utilizing planned stoppage times to make changes in shifts and line configurations. The availability of spare parts significantly impacts operational costs, with sensor technologies enabling condition monitoring and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management, proposing a stochastic dynamic program to minimize expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this context, showing optimal time between long stoppages and spare parts order quantity when prior information about degradation is accurate. The methodology can analyze sensitivity of optimal solutions to changes in accuracy and bias of prior distributions of model parameters, cost structure, and number of machines in the system.