Bayesian degradation modelling for spare parts inventory management. (13th May 2020)

• Record Type: Journal Article
• Authors: Ruiz, Cesar; Pohl, Edward; Liao, Haitao
• Is Part Of: IMA journal of management mathematics. Volume 32:Number 1(2021)
• Page Start: 31
• Page End: 49
• Publication Date: 2020-05-13
• Subjects: Bayesian degradation modelling -- stochastic dynamic programming -- spare parts inventory management; Management -- Mathematical models -- Periodicals; Management science -- Mathematical models -- Periodicals; Business mathematics -- Periodicals; 650.01513
• DOI: 10.1093/imaman/dpaa008
• Languages: English
• ISSNs: 1471-678X

Abstract

Abstract: 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.