Forecasting spare parts demand using condition monitoring information

Abstract

Purpose

The control of an inventory where spare parts demand is infrequent has always been difficult to manage because of the randomness of the demand, as well as the existence of a large proportion of zero values in the demand pattern. The purpose of this paper is to propose a just-in-time (JIT) spare parts availability approach by integrating condition monitoring (CM) with spare parts management by means of proportional hazards models (PHM) to eliminate some of the shortcomings of the spare parts demand forecasting methods.

Design/methodology/approach

In order to obtain the event data (lifetime) and CM data (first natural frequency) required to build the PHM for the spares demand forecasting, a series of fatigue tests were conducted on a group of turbomachinery blades that were systematically fatigued on an electrodynamic shaker in the laboratory, through base excitation. The process of data generation in the numerical as well as experimental approaches comprised introducing an initial crack in each of the blades and subjecting the blades to base excitation on the shaker and then propagating the crack. The blade fatigue life was estimated from monitoring the first natural frequency of each blade while the crack was propagating. The numerical investigation was performed using the MSC.MARC/2016 software package.

Findings

After building the PHM using the data obtained during the fatigue tests, a blending of the PHM with economic considerations allowed determining the optimal risk level, which minimizes the cost. The optimal risk point was then used to estimate the JIT spare parts demand and define a component replacement policy. The outcome from the PHM and economical approach allowed proposing development of an integrated forecasting methodology based not only on failure information, but also on condition information.

Research limitations/implications

The research is simplified by not considering all the elements usually forming part of the spare parts management study, such as lead time, stock holding, etc. This is done to focus the attention on component replacement, so that a just-in-time spare parts availability approach can be implemented. Another feature of the work relates to the decision making using PHM. The approach adopted here does not consider the use of the transition probability matrix as addressed by Jardine and Makis (2013). Instead, a simulation method is used to determine the optimal risk point which minimizes the cost.

Originality/value

This paper presents a way to address some existing shortcomings of traditional spare parts demand forecasting methods, by introducing the PHM as a tool to forecast spare parts demand, not considering the previous demand as is the case for most of the traditional spare parts forecasting methods, but the condition of the parts in operation. In this paper, the blade bending first mode natural frequency is used as the covariate in the PHM in a laboratory experiment. The choice of natural frequency as covariate is justified by its relationship with structural stiffness (and hence damage), as well as being a global parameter that could be measured anywhere on the blade without affecting the results.

Citation

Lelo, N.A., Heyns, P.S. and Wannenburg, J. (2020), "Forecasting spare parts demand using condition monitoring information", Journal of Quality in Maintenance Engineering, Vol. 26 No. 1, pp. 53-68. https://doi.org/10.1108/JQME-07-2018-0062