Optimizing product support (spare parts procurement) strategy by considering system operating environment – A case study (original) (raw)
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Reliability and operating environment‐based spare parts estimation approach
Journal of Quality in Maintenance Engineering, 2005
The required spare parts planning for a system/machine is an integral part of the product support strategy. The number of required spare parts can be effectively estimated on the basis of the product reliability characteristics. The reliability characteristics of an existing machine/system are influenced not only by the operating time, but also by factors such as the environmental parameters (e.g. dust, humidity, temperature, moisture, etc.), which can degrade or improve the reliability. In the product life cycle, for determining the accurate spare parts needs and for minimizing the machine life cycle cost, consideration of these factors are useful. Identification of the effects of operating environment factors (as covariates) on the reliability may facilitate more accurate prediction and calculation of the required spare parts for a system under given operating conditions. The Proportional Hazard Model (PHM) method is used for estimation of the hazard (failure) rate of components under the effect of covariates. The existing method for calculating the number of spare parts on the basis of the reliability characteristics, without consideration of covariates, is modified and improved to arrive at the optimum spare parts requirement. In this research, an approach has been developed to forecast and estimate accurately the spare parts requirements considering operating environment and to create rational part ordering strategies. Subsequently, two models (exponential and Weibull reliability based) considering environmental factors are developed to forecast and estimate the required number of spare parts within a specific period of the product life cycle. This study only discusses non-repairable components (changeable/service parts), which must be replaced upon failure. To test the models, the data collection and classification was carried out from two mining companies in Iran and Sweden and then the case studies concerning spare parts planning based on the reliability characteristics of parts, with/without considering the operating environment were done. The results show clearly the differences between the consumption patterns for spare parts with and without taking into account the effects of covariates (operating environment) in the estimation. The final discussion treats a risk analysis of not considering the system's working conditions through a non-standard (new) event tree approach in which the organizational states and decisions were included and taken into consideration in the risk analysis. In other words, we used the undesired states instead of barriers in combination with events and consequent changes as a safety function in event tree analysis. The results of this analysis confirm the conclusion of this research that the system's operating environment should be considered when estimating the required spare parts.
Product support and after sales services are among the important areas which have attracted the attention of managers and decision makers, especially in the field of supply chain and logistics management. Supplying the spare parts of products to guarantee the desired operation of product during its life time is in the focus of attention of logistics and supply chain managers. What makes the demand forecasting possible and real is the correct identification of demand affecting factors and their relation. So, the best model of spare parts' demand forecasting is one which incorporates all factors influencing the failure rate of the parts. This article presents a model which incorporates the environmental covariates influencing the failure rate as well as the reliability characteristics of parts. In addition, a portion of spare parts demand is due to the interaction of different parts in a system which is known as failure interaction. This factor is regarded in the model as another ...
2012 IEEE International Conference on Industrial Engineering and Engineering Management, 2012
Spare parts provision is a complex problem and requires an accurate model to analysis all factors that may affect the required number of spare parts. The number of spare parts required can be effectively estimated based on the reliability performance of the item. The reliability characteristics of an item are influenced not only by the operating time, but also by factors such as the operational environment. Therefore, for spare parts provisioning to be effective, the impact of these influence factors on the reliability performance of the item should be quantified. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability performance analysis that takes influence factors into account is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. In this paper the application of PHM to spare parts provision is discussed and demonstrated by a case study.
Strategies in spare parts management using a reliability engineering approach
Engineering Costs and Production Economics, 1991
Maintenance in a process industry, such as chemical and oil companies and food processing, needs frequent part replacements to assure continued operations. These parts represent non-repairable items which are best purchased, such as bearings and gaskets. Based on limited information on the time between failures of such systems, one can forecast the need for spare parts for a given planning horizon, which may be from one to several years. In this paper we discuss the methodology to calculate the spare part requirements for non-repairable systems and purchasing strategies for these parts. These strategies are based on a stochastic characterization of the time between failures of the system. The inventory level to be maintained will depend upon the need of such parts in consecutive time intervals or inventory cycles to satisfy the maintenance requirements with an acceptable risk level.
2012
Spare parts provision is a complex problem and requires an accurate model to analysis all factors that may affect the required number of spare parts. The number of spare parts required can be effectively estimated based on the reliability performance of the item. The reliability characteristics of an item are influenced not only by the operating time, but also by factors such as the operational environment. Therefore, for spare parts provisioning to be effective, the impact of these influence factors on the reliability performance of the item should be quantified. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability performance analysis that takes influence factors into account is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. In this paper the application of PHM to spare parts provision is discussed and demonstrated by a case study.
2012 IEEE International Conference on Industrial Engineering and Engineering Management, 2012
Spare parts provision is a complex problem and requires an accurate model to analysis all factors that may affect the required number of spare parts. The number of spare parts required can be effectively estimated based on the reliability performance of the item. The reliability characteristics of an item are influenced not only by the operating time, but also by factors such as the operational environment. Therefore, for spare parts provisioning to be effective, the impact of these influence factors on the reliability performance of the item should be quantified. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability performance analysis that takes influence factors into account is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. In this paper the application of PHM to spare parts provision is discussed and demonstrated by a case study.
Assessment on Spare Parts Requirement Based on Reliability Theory
The publications of the MultiScience - XXX. MicroCAD International Scientific Conference, 2016
The purpose of this paper is to estimate the amount of spare parts, for a specified operation period of a machine or equipment considering the renewal process, in order to forecast the need for spare parts, which is based on the renewal theory implying the explicit expression of the renewal function, the degree of failure also called the renewal rate function.
Assessment of Spare Parts Requirement by Reliability: A Case Study
International Journal of Reliability, Risk and Safety: Theory and Application
Spare parts provision is a complex problem and requires an accurate model to analyze all factors that may affect the required number of spare parts. The number of spare parts required for an item can be effectively estimated based on its reliability. The reliability characteristics of an item are influenced by different factors such as the operational environment, maintenance policy, operator skill, etc. However, in most reliability-based spare parts provision (RSPP) studies, the effect of these influence factors has not been considered. Hence, the statistical approach selected for reliability performance analysis must be able to handle the effect of these factors. One of the important models for reliability analysis by considering risk factors is the proportional hazard model (PHM), which has received less attention in the field of spare parts provisioning. Thus, this paper aims to demonstrate the application of the available reliability models with covariates in the field of spare part predictions using a case study. The proposed approach was evaluated with data from the system of fleet loading of the Jajarm Bauxite mine in Iran. The outputs represent a significant difference in spare parts forecasting and inventory management when considering covariates.
Joint optimization of component reliability and spare parts inventory for capital goods
2008
We consider an OEM who is responsible for the availability of its systems in the field through service contracts. Upon a failure of a critical part in a system during the exploitation phase, the failed part is replaced by a ready-for-use part from a spare parts inventory. In an out-of-stock situation, a costly emergency procedure is applied. The reliability levels and spare parts inventory levels of the critical components are the two main factors that determine the downtime and corresponding costs of the systems. We introduce a quantitative model for the joint optimization of these two levels. We formulate the portions of Life Cycle Costs (LCC) which are affected by a component's reliability and its spare parts inventory level. These costs consist of design costs, production costs, and maintenance and downtime costs in the exploitation phase. We conduct exact analysis and provide an efficient optimization algorithm. In our numerical experiment which is based on real-life data, our method leads to significant cost reductions in comparison to a method that ignores costs in the exploitation phase when the reliability level is determined. We also show that the optimal reliability level also strongly depends on the component type (cheap or expensive), the size of installed base, the downtime penalty rate, and the lifetime of the system in our experiment.