Spare Parts Estimation for Machine Availability Improvement Addressing Its Reliability and Operating Environment — Case Study

Abstract

Industrial operation cost analysis shows that, in general, maintenance represents a significant proportion of the overall operating costs. Therefore, the improvement of maintenance follows the final goal of any company, namely, to maximize profit. This paper studies spare parts availability, an issue of the maintenance process, which is an important way to improve production through increased availability of functional machinery and subsequent minimization of the total production cost. Spare parts estimation based on machine reliability characteristics and operating environment is performed. The study uses an improved statistical-reliability (S-R) approach which incorporates the system/machine operating environment information in systems reliability analysis. For this purpose, two methods of Poisson process and renewal process are introduced and discussed. The renewal process model uses a multiple regression type of analysis based on Cox's proportional hazards modeling (PHM). The parametric approaches with baseline Weibull hazard functions and time-independent covariates are considered, and the influence of operating environment factors on this model is analyzed. The outputs represent a significant difference in the required spare parts estimation when considering or ignoring the influence of the relevant system operating environment. The difference is significant in the sense of spare parts forecasting and inventory management which can enhance the parts and consequently machine availability, leading to economical operation and savings.

OR in spare parts management: A review

“…Therefore, a method was developed to forecast and estimate accurately the spare parts requirements considering the operating environment and to create rational part ordering strategies. Subsequent studies of the same topic (see Ghodrati and Kumar (2005), Ghodrati (2006), and Ghodrati et al (2013)) showed that there is a significant difference in spare parts estimation when considering or ignoring the influence of the operating environment. Similarly, Barabadi (2012) presented a proportional hazard model and its extension in order to analyze the effect of the operational environment for spare parts provision.…”

Prevision and Optimisation of Repairable Spare Parts: A Case Study in the Petroleum Industry

“…This problem was addressed by emphasising the time between failures [7] using the queuing theory or Markov chains [8,9]. However, as pointed out by Ghodrati and Kumar [10], the queuing theory-based models have theoretical drawbacks, such as constant failure and repair rates, which prevent them from representing real-world systems.…”

“…Several techniques based on statistical methods [13], artificial intelligence [14], Monte Carlo simulation [15], and non-linear programming [16] have been used to forecast spare part demands. Since reliability characteristics are a function of operation time and environmental operating factors [10,17], the estimates made by the above reliability-based models lack the confidence of maintenance engineers. Environmental operating conditions are important for asset reliability, reflecting the demand for spare parts.…”

“…Based on the results obtained in case studies [10,17] that observed a 20 per cent reduction in the number of spare parts when operating conditions were assessed, a number of more recent investigations also used industrial reliability and covariates to decrease replacements costs [18] and to predict and optimise spare parts [17,19,20]. However, the papers using covariates address the problem of non-repairable equipment/components; and the analysed items can be maintained using preventive or condition-based maintenance.…”

A framework for investigating optimization of service parts performance with big data

“…A review of literature supports these findings. Extensive research has been conducted in this area to include studies on machine availability (Ghodrati et al 2013), preventative maintenance (Jiang et al 2015), and level of repair Chang et al (2005) across industry segments such as Original Equipment Manufacturers (Behfard et al 2015), third-party providers (Kazemi Zanjani and Nourelfath 2014), and electronics repair (Schroter and Spengler 2004). Despite research demonstrating the impact of service parts availability on overall cost (Louit et al 2011) and customer service (Dekker et al 1998), the data from the current research suggests a gap between extant research and managerial application in that most research examines only direct relationships between service parts management and one performance goal.…”