scholarly journals Preventive Maintenance Interval Prediction: a Spare Parts Inventory Cost and Lost Earning Based Model

2015 ◽  
Vol 5 (3) ◽  
pp. 811-817
Author(s):  
O. A. Adebimpe ◽  
V. Oladokun ◽  
O. E. Charles-Owaba
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Manufacturing Firms ◽  
Inventory Cost ◽  
Manufacturing Firm ◽  
Interval Prediction ◽  
Spare Parts Inventory ◽  
Optimal Maintenance

In this paper, some preventive maintenance parameters in manufacturing firms were identified and used to develop cost based functions in terms of machine preventive maintenance. The proposed cost based model considers system’s reliability, cost of keeping spare parts inventory and lost earnings in deriving optimal maintenance interval. A case of a manufacturing firm in Nigeria was observed and the data was used to evaluate the model.

A dynamic predictive maintenance model considering spare parts inventory based on hidden semi-Markov model

2012 ◽  
Vol 227 (9) ◽  
pp. 2090-2103 ◽  
Author(s):  
Qinming Liu ◽  
Ming Dong ◽  
Ying Peng
Keyword(s):  
Dynamic Programming ◽  
Markov Model ◽  
Transition Probabilities ◽  
Spare Part ◽  
Spare Parts ◽  
Stochastic Dynamic ◽  
Maintenance Strategies ◽  
Spare Parts Inventory ◽  
Optimal Maintenance ◽  
Maintenance Model

The maintenance strategies optimization can play a key role in the industrial systems, in particular to reduce the related risks and the maintenance costs, improve the availability, and the reliability. Spare part demands are usually generated by the need of maintenance. It is often dependent on the maintenance strategies, and a better practice is to deal with these problems simultaneously. This article presents a stochastic dynamic programming maintenance model considering multi-failure states and spare part inventory. First, a probabilistic maintenance model called hidden semi-Markov model with aging factor is used to classify the multi-failure states and obtain transition probabilities among multi-failure states. Then, spare parts inventory cost is integrated into the maintenance model for different failure states. Finally, a double-layer dynamic programming maintenance model is proposed to obtain the optimal spare parts inventory and the optimal maintenance strategy through which the minimum total cost can be achieved. A case study is used to demonstrate the implementation and potential applications of the proposed methods.

scholarly journals Risk-Based Maintenance Assessment in the Manufacturing Industry: Minimisation of Suboptimal Prioritisation

2017 ◽  
Vol 8 (1) ◽  
pp. 38-45 ◽  
Author(s):  
R.M. Chandima Ratnayake ◽  
Katarzyna Antosz
Keyword(s):  
Risk Assessment ◽  
Manufacturing Industry ◽  
Fuzzy Inference ◽  
Optimal Level ◽  
Spare Parts ◽  
Manufacturing Firms ◽  
Rule Base ◽  
Risk Matrix ◽  
Inference System ◽  
Optimal Maintenance

Abstract Manufacturing firms continuously strive to increase the efficiency and effectiveness in the maintenance management processes. Focus is placed on eliminating the unexpected failures which cause unnecessary costs and the production losses. Risk-based maintenance (RBM) strategies enable to address the above through the identification of probability and consequences of potential failures whilst providing a way for prioritisation of maintenance actions based on the risk of possible failures. Such prioritisations enable to identify the optimal maintenance strategy, intervals of maintenance tasks, and optimal level of spare parts inventory. However, the risk assessment activities are performed with the support of a risk matrix. Suboptimal classifications and/or prioritisations arise due to the inherent nature of the risk matrix. This is caused by the fact that there are no means to incorporate actual circumstances at the boundary of the input ranges or at the levels of linguistic data and risk categories. In this paper, a risk matrix is first developed in collaboration with one of the manufacturing firms in Poland. Then, it illustrates the use of fuzzy logic for minimisation of suboptimal prioritisation and/or classifications using a fuzzy inference system (FIS) together with illustrative membership functions and a rule base. Finally, an illustrative risk assessment is also demonstrated to illustrate the methodology.

scholarly journals Maintenance System of Universal Goss Printing Machine based on failure data using RCM and RCS method

2018 ◽  
Vol 2 (01) ◽  
Author(s):  
Judi Alhilman
Keyword(s):  
Preventive Maintenance ◽  
Spare Parts ◽  
Maintenance System ◽  
Failure Data ◽  
Spare Parts Inventory ◽  
Printing Machine ◽  
Critical Subsystem

OverBased on RCM analysis for each critical subsystem obtained interval preventive maintenance for transfer roller 127.60 hours, Ink fountain roller 24.45 hours, ink form roller 29.23 hours respectively, and the wash-up device is no scheduled maintenance. For spare parts inventory strategies the result using RCS method are: transfer roller104 units, ink fountain roller requires 32 units, ink form roller 36 units and are holding spare policy required, and a wash-up device no holding spare parts. Inserted: r Inserted: u Inserted: ng Inserted: foll Inserted: polic Inserted: d Inserted: the Deleted:in acc Deleted:rdance Deleted:th Deleted:i Deleted: accor Deleted:anc Deleted: with Deleted:strateg

scholarly journals Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Cai ◽  
Yibing Yin ◽  
Li Zhang ◽  
Xi Chen
Keyword(s):  
Inventory Management ◽  
Preventive Maintenance ◽  
Spare Parts ◽  
Combination Method ◽  
Joint Optimization ◽  
Inventory Level ◽  
Cost Rate ◽  
Proposed Model ◽  
Spare Parts Inventory ◽  
The Cost

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

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