Search results

1 – 10 of 720
Article
Publication date: 10 March 2022

Na Tao

In this study, the focus was shifted from repairing durable goods to achieving healthier ecology, making durable goods more secure in turn. This study introduced preventive

Abstract

Purpose

In this study, the focus was shifted from repairing durable goods to achieving healthier ecology, making durable goods more secure in turn. This study introduced preventive maintenance behavior to trace the ex-post control of “curling” back to the ex-post control of “self-healing.” This study tries to close the gap between the human repair of machines and their “self-curing.” Finally, the author makes the machines healthier.

Design/methodology/approach

The paper constructed a mathematical model of preventive maintenance behavior during a specific period for durable consumer goods. The author builds a simulation function of the two-stage preventative maintenance behavior relations. The study used simulations to analyze the influencing relationship and differences between three preventive maintenance behavior elements to basic warranty preventive maintenance (BWPM) behavior and extended warranty preventive maintenance (EWPM) behavior.

Findings

Both BWPM behavior and EWPM behavior were affected by the preventive maintenance (PM) behavioral components in different ways. The influence paths of the two warranty periods affected by PM behavior were also different.

Research limitations/implications

This study introduced PM behavior to trace the ex-post control of “curling” back to the ex-post control of “self-healing.” This study adopted the human–machine interaction mode to improve durable goods' self-healing ability during operation and enable a more effective and sustainable development.

Practical implications

This study’s conclusions may help manufacturers guide PM behavior in a way that achieves “self-healing” of the durable goods.

Originality/value

The author opened a “black box” of PM behaviors and analyzed their components. The internal structure relation of PM behavior is built and the closed-loop system of spatial structure is formed.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 3 March 2023

Lazhar Tlili, Chelbi Anis and Mokhles Bouazizi

This paper deals with a particular type of leasing contracts according to which an equipment is leased for free with the condition for the lessee to purchase a predetermined…

Abstract

Purpose

This paper deals with a particular type of leasing contracts according to which an equipment is leased for free with the condition for the lessee to purchase a predetermined minimum quantity of consumables during each leasing period. Maintenance actions are performed by the lessor and borne by him. Imperfect preventive maintenance is carried out every t time units throughout the leasing period. Minimal repairs are performed following equipment failures. At the end of the leasing period, an overhaul which restores the equipment to “as good as new” state is performed. The equipment is leased many times during its life cycle. The purpose of this paper is to determine the values of the decision variables for the lessor, which are the preventive maintenance (PM) period and the minimum quantity of consumables to be sold to ensure profit.

Design/methodology/approach

A mathematical model is developed to express the expected maintenance cost per time unit incurred by the lessor as well as his expected profit over the equipment life cycle. The optimal PM period minimizing the maintenance cost is determined first. Then, given the corresponding minimum maintenance cost, the minimum quantity of consumables (the lessor's break-even point) to be purchased by the lessee is computed. A numerical example and a sensitivity study are presented, and the obtained results are discussed.

Findings

The outcome of this work is supposed to help the lessors determining two key values to be included in each leasing contract, namely: (1) the periodicity according to which they will commit to perform preventive maintenance actions such that their average total cost of maintenance is minimized, (2) the minimum quantity of consumables that the lessee must commit to purchasing during the leasing period. This quantity must be between the break-even point and the maximum quantity associated with the capacity of the equipment.

Practical implications

Practically, the objective of this work is first to determine the optimal strategy to be adopted by the lessor in terms of effort relating to PM and second to determine the minimum quantity of consumables that the lessee must purchase during the leasing period such as profit is insured for the lessor.

Originality/value

This type of leasing (for free) has not been addressed in the literature particularly when considering maintenance strategies.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 10 November 2020

Azmat Ullah, Muhammad Ayat, Hakeem Ur Rehman and Lochan Kumar Batala

The purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product…

Abstract

Purpose

The purpose of this paper is to develop a model that determines whether how much effort of preventive maintenance action is worthwhile for the consumer over the post-sale product life cycle of a repairable complex product where the product is under warranty and subject to stochastic multimode failure process, that is, damaging failure and light failure with different probabilities.

Design/methodology/approach

The expected life cycle cost is designed for a warranted product from the consumer perspective. The product failure is quantified with failure rate function, which is the number of failures incurred over the product life cycle. The authors consider the failure rate function reduction method in their model where the scale parameter of a failure rate function is maximized by applying the optimal preventive maintenance level. The scale parameter of any failure distribution refers to the meantime to failure (MTTF). The first-order condition is applied with respect to the maintenance level in order to achieve the convexity of the nonlinear function of the expected life cycle cost function.

Findings

The authors have found analytically the close form of the preventive maintenance level, which can be used to find the optimal reduced form of the failure rate function of the product and the minimum product expected life cycle cost under the given condition of multimode stochastic failure process. The authors have suggested different maintenance policies to consumers in order to implement the proposed preventive maintenance model under different conditions. A numerical example further illustrated the analytical model by considering the Weibull distribution.

Practical implications

The consumer may use this study in the accurate modeling of the life cycle cost of a product that is under warranty and fails with a multimode failure process. Also, the suggested preventive maintenance approach of this study helps the consumer in making appropriate maintenance decisions such as to minimize the expected life cycle cost of a product.

Originality/value

This study proposes an accurate estimation of a life cycle cost for a product that is under the support of warranty and fails with multimode. Furthermore, for such a kind of product, which is under warranty and fails with multimode, this study suggests a new preventive maintenance approach that assures the minimum expected life cycle cost.

Details

International Journal of Quality & Reliability Management, vol. 38 no. 5
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 January 2006

J. Pongpech, D.N.P. Murthy and R. Boondiskulchock

The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs).

1826

Abstract

Purpose

The aim of this research is to determine the optimal upgrade and preventive maintenance actions that minimize the total expected cost (maintenance costs+penalty costs).

Design/methodology/approach

The problem is a four‐parameter optimization with two parameters being k‐dimensional. The optimal solution is obtained by using a four‐stage approach where at each stage a one‐parameter optimization is solved.

Findings

Upgrading action is an extra option before the lease of used equipment, in addition to preventive maintenance action. Upgrading action makes equipment younger and preventive maintenance action lowers the ROCOF.

Practical implications

There is a growing trend towards leasing equipment rather than owning it. The lease contract contains penalties if the equipment fails often and repairs are done within reasonable time period. This implies that the lessor needs to look at optimal preventive maintenance strategies in the case of new equipment lease, and upgrade actions plus preventive maintenance in the case of used equipment lease. The paper deals with this topic and is of great significant to business involved with leasing equipment.

Originality/value

Nowadays many organizations are interested in leasing equipment and outsourcing maintenance. The model in this paper addresses the preventive maintenance problem for leased equipment. It provides an approach to dealing with this problem.

Details

Journal of Quality in Maintenance Engineering, vol. 12 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 23 March 2012

Hamid Reza Golmakani and Ali Namazi

In many manufacturing systems, machines are subject to preventive maintenance. This paper aims to schedule the operations of jobs and preventive maintenance tasks in such a way…

Abstract

Purpose

In many manufacturing systems, machines are subject to preventive maintenance. This paper aims to schedule the operations of jobs and preventive maintenance tasks in such a way that the completion time of jobs and preventive maintenance tasks is minimized.

Design/methodology/approach

An heuristic approach based on artificial immune algorithm is proposed for solving the multiple‐route job shop‐scheduling problem subject to fixed periodic and age‐dependent preventive maintenance tasks. Under fixed periodic assumption, the time between two consecutive preventive maintenance tasks is assumed constant. Under age‐dependent assumption, a preventive maintenance task is triggered if the machine operates for a certain amount of time. The goal is to schedule the jobs and preventive maintenance task subject to makespan minimization.

Findings

In addition to presenting mathematical formulation for the multiple‐route job shop‐scheduling problem, this paper proposes a novel approach by which one can tackle the complexity that is raised in scheduling and sequencing the jobs and the preventive maintenance simultaneously and obtain the required schedule in reasonable time.

Practical implications

Integrating preventive maintenance tasks into the scheduling procedure is vital in many manufacturing systems. Using the proposed approach, one can obtain a schedule that defines the production route through which each part is processed, the time each operation must be started, and when preventive maintenance must be carried out on each machine. This, in turn, results in overall manufacturing cost reduction.

Originality/value

Using the approach proposed in this paper, good solutions, if not optimal, can be obtained for scheduling jobs and preventive maintenance task in one of the most complicated job shop configurations, namely, multiple‐route job shop. Thus, the approach can dominate all other simpler configurations.

Details

Journal of Quality in Maintenance Engineering, vol. 18 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 1 April 1991

Festus O. Olorunniwo and Ariwodo Izuchukwu

Although each maintenance task performed on an item of equipment may enhance its reliability, neither preventive nor overhaul maintenance can return the equipment to good‐as‐new…

Abstract

Although each maintenance task performed on an item of equipment may enhance its reliability, neither preventive nor overhaul maintenance can return the equipment to good‐as‐new condition. Applying the concept of maintenance improvement factors to both types of maintenance, mathematical models are developed that are used to generate preventive and overhaul maintenance schedules. Examples are provided to demonstrate the sensitivity of the schedules to model parameters.

Details

International Journal of Quality & Reliability Management, vol. 8 no. 4
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 1 July 1999

Hans Löfsten

One of the main problems, when choosing between preventive or corrective maintenance for the production and maintenance departments in the eight firms studied, is to attempt to…

3843

Abstract

One of the main problems, when choosing between preventive or corrective maintenance for the production and maintenance departments in the eight firms studied, is to attempt to establish the state of a particular production system or individual production line. In order to carry out a cost analysis it is imperative to be able to measure how preventive maintenance will both reduce the deterioration of the state of the object and improve the state of the object at the point in time that the maintenance is carried out. This can be explained by the fact that the departments lack methods for measuring and estimating the effects. The model presented in this paper determines whether to schedule preventive maintenance and the model trades off the capital costs of preventive maintenance and the sum of corrective maintenance and down‐time costs based on the production line’s state.

Details

International Journal of Operations & Production Management, vol. 19 no. 7
Type: Research Article
ISSN: 0144-3577

Keywords

Article
Publication date: 9 July 2020

Nadia Bahria, Imen Harbaoui Dridi, Anis Chelbi and Hanen Bouchriha

The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.

Abstract

Purpose

The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.

Design/methodology/approach

The proposed integrated policy is defined and modeled mathematically.

Findings

The paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.

Practical implications

The paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every α inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.

Originality/value

The existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

Details

Journal of Quality in Maintenance Engineering, vol. 27 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 25 September 2009

Sophie Hennequin, Gabriel Arango and Nidhal Rezg

This paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the…

Abstract

Purpose

This paper aims to propose an approach for the optimization of imperfect preventive maintenance and corrective actions performed on a single machine. After maintenance, the machine returns to an age between “as good as new” and “as bad as old”.

Design/methodology/approach

The approach is based on fuzzy logic and simulation‐based optimization. Fuzzy logic is preferred over crisp logic because it is relatively easy to implement in this situation considering that the human factor is hardly interpreted by analytical methods because of its unpredictable nature. Simulation‐based optimization is used to have a more reactive and accurate tool for practitioners.

Findings

Taking into account the impact of the imperfections due to human factors, the period for preventive maintenance, which minimizes the expected cost rate per unit of time or maximizes the availability of the system, is evaluated by the simulation‐based optimization.

Research limitations/implications

Different and more realistic maintenance levels must be considered and the traceability of a specific system could be used to determine the most appropriate failure law. For this study, cost reduction was considered as the priority, but the model can be adjusted according to the user's preferences.

Practical implications

This paper considers a single repairable machine as a system that undergoes periodic preventive and corrective maintenance actions. Considering maintenance imperfections, rule‐based fuzzy logic can be integrated into the maintenance program to determine the times for the periodic preventive maintenance actions.

Originality/value

Considering human factors in maintenance programs is indispensable to assure more accurate and realistic results. However, due to the difficulty engendered by their modeling, most theoretical maintenance models do not consider these factors. Therefore, the proposed fuzzy model in the paper can be an important tool to include them.

Details

Journal of Quality in Maintenance Engineering, vol. 15 no. 4
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 17 December 2021

Farouq Alhourani, Jean Essila and Bernie Farkas

The purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability…

Abstract

Purpose

The purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability factor.

Design/methodology/approach

Similarity coefficient method in group technology (GT) philosophy is used. Machines’ reliability factor is considered to develop virtual machine cells based on their need for maintenance according to the type of failures they encounter.

Findings

Using similarity coefficient method in GT philosophy for PM planning results in grouping machines based on their common failures and maintenance needs. Using machines' reliability factor makes the plan more efficient since machines will be maintained at the same time intervals and when their maintenance is due. This helps to schedule a standard and efficient maintenance process where maintenance material, tools and labor are scheduled accordingly.

Practical implications

The proposed procedure will assist maintenance managers in developing an efficient and effective PM plans. These maintenance plans provide better inventory management for the maintenance materials and tools needed using the developed virtual machine cells.

Originality/value

This paper presents a new procedure to implement PM using the similarity coefficient method in GT. A new similarity coefficient equation that considers machines reliability is developed. Also a clustering algorithm that calculates the similarity between machine groups and form virtual machine cells is developed. A numerical example adopted from the literature is solved to demonstrate the proposed heuristic method.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

1 – 10 of 720