Optimum maintenance strategy: a case study in the ...

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Int. J. Services and Operations Management, Vol. 12, No. 3, 2012

Optimum maintenance strategy: a case study in the mining industry Arash Shahin Department of Management, University of Isfahan, 1.242, Saeb Avenue, 81848-13713, Isfahan, Iran E-mail: [email protected]

Hadi Shirouyehzad and Ehsan Pourjavad* Department of Industrial Engineering, Najafabad Branch, Islamic Azad University, Isfahan, Iran E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: Managers face with the problem of decision-making for selecting suitable maintenance strategy because of the emergence of different maintenance strategies for systems and equipments. Considering implementation methods and strengths and weaknesses of each maintenance strategy, simultaneous employment of these strategies may result in improvement or reduction of organisational performance. The main purpose of this paper is to consider interdependency of maintenance strategies to find the most suitable maintenance strategy for equipments. While the problem of selecting a suitable maintenance strategy can be solved by multi-criteria decision-making, the approach of analytic network process (ANP) has been suggested due to its network structure and its capability in counting the interdependency of maintenance strategies. As a case study, an attempt has been made to propose the most suitable maintenance strategies for a category of equipments in Chadormalu Mining-Industrial Company by the ANP approach. The findings imply that the priorities of maintenance strategies include TPM, CBM, DOM, TBM, EM, respectively. Also, maintainability and reliability have been found as the main reasons of higher rankings of TPM and CBM strategies. Keywords: analytic network process; interdependency; mining industry.

ANP;

maintenance

strategy;

Reference to this paper should be made as follows: Shahin, A., Shirouyehzad, H. and Pourjavad, E. (2012) ‘Optimum maintenance strategy: a case study in the mining industry’, Int. J. Services and Operations Management, Vol. 12, No. 3, pp.368–386. Biographical notes: Arash Shahin graduated in Iran in 1995 and 1998 with BS and MS, respectively in Industrial Engineering. He received a PhD in 2003 in UK from the University of Newcastle for his studies on Quality Engineering. From 1992 to 1995, he was the Quality Manager of car parts production companies in Isfahan. From 1995 to 2003, he was the Executive Manager of Amin Cara Engineering Consulting Co. in Isfahan. Currently, he is a full-time Copyright © 2012 Inderscience Enterprises Ltd.

Optimum maintenance strategy: a case study in the mining industry

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Assistant Professor at the Department of Management, University of Isfahan. He is the author of five books and 190 published papers at national and international levels in refereed journals and conferences since 1994. Hadi Shirouyehzad Graduated in Iran 1999 and 2002 with BS and MS degrees, in Industrial Engineering. He is PhD candidate of Industrial Engineering at Research and Science Branch, Islamic Azad University, Tehran, Iran. Currently He is the faculty member of industrial engineering department of Islamic Azad University of Najafabad. He is the author of two books and more than 35 published papers at national and international levels in refereed journals and conferences since 2003. Ehsan Pourjavad is a postgraduate student at Najaf Abad University, Iran. He received a Bachelor of Engineering in Industrial Engineering from Najaf Abad University in 2007. His research interests include operation research, maintenance management, and optimisation of manufacturing systems.

1

Introduction

Rapid change of manufacturing environments has been recognised as a critical factor for increasing competitiveness in manufacturing systems. Manufacturing firms have been investing a lot to make better their manufacturing performance in terms of cost, quality, and flexibility in an effort to compete with other companies in the global marketplace (Karsak and Tolga, 2001). In manufacturing firms diverse problems exist that can influence the manufacturing cost, product quality and delivery time of products to customers; such as manufacturing technology selection, maintenance strategy selection, machine location and evaluation of quality function. Maintenance, as a system, plays a key role in decreasing cost, minimising equipment downtime, enhancing quality, increasing productivity and providing reliable equipment and as a result, achieving organisational targets and objectives. The aim of the maintenance function is to contribute towards an organisation’s profit, clearly bringing the need for maintenance operation to be in harmony with the corporate objective (Sharma et al., 2011). One of the main expenditure items of manufacturing companies is maintenance cost, which can make 15% to 70% of production costs, varying according to the type of industry (Bevilacqua and Braglia, 2000). On the other hand, one third of all maintenance costs is wasted as the result of unnecessary or unsuitable maintenance activities (Mobley, 2002). Therefore, selection of suitable maintenance strategies can highly influence the manufacturing expenditures. Maintenance of equipment has a strong impact on achieving a fully operational mode; hence, maintenance strategies represent a distinct sub-topic in the field of operations management (Gebauer et al., 2008). According to Simoes et al. (2011), approaching maintenance management strategically and systematically has become essential to make the right choices, especially in capital-intensive industries. It is important to note that each maintenance strategy might have strength and weakness. Therefore, selection of the appropriate strategy is one of the most important problems for maintenance managers. The degree of importance of selecting a suitable maintenance strategy is different in various manufacturing systems. In continuous manufacturing systems, the problem of selecting suitable maintenance strategy is more

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critical than other systems, because stoppage of equipment leads to stoppage of manufacturing line. Considering the reviewed literature it is apparent that selection of suitable maintenance strategy is almost a multi-criteria decision-making problem, and selection of optimum maintenance strategy needs consideration of various criteria. In spite of differences in performance of maintenance strategies, they might have effects on each others. Employing strategies, simultaneously, may lead to improvement or reduction of performance. To have a good selection, it is necessary to consider the effects of strategies on each other. The reviewed literature also reveals that in almost all of the conducted studies, the interdependency of maintenance strategies has not been investigated. Therefore, the main aim of this paper is to propose an approach for determining suitable maintenance strategies considering their interdependency. In fact, in practice a number of maintenance strategies might be applied simultaneously, while each might have strength and weakness and considering their potential interdependencies, system’s performance might be negatively affected. In a wider perspective with the consideration of interdependency of criteria and interdependency of maintenance strategies and the interdependency of strategies and criteria, analytic network process (ANP) as a multiple criteria decision-making (MCDM) approach is proposed for selecting suitable maintenance strategies. MCDM problems involve multiple criteria that can be both qualitative and quantitative (Gnanasekaran, 2010). ANP is an effective approach for determining the interdependency of strategies. In this study, five strategies of emergency maintenance (EM), condition-based maintenance (CBM), time-based maintenance (TBM), design out maintenance (DOM) and total productive maintenance (TPM) are considered as alternatives and reliability, availability, maintainability and cost of equipments are considered as criteria. Since companies in the manufacturing and construction business are heavily dependent on their machinery and equipment in securing a competitive advantage (Al-Turki, 2011), the Chadormalu Mining-Industrial Company (CMIC) has been selected as a case study. It is important to note that based on the investigation of Simoes et al. (2011) on 137 companies, only six maintenance management case studies had been conducted in the mining industry, which highlights the contribution of this study to the associated literature. In the following, after a literature review, the maintenance strategies and criteria are introduced. The research methodology is then explained and in the case study, the equipments of Chadormalu Company are categorised in five classes based on their performance. Out of the five classes, the Mills equipment is selected for analysis by the step-by-step approach of ANP, by which the interdependency of maintenance strategies is considered according to each of the criteria. The findings are then discussed and conclusions are made.

2

Literature review

Many studies have been conducted to determine the suitable maintenance strategy. Luce (1999) and Okumura and Okino (2003) suggested an approach to select the most effective maintenance strategy based on different production loss and maintenance costs acquired by various maintenance strategies. Although the calculation of the related costs is reasonable, the money spent on maintenance is only one of the factors that must be taken into account when selecting maintenance strategies. Azadivar and Shu (1999) proposed a method for selecting an appropriate maintenance strategy for each class of


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systems in a just-in-time environment, exploring 16 characteristic factors that could play a considerable role in maintenance strategy selection. However, this method is not suitable to process plants because of the difference between separated manufacturing plants and process plants. In the study of Bevilacqua and Braglia (2000), the original approach for the selection of maintenance strategies in an important Italian oil refinery was given, and the application of the analytic hierarchy process (AHP) for selecting the best maintenance strategy was delineated. The criteria they considered seem adequate, but a crisp decision-making method as the traditional AHP is not proper because many of the maintenance goals taken as criteria are non-monetary and difficult to be quantified. Al-Najjar and Alsyouf (2003) and Sharma et al. (2005) appraised the most popular maintenance strategies using the fuzzy inference theory and fuzzy MCDM approach. While the application of the fuzzy theory seems is a good solution, only a few failure causes were considered as criteria in their studies. Mechefske and Wang (2003) suggested evaluating and selecting the optimum maintenance strategy and condition monitoring technique by the use of fuzzy linguistics. The fuzzy methodology based on qualitative verbal estimation inputs is practical since most of the overall maintenance objectives of the organisation are intangible. Meanwhile, the method of Mechefske and Wang (2003) is very subjective to directly assess the significance of each maintenance goal and the ability of each strategy to achieve maintenance purposes. Almeida and Bohoris (1995) debated the application of decision-making theory in maintenance with particular attention to multi attribute utility theory. Triantaphyllou et al. (1997) proposed the use of AHP with consideration of four maintenance criteria of cost, reparability, reliability and availability. Murthy and Asgharizade (1999) proposed an approach for decision-making when a company out-sources maintenance activities. They used game theory to conduct a decision when the customer (the receptionist of maintenance) wants to decide whether having a service contract. Löfsten (1999) suggested a cost analysis model to select corrective or preventive maintenance (PM). Ivy and Nembhard (2005) integrated statistical quality control (SQC) and partially observable Markov decision processes (POMDPs) for the estimation of maintenance policies in case of limited information. Tahir et al. (2008) used a new maintenance optimisation model to carry out the computations for calculating frequency of failures and downtime as the maintenance data problems using decision-making grid (DMG) with fuzzy logic in maintenance decision support system (DSS). Jafari et al. (2008) suggested a new approach which can determine the best maintenance strategy by considering the uncertainty level and the variety in maintenance criteria. Bertolini and Bevilacqua (2006) utilised a ‘lexicographic’ goal programming (LGP) approach to explain the optimum strategies for the maintenance of critical centrifugal pumps in an oil refinery. Wang et al. (2007) evaluated different maintenance strategies [such as corrective maintenance, time-based PM, CBM, and predictive maintenance (PDM)] for different equipments. They performed a fuzzy modification of AHP, where doubtful and imprecise judgments of decision makers were translated into fuzzy numbers. In their case study at a power plant, they found that the PDM strategy is the most appropriate for boilers. Bashiri et al. (2011) proposed a new approach for selecting optimum maintenance strategy using qualitative and quantitative data through interaction with the maintenance specialists. This approach was based on linear assignment method (LAM) with some modifications to improve interactive fuzzy linear assignment method (IFLAM). Zhaoyang et al. (2011) improved an application of the AHP to select the most practicable maintenance strategy for equipments with high risk. To arrange the hierarchic structure and evaluation, four main criteria were

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defined for pair-wise judgments. Zhou et al. (2011) developed a partially observable semi-Markov decision process (POSMDP) for maintenance strategy optimisation when the health state of an asset can be only partially observed. Compared with existing POMDP methods that optimise the maintenance strategies, the proposed POSMDP do not have the assumption of discrete time and state. Without these two assumptions, degradation processes can be modelled more accurately and more cost-effective maintenance strategies can be developed, respectively. In addition, the POSMDP can derive more flexible maintenance strategies when multiple maintenance activities are involved. Meselhy et al. (2010) introduced a novel general metric for quantifying periodicity, which is required to ensure the system functional stability throughout its life. The proposed periodicity metric can be used to quantitatively compare the resetting ability of different maintenance policies, which combined with other performance metrics like cost, availability and product quality, can vastly enhance decision-making in selecting appropriate maintenance strategies. Yeh et al. (2011) developed two periodical age reduction PM models, one with a fixed maintenance degree, and the other with an age threshold value, for second-hand products. The structural properties of the optimal policy are investigated for Weibull distribution of life time and algorithms are provided to search for the optimal solutions. Arunranj and Maiti (2010) presented an approach of maintenance selection based on risk of equipment failure and cost of maintenance. They used AHP and goal programming (GP) for maintenance policy selection. Joo and Min (2011) proposed a dynamic GP model that aims at determining the optimal number of spare modules, while meeting the scheduled due dates for PM under budget constraints.

3

Maintenance strategies

The importance of maintenance for manufacturing systems, especially for continues manufacturing systems leads to create various maintenance strategies. Each maintenance strategy might have strength and weakness. In addition, each maintenance strategy is applicable for a specific manufacturing system. In fact, these is no perfect maintenance strategy resulting that, selecting a strategy or a suitable combination of strategies has become one of the most important problems for maintenance managers. Variety of maintenance strategies leads to an important challenge for industrial managers to select the most suitable strategy or combination of strategies. However, in this paper, five strategies of EM, TBM, CBM, DOM and TPM are considered according to the DMG suggested by Labib (1998). The five strategies are further demonstrated in the following.

3.1 Time-based maintenance In TBM, maintenance is performed at fixed time gaps, whether a problem is apparent or not, to shun failure of the items while the system operates (Khazraei and Deuse, 2011). According to reliability characteristics of equipments, maintenance is planned and performed periodically to decrease frequent and unexpected failure. This maintenance strategy is called time-based preventive maintenance, where the term ‘time’ may refer to calendar time, operating time or age. Time-based PM is applied widely in industries. For implementation of time-based PM, a DSS is needed, and it is often hard to explain the most effective maintenance intervals because of lacking sufficient historical data (Mann et al., 1995). In many cases when TBM strategies are used, most machines are maintained


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with an important amount of useful life remaining (Mechefske and Wang, 2003). This often leads to redundant maintenance, even deterioration of machines if incorrect maintenance is implemented.

3.2 Condition-based maintenance CBM is a maintenance strategy that recommends maintenance actions based on the information collected through condition monitoring and its goal is often to improve the equipment’s reliability, availability, or its associated life cycle costs (Golmakani and Fattahipour, 2011). In CBM, maintenance decision is made depending on the measured data from a set of sensors system when using the CBM strategy. To date a number of monitoring techniques are already attainable such as vibration monitoring, lubricating analysis, and ultrasonic testing. The monitored data of equipment parameters could tell engineers whether the situation is normal, allowing the maintenance staff to perform necessary maintenance before failure occurs. This maintenance strategy is often designed for rotating and reciprocating machines, e.g., turbines, centrifugal pumps and compressors. However, it should be noted that limitations and deficiency in data coverage and quality reduce the effectiveness and exactness of the CBM strategy (Al-Najjar and Alsyouf, 2003).

3.3 Emergency maintenance This alternative maintenance strategy is also named as fire-fighting maintenance, failure-based maintenance or breakdown maintenance. When the EM strategy is applied, maintenance is not implemented until failure occurs (Swanson, 2001). In fact, maintenance is done when a machine is failed and there is no endeavour to trim down the number of failures (Khazraei and Deuse, 2011). EM is the original maintenance strategy appeared in industries (Waeyenbergh and Pintelon, 2002; Mechefske and Wang, 2003). It is considered as a feasible strategy in the cases where profit margins are high (Sharma et al., 2005). However, such a fire-fighting mode of maintenance often causes serious damage of related facilities, personnel and environment. Furthermore, expanding global competition and small profit margins have forced maintenance managers to apply more effective and reliable maintenance strategies.

3.4 Total productive maintenance TPM is a maintenance programme philosophy which is similar in nature to total quality management (TQM) in several aspects, including the total commitment of upper-level management to the TPM programme, employees must be empowered to take initiatives and corrective actions, and continuity and long-term strategy is needed as TPM is a continuous process (Graisa and Al-Habaibeh, 2011). It can be considered as the medical science of machines. TPM is a maintenance programme which involves a newly defined concept for maintaining plants and equipment. The aim of the TPM programme is to markedly increase production while, at the same time, increasing employee morale and job satisfaction. TPM brings maintenance into focus as a necessary and vitally important part of the business. It is no longer regarded as a non-profit activity. Down time for maintenance is scheduled as a part of the manufacturing day and, in some cases, as an

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integral part of the manufacturing process. The aim is to hold emergency and unscheduled maintenance to a minimum (Chan et al., 2005).

3.5 Design out maintenance In DOM, the goal is to minimise the effect of failures and in fact to eliminate the cause of maintenance. Although it is an engineering design problem, yet it is often a responsibility of maintenance department. This is preferred for items of high maintenance cost that are due to poor maintenance, poor design or poor design outside design specifications (Waeyenbergh and Pintelon, 2004). If there are recurrent faults of the same type occurring after a system is commissioned and put into service, then the maintenance staff should carry out investigation on the root causes of the problem and re-design the system if appropriate to eliminate the problem. It is aimed to improve the design in order to make maintenance easier or even eliminated (Khazraei and Deuse, 2011). Figure 1

4

Criteria, strategies and their interrelationships

Criteria for selecting optimum maintenance strategy

Industrial managers consider a variety of different criteria such as feasibility, added-value, cost, safety, maintainability, reliability, etc., for selecting a suitable maintenance strategy. Triantaphyllou et al. (1997) recommended reliability, availability, maintainability and cost as the most suitable criterion for selecting maintenance strategy. In this research, these four criteria are used to compare maintenance strategies as it is illustrated in Figure 1. According to the figure and considering the aim of this research, by the use of the ANP approach, comparisons can be made among criteria, among strategies and among strategies and criteria and finally, the most suitable strategies are determined. The criteria are further demonstrated in the following.

4.1 Reliability Reliability (R), a probabilistic measure of the failure-free operation, is the probability of the equipment functioning without failure during a given time period under certain conditions (Kumar et al., 1992), which is often expressed as equation (1). It can be enhanced by decreasing failure frequency.


)

R (t ) = exp ⎢⎣( −ti ) ⎥⎦ MTBF = exp ( − ⎣⎢(λ t ⎦⎥ i )

375 (1)

where ti, λ and MTBF represent operation time, a constant defined as the failure frequency, and mean time between failures (MTBF), respectively. Reliability determines whether the output of the plant is as expected or whether the business can be beneficial, so it is of great concern in terms of engineering application, and it helps determine what and how much maintenance must be carried out. Equipment with a long failure-free period can decrease accessories, reserves and maintenance cost. High-reliability can increase equipment availability while reducing outrage time, maintenance cost and secondary failure loss, and thus contribute to a huge benefit for the company. The key indicators which describe reliability include MTBF, mean time to failure (MTTF), mean life of components, failure frequency, maximum number of failures permitted in a specific time-interval, etc.

4.2 Availability Availability (A) is explained as the capability of equipment functioning well during a definite period or even beyond it. It gives an indication of available working time during operation (Kumar et al., 1992), and can be expressed as in equation (2). Availability = MTBF / ( MTBF + MTTR )

(2)

where MTTR is mean time to repair. Increasing failure-free time and reducing downtime can increase availability, which can be changed into reliability and maintainability requirements in terms of acceptable failure frequency and outage hours.

4.3 Maintainability Maintainability (M) is the ability that equipment can restore to normal function in a specified period of time or beyond it (Kumar et al., 1992). It correlates with design and installation quality. Maintainability indicator can be used to evaluate, clarify and illustrate maintenance programmes and requirements. Maintenance project, personnel, organisation, preparation and procedures all affect maintainability, which is often expressed in equation (3). Designed maintenance procedures and maintenance time are the baseline of maintainability, and the key figure-of-merit for maintainability is MTTR. M (t ) = 1 − exp ( −t0i / MTTR )

(3)

where t0i represents repair time. The shorter MTTR is, the higher the maintainability will be. Three main parameters of repair time (which is the function decided by equipment design, and it is related to the training and skill of the personnel in charge of maintenance), logistic time (i.e., time for supplying parts) and administrative time (a function of operational structure of the organisation, standard maintenance procedure, and maintenance quality assurance document) are concerned with downtime.

4.4 Cost (C) Different maintenance strategies have different expenditure of hardware, software, and personnel training (Wang et al., 2007).

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•

hardware: for CBM and PDM, a number of sensors and some computers are essential

•

software: software is needed for analysing measured parameters data when using CBM and PDM strategies

•

personnel training: only after adequate training can maintenance staff make full use of the related tools and techniques, and obtain the maintenance goals.

5

Research methodology

Yüksel and Dagdeviren (2007) argued that AHP is the most effective approach for solving complex decision-making problems. AHP was first presented by Saaty (1980) and used in various decision-making process (Bozdag et al., 2003; Kahraman et al., 2003, 2006; Tolga et al., 2005). The AHP technique performs pairwise comparisons to measure the relative importance of elements at each level of the hierarchy and evaluates alternatives at the lowest level of the hierarchy in order to make the best decision among multiple alternatives (Sipahi and Timor, 2010). The basic assumption of AHP is the condition of functional independence of the upper part of the hierarchy, from all its lower parts, and from the criteria or items in each level. Many decision-making problems cannot be organised hierarchically because they involve interaction of various factors, with high-level factors occasionally depending on low-level factors (Saaty and Takizawa, 1986; Saaty, 1996). Saaty (1996) proposed the use of AHP to solve the problem of independency among alternatives/criteria, and the use of ANP to solve the problem of dependency among alternatives/criteria. The ANP, also initiated by Saaty (1996), is a generalisation of the AHP. Whereas AHP represents a framework with a unidirectional hierarchical AHP relationship, ANP allows for complex interrelationships among decision levels and attributes. In ANP, like AHP, pairwise comparisons of the elements in each level are conducted with respect to their relative importance towards their control criterion (Dikmen et al., 2010). The ANP feedback approach replaces hierarchies with networks in which the relationships between levels are not easily outlined as higher or lower, dominant or subordinate, direct or indirect (Meade and Sarkis, 1999). For instance, not only does the significance of the criteria determine the importance of the alternatives, as in a hierarchy, but also the importance of the alternatives may have influence on the importance of the criteria (Saaty, 1996). Therefore, a hierarchical structure with a linear top-to-bottom form is not appropriate for a complex system. Majority of problems which employ ANP approach, formulise the problem with super-matrix. They rank criteria and alternatives of the problem by solving super-matrix. In this article, the problem will not be solved by formation of a super-matrix; instead, a step-by-step approach of ANP method is employed. In the following, this approach is described.

5.1 Step-by-step approach of ANP The step by step approach is one of the ANP approaches for problem solving. In fact this approach solves problem in two major steps; first, the criteria and then the alternatives are prioritised. This approach includes five stages in which, instead of super matrix, the


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multiplication of matrixes is used for finding the appropriate result. In order to determine the inner dependency of alternatives, the interaction of alternatives is computed based on each criterion. This helps the researcher to consider all of the interdependencies of criteria and alternatives together with their inner dependencies in problem solving. It is important to note that the computation does not necessary require any specific software and multiplication of the matrixes in different stages is sufficient for deriving the appropriate result. The methodology steps for selecting suitable maintenance strategy are as follows (Das and Chakraborty, 2011; Karsak et al., 2002; Wey and Wu, 2007): •

Step 1: paired comparison of criteria considering the goal At this step, with the help of paired comparisons of criteria considering the goal, the importance of each criterion for the goal is identified. Saaty’s (1980) fundamental scale is used to determine the importance value (i.e., Table 1).

Table 1

Saaty’s fundamental scale

Value

Definition

Explanation

1

Equally important

Two decision elements equally influence the parent decision element

3

Moderately more important

5

Much more important

7

Very much more important

One decision element has significantly more influence over the others

9

Extremely more important

The difference between influences of the two decision elements is extremely significant

2, 4, 6, 8

Intermediate judgment values

Judgment values between equally, moderately, much, very much and extremely

One decision element is moderately more influential than the others One decision element has more influence than the others

Source: Saaty (1980)

•

Step 2: paired comparison of criteria considering the criteria At this step, with consideration of interdependency of criteria, the weight of each criterion is determined. Each criterion is compared with other criteria individually, considering one specific criterion. Based on the results, the W2-matrix is arranged. As an example, W21 presents the weight of criteria related to the first criterion. W2 = (W21 ,…… , W2 n )

Then, W2 (rows and columns are equal to criterion) and W1 are multiplied and according to the weight of each criterion, they will be ranked. WC = W2 ∗ W1

•

Step 3: paired comparison of alternatives considering criterion At this step, with this assumption that alternatives are not independent, paired comparison of alternatives and criteria is performed; then, the weight of each alternative related to criteria is determined. For example, W31 presents the weight of

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A. Shahin et al. options related to the first criterion. To calculate W31, the matrix of paired comparison related to first criterion is arranged and finally, with geometric average method, the weight of each alternative is evaluated. W3 = (W31 ,…… , W3n )

•

Step 4: paired comparison of alternative related to alternatives At this step, the weight of each alternative with consideration of interdependency of alternatives is evaluated. Respectively, interdependency of alternatives considering each criterion is calculated. In fact, the effect of alternatives on each other related to a specific criterion is determined. For example, W41 is the weight of alternatives considering interdependency of alternatives related to the first criterion. Then with arranging WA, priority of options related to criteria considering interdependency of alternatives is calculated. Therefore, W41 is normalised and multiplied by W31 and WA1 is evaluated. The same process is followed for other criteria. Finally, WA is arranged (WA is a matrix with columns equal to number of criteria and rows equal to number of alternatives). WA1 = W41 ∗ W31 WA2 = W42 ∗ W32 WAm = W4 m ∗ W3n

WA = (WA1 ,…… , WAm ) .

•

Step 5: determining priorities of alternatives At the last step, WA and WC are multiplied to evaluate priorities of alternatives in ANP. WANP = WA ∗ WC

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Case study and findings

CMIC is located between the cities of Yazd and Tabas at the centre of Iran and produces iron concentrate. This factory, converts iron stone to iron concentrate through chemical and physical operations. Due to the great volume of production and the critical role of this product in industrial development of Iran, this company is considered as one of the most important production companies of the country. Considering high importance of maintenance system for plants with continuous manufacturing lines, the problem of selecting a suitable maintenance strategy for equipment in this company is very critical. To determine the suitable maintenance strategy for CMIC, its equipments are categorised into five categories and among them, Mills are selected for study due to its higher importance in the manufacturing system. To perform paired comparisons, a sample of 58 managers and maintenance experts has been selected. In order to collect and combine the answers, the group AHP technique is applied. In the following, the process of determining the appropriate strategy for mills based on the methodology steps is described.

Optimum maintenance strategy: a case study in the mining industry •

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Step 1: paired comparison of criteria considering the goal The weight of each RAM+C criterion (reliability, availability, maintainability, cost) is evaluated. Table 2, presents group matrix of paired comparisons related to the goal. Local priority presents the weight of each criterion. Considering the results, the weight of each criterion is determined as: W1 = (R, A, M, C) = (0.308, 0.282, 0.289, 0.121). As it is clear, ‘reliability’ has the highest weight based on the evaluators’ point of view.

Table 2

Comparison of criteria considering the goal

Local priority

•

C

M

A

R

0.308

0.582

0.581

0.601

0.591

R

0.282

0.526

0.559

0.544

0.534

A

0.289

0.574

0.548

0.533

0.557

M

0.121

0.232

0.222

0.240

0.236

C

Step 2: paired comparison of criteria considering criterion RAM+C criteria have interdependency and therefore, increase or reduction of each criterion has effect on others. For example, the increasing availability of equipment results in increase of reliability. At this step, by paired comparison, the interdependency of criteria for mills is evaluated. To determine the interdependency of criteria, for instance the question of “how much availability has effect on reliability?” is considered. Then, interdependency of criteria related to others is calculated and finally W2 = (W21, W22, W23, W24) is arranged. For example W21 presents the weight of other criteria related to reliability. In order to determine the final priorities of criteria, W1 is multiplied by W2. ⎡ 0.242 ⎢ 0.325 WC − W2 + W1 − ⎢ ⎢0.277 ⎢ ⎣ 0.156

0.300 0.245 0.280 0.174

0.292 0.223 0.249 0.236

0.247 ⎤ ⎡0.308 ⎤ 0.253⎥⎥ ⎢⎢0.282 ⎥⎥ ∗ 0.250 ⎥ ⎢0.289 ⎥ ⎥ ⎢ ⎥ 0.249 ⎦ ⎣ 0.121⎦

Wc presents the priorities of criteria for the problem of selecting the suitable maintenance strategy. According to the calculations, the priorities of criteria are: Wc = (0.273, 0.264, 0.267, 0.196). The final result indicates that reliability is more preferable based on the evaluators’ point of view. •

Step 3: paired comparisons of alternatives related to criteria Five strategies of EM, TBM, CBM, TPM and DOM are compared with RAM+C individually and the weight of each strategy is identified. To determine the importance of each strategy related to criteria, questions such as “how much employing EM strategy affects reliability of equipment?” are asked. W31, W32, W33, and W34 denote the weight of maintenance strategies related to RAM+C, respectively.

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A. Shahin et al. ⎡ 0.065 ⎤ ⎡ 0.088 ⎤ ⎡ 0.068 ⎤ ⎡ 0.078 ⎤ ⎢ 0.221⎥ ⎢0.237 ⎥ ⎢0.217 ⎥ ⎢ 0.155 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ W31 ⎢ 0.307 ⎥ , W32 = ⎢ 0.306 ⎥ , W33 = ⎢ 0.248 ⎥ , W34 = ⎢ 0.188 ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ 0.249 ⎥ ⎢ 0.306 ⎥ ⎢ 0.308 ⎥ ⎢ 0.273⎥ ⎢⎣ 0.159 ⎥⎦ ⎢⎣ 0.063 ⎥⎦ ⎢⎣ 0.159 ⎥⎦ ⎢⎣0.306 ⎥⎦

•

Step 4: paired comparison of alternative related to alternatives At this step, strategies are compared together with consideration of the effects of maintenance strategies on each other and the weight of each is evaluated. For this purpose, the effect of each maintenance strategy on others related to each of the criteria should be calculated. In order to determine the interdependency of strategies, questions such as “considering reliability, does employing TBM strategy affect EM strategy? and if yes, how much?” is asked. For example W41 represents the interdependency of maintenance strategy related to reliability. To determine priorities of options related to reliability (WA1), W41 is multiplied by W31. In the following, this process is performed for each criterion.

WA1

⎡ 0.082 ⎢ 0.424 ⎢ = W41 ∗ W31 = ⎢ 0.590 ⎢ ⎢ 0.591 ⎢⎣ 0.339

0.031 0.091 0.093 0.066 ⎤ ⎡ 0.065 ⎤ 0.159 0.145 0.164 0.257 ⎥⎥ ⎢⎢ 0.221⎥⎥ 0.720 0.659 0.656 0.652 ⎥ ∗ ⎢0.307 ⎥ ⎥ ⎢ ⎥ 0.653 0.677 0.674 0.655 ⎥ ⎢ 0.249 ⎥ 0.169 0.278 0.282 0.274 ⎥⎦ ⎢⎣ 0.159 ⎥⎦

⎡ 0.098 ⎢ 0.363 ⎢ = ⎢ 0.649 ⎢ ⎢ 0.583 ⎢⎣ 0.311

0.034 0.098 0.119 0.074 ⎤ ⎡ 0.088 ⎤ 0.127 0.126 0.124 0.157 ⎥⎥ ⎢⎢0.237 ⎥⎥ 0.653 0.643 0.639 0.691⎥ ∗ ⎢ 0.306 ⎥ ⎥ ⎢ ⎥ 0.722 0.714 0.707 0.661⎥ ⎢ 0.306 ⎥ 0.189 0.219 0.250 0.234 ⎥⎦ ⎢⎣ 0.063⎥⎦

⎡ 0.098 ⎢ 0.270 ⎢ = W43 ∗ W33 = ⎢ 0.561 ⎢ ⎢ 0.689 ⎢⎣ 0.351

0.047 0.073 0.113 0.099 ⎤ ⎡ 0.068 ⎤ 0.133 0.077 0.157 0.384 ⎥⎥ ⎢⎢0.217 ⎥⎥ 0.721 0.417 0.382 0.549 ⎥ ∗ ⎢ 0.248 ⎥ ⎥ ⎢ ⎥ 0.667 0.861 0.790 0.643⎥ ⎢ 0.308 ⎥ 0.124 0.271 0.438 0.357 ⎥⎦ ⎢⎣ 0.159 ⎥⎦

WA2 = W42 ∗ W32

WA3

⎡ 0.091 ⎢ 0.298 ⎢ WA1 − W44 ∗ W34 − ⎢ 0.411 ⎢ ⎢0.542 ⎢⎣ 0.663

0.031 0.035 0.076 0.116 ⎤ ⎡0.078 ⎤ 0.103 0.034 0.076 0.141⎥⎥ ⎢⎢ 0.155 ⎥⎥ 0.479 0.159 0.089 0.242 ⎥ ∗ ⎢0.188 ⎥ ⎥ ⎢ ⎥ 0.615 0.813 0.455 0.438⎥ ⎢ 0.273⎥ 0.617 0.557 0.879 0.846 ⎥⎦ ⎢⎣0.306 ⎥⎦


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Finally, WA as the final priorities of alternatives related to criteria is arranged. WA = (WA1 , WA2 , WA3 , WA4 ) ⎡ 0.074 ⎢ 0.188 ⎢ WA = ⎢ 0.666 ⎢ ⎢ 0.662 ⎢⎣ 0.258

•

0.088 0.086 0.075 ⎤ 0.149 0.176 0.109 ⎥⎥ 0.649 0.503 0.235 ⎥ ⎥ 0.699 0.751 0.549 ⎥ 0.230 0.309 0.751⎥⎦

Step 5: determining the priorities of alternatives At the last step, WA is multiplied by WC and the maintenance strategies are ranked.

WANP

⎡ 0.074 ⎢ 0.188 ⎢ = WA ∗ WC = ⎢ 0.666 ⎢ ⎢ 0.662 ⎢⎣ 0.258

0.88 0.149 0.649 0.699 0.230

0.086 0.075 ⎤ ⎡ 0.081⎤ ⎡ 0.273⎤ ⎢ ⎥ ⎥ 0.176 0.109 ⎥ ⎢ ⎥ ⎢0.159 ⎥ 0.264 ⎥ = ⎢0.534 ⎥ 0.503 0.235 ⎥ ∗ ⎢ ⎥ ⎢ 0.267 ⎥ ⎢ ⎥ 0.751 0.549 ⎥ ⎢ ⎥ ⎢ 0.673⎥ 0.196 ⎣ ⎦ ⎢ 0.309 0.751⎥⎦ ⎣ 0.361⎥⎦

Weights of the maintenance strategies derived from the ANP approach are as follows: WANP = (WEM , WTBM , WCBM , WTPM , WDOM ) = (0.081, 0.159, 0.534, 0.673, 0.361)

Respectively for Mills, the strategies of TPM and CBM have higher rankings.

7

Discussion and conclusions

In this paper, interdependency of maintenance strategies for selecting the most suitable strategy was studied. The ANP approach with network structure and consideration of interdependency of criteria and interdependency of strategies and mutual dependence of strategies and criteria was employed. With a step-by-step approach of ANP, five maintenance strategies were compared according to four criteria of reliability, availability, maintainability and cost. According to the results, the priorities of maintenance strategies for mills in CMIC were determined as TPM, CBM, DOM, TBM and EM. Also, the criteria were ranked as reliability, maintainability, availability and cost. Therefore it is concluded that TPM and CBM strategies are the two most important strategies and improving reliability and maintainability of the equipments are critically important at CMIC. Figure 2 illustrates the influence of criteria on the maintenance strategies and highlights the considerable contribution of maintainability and reliability in the TPM and CBM strategies.

382 Figure 2

A. Shahin et al. Effects of different criteria on maintenance strategies

The Mills at the studied company have liners which are used as a cover for the body of the equipment. Usually, after a three months period the equipment stops and necessary change/repair is done on its parts. In fact, at the time of this investigation, the strategies of TPM and CBM were not applied for the equipment. After the investigation, the labour force has been divided into two groups for each part of the mills; their skills have been improved and the implementation of TPM has become facilitated. Also by supplying modern instruments for monitoring the condition of equipments (e.g., measurement of the vibration of gearboxes) and supplying suitable log sheets, regular inspection has been done and employing CBM strategy has been initiated. The findings of ranking strategies indicate that TPM and CBM strategies are more important than other strategies, while literature review implies different results. Bevilacqua and Braglia (2000) compared opportunistic maintenance (OPM), CBM, CM, PM and PDM strategies by AHP according to cost, added value, applicability and damages in three equipment categories at one of the oil industries of Italy. In each category, the ranking of strategies was different. For example the priority of maintenance strategies for air compressor was CM, PM, OPM, CBM and PDM. Bertolini and Bevilacqua (2006), with a combination of AHP and GP methods, compared PM, PDM and CM maintenance strategies according to delectability, occurrence and severity and PDM strategy was found as suitable. Wang et al. (2007) compared CBM, TBM, CM and PDM strategies by fuzzy AHP in an electricity plant. The comparison was in view of feasibility, added value, cost and safety and the strategy of PDM was selected as the suitable strategy. Arunranj and Maiti (2010) compared EM, CBM, TBM and CM strategies related to cost and risk by combination of AHP and GP approaches. Their findings highlighted that based on risk, CBM strategy was better than TBM strategy and based on cost, CM strategy was the best. The AHP-GP approach resulted in CBM as the best strategy. Zhaoyang et al. (2011) compared RCM, CM, CBM and PM in view of considering safety, cost, added value and feasibility by combining risk-based inspection (RBI) and AHP approaches. They applied their approach in one of the petroleum industries of china. Initially, they categorised the equipments based on risk by RBI approach and then, they found suitable maintenance strategy for each category by AHP


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approach. In almost all of the above addressed studies, interdependency of criteria and mutual dependency of strategies and criteria are not considered, while in this paper due to the network structure of ANP, all of the dependencies were considered.

7.1 Research limitations and managerial implications Applying any of the approaches for selecting the suitable maintenance strategy might have limitations. One of the limitations of ANP is its wide range of calculations. While in this paper, only five maintenance strategies and four criteria were considered, increase in the numbers of strategies and criteria will result in an increase in calculations. Another important subject is that the results of this approach are dependent upon experts’ conceptual opinions. Therefore, it is critically important that experts who make the comparisons be familiar with the strategies and criteria of maintenance. In a company with various equipments and machinery it is necessary to categorise them considering their performance and facilities and then selecting maintenance strategies for each category. Different criteria could be used for classifying of equipments; for instance, they could be classified based on risk and suitable maintenance strategy could be determined for each of the categories, respectively. Since the number of decision criteria might be enormous in action, it is important to select those criteria which are more important than others for further analysis. For example in addition to the four addressed criteria of this study, safety is an important criterion in the oil refinery industry. It should be noted that this study was limited to only one category of equipments and in one plant and therefore, the findings could not be generalised to other types of equipments and plants. Prior to the comparison of maintenance strategies for an industrial unit, it is necessary to study the feasibility of their application. In fact the application of the maintenance strategies is dependent on hardware and software requirements.

7.2 Future study In this research, the ANP approach was only employed and due to its considerable calculations its integration with other multi-criteria decision-making approaches such as TOPSIS and fuzzy ANP might facilitate and accurate analysis. In this paper, the maintenance strategies were compared according to only four criteria; therefore, the use of more criteria and studying its impact on the approach and it findings is recommended. While in this research, the step-by-step of ANP was applied and the interdependency of strategies was considered, problem solving by arranging super matrix and without consideration of interdependency of strategies seems to provide an opportunity for future studies.

Acknowledgements The authors would like to acknowledge the contribution of managers and experts of the maintenance department of the Chadormalu Mining-Industrial Company in facilitating this research.

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