Maintenance policy selection model – a case study in the palm oil industry

Maintenance policy selectionmodel – a case study

in the palm oil industrySiew-Hong Ding, Shahrul Kamaruddin and Ishak Abdul Azid

School of Mechanical Engineering,Universiti Sains Malaysia, Nibong Tebal, Malaysia

Abstract

Purpose – An optimal maintenance policy is key to the improvement of the availability andreliability of a system at an acceptable level without a significant increase in investment. However,the selection process is a complicated task because it requires in-depth knowledge on maintenancepolicies and on the technical requirements of maintenance. The difficulties and complexity of theselection process arise from the combination of conflicting maintenance constraints such as availablespares, size of workforce, and maintenance skills. The paper aims to discuss these issues.

Design/methodology/approach – The proposed maintenance policy selection (MPS) model isseparated into three major phases. The first phase identifies the critical system (CS) based on failurefrequency. The failure mechanism in the CS is then analyzed by using a failure mode and effectanalysis in the second phase. In the third phase, a multi-criteria decision making method, called thetechnique for order of preference by similarity to ideal solution, is adopted to identify an optimalmaintenance policy that can minimize the failures.

Findings – Through a case study, preventive maintenance was selected as the optimal maintenancepolicy for the reduction of system failures. The results obtained from the case study not only provideevidence of the feasibility and practicability of the developed model, but also test the acceptability andrationale of the developed model from the industry perspective. Valuable knowledge and experiencefrom employees were extracted and utilized through the proposed model to rank the optimalmaintenance policy based on the capability to reduce failure.

Originality/value – The practicality of the MPS model is justified through an implementation in thepalm oil industry. The application of the MPS model can also be extended to other manufacturingindustries.

Keywords Decision making, Maintenance, Critical analysis, Maintenance policy selection

Paper type Research paper

1. IntroductionA machine is the primary component of any manufacturing industry. However,a machine is incapable of preventing failure. The occurrence of failure, whether seriousor not, results in uncertain losses in terms of money, time and life. Maintenance is thusnecessary to reduce losses. Proper maintenance, which keeps life cycle costs down,not only helps extend the system lifetime, but also positively contributes to theoverall performance of the company (Jou et al., 2009). However, maintenance alsocontributes significantly to total production cost. The maintenance cost of a firm can

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The authors wish to acknowledge the support of the Fundamental Research Grant Scheme(FRGS) from the Ministry of Higher Education (MOHE) for funding this research. The authorsare gratefully appreciated to the anonymous referees for their constructive comments thatenabled the improvement of the paper.

Received 8 March 2012Revised 13 September 2012

8 November 20126 December 20129 December 2012

Accepted 12 December 2012

Journal of Manufacturing TechnologyManagement

Vol. 25 No. 3, 2014pp. 415-435

q Emerald Group Publishing Limited1741-038X

DOI 10.1108/JMTM-03-2012-0032

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model

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vary from 15 to 70 per cent of the production costs, depending on the industry type(Waeyenbergh and Pintelon, 2004; Savsar, 2011). This cost often forms the basis for theperformance improvement demands on the maintenance department. Over the years,several maintenance policies have been introduced, including preventive maintenance(PM) and predictive maintenance (PdM), to improve system performance by enhancingsystem availability and reliability. However, not every maintenance policy is suitablefor implementation in a system because each maintenance policy has its own attributesand produces different effects when implemented. Therefore, a selection model isneeded to identify the optimal maintenance policy. The optimal maintenance policyis necessary for the improvement of system performance by increasing the availabilityand reliability levels of the system without a significant increase in investment(Wang et al., 2007).

Given the significance and difficulty of selecting the optimal maintenance policy,different models have been developed for this goal. For instance, Bevilacqua andBraglia (2000) presented a multi-criteria decision making (MCDM) model for theselection of the optimal maintenance policy in an Italian oil refinery processing plant.The optimal maintenance policy was selected by using the analytical hierarchy process(AHP) according to various features such as economics, applicability, cost and safety.A similar model was also proposed by Bertolini and Bevilacqua (2006). Moreover,a model that employs an integration of the weighted sum method with fuzzy logic wasproposed by Al-Najjar and Alsyouf (2003) for the selection of the most cost-effectivemaintenance policy. Li and Xu (2007) also proposed an MCDM model by integratingthe elimination and choice translating reality with fuzzy logic in identifying theoptimal maintenance policy for a compressor. Safety, cost, added value and informationwere the five criteria used to evaluate the maintenance policy. The technique for orderof preference by similarity to ideal solution (TOPSIS) was also adopted by Shyjith et al.(2008) for the selection of the optimal maintenance policy in the textile industry.Different aspects including environment, machine, workforce and the maintenancepolicy itself were considered in the selection process. Moreover, an integration of fuzzyAHP with TOPSIS was also developed by Ilangkumaran and Kumanan (2009) todetermine the optimal maintenance policy for the textile industry.

Majority of the studies agreed that most problems associated with system reliability,availability and maintainability can be solved by implementing the optimal maintenancepolicy. The developed maintenance selection models typically focus on determininghow much maintenance must be conducted on the components and how frequent thecomponents must be replaced. These maintenance selection models do not focus onwhich system must be improved and on what maintenance policy is required. Thus,the studies were directly focused on a specific maintenance action without performingany analysis on the suitability of the maintenance policy for the system.

In addition, the practicability of the model in the industry must also be considered.Therefore, a model that integrates failure analysis and the MCDM method on thesystem is proposed for the selection of the optimal maintenance policy. Furthermore,the MCDM method, which utilizes expert knowledge in collecting data for analysis,is more practical. This practicality stems from the fact that experts with the mostknowledge and experience can be easily identified. In addition, the MCDM method canconsider a large number of criteria in selection process, thus improving the overallreliability of the selected maintenance policy. Through a selection process that uses

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the suggested model, the maintenance policy is expected to reduce the failure impactto a minimum level. By using this model, management can plan for and implement amore effective maintenance process at a minimal amount of time.

The remainder of this paper is organized as follows: Section 2 describes the generalprinciple of the maintenance policy selection (MPS) model. Section 3 describes theapplication of the MPS model in the palm oil industry. The results of the MPS modelare discussed in Section 4. Section 5 draws the conclusion of this paper.

2. MPS modelThe basic principle of the MPS model is that the selection of the optimal maintenancepolicy should be based on the causes of system failure. However, most industries lack acomplete data recording system. This inadequacy may be attributed to the lack ofawareness of the management regarding the importance of data recording. Moreover,setting up a complete data recording system requires a certain amount of investment,which is why numerous companies are not keen on this aspect. The lack of a completedata recording system has increased the difficulty of collecting accurate and precisedata, which adversely affects the accuracy of the final result obtained from the model.Technically, these data reside within the workers under the form of skills, know-how,and capabilities. Therefore, expert judgment is the most suitable methodology that canbe used to collect data from workers.

The MPS model proposed in this study was developed according to three principles:problem identification, analysis, and solution finding. Based on these three principles,the model was developed by integrating different approaches, including tally chart,functional block diagram (FBD), functional failure mode and effect analysis (FMEA),expert judgment and TOPSIS. The model is generally divided into three modules: scopeidentification, functional analysisand maintenancepolicy evaluation, as shown in Figure 1.

As shown in Figure 1, the objective of Module I is to specify the scope of analysis inorder to have effective analysis by focusing on the fundamental area. In Module II,a series of analysis is conducted to investigate and relate the failure causes andassociate effects on the critical system (CS). An optimal maintenance policy that is ableto reduce the failure effect is selected in Module III by referring to the results obtainedin Module II. The details of each module are explained in following paragraph.

Figure 1.MPS model

Module IScope Identification

Module II

Critical SystemAssessmment

Module III

Maintenance Policy Evaluattion

• Production process description• System separation• Critical system selection

• Functional identification• Functional failure analysis

• Maintenance policy proposition• Maiintenance ppolicy rankking


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Module I: scope identificationModule I aims to study and to separate the entire production line into several systemsaccording to the main functions of the system. The operational flow and the functionsof the related system are studied through actual operation observations, productionhandbooks and operation manuals. These functions are illustrated by using FBD.The adoption of FBD provides a diagrammatical and structural breakdown of acomplex system in functional terms. The knowledge acquired is then used to separatethe production line into several systems to simplify the analysis process.

In a CS identification process, failure frequency is used to prioritize the criticality levelof each system on the production line for effective analysis for a limited duration. Failurefrequency was collected from historical failure records in maintenance documents andthen recorded in a tally chart. A tally chart is an easy and efficient method for thecollection of occurrences such as statistical information to show the relative occurrencefrequency (Martin, 2008; NSW Health Department, 2002). The final goal of Module Iis to determine which system has the highest failure frequency. This system thenbecomes the analysis subject in Module II.

Module II: CS assessmentActivities in Module II can be separated into two major steps including functionalidentification and functional failure analysis. Second-level FBD broken down from theFBD that was produced in Module I is used to illustrate functional flow of CS. The listsof functional modes are identified according to the second-level FBD. Functional modeused in this identification process is defined as the activities assigned to a system toaccomplish specific function. Once the functions are identified, a functional failureanalysis is conducted to identify the failure mode and the failure causes of the CS byusing functional FMEA. Functional FMEA implemented in this model is to providebetter understanding about failure mechanism including how the failure occurs,why the failure occurs and what are the impacts when the failure occurs.

The functional failure modes are identified based on the results obtained in functionalidentification. In this identification process, the functional failure mode is described asthe sub-system state when it lost its function. Whereas functional failure causes aredefined as the physical processes that directly cause a system failure. As a functionalfailure mode may have more than one failure causes; all probable causes for eachfunctional failure mode shall be identified and described. Once the failure causes arerecognized, failure impact computation of each failure cause will be conducted.Functional failure impact is a relative measure of the consequences of a functionalfailure cause. Each functional failure cause creates different impact on the CS.The quantification of the failure impact is required in order to integrate the value of failureimpact into the maintenance policies selection process. Risk priority number (RPN) is usedto quantify the failure impact of the functional failure cause (Yang et al., 2008). Theindication process is based on three criteria which are occurrence, severity and detection.

In order to perform an overall evaluation from failure severity to the effectiveness ofexisting failure detection possibility, a RPN rating scale that suit to the evaluationscenario are developed from the discussion with FMEA team. Table I tabulatedthe evaluation rating of occurrence. The definition of occurrence in this model is theduration between two related functional failure causes occurred that named as meantime between failures (MTBF).

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The developed severity rating is tabulated in Table II. The severity is defined as theseriousness of the functional failure cause on the CS when it occurs. In a continuousproduction flow with preponderance of unrefined product that is in fluid form, anynature of component failure will cause certain duration of stoppage. Therefore, severityof the functional failure effect is justified according to the duration of stoppage whenthe functional failure cause occurs.

The third criterion in the criticality evaluation is detection where it is the possibilityof the functional failure cause being detected before failure. The rating scale ispresented in Table III. The percentage of detection is based on average ten timesfailure, how many percent the failure could be detected before failure out of ten timesfailures.

The RPN is computed according to equation (1):

RPN ¼ OFJ£ SFJ

£ DFJð1Þ

where OFJrepresents the occurrence of the failure cause, Fj; SFJ

represents the severity ofFj; and DFJ

represents the possibility of detecting Fj.The list of failure causes serves as the evaluation criterion during the maintenance

policy evaluation conducted in Module III. The RPN is the criterion that is weighted inthe TOPSIS computation.

Rating MTBF

10 Less than 1 hour9 1-8 hours8 9-16 hours7 Two to six days6 One to three weeks5 One to two months4 Three to five months3 Six to nine months2 Ten to 12 months1 Greater than one years

Table I.Occurrence rating criteria

Rating Description (evaluation criteria)

1 Cause delay less than 15 minutes. No defective component2 Cause delay less than 15 minutes, parts defect3 Systems down less than 30 minutes, parts defect4 Systems down for 30 minutes to 1 hour, parts defect5 Systems down for 1-2 hours, parts defect6 Systems down for 3-5 hours, parts defect7 Systems down for 6-8 hours, parts defect8 Systems down more than 9 hours, parts defect9 Failure mode affects operator safety or involves non-compliance with

government regulation with warning10 Failure mode affects operator safety or involves non-compliance with

government regulation without warningTable II.

Severity rating criteria


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Module III: maintenance policy evaluationDiscussion with experts in the corresponding organization is performed to identify thefeasible maintenance policies that could be implemented according to the capabilitiesand constraints of the organization. With the same group of experts, evaluations areconducted to rate the potential performance of the proposed maintenance policies interms of reducing the failure impact from related failure causes. TOPSIS is then usedin the MPS model to rank the proposed maintenance policies. The equations used inTOPSIS can be found in the study by Yang and Hung (2007). The rating is thenorganized into a matrix, as shown in equation (2):

F1 F2 . . . Fj . . . Fn

D ¼

A1

A2

..

.

Ai

..

.

Am

�p11 �p12 . . . �p1j . . . �p1 n

�p21 �p22 . . . �p2 j . . . �p2 n

..

. ...

. . . ...

. . . ...

�pi1 �pi2 . . . �pi j . . . �pi n

..

. ...

. . . ...

. . . ...

�pm1 �pm2 . . . �pmj . . . �pmn

266666666666664

377777777777775

ð2Þ

where Ai denotes the proposed maintenance policies stated in Section 3.3.1 for i ¼ 1, 2,3, . . . , m; Fj represents the functional failure causes identified in Module II for j ¼ 1, 2,3, . . . , n; and �pFjAi

is the average maintenance performance rating of each maintenancepolicy Ai with respect to each functional failure cause Fj.

Once the matrix representation �p has been completed, it will be normalized totransform the different scales and units among the functional failure causes intocommon measurable units to enable comparisons across the criteria (Chen, 2004).The normalized value rij is calculated as follows:

rij ¼pijffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPnj¼1p

2ij

q ; j ¼ 1; . . . ; n; i ¼ 1; . . . ;m ð3Þ

Rating Description (%)

1 More than 902 80-893 70-794 60-695 50-596 40-497 30-398 20-299 10-19

10 Less than 10Table III.Detection rating criteria

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where rij is the normalized preference measure of the ith maintenance policy in termsof the jth functional failure cause.

Each functional failure cause produces a different failure impact to the system andmust have a different priority in the MPS process. The functional failure cause that hasa higher failure impact must be the main concern in the selection process. Thus, theRPN computed in Module II is converted to represent the weight (w) of each functionalfailure cause to form a normalized weighted decision matrix. The weight is given in thefollowing equation:

wFj¼ RPNFj

ð4Þ

where wFjrepresents the weight for each functional failure cause Fj, for j ¼ 1, 2, 3, . . . ,

n; and RPN is the risk priority number obtained from equation (1).The normalized weighted decision matrix V is then formed as equation (5):

V ¼ RW ¼

wF1r11 wF2r12 . . . wFjr1i . . . wFnr1n

wF1r21 wF2r22 . . . wFjr2i . . . wFnr2n

..

. ...

. . . ...

. . . ...

wF1rm1 wF2rm2 . . . wFjrji . . . wFnrmn

26666664

37777775

ð5Þ

where w represents the weight of the functional failure cause, Fj, for j ¼ 1, 2, 3, . . . , n.After V is formed, the ideal (Aþ

* ) and negative ideal (A2* ) maintenance solutions of

each functional failure cause are determined, denoted as:

Aþ* ¼ {ðmax vijj j [ J 1Þ; i ¼ 1; 2; 3; . . . ;m} ¼ {v1þ ; v2þ ; . . . ; vnþ}; ð6Þ

A2* ¼ {ðmin vijj j [ J 1Þ; i ¼ 1; 2; 3; . . . ;m} ¼ {v1; v2; . . . ; vn2} ð7Þ

where vij is the weighted normalized value that indicates the average performancerating of each maintenance policy Ai with respect to each functional failure cause Fj, forJ1 ¼ {j ¼ 1, 2, 3, . . . , n}.

Based on equations (6) and (7), the most preferable and the least preferablemaintenance solutions for each functional failure cause are identified. Once thesemaintenance solutions are computed, the overall performance of the maintenance policyis then determined by using the n-dimensional Euclidean distance to the ideal andnegative ideal maintenance solutions. Aþ is the distance of each maintenance policyfrom the ideal maintenance solution and is defined as:

Aþ ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXnj¼1

vij 2 vþj

� �2

vuut for i ¼ 1; 2; 3; . . . ;m: ð8Þ

The distance from the negative ideal maintenance solution (A2 ) is defined as:

A2 ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXnj¼1

vij 2 v2j

� �2

vuut ; for i ¼ 1; 2; 3; . . . ;m: ð9Þ


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After obtaining each of the maintenance policy separation distances, the relativecloseness of each maintenance policy to the ideal maintenance solution is calculated torank the proposed maintenance policies. The relative closeness to the ideal maintenancesolution is defined as:

C ¼A2

i

Aþi þ A2

i

; ð10Þ

where C denotes relative closeness, with 0 , C , 1 for i ¼ 1, 2, 3, . . . , m.The maintenance policies can then be ranked by preference according to the

descending order of C (equation (10)). A larger index value denotes better performanceof the maintenance policy in preventing failure modes. The maintenance policy thatobtains the highest performance index is selected and then proposed as the optimalmaintenance policy for the CS.

3. Case studyA case study of a processing company in Malaysia was conducted to verify and tovalidate the MPS model. The company is in the agriculture industry and is in thebusiness of extracting crude palm oil (CPO) from palm fruits. The company is small,having approximately 40 employees with production running in two shifts.The company management has set a target to achieve the maximum throughput ofthe production line at 30 tons of palm fruits processed each hour. The whole productionline in the palm oil mill (POM) is constructed in a serial configuration. When thecontinuous production line is in a serial configuration, the occurrence of failure causesa stoppage in the whole production line. The stoppage has serious effects either on themaintenance department or on the production department. Before the case study,the high failure frequency that occurred because of unplanned maintenance affected theproduction line throughput. The failure occurrences were largely attributed to the failureon the part of the maintenance management to have proper guidelines to analyze failuresand subsequently identify a suitable solution either at the system level or componentlevel. Appropriate maintenance must be identified at the system level before proceedingto an analysis for planning maintenance actions at the component level. Therefore,the case study focused on selecting an optimal maintenance policy for the CS in theproduction line based on the developed selection model. The application of the selectionmodel is discussed in detail in the following section.

3.1 Module I: scope identificationReferring to the MPS model shown in Figure 1 the most important CS must beidentified at the initial stage. Module I primarily aims to obtain information on themajor functions of the production line. The production line is then separated intoseveral systems based on their functions. Failure frequencies are collected and thenused to identify the CS as the subject of analysis in Module II. Module I is initiated byprocess descriptions, followed by system separation, and then CS identification.

3.1.1 Production process description. The verification and validation process isinitiated by studying the POM production line. Fresh fruit bunches (FFBs), which are theunprocessed fruit that are in bunches or in loose form, are generally derived from Elaeisguineensis. FFBs are harvested when ripe and then transported to the POM by lorry. TheFFBs are tipped into a chute at the loading ramp. The FFBs are then loaded into the cages

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beneath the chute and pushed into horizontal sterilizers for cooking. The sterilizationprocess aims to inactivate the enzymes that produce free fatty acids, loosen the fruitsfrom bunches, soften the fruits, condition the nuts, and to coagulate the proteins.

Once the sterilization process is completed, the cages are hoisted up and then emptiedinto the feed hopper of the bunch stripper. The stripping process comprises a horizontalrotating drum, in which each bunch is lifted and dropped several times to shake out thefruits. The separated fruits are then transferred for an oil-extracting process. The emptybunches that are produced from the separation process are conveyed to the dump site.The fruits are elevated to digesters, which consist of a steam-jacketed vessel and arefitted with stirring arms. The action of the arms breaks up the fruit, particularly theoil-containing cells. The digester is linked to a continuous double-screw press. The presssqueezes out crude oil and press cake, which contain fruit fiber and nuts. The extractedcrude oil that comprises oil, water and dirt is clarified in the oil room to produce purecrude oil.

The extracted crude oil flows through a vibrating screen into a settling tank toseparate the clean oil and the sludge layer. Dirt and moisture are removed from the oillayer in a hermetically sealed purifying centrifuge, and the oil is dried in a vacuumdrier before storage. The sludge layer is passed through a small de-sanding cyclonebefore going into a sludge centrifuge. The recovered oil from the centrifugal stage isreturned to the settling tank and the final sludge is sent to a waste treatment system.Fiber, shell and solids that are removed by the screen are recycled to the digester.

The cake from the screw press passes down to the cake breaker conveyor.The conveyor is specially designed to break up and to dry the cake by evaporation.The broken-up cake, nuts and fiber are then fed into moving air columns that suckmost of the fiber. The nuts are dropped into a nut-polishing drum, which frees the restof the fibers so that these fibers can also be removed in the air stream. The nuts arestored in a nut silo to reduce their moisture content by using a stream of heated airbefore they are cracked. The nuts are cracked in a nut cracker for kernels extraction.The mixture of kernels and shell fragments is first separated in a pneumatic column andthen in a hydro cyclone. The separated shell fragments are then sent to a dump site, andthe kernels are dried in a silo by using hot air before being sent for further processing.

Through these steps, all functions in the production line were studied and charted,as discussed in Section 2. The knowledge gained regarding these functions is vital forthe preparation of the system separation process, which is conducted through the stepsoutlined below.

3.1.2 System separation. According to the process description in the previous section,the whole production line is divided into six systems based on their main functions.The FFB are processed through six systems, which include sterilization, stripping, crudeoil extraction, crude oil clarification, nut cracking and kernel separation. These systemsare each numbered by using a functional code. A functional code is a numbering systemthat is used for the breakdown order of the functional system. This code is essential toprovide traceability through each level of indenture. The functions that are identified inthe FBD at each level are numbered in a manner that preserves the continuity of thefunctions and provides information on the function origin throughout the system.The complete flow of the process is represented in the FBD depicted in Figure 2.

Referring to Figure 2, the function of the sterilization system is to sterilize the FFB.Then, the sterilized fruits are transferred to the stripping system for separating


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the fruits from bunches. Once the fruits have been completely separated, it will bemove to crude oil extraction system for extracting the crude oil. The extracted crude oilis clarified in the crude oil clarification system to obtain a pure CPO. The nut extractedfrom the fruits during crude oil extraction process is transferred to the nut crackingsystem. The nut cracking system is used to extract the kernels from nuts by crackingthe shells. The final process is to separate the mixture of kernel and shells and this iscarried out in the kernel separation system.

The most critical issue in adopting FBD is defining the boundary between systems.The reason is that the production line involved in this case study is a continuous processand grouping machines that are located between systems is difficult. This approachusually involves a transport machine such as a conveyor or an elevator. In this casestudy, however, the grouping of these kinds of machines was determined based on theprocessing stage and the function of the production line. When the production line hadbeen separated into systems, the process of critical identification is performed during theCS selection stage.

3.1.3 CS selection. System failure frequency is the main reference for the CSselection process. Failure frequency is defined as the number of failure occurrenceswithin a specific period. The failure frequency of each system was collected based onthe maintenance records from the maintenance department. To achieve betteraccuracy in terms of collected failure frequency, failure records from the productiondepartment were referenced. The total failure frequency of each system is shown inFigure 3.

Based on the collected failure data shown in Figure 3, the stripping system had thehighest failure frequency recorded that is 26 times per month, followed by nutcracking, kernel separation, sterilization, crude oil clarification. Crude oilextraction system has a only seven times failure occurrence per month. This is thelowest failure frequency among these systems. Thus, the stripping system whichhad the highest failure frequency was assigned as CS and will be the focused inModule II.

Figure 2.FBD of palm oilproduction line

1.0Sterilization

2.0Stripping

3.0Crude oil extraction

5.0Nut cracking

Fresh fruit bunches(FFB)

Palm kernel

6.0Kernel separation

4.0Crude oil clarification

Crude palm oil(CPO)

Empty bunches

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3.2 Module II: CS assessmentCS assessment is the second module in the MPS model that will be validated. Thismodule comprises two sections: identification of operational flow and evaluation offailure occurrence by using functional FMEA and expert judgment. The first step inModule II is the detailing of the operational flow in the stripping system. Whenoperational flow is studied, critical analysis is performed to identify the failuremechanisms involved in the stripping system. The failure impact is then quantified.

3.2.1 Functional identification. The sub-functional systems of the stripping systemare studied in this section. Each sub-functional system was given a code to simplify thereorganization of each functional system in the stripping system. The sub-functionalsystems were coded based on the FBD coding system. The coded functional flow isshown in Figure 4.

As shown in Figure 4, the stripping system comprises six sub-functional systems.The cage that contains the sterilized FFBs is lifted and discharged into the feeder. Thesterilized FFBs are then dispensed into the stripping drum for the separation processthrough an auto feeder. Separating the fruits from the FFB bunches in the strippingdrum is the third sub-function. Then, the separated fruits are conveyed into the fruitelevator using a screw conveyor. Finally, the separated fruits are transferred to the crudeoil extraction system through a fruit elevator. The empty bunches produced during theseparation process are then moved to the dumpsite by the empty bunches conveyor.

When the FBD system is developed, functional failure analysis can be performedsystematically by referring to the identified sub-function. Thus, the failure mechanismunderlying the sub-functions, which includes failure modes, causes, and failure effects,can be investigated.

3.2.2 Functional failure analysis. Functional failure analysis is the second section ofModule II in terms of selection model validation. Functional FMEA was adoptedin this step to perform failure analysis on the CS. The first step in functional FMEA isthe selection of experts. The process comprises two stages that will be elaborated indetail below.

Figure 3.Failure frequency of

separated systems

16

26

7

11

22

18

0

5

10

15

20

25

30

Sterilization Stripping Crude oilextraction

Crude oilclarification

Nutcracking

Kernelseparation


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(a) Selection of experts. Qualified experts were identified based on the developedcriteria. The three criteria were: minimum of ten years of working experience inworkshop and maintenance with a minimum of seven years of industrial experience ina related industry, capable of dedicating the required time to perform evaluations andcommitted to participate, as required. Six candidates were interviewed. Thesecandidates held different positions, including mill manager, mill assistant (responsiblefor the entire production process), and maintenance planner. Four technical personnelin the maintenance department were also interviewed. Eventually, a team of fivequalified experts comprising engineers and foremen were invited to participate in thefunctional FMEA elicitation. When the qualified experts were identified, a meeting wasconducted to introduce the project, to familiarize them with the expert judgment andthe elicitation process and to foster critical discussions of key evidence relevant tothe questions posed. This step aimed at reducing potential bias in judgment(Roman et al., 2008).

(b) Functional FMEA elicitation. Functional FMEA elicitation was conducted at thisstage. This step comprised two stages. The first stage was in the form of a groupdiscussion aimed at collecting information on functional failure modes and failurecauses in the stripping system. The second stage involved individual face-to-faceinterviews aimed at quantifying the criticality of each functional failure cause that wasidentified during the group discussion.

Figure 4.FBD of stripping system

2.1 Lifting the cage consists of sterilized

FFB and discharging into hopperfeeder

2.2Feeding the sterilized FFB into the

stripping drum for separation processfeeder

2.3 Separating the fruits in the stripper

drum

2.6Transferring the emptybunches the dump site

Cage withsterilized FFB

Empty bunches 2.4

Transfer the fruits intothe fruit elevator

2.5Transferring the separated fruitsto the crude oil extraction system

Separated fruits

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During the discussion, the first priority was to determine the functional failure modesin each of the functional subsystems identified in Section 3.2.1. When the functionalfailure modes were known, the causes for each were also identified. During thediscussion, 19 functional failure modes were identified. Considering that the occurrenceof each functional failure cause has different impacts on the system, a second sectionof elicitation was conducted to quantify these impacts. As previously mentioned,this step was an individual activity.

The second section of the functional failure cause impact evaluation process involvedquantification based on the experts’ experience and knowledge. As generally known,human judgment is very subjective and the answers provided will have a significanteffect on the final results. Thus, a simple statistical analysis was performed to test theinter-rater reliability among the five experts. The inter-rater reliability was used to testthe degree of consistency on the answer provided by different experts in terms of scaleand to determine the consistency and reliability of the given results.

In the reliability testing, Cronbach’s a was calculated based on three criteria:occurrence, severity and detection. The consistency in evaluation among the five expertsfor each criterion can be judged through this test. The values of the coefficients aretabulated in Table IV.

The Cronbach’s a coefficients for the three criteria were 0.953, 0.913 and 0.954.The Cronbach’s a coefficients were higher than 0.7. From the analysis, the rating givenby the experts were rationally explainable and did not show significant bias. Thisresult indicates that, by referring to these data, the final outcome is reliable.

(c) RPN computation. When the rating of each criterion was determined as reliable,these data were then used in the RPN computation process. When identification andcalculation were completed, data were documented by using an FMEA worksheet.This data set comprises the results of the functional failure analysis stage in Module II.The functional FMEA data that were collected from the maintenance experts wererecorded in the FMEA worksheet that is attached in the Appendix.

The determination of functional failure causes and their RPN values is essentialfor the maintenance policy evaluation in Module III because the performance of themaintenance policies will be rated based on these failure causes. The RPN values of thefailure causes were converted into weighted values during the maintenance policyranking in Module III.

3.3 Module III: maintenance policy evaluationMaintenance policy evaluation is the final module in the MPS model. This module isdivided into two stages. The first stage is the proposal of maintenance policies that areapplicable to the selection. The second stage is the evaluation and ranking (by usingTOPSIS) of the performance of these maintenance policies to reduce the failure impactof the failure causes identified in Module II.

Criteria Number of expert Number of question Cronbach’s a Judgment

Occurrence 5 128 0.953 GoodSeverity 5 128 0.913 GoodDetection 5 128 0.954 Good

Table IV.Reliability statistics


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3.3.1 Proposal of maintenance policies. Four maintenance policies (i.e. the autonomousmaintenance policy, (AM, A1), corrective maintenance policy, (CM, A2), PdM policy,(A3) and PM (A4)] were proposed as options in this research.

3.3.2 Ranking of maintenance policies. After maintenance policies were proposed,the performances of these proposed maintenance policies were evaluated by a selectedexpert (identified in Module II). The process was conducted in two stages; the firststage was the maintenance policy performance elicitation, and the second stage wasthe performance analysis.

(a) Maintenance policy performance elicitation. The five experts who participated infunctional FMEA were asked to give their respective performance ratings on theproposed maintenance policy. During the interview, the experts were asked to assessthe performance of each of the maintenance policies in reducing the failure effectaccording to three criteria: occurrence (O), severity (S) and detection (D). At the end ofthe elicitation process, the performances of the maintenance policies corresponding tothe three maintenance criteria were rated and the analyzed by using TOPSIS.

To identify the reliability and bias of the rating, the two-way mixed effects modeltype with 95 percent confidence interval intra class correlation was used to compute theCronbach’s a coefficient. In the reliability computations, the Cronbach’s a coefficientwas calculated based on three criteria: failure frequency, failure duration and detection.The consistency in the evaluation of each criterion among the five experts can be judgedthrough this analysis. The values of the coefficients are tabulated in Table V.

All three criteria had Cronbach’s a values of more than 0.7. This result suggests thehigh reliability of the experts. From the analysis, the ratings given by the experts wererationally explainable and did not show significant bias. By referring to these data,the final outcome will be reliable. When the first stage elicitation of the proposedmaintenance policy is computed, performance analysis can be conducted.

(b) Maintenance policy performance analysis. Referring to the first stage, five sets ofdata on the maintenance policy ratings were obtained from five maintenance experts.Among the four maintenance policies, all experts gave the lowest rating tomaintenance policy A2 (CM), followed by A1 (AM). A3 (PdM) and A4 (PM) had almostsimilar and higher ratings compared with A1 and A2. Every functional cause had adifferent significance to system function. Thus, identifying the weight of thesignificance of each functional failure cause and incorporating such significance intothe normalized decision matrix is necessary. The maximum and minimum values ofeach functional failure cause in the weighted normalized decision matrix wereidentified. The maximum value is the positive ideal solution, and the minimum value isthe negative ideal maintenance. The results are tabulated in Table VI.

Referring to the results in Table VI, A2 had the farthest distance (578) from thepositive ideal solution. A1 had been separated by 262 from positive ideal distance.A3 and A4 had similar distances between their Aþ and A2 . The relative closeness ofeach maintenance policy was determined. When the calculation of relative closeness

Criteria Number of experts Number of question Cronbach’s a Judgment

Occurrence 5 128 0.911 GoodSeverity 5 128 0.890 GoodDetection 5 128 0.901 Good

Table V.Reliability statistics

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was completed, the maintenance policies were ranked based on a descending value ofrelative closeness. From the results shown in Table VII, A4 had the largest relativecloseness distance to ideal maintenance, followed by A3 and A1. A2 had the farthestmaintenance policy from ideal maintenance.

PM had the highest ranking, although it had a similar value as PdM (A3). However,the objective of this model is to select the optimal maintenance policy. Thus, the PMpolicy was chosen as the optimal maintenance policy for the stripping system.

4. DiscussionThe MPS model was validated by using a case study from a certain industry. Thepurpose of model validation is to make the model useful in the sense that the modeladdresses the appropriate problem, provides accurate information about the systembeing modelled and ensures that the model is applicable in the industry. Validation byusing a case study not only provides evidence of the feasibility and practicability of thedeveloped model but also tests the acceptability and the rationale of the developedmodel from the industry perspective. In addition, by conducting a case study, a clearand complete picture of the industrial environment including a company’s needs andexpectations as well as the constraints that are actually faced in the industry can alsobe considered.

In this paper, model validation focused on identifying the optimal maintenancepolicy for a system to demonstrate that the developed model is applicable in actualconditions. Effectiveness remains an important issue in any industry. Everything mustbe fast and effective, with the best outcome obtained within the shortest time. One ofthe ways to achieve the highest effectiveness is by placing high importance on thecritical issue. In this case study, the system that caused the most problems and hasbecome the bottle-neck in the production line was identified before attempting to findthe solution. According to Krishnan (1992), different criteria such as downtime, failurefrequency and spare parts may be referenced as identification guidelines. A similarcriterion, failure frequency, has also been adopted. Consequently, the CS can be easilyand clearly identified. Through the selection process, a CS was chosen, which provesthe robustness of Module I of the MPS model.

Maintenance polices Positive distance, Aþ Negative distance, A2

A1 262 378A2 578 0A3 162 484A4 162 503

Table VI.Positive distance and

negative distance ofmaintenance policy

Maintenance polices Relative closeness, C Ranking

A1 0.410 3A2 0.000 4A3 0.749 2A4 0.756 1

Table VII.Relative closeness of

maintenance policy to theideal maintenance


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Technical analysis in Module II was performed to obtain useful insight into the failuremechanism underlying the CS. The obtained results show that the model is valid interms of credibility and plausibility. The result of the discussion among experts with anaverage of 19 years of working experience was reliable, which is consistent withDimattia et al. (2005), who stated that experts with more than ten years workingexperience in a related industry could provide reliable judgment. However,all measurements and judgments were subject to experimental and judgment errors(Park and Lee, 2008). Relative to error, one of the crucial problems is that error can resultin inconsistency. A built-in consistency checking mechanism will improve the accuracyof a judgment, in addition to the overall consistency.

In the model, the consistency of the information provided as well as the reliability ofthe experts was confirmed by using intra-judgment consistency. According to Sekaran(2002), a Cronbach’s a coefficient higher than 0.7 shows good inter-rater reliability,which is consistent with the results obtained from individual judgments (referring toTable IV). Thus, these results provide evidence that the judgments provided by the fiveexperts are consistent and precise, thereby supporting the validity and reliability ofexpert judgments and of the results generated by the model. The robustness of the modelwas also proven.

When the failure mechanisms were known, a suitable maintenance policy wasselected in Module III. Similar to Module II, an expert judgment was adopted toevaluate the maintenance policy performance. The data were validated and foundreliable because the Cronbach’s a coefficient was more than 0.7. This result also provesthe trustworthiness of the experts. The final results obtained from the developed modelclearly show the ranking of each proposed maintenance policy. The results wereconsistent with those of several others, such as those of Bevilacqua and Braglia (2000),Al-Najjar and Alsyouf (2003) and Waeyenbergh and Pintelon (2004), who performedcase studies of different manufacturing industries aimed at reducing the failureimpacts in these companies. These results prove that similar results were obtainedwith greater ease and simplicity by using the MPS model compared with the modeldeveloped by the authors.

The good results obtained in this case study can largely be attributed to theintegration of different methods, including tally chart, FMEA and TOPSIS, thus makingthe MPS model unique. Moreover, the integration of quantitative expert judgment toquantify subjective value increased the accuracy of our results. This scheme issupported by Spurgin (2002), who stated that high-quality results depend on experts inrelated fields and on feedback based on their experience. Waeyenbergh and Pintelon(2002) also agreed that experienced and knowledgeable personnel have adequate abilityto provide useful judgment. The reason is that such personnel are the most familiar withthe actual operation of the production line and thus have adequate knowledge in themaintenance aspect.

Another significant aspect of the MPS model is the integration of the quantitativeaspect that enabled the expression of the MPS outcome to be in a numerical form.Quantitative expression is not only easier to understand compared with qualitativeexpression, but also brings more accurate meaning that will increase the accuracy ofjudgment. This hypothesis is supported by Keeney and Winterfeldt (1989), who stated thatqualitative terms, such as “small chance”, have large ranges of interpretation, dependingon who had been asked. Moreover, evaluations performed by using quantification

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values can also be analyzed based on consistency and reliability. The analysis will bolsterthe reliability of the results that were obtained from the model.

Hence, the developed model is reliable, valid and applicable in the manufacturingindustry. The MPS model can be used as a new MPS method with full assurance.Through the discussion of the significant aspects of the MPS model, the developedmodel was proven robust based on validity, accuracy, reliability and practicability.In other words, the MPS model can be applied in the selection of a maintenance policyin the manufacturing industry.

Optimal maintenance policy is a key decision in the manufacturing industry for theimprovement of productivity performance (Lu and Sy, 2009). With the appropriatemaintenance policy, maintenance management becomes more effective and agile byhaving better planning on maintenance, human resources and spare parts arrangementwithout disrupting the production schedule. The optimal maintenance policy that wasdetermined by using the MPS model is expected to reduce the failure impact of thesystem based on product, system, people and environment. Apparently, malfunctioncould affect productivity as well as product quality. Failures eventually reduce theuseful life of the system. Notably, possible employee injury caused by these failurescan be reduced by the maintenance policy that was determined from the MPS model.The policy can further enhance productivity, which heavily relies on employeeperformance. On the environmental aspect, a maintenance policy is expected to preventcatastrophic failures that may cause pollution to the environment and againstgovernment regulations. Particularly, the optimal maintenance policy determinedfrom the MPS model can reduce not only the failure impact on the system but alsocompany losses. This effect will enhance competitiveness among manufacturingindustries.

5. ConclusionAn example of a successful implementation of a model for the selection of an optimalmaintenance policy was demonstrated in this paper. The MPS model can be effectivelyused for quantifying the performance of each maintenance policy, thereby reducing thefailure impact on the system and selecting the optimal maintenance policy according toquantified performance values. On the basis of the obtained results, this model can aidcompanies in selecting the most suitable maintenance policy for the system and inachieving more effective maintenance planning. The ultimate aim is to enhance thecompany’s maintenance effectiveness by implementing the optimal maintenancepolicy. Moreover, the developed model is capable of providing accurate results withina short time, which is suitable for industries that require effective decisions within ashort time.

Regarding future studies and developments, a computerized integration with thedeveloped maintenance selection model should be developed to attain better resultswithin a significantly shorter time. In actual industries, everything must be fast andaccurate, and the maintenance team must be capable of adapting well. Developing asimple but effective information collection system must also be considered. Duringanalysis, the common problems found were the lack of necessary information and theaccuracy of information. Thus, a systematic procedure for recording information foranalysis is necessary. The developed information collection system must be userfriendly to enhance its practical applicability.


model

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References

Al-Najjar, B. and Alsyouf, I. (2003), “Selecting the most efficient maintenance approach usingfuzzy multiple criteria decision making”, International Journal of Production Economics,Vol. 84 No. 1, pp. 85-100.

Bertolini, M. and Bevilacqua, M. (2006), “A combined goal programming – AHP approach tomaintenance selection problem”, Reliability Engineering & System Safety, Vol. 91 No. 7,pp. 839-848.

Bevilacqua, B. and Braglia, M. (2000), “The analytic process applied to maintenance strategyselection”, Reliability Engineering & System Safety, Vol. 70 No. 1, pp. 71-83.

Chen, M.F. (2004), “Combining grey relation and TOPSIS concepts for selecting an expatriatehost country”, Mathematical and Computer Modelling, Vol. 40 No. 13, pp. 1473-1490.

DiMattia, D.G., Khan, F.I. and Amyotte, P.R. (2005), “Determination of human error probabilitiesfor offshore platform musters”, Journal of Loss Prevention in the Process Industries, Vol. 18,pp. 488-501.

Ilangkumaran, M. and Kumanan, S. (2009), “Selection of maintenance policy for textile industryusing hybrid multi-criteria decision making approach”, Journal of ManufacturingTechnology Management, Vol. 20 No. 7, pp. 1009-1022.

Jou, Y.T., Wee, H.M., Chen, H.C., Hsieh, Y.H. and Wang, L. (2009), “A neural network forecastingmodel for consumable parts in semiconductor manufacturing”, Journal of ManufacturingTechnology Management, Vol. 20 No. 3, pp. 404-412.

Keeney, R.L. and Winterfeldt, D.V. (1989), “On the uses of expert judgment on complex technicalproblems”, IEEE Transactions on Engineering Management, Vol. 36 No. 2, pp. 83-86.

Krishnan, N.T.B. (1992), “A simulation model for maintenance planning”, Reliability andMaintainability Symposium 1992 Proceedings of the International Conference, Las Vegas,NV, USA, pp. 109-118.

Li, C. and Xu, M. (2007), “ELECTRE III based on ranking fuzzy numbers for deterministic andfuzzy maintenance strategy decision problems”, Automation and Logistics Proceedings ofthe IEEE International Conference, Jinan, China, pp. 309-312.

Lu, K.Y. and Sy, C.C. (2009), “A real-time decision-making of maintenance using fuzzy agent”,Expert Systems with Applications, Vol. 36 No. 2, pp. 2691-2698.

Martin, F. (2008), “A performance technologist’s approach to process performanceimprovement”, International Society for Performance Improvement, Vol. 47 No. 2, pp. 30-40.

Park, K.S. and Lee, J.I. (2008), “A new method for estimating human probabilities: AHP-SLIM”,Reliability Engineering & System Safety, Vol. 93 No. 4, pp. 578-587.

Roman, H.A., Walker, K.D., Richmond, H.M., Hubbell, B.J. and Kinney, P.L. (2008), “Expertjudgment assessment of the morality impact of changes in ambient fine particulate matterin the US”, Environmental Science & Technology, Vol. 42 No. 7, pp. 2268-2274.

Savsar, M. (2011), “Analysis and modeling of maintenance operations in the context of an oilfilling plant”, Journal of Manufacturing Technology Management, Vol. 22 No. 5,pp. 679-697.

Sekaran, U. (2002), Research Methods for Business: A Skill Building Approach, Wiley,Hoboken, NJ.

Shyjith, K., Ilangkumaran, M. and Kumanan, S. (2008), “Multi-criteria decision-making approachto evaluate optimum maintenance strategy in textile industry”, Journal of Quality inMaintenance Engineering, Vol. 14 No. 4, pp. 375-386.

Spurgin, A.J. (2002), “Critique of current human reliability analysis methods”, Human Factor andPower Plant 2002 Proceedings of the International Conference, pp. 12-18.

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Waeyenbergh, G. and Pintelon, L. (2002), “A framework for maintenance concept development”,International Journal of Production Economics, Vol. 77 No. 3, pp. 299-313.

Waeyenbergh, G. and Pintelon, L. (2004), “Maintenance concept development: a case study”,International Journal of Production Economics, Vol. 89 No. 3, pp. 395-405.

Wang, L., Chu, J. and Wu, J. (2007), “Selection of optimum maintenance strategies based on afuzzy analytical hierarchy process”, International Journal of Production Economics,Vol. 107 No. 1, pp. 151-163.

Yang, T.H. and Hung, C.C. (2007), “Multiple-attribute decision making methods for plant layoutdesign problem”, Robotics and Computer Integrated Manufacturing, Vol. 23 No. 1,pp. 126-137.

Yang, Z., Bonsall, S. and Wang, J. (2008), “Fuzzy rule-based Bayesian reasoning approach forprioritization of failures in FMEA”, IEEE Transactions on Reliability, Vol. 57 No. 3,pp. 517-528.

Further reading

Bogonovo, E., Marseguerra, M. and Zio, E. (2000), “A Monte Carlo methodological approach toplant availability modelling with maintenance, aging and obsolescence”, ReliabilityEngineering & System Safety, Vol. 67 No. 1, pp. 61-73.

About the authorsSiew-Hong Ding received the BEng degree from Universiti Sains Malaysia, Malaysia in 2007, theMSc degree also from Universiti Sains Malaysia, Malaysia, in 2010. Currently, Siew-Hong Ding isa PhD candidate in the School of Mechanical Engineering at the University Sains Malaysia.Her research interest is on industrial maintenance management.

Shahrul Kamaruddin received the BEng (Hons) degree from University of Strathclyde,Glasgow, Scotland in 1996, the MSc degree from University of Birmingham, UK, in 1998, and thePhD from University of Birmingham, in 2003. Currently, Shahrul Kamaruddin is an AssociateProfessor with the School of Mechanical Engineering (under the manufacturing engineering withmanagement programme), Universiti Sains Malaysia. He has various past experiences withmanufacturing industries from heavy to electronics industries especially in the field of industrialengineering, manufacturing processes and product design. He has more than 20 publications inreputed international and national journals/conferences. His current research interests includesimulation and modelling of manufacturing systems, production planning and control,maintenance management and application of artificial intelligence techniques in manufacturing.Shahrul Kamaruddin is the corresponding author and can be contacted at: [email protected]

Ishak Abdul Azid is an Associate Professor in the School of Mechanical Engineering at theUniversity Sains Malaysia. His research interests included structural analysis, finite elementmethod (FEM) and genetic algorithm in optimization.

(The Appendix follows overleaf.)


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Appendix

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Documents

Maintenance policy selection model – a case study in the palm oil industry