Maintenance Strategy Selection A Case Study.pdf

8/11/2019 Maintenance Strategy Selection A Case Study.pdf

1/22

Maintenance Strategy Selection: A Case Study

SelimZaimMarmara University, Department of Mechanical Engineering,Goztepe, Istanbul, Turkey,

[email protected]

Ali Turkylmaz

Fatih University, Department of Industrial Engineering, Buyukcekmece, Istanbul, Turkey,

[email protected]

Mehmet F. Acar

Fatih University, Department of Management, Buyukcekmece, Istanbul,

Turkey,[email protected]

Umar Al-TurkiKing Fahd University of Petroleum & Minerals, Systems Engineering Department, Dhahran,

Saudi Arabia,[email protected]

Omer F. Demirel

Fatih University, Department of Industrial Engineering, Buyukcekmece, Istanbul,

Turkey,[email protected]

Corresponding Author: Omer F. [email protected]

Acknowledgement:The authors acknowledge the support of both Fatih University and King

Fahd University for their support. They also acknowledge the anonymous referees for their

constructive comments.

Abstract

Purpose The purpose of this paper is to demonstrate the use of two general purpose

decision making techniques in selecting the most appropriate maintenance strategy for

organizations with critical production requirements.

Design/methodology/approach - The Analytical Hierarchical Process (AHP) and the

Analytical Hierarchical Process (ANP) are used for the selection of the most appropriate

maintenance strategy in a local newspaper printing facility in Turkey.

Finding - The two methods where shown to be effective in choosing a strategy for

maintaining the printing machines. The two methods resulted in almost the same results. Both
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


2/22

methods take into account the specific requirements of the organization through its own

available expertise.

Practical Implications - The techniques demonstrated in this paper can be used by all types

of organizations for selecting and adopting maintenance strategies that have higher impact on

maintenance performance and hence overall business productivity. The two methods are

explained in a step by step approach for easier adaptation by practitioners in all types of

organizations.

Originality/Value - The value of the paper is in applying AHP and ANP decision making

methodologies in maintenance strategy selection. These two methods are not very common in

the area of maintenance, and hence add to the pool of techniques utilized in selecting

maintenance strategies.

Keywords: Maintenance planning, AHP, ANP, maintenance strategy, strategy selection

Article Classification:Case Study

Running Heads:Maintenance strategy selection


3/22

1. Introduction

The cost of maintenance is becoming increasingly critical with the increasingcompetition in

the business environment. The competition is leading to more focus on cost reduction in

operations and maintenance. Cost reduction may immediately be reflected on pricing and

hence, gaining edge over competitors. Maintenance cost constitutes a major portion of total

operations cost and hence is central to most cost reduction programs. Such programs should

be done with care so that other requirements such as quality are not sacrificed.

Maintenance costs can reach to 15-70% of production costs according to different sectors

(Bevilacqua and Braglia, 2000). Moreover, maintenance directly or indirectly influences

product quality, safety and reliability. Nowadays, maintenance is considered as profit

contributor and partner for world class competitiveness (Waeyenberg and Pintelon.2002).

Rausand (1998) identified the four probable consequences of failure, i.e. safety of personnel,

environmental impact, production availability and cost of material loss.

Maintenance is one of the most crucial issues in todays competitive manufacturing

environment. Machine failure may cause various business related problems such as; missing

delivery dates, loss of image and direct and indirect loss of profit and opportunity loss. As

such, maintenance should be carefully dealt with in terms of planning, investment, and

control. In terms of planning, appropriate maintenance strategies should be selected that are in

line with companys global and operational objectives. However, maintenance strategies

change rapidly with new options and practices. In fact any change in operations requires some

adjustment or major change in the adopted maintenance strategy to be compatible with the

new requirements. The selection process itself is becoming crucial for achieving highest

performance. Such decisions that highly impact technology are usually dealt with in

technically founded manner.


4/22

The motivation of this work is the existing need for some technical methodologies for

optimum selection of the most fit maintenance strategies. In this research, two of the

commonly methods fordecision making, namely the Analytical Network Process (ANP) and

the Analytical Hierarchy Process (AHP), are used for the selection of the best maintenance

policy. The two methods are simple but powerful in making decisions at different business

and functional levels under high complex and uncertain conditions.

This paper is organized as follows. Firstly, literature review is written about maintenance and

maintenance selection, then AHP and ANP are overviewed and proposed model is introduced.

Lastly, case study is introduced in this paper. The method is demonstrated through a case

study from local industry in Turkey. Results are discussed and benefits are identified.

2. Literature Review

Maintenance is classified into two main categories: corrective and preventive (Li, et al. 2006;

Waeyenberg and Pintelon, 2004). Corrective maintenance is performed after system failure

and preventive maintenance is performed before its failure (Wang, 2002). Corrective

maintenance, also called breakdown maintenance, is the oldest strategy in the industry

(Waeyenberg and Pintelon, 2002,Mechefske and Wang, 2003, Wang, 2007). For large profit

margin organizations, this policy can be seen as feasible strategy (Sharma, et al. 2005).

Preventive maintenance, in practice has two forms; periodic and predictive. In periodic

maintenance, as the name suggests, maintenance is performed periodically to prevent sudden

failure (Wang, 2007). This strategy is also called time-based maintenance and is used by

many firms in the industry following manufacturers recommendations which sometimes

results in unnecessary maintenance activities.

In predictive maintenance, maintenance decisionsare made based on information collected

from special measurement instruments like sensor systems, monitoring techniques, vibration


5/22

monitoring, lubrication analysis and ultrasonic testing (Wang, 2007). This strategy is also

known as condition-based maintenance.

In addition to these, opportunistic maintenance is used by some large scale industries such as

petroleum and petrochemical industries. Bevelacqua and Braglia (2000) defined the

opportunistic maintenance as maintenance can lead to the whole plant being shut down at set

times to perform all relevant maintenance interventions at the same time.

Studies on maintenance systems in practice show that some managers are unaware of the

different types of maintenance policies (Shorrocks, 2000; Shorrocks and Labib, 2000) and

selection methods.Luce (1999), Okumura and Okino (2003) presented the maintenance

selection method based on production loss and maintenance cost. Azadivar and Shu (1999)

showed the effective methods of selecting appropriate (optimum) maintenance strategies for

just in time production systems. Bevilacqua and Braglia (2000) used Analytical Hierarchy

Process (AHP) for maintenance selection in an oil refinery and they described some features

in the selection of maintenance strategy, such as: economic factors, applicability, costs and

safety. Al-Najjar and Alsyouf (2003), Sharma, et al. (2005) used fuzzy inference theory and

fuzzy multiple criteria decision making methodology. Moreover, Mechefske and Wang

(2003) showed a new method for selecting the optimum maintenance strategy and condition

monitoring technique. Almeida and Bohoris (1995) developed a new method using decision

making theory especially the multi-attribute utility theory. Triantaphyllou, et al. (1997)

presented AHP model with four maintenance criteria: cost, reparability, reliability and

availability. In addition to these, Bertolini and Bevilacqua (2006) proposed a combined goal

programming and AHP for maintenance selection. Wang, et al. (2007) developed a fuzzy

AHP model for selection of optimum maintenance strategy.

Labib et.al (1998) developed a model of maintenance decision making which includes AHP.

In the first stage, criteria are identified and then in the second stage AHP is applied. Lastly,


6/22

machines are ranked according to their importance. Arunraj and Maiti (2010) used AHP and

goal programming for maintenance policy selection according to risk of failure and cost of

maintenance in a chemical factory. They concluded that if risk is chosen as a criterion,

predictive maintenance is preferred policy over periodic maintenance. Similarly, if cost is

chosen as a criterion, corrective maintenance is preferred. Nevertheless, if both risk and cost

are considered, AHP-GP results show that predictive maintenance and corrective maintenance

are best for high risk equipment and low risk equipment, respectively. Labib (2004) also

developed a model for maintenance policy selection using a computerized maintenance

management system. In this study, fuzzy logic and AHP are used. HajShirmohammadi and

Wedley (2004) used an AHP model for maintenance management for centralization and

decentralization. Centralized system means that all maintenance systems are managed from a

centrally administered location. However, decentralized system implies that each production

area manages its own maintenance systems.

Shyjith, et al (2008) developed a model using AHP and TOPSIS for maintenance selection in

textile industry and then Ilangkumaran and Kumanan (2009) integrated fuzzy AHP and

TOPSIS algorithm to select the maintenance policy for textile industry.

It is clear from the literature that AHP has proven success in maintenance strategy selection as

it did for many other decision making problems. As such it was selected to be the major tool

in this paper.

3. Theoretical Background

3.1 The Analytic Hierarchy Process (AHP) Method

The analytic hierarchy process (AHP) methodology, which was developed by Saaty (1980), is

a powerful tool in solving complex decision problems. The AHP helps the analysts organize

the critical aspects of a problem into a hierarchical structure similar to a family tree. By


7/22

reducing complex decisions to a series of simple comparisons and rankings, then synthesizing

the results, the AHP not only helps the analysts arrive at the best decision, but also provides a

clear rationale for the choices made (Chin et al., 1999).

In the AHP approach, the decision problem is structured hierarchically at different levels with

each level consisting of a finite number of decision elements. The upper level of the hierarchy

represents the overall goal, while the lower level consists of all possible alternatives. One or

more intermediate level embody the decision criteria and sub-criteria (Partovi, 1994).

3.2 The Analytical Network Process (ANP)

The ANP method is an improved version of AHP method and it is more accurate with many

complicated models in which many criteria feedback and interrelations among criteria are

used.

The ANP method evaluates all the relationships systematically by adding all interactions,

interdependences, and feedbacks in decision making systems. The powerful side of our model

is to represent the decision making problem that involves many complicated relationships

easily. This technique does not only enable the pair wise comparisons of the sub-criteria

under main criteria, but also enables us to compare independently all the interacting sub-

criteria.

Decision making problems that occur in firms cannot be explained by only hierarchical

structures. The criteria and alternatives in a problem can have interactions. At these

circumstance, complicated analyzes can be necessary to find out the weights of all

components. ANP technique is used for such as that kind of problems and it is based on

pairwise comparisons as it is in AHP. For pairwise comparisons the 1-9 scale of Saaty (1980)

is used in Table 1. In ANP model all the components and relationships are defined and the

relationships are determined as two way interactions. In the model the network structure is


8/22

used and all the relationships in a cluster (that is relationships among sub-criteria in a cluster)

and relationships between sub-criteria under different clusters are considered. Because of such

relationships, the ANP method is useful for getting more accurate and effective results is a

complex and crucial decision making problems.

In ANP method there are three matrix analyses; super matrix, weighted super matrix and limit

matrix. The super matrix provides relative importance of all components and weighted super

matrix finds out the value that is obtained by the super matrix values and the value of each

cluster. In the limit matrix, the constant values of each value are determined by taking the

necessary limit of the weighted super matrix. The results of the decision making problem is

gained from the limit matrix scores. It is important to value the criteria and alternatives by the

experts in order to get more consistent and reliable results.

Table 1. Intensity comparison scale

Option Numerical Value(s)

Equal 1

Marginally strong 3

Strong 5

Very strong 7

Extremely strong 9

Intermediate values to reflect fuzzy inputs 2,4,6,8

Source: Bhushan and Rai (2004)

4. Case Study

The method proposed for selecting maintenance strategy is based on a hierarchical model

composed of a set of criterion and sub-criterion as developed by Saaty. Both AHP and ANP


9/22

methods are demonstrated by the following case study from the newspaper printing industry.

The use of the two methods is reported along with their resulting solutions.

One of the most selling newspapers in Turkey, ZAMAN, is the subject of the case study in

this research. It publishes national and international news in the fields of politics, business,

economics, arts, cultures, sports, etc... It is published seven days a week with approximately

30 pages in addition to publishing TODAYSZAMAN, the most circulated English

newspaper in Turkey, and special supplements in weekends and special occasions. It won

different awards in several design competitions.

To meet its publication daily schedule, machines and equipments in its printing house must be

kept continuously ready for production which puts high pressure on operations and

maintenance. Thus maintenance is highly crucial for this firm and therefore selected to be the

focus of this paper. The objective of this study is to select the best maintenance strategy that

meets the operations objectives. Three alternative maintenance strategies are considered, these

are; corrective, periodic (time-based) and predictive (condition-based) maintenance policies.

Opportunistic maintenance is not considered because long time shut down of equipments and

machines is not expected.

Four selection criteria are considered. These are; added value, cost, safety and

implementation. Moreover, different sub-criteria are added to the model. According to the

proposed model, problems, criteria and alternatives are found and these are described in the

steps below.

Step 1: Form a focus group composed of key managers and engineers: The purpose is to

determine and examine current problems and their impact at the business level of the

company. In this case, a project team is established. The project team is composed of five

managers from production planning and control and maintenance in addition to some experts

from several universities.


10/22

Step 2:Evaluate the problems:Facilitate a focus group meeting to identify issues and problems

related to maintenance and their possible causes mapped into categories and subcategories.

The group also constructs maintenance method selection criteria and sub-criteria. In this case,

the team identified several issues related to maintenance in ZAMAN printing house. Some

of the issues found to be crucially related to maintenance strategy. One of the most important

problems is the firms image. If machines breakdown and production stops, newspaper may

not be issued and this situation negatively affects the image of the firm. Another problem is

found to be cost. In case of shutdown, the firm may need to outsource the printing of the

newspaper and this causes an extra cost for the firm.

Step 3: Determine the alternative strategies. Some maintenance strategies might not be

suitable for a certain organization. That strategy can be eliminated by the focus group with

more attention and analysis may be conducted for feasible strategies.Throughout the

discussions held with the formed group members, three possible alternative maintenance

strategies are identified, these are; corrective, periodic and predictive maintenance policies.

Opportunistic maintenance is not considered because long time shut down of equipments and

machines is not expected.

Step 4: Construct a hierarchical model: Using criteria, sub-criteria and alternatives, a

hierarchical model is constructed to apply AHP and ANP algorithms. Then relationship

among criteria and sub-criteria are determined and reflected in the hierarchical model.

Maintenance strategy selection criteria were determined based on the review of prior literature

and semi-structured interviews undertaken with 22 managers from relevant departments

including purchasing, manufacturing, quality assurance and maintenance. Figure 2 shows the

hierarchical structure of the maintenance strategy selection problem, which includes four

levels. The top level of the hierarchy represents the ultimate goal of the problem, while the

second level of the hierarchy consists of four main maintenance policy selection criteria,


11/22

which are namely added value, cost, safety, and implementation. These criteria are

decomposed into various sub-criteria that may affect the managers decision for a particular

maintenance policy. Finally, the bottom level of the hierarchy represents the alternative

maintenance policies. Each selection criterion in the tree diagram is briefly described below.

The sub-criteria for each main criteria are identified as follows:

A. Value adding is viewed in four possible dimensions (sub-criteria) as follows:

1. On Time Delivery: During the production process, some machines may fail causing

delays in order delivery.

2.

Profit: Excessive failures increase maintenance cost and decrease in profit.

Material paper and production time wastages are examples of direct poor

maintenance costs.

3. Quality: Some machine failures may cause drop in product quality showing as

damaged paper or unreadable text.

4. Image: The image of the firm is largely affected by production and maintenance

performance. Late deliveries, low quality printing, shortage in quantities are some

examples causing image damage.

B. The cost criterion includes the following:

1. Hardware: To apply predictive maintenance, the firm may need to acquire some

new machines or equipments.

2.

Software: Different software may be required to evaluate information which is

obtained from equipments used for predictive maintenance.

3. Training:Technicians or managers may be required to go through special training

for effective use of equipments and software that are used in predictive

maintenance.


12/22

4. Inventory of spare parts: Maintenance strategies, especially corrective

maintenance, some spare parts should be available in inventory. The cost of

holding spareparts adds to the overall maintenance cost.

5. Cost of advising and consulting: For corrective and periodic maintenance

strategies, the firmmay need some special maintenance experts to plan and control

maintenance operations.

These costs are mostly necessary regardless of the type of maintenance strategy adopted

whether corrective, periodic or predictive. However, the costing elements vary in amount

between strategies.

C. The safety criterion consists of the following:

1. Internal Environment: Safety policies and procedures maintains healthy working

environment. Interruptions in operations due to failure may form a source of

hazard to people and the whole internal environment.

2. External Environment: Safety outside the factory is another crucial element,

especially for nuclear or chemical plants. In case of fire or chemical spills in the

printing house may cause unrecoverable damage to the surrounding environment.

3. Personnel: Lastly, some breakdowns and/or maintenance activities may directly or

indirectly harm workers. Therefore, it is essential to seek their opinion about the

possible maintenance practices.

D.

The implementation criterion includes the following:

1. Technology: Technology is an important for predictive maintenance, because there

are no special equipments for some machines to apply condition-based

maintenance.

2. Desire of workers: Some of workers may not want to predictive maintenance,

because workers do some extra duties in condition-based maintenance.


13/22

3. Desire of top management: Sometimes top managers do not want to apply

predictive maintenance, because its setup cost which is sourced from buying of

hardware and software is high.

4. Decision of Service Company: There are lots companies which supply

maintenance service as business for other companies, so this is a criterion for

firms.

Based on the above identified criteria and sub-criteria, a hierarchical model is constructed and

relations are determined for our case study. The model is shown in Figure 1.

Step 5: Pair wise comparison among criteria and sub-criteria: Pair wise comparisons are

done among related criteria and sub-criteria following the scale suggested bySaaty.A special

questionnaire form is used to complete the pair-wise comparison matrix. In this comparison,

criteria, sub criteriaare used for comparing alternative maintenance strategiesby experts in the

field.

Overall Goal (G)

Machine availability

Value Adding

V

Cost

C

Safety

SImplementation

(I)

Delivery

V4

Profit (V3)

Image (V1)

Quality (V2)

Hardware

C1

Software

C4

Training

C2

Spare parts

C5

ConsultationC3

External

S1

Internal

S2

Personnel

S3

Technology

I2

Workers

acce tance I1

Management

acce tance I3

Predictive

Maintenance (P1)

Periodic

Maintenance (P2)

Corrective

Maintenance (P3)


14/22

Figure 1: The hierarchical model for maintenance strategy selection criteria

Step 6:Applying AHP and ANPalgorithms:

These algorithms give weight to each alternative based on which the best strategy is chosen.

In the AHP approach, the weights of the criteria and the scores of the alternatives, which are

called local priorities, are considered as decision elements in the second step of the decision

process. The decision-maker is required to provide his preferences by pairwise comparisons,

with respect to the weights and scores. The values of the weights iv and scores ijr are elicited

from these comparisons and represented in a decision table. The last step of the AHP

aggregates all local priorities from the decision table by a weighted sum of the type

=i

ijij rvR

The global prioritiesj

R thus obtained are finally used for ranking of the alternatives and

selection of the best one.In the ANP approach,two matrices are calculated; the weighted super matrix and the limit

matrix.The weighted super matrix permits a resolution of the interdependencies that exist

among the components of a system. It is a partitioned matrix where each sub-matrix is

composed of a set of relationships between and within the levels, as represented by the model.

The entries of the super matrix are imported from the pair-wise comparison matrices of

interdependencies. Since there are 20 such pairwise comparisons matrices, one for each

interdependent criterion, the super matrix contains 20 non-zero columns. The weighted

supermatrix is obtained by multiplying all the elements in a component of the unweighted

supermatrix by the corresponding cluster weight. In other words, the values in the cluster

matrix are used to weight the unweighted supermatrix by multiplying the value in the cell of

the cluster matrix times the value in each cell in the component of the unweighted


15/22

supermatrix to produce the weighted supermatrix. The resulting weighted super matrix is

shown in Table 2. The limit supermatrix is obtained by raising the weighted supermatrix to

the power 2k+1 where k is an arbitrarily large number, allows convergence of the

interdependent relationships. When the column of numbers is the same for every column, the

limit matrix has been reached and the matrix multiplication process is halted. The limit

supermatrix for the Model is shown in Table 3.


16/22

Table 2.Weighted Super Matrix

P1 P2 P3 G S1 S2 S3 V1 V2 V3 V4 C1 C2 C3 C4 C5 I1 I2 I3

P1 0.000 0.000 0.000 0.000 0.709 0.287 0.290 0.290 0.467 0.176 0.290 1.000 1.000 0.000 1.000 0.000 0.077 1.000 0.091

P2 0.000 0.000 0.000 0.000 0.179 0.635 0.655 0.655 0.467 0.280 0.655 0.000 0.000 0.750 0.000 0.000 0.462 0.000 0.091

P3 0.000 0.000 0.000 0.000 0.113 0.078 0.055 0.055 0.067 0.044 0.055 0.000 0.000 0.250 0.000 1.000 0.462 0.000 0.818

G 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

S1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

S2 0.000 0.000 0.000 0.400 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

S3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.333 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.111 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V4 0.000 0.000 0.000 0.278 0.000 0.000 0.000 0.000 0.000 0.056 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

C1 0.181 0.075 0.042 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

C2 0.085 0.075 0.042 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

C3 0.017 0.677 0.589 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

C4 0.202 0.075 0.042 0.144 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

C5 0.015 0.097 0.286 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

I1 0.100 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

I2 0.300 0.000 0.000 0.178 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

I3 0.100 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000


17/22

Table 3.Limit Matrix

P1 P2 P3 G S1 S2 S3 V1 V2 V3 V4 C1 C2 C3 C4 C5 I1 I2 I3

P1 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227 0.227

P2 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146 0.146

P3 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127 0.127

G 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

S1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

S2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

S3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V1 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V3 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

V4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

C1 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057 0.057

C2 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036 0.036

C3 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178 0.178

C4 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062 0.062

C5 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054 0.054

I1 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023

I2 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068 0.068

I3 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023 0.023


18/22

Step 7: Prioritization: It simply means listing alternatives in descending order of their

weights according to both AHP and ANP algorithms.

The AHP algorithm resulted in the following ranking (best to worst) of maintenance

strategies; predictive, periodic and corrective maintenance respectivelyand using the ANP

algorithm; the resulting strategy ranking (best to worst) is; predictive, periodic and corrective

maintenance, respectivelyas shown in Figure 2.

Figure 2: AHP and ANP scores

Step 8: Compare results and make the decision: The two solutions, AHP and ANP, are

compared and evaluated by experts to make the best decision.

The analysis clearly shows that predictive maintenance is to be the best strategy by both AHP

and ANP methods. However, in the real situation, ZAMAN is using periodic maintenance

for maintainingits printing house. In fact, predictive maintenance is shown by both methods to

cause unnecessary expenditure for ZAMAN. This is not recognized by the firm since the

maintenance effectiveness is quite high on the expense of efficiency in resource utilization.

Furthermore, technicians and experts are occasionally interfering with the maintenance

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

Predictive M. Periodic M. Corrective M.

AHP

ANP


19/22

operations based on their intuitions before the time of periodic maintenance. This can be seen

as predictive maintenance, hence, in fact, both predictive and periodic maintenance are used

in ad-hoc basis.

Currently there are plans underway in ZAMAN to adopt an ERP system for planning and

controlling maintenance operations and at the same time be used to estimate and report the

cost of maintenance.

5. Conclusion

In this research, some criteria are determined about maintenance selection and according to

these criteria, AHP and ANP models are constituted. Three maintenance policies are

considered; these are corrective, periodic (time-based) and predictive (condition-based)

maintenance. Moreover, with the help of experts and engineers, these AHP and ANP models

are used for machines in printing house of the daily newspaper, ZAMAN. At the end of

these analyses, weights of three different maintenance policies are determined. This research

shows that predictive maintenance is the most suitable maintenance policy for this newspaper

firm in both AHP and ANP analyses. In the future, AHP and ANP models can be used with

fuzzy logic for maintenance selection and new models can be done for firms which are in

other sectors.Further research ontesting other decision making tools including fuzzy logic,

may be done. This study can also be extended by adding a new selection factor to the existing

model. In addition, other well known multi-criteria methods such as TOPSIS, ELECTRE can

be used to compare the results of this work.

REFERENCES

Almeida, A.T. and Bohoris, G.A. (1995), Decision theory in maintenance decision making,

Journal of Quality in Maintenance Engineering, Vol.1 No.1, pp.39-45.


20/22

Al-Najjar, B. and Alsyouf, I., (2003), Selecting the most efficient maintenance approach

using fuzzy multiple criteria decision making, International Journal of Production

Economics, Vol.84, pp.85100.

Arunraj, N.S., Maiti J. (2010), Risk-based maintenance policy selection using AHP and goal

programming, Safety Science, Vol. 48, pp.238-247

Azadivar, F. and Shu, V., (1999), Maintenance policy selection for JIT production systems,

International Journal of Production Research, Vol.37 No.16, pp.37253738.

Bertolini, M. and Bevilacqua, M. (2006), A combined goal programming-AHP approach to

maintenance selection problem,Reliability Engineering and System Safety, Vol. 91,

pp.839-848.

Bevilacqua, M. and Braglia, M. (2000), The analytic hierarchy process applied to

maintenance strategy selection,Reliability Engineering and System Safety, Vol. 70,

pp.71-83.

Bhushan, N. and Rai K. (2004), Strategic Decision Making, Applying the Analytical

Hierarchy Process, Springer-Verlag, London.

Chin, K-S., Chiu, S., and Tummalo, R.V.M. (1999), An evaluation of success factors using

the AHP to implement ISO 14001-based EMS,International Journal of Quality &

Reliability Management, Vol. 16 No. 4, pp. 341-362.

HajShirmohammadi, A. and Wedley W.C. (2004), Maintenance Management an AHP

application for centralization/decentralization, Journal of Quality in Maintenance

Engineering, Vol.10 No.1, pp.16-25.

Ilangkumaran, M. and Kumanan, S. (2009), Selection of maintenance policy for textile

industry using hybrid multi-criteria decision making approach, Journal of

Manufacturing Technology Management,Vol. 20 No 7, pp.1009-1022.


21/22

Labib, A.W. (2004), A decision analysis model for maintenance policy selection using a

CMMS,Journal of Quality in Maintenance Engineering, Vol. 10 No. 3, pp.191-202.

Labib, A.W., OConnor R.F., Williams G.B. (1998), An effective maintenance system using

the analytic hierarchy process, Integrated Manufacturing Systems, Vol.2 No. 9,

pp.87-98.

Li, J.R., Khoo, L.P., and Tor, S.B., (2006), Generation of possiblemultiple components

disassembly sequence for maintenance using a disassembly constraint graph,

International Journal of Production Economics, Vol. 102, pp.51-65.

Luce, S., (1999), Choice criteria in conditional preventive maintenance, Mechanical

Systems and Signal Processing, Vol. 13 No.1,pp.163-168.

Mechefske, C.K. and Wang, Z., (2003), Using fuzzy linguistics to select optimum

maintenance and condition monitoring strategies, Mechanical Systems and Signal

Processing, Vol.17 No.2, pp.305316

Okumura, S., and Okino, N., (2003), A maintenance policy selection method for a critical

single-unit item in each workstation composing a FMS with CBM optimization,

International Journal of COMADEM, Vol.6 No.2, pp.3-9.

Partovi, Y.F. (1994), Determining What to Benchmark: An Analytic Hierarchy Process

Approach,International Journal of Operations and Production Management, Vol. 14

No. 6, pp. 25-39

Rausand, M. (1998), Reliability centered maintenance,Reliability Engineering System

Safety, Vol.60 No.2, pp.121-32.

Saaty, T.L. (1980), The Analytic Hierarchy Process, McGraw-Hill, New York

Saaty, T.L. (2001), Decision Making with Dependence and Feedback: Analytic

NetworkProcess,RWS Publications, Pittsburgh.


22/22

Sharma, R.K., Kumar, D., and Kumar, P. (2005), FLM to select suitable maintenance

strategy in process industries using MISO model,Journal of Quality in Maintenance

Engineering, Vol. 11 No.4, pp.359374.

Shyjith, K., Ilangkumaran M., and Kumanan S. (2008), Multi-criteria decision-making

approach to evaluate optimum maintenance strategy in textile industry, Journal of

Quality in Maintenance Engineering, Vol 14 No. 4, pp.375-386.

Shorrocks, P. (2000), Selection of the most appropriate maintenance model using a decision

support framework,unpublished report, UMIST, Manchester.

Shorrocks, P. and Labib, A.W. (2000), Towards a multimedia based decision support system

for word class maintenance, Proceedings of the 14th ARTS (Advances in Reliability

Technology Symposium), IMechE, University of Manchester.

Triantaphyllou, E., Kovalerchuk, B., Mann, L., Knapp, G.M. (1997), Determining the most

important criteria in maintenance decision making, Journal of Quality in

Maintenance Engineering, Vol.3 No.1, pp.1628.

Waeyenbergh, G., Pintelon, L., (2002), A framework for maintenance concept

development,International Journal of Production Economics, Vol. 77, pp.299-313.

Waeyenbergh, G. andPintelon, L. (2004), Maintenance concept development: A case study,

International Journal of Production Economics, Vol. 89, pp.395-405.

Wang, H., (2002). A survey of maintenance policies of deteriorating systems, European

Journal of Operational Research, Vol. 139, pp.469-489.

Wang l., Chu J., Wu J. (2007), Selection of optimum maintenance strategies based on a

fuzzy analytical hierarchy process,International Journal of Production Economics,

Vol. 107, pp.151-163.

Maintenance Strategy Selection A Case Study.pdf

Documents

Transcript of Maintenance Strategy Selection A Case Study.pdf