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The analytic hierarchy process applied to maintenance strategy selectionM. Bevilacquaa, M. Bragliab,*

aDipartimento di Ingegneria Industriale, Universita degli Studi di Parma, Viale delle Scienze, 43100 Parma, ItalybDipartimento di Ingegneria Meccanica, Nucleare e della Produzione, Universita degli Studi di Pisa, Via Bonanno Pisano 25/B, 56126 Pisa, Italy

Received 12 October 1999; accepted 24 March 2000

Abstract

This paper describes an application of the Analytic Hierarchy Process (AHP) for selecting the best maintenance strategy for an importantItalian oil refinery (an Integrated Gasification and Combined Cycle plant). Five possible alternatives are considered: preventive, predictive,condition-based, corrective and opportunistic maintenance. The best maintenance policy must be selected for each facility of the plant (about200 in total). The machines are clustered in three homogeneous groups after a criticality analysis based on internal procedures of the oilrefinery. With AHP technique, several aspects, which characterise each of the above-mentioned maintenance strategies, are arranged in ahierarchic structure and evaluated using only a series of pairwise judgements. To improve the effectiveness of the methodology AHP iscoupled with a sensitivity analysis. ! 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Maintenance; Process plant; Failure mode effect and criticality analysis technique; Analytic hierarchy process

1. Introduction

Many companies think of maintenance as an inevitablesource of cost. For these companies maintenance operationshave a corrective function and are only executed in emer-gency conditions. Today, this form of intervention is nolonger acceptable because of certain critical elements suchas product quality, plant safety, and the increase in main-tenance department costs which can represent from 15 to70% of total production costs.The managers have to select the best maintenance policy

for each piece of equipment or system from a set of possiblealternatives. For example, corrective, preventive, opportu-nistic, condition-based and predictive maintenance policiesare considered in this paper.It is particularly difficult to choose the best mix of main-

tenance policies when this choice is based on preventiveelements, i.e. during the plant design phase. This is thesituation in the case examined in this paper, that of an Inte-grated Gasification and Combined Cycle plant which isbeing built for an Italian oil company. This plant willhave about 200 facilities (pumps, compressors, air-coolers,etc.) and the management must decide on the maintenanceapproach for the different machines. These decisions willhave significant consequences in the short-medium term formatters such as resources (i.e. budget) allocation, technolo-

gical choices, managerial and organisational procedures,etc. At this level of selection, it is only necessary to definethe best maintenance strategy to adopt for each machine,bearing in mind budget constraints. It is not necessary toidentify the best solution from among the alternatives thatthis approach presents. The maintenance manager onlywants to recognise the most critical machines for a pre-allocation of the budget maintenance resources, withoutentering into the details of the actual final choice. Thisfinal choice would, in any case, be impossible because theplant is not yet operating and, as a consequence, totalknowledge of the reliability aspects of the plant machinesis not yet available. In other words, the problem is notwhether it is better to control the temperature or the vibra-tion of a certain facility under analysis, but only to decide ifit is better to adopt a condition-based type of maintenanceapproach rather than another type. The second level of deci-sion making concerns a fine tuned selection of the alterna-tive maintenance approaches (i.e. definition of the optimalmaintenance frequencies, thresholds for condition-basedintervention, etc.). This level must be postponed until datafrom the operating production system becomes available.Several attributes must be taken into account at this first

level when selecting the type of maintenance. This selectioninvolves several aspects such as the investment required,safety and environmental problems, failure costs, reliabilityof the policy, Mean Time Between Failure (MTBF) andMean Time To Repair (MTTR) of the facility, etc. Severalof these factors are not easy to evaluate because of their

Reliability Engineering and System Safety 70 (2000) 71–83

0951-8320/00/$ - see front matter ! 2000 Elsevier Science Ltd. All rights reserved.PII: S0951-8320(00)00047-8

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* Corresponding author. Fax: ! 39-050-913-040.E-mail address: [email protected] (M. Braglia).

Alexander Lindgren

Alexander Lindgren

Alexander Lindgren

intangible and complex nature. Besides, the nature of theweights of importance that the maintenance staff must giveto these factors during the selection process is highly subjec-tive. Finally, bearing in mind that the plant is still in theconstruction phase, some tangible aspects such as MTBFand MTTR can be only estimated from failure data concern-ing machines working in other plants (in this case oil refi-neries) under more or less similar operating conditions.Furthermore, they will affect each single facility analysedin a particular way and, as a consequence, the final main-tenance policy selection.It is therefore clear that the analysis and justification of

maintenance strategy selection is a critical and complex taskdue to the great number of attributes to be considered, manyof which are intangible. As an aid to the resolution of thisproblem, some multi-criteria decision making (MCDM)approaches are proposed in the literature. Almeida andBohoris [1] discuss the application of decision makingtheory to maintenance with particular attention to multi-attribute utility theory. Triantaphyllou et al. [2] suggestthe use of Analytical Hierarchy Process (AHP) consideringonly four maintenance criteria: cost, reparability, reliabilityand availability. The Reliability Centered Maintenance(RCM) methodology (see, for example, Ref. [3]) is probablythe most widely used technique. RCM represents a methodfor preserving functional integrity and is designed to mini-mise maintenance costs by balancing the higher cost ofcorrective maintenance against the cost of preventive main-tenance, taking into account the loss of potential life of theunit in question [4].One of the tools more frequently adopted by the compa-

nies to categorise the machines in several groups of risk isbased on the concepts of failure mode effect and criticalityanalysis technique (FMECA). This methodology has beenproposed in different possible variants, in terms of relevantcriteria considered and/or risk priority number formulation[5]. Using this approach, the selection of a maintenancepolicy is performed through the analysis of obtained priorityrisk number. An example of this approach has also beenfollowed by our oil company, which has developed itsown methodology internally. This approach makes itpossible to obtain a satisfying criticality clustering ofthe 200 facilities into three homogeneous groups. Theproblem is to define the best maintenance strategy foreach group.To integrate the internal “self-made” criticality approach,

this paper presents a multi-attribute decision method basedon the AHP approach to select the most appropriate main-tenance strategy for each machine group. In this procedure,several costs and benefits for each alternative maintenancestrategy are arranged in a hierarchic structure and evaluated,for each facility, through the use of a series of pairwisejudgements. Finally, considering that the maintenancemanager can never be sure about the relative importanceof decision making criteria selected when dealing withthis complex maintenance problem, to improve the AHP

effectiveness the methodology is coupled with a sensitivityanalysis phase.

2. The API oil refinery IGCC plant: a brief description

The Integrated Gasification and Combined Cycle(IGCC) plant [6], currently being assembled at TheFalconara Marittima API oil refinery, will make itpossible to transform the oil refinement residuals intothe synthesis gases which will be used as fuel toproduce electricity. The IGCC plan will be placed ina 47,000 m2 area inside the oil refinery.The electricity produced by the IGCC plant will be sold to

ENEL (Italian electrical energy firm) while some65,000 ton/h of steam will be used inside the oil refineryfor process requirements. The total cost of the projectamounts to about 750 million dollars.In recent years, economic and legislative changes

have led to increased co-operation between petrochem-ical and electrical firms. The adoption of strict environ-mental standards, both in Europe and in the UnitedStates, is forcing oil refinery firms to reduce the emis-sions of pollutants from the process plants and reducethe potential pollution of the refined products. The samepollution control requirements, mainly a reduction in thelevel of nitrogen and sulphur oxides, together with theincreasing need to control operating and investmentscosts, is pushing electrical firms to search for moreeconomic and cleaner production methods.The combined effect of the above-mentioned factors has

led several oil refineries to adopt IGCC technology for oilrefinement heavy residuals processing. IGCC technologyhas proved to be a valid solution to the market requirementof efficient, clean, low consuming and environmentallyorientated production technologies.The API oil refinery uses a thermal conversion process

and has a production capacity of about 4,000,000 tons of oilper year (80,000 barrels per day). The production cycle istypical of oil refineries with a similar production capacity:the current distilled yield is higher than 70% and the resi-duals are used to produce fuel oil and bitumen. Oil refine-ment heavy residuals with a high sulphur content will bepartly converted into the synthesis gases “syngas” (whichwill be cleaned in the IGCC gasifiers) and partly used toproduce bitumen.The three main objectives of the oil refinery management

are the following:

1. the elimination of heavy residuals used to produce fueloil with high and low sulphur content;

2. the ability to process almost every type of heavy oil witha high sulphur content;

3. the substitution of the present low efficiency thermoelec-trical power plant with a more efficient system, withlower levels of pollutant emissions.

M. Bevilacqua, M. Braglia / Reliability Engineering and System Safety 70 (2000) 71–8372

3. Possible alternative maintenance strategies

Five alternative maintenance policies are evaluated in thiscase study. Briefly, they are the following.

• Corrective maintenance. The main feature of correctivemaintenance is that actions are only performed when amachine breaks down. There are no interventions until afailure has occurred.

• Preventive maintenance. Preventive maintenance isbased on component reliability characteristics. Thisdata makes it possible to analyse the behaviour of theelement in question and allows the maintenance engineerto define a periodic maintenance program for themachine. The preventive maintenance policy tries todetermine a series of checks, replacements and/orcomponent revisions with a frequency related to the fail-ure rate. In other words, preventive (periodic) mainte-nance is effective in overcoming the problemsassociated with the wearing of components. It is evidentthat, after a check, it is not always necessary to substitutethe component: maintenance is often sufficient.

• Opportunistic maintenance. The possibility of usingopportunistic maintenance is determined by the nearnessor concurrence of control or substitution times for differ-ent components on the same machine or plant. This typeof maintenance can lead to the whole plant being shutdown at set times to perform all relevant maintenanceinterventions at the same time.

• Condition-based maintenance. A requisite for the appli-cation of condition-based maintenance is the availabilityof a set of measurements and data acquisition systems tomonitor the machine performance in real time. Thecontinuous survey of working conditions can easily andclearly point out an abnormal situation (e.g. the exceed-ing of a controlled parameter threshold level), allowingthe process administrator to punctually perform thenecessary controls and, if necessary, stop the machinebefore a failure can occur.

• Predictive maintenance. Unlike the condition-basedmaintenance policy, in predictive maintenance theacquired controlled parameters data are analysed to finda possible temporal trend. This makes it possible topredict when the controlled quantity value will reach orexceed the threshold values. The maintenance staff willthen be able to plan when, depending on the operatingconditions, the component substitution or revision isreally unavoidable.

4. The IGCC plant maintenance program definition

An electrical power plant based on IGCC technology is avery complex facility, with a lot of different machines andequipment with very different operating conditions. Decid-ing on the best maintenance policy is not an easy matter,

since the maintenance program must combine technicalrequirements with the firm’s managerial strategy. TheIGCC plant complex configuration requires an optimalmaintenance policy mix, in order to increase the plant avail-ability and reduce the operating costs.Maintenance design deals with the definition of the best

strategies for each plant machine or component, dependingon the availability request and global maintenance budget.Every component, in accordance with its failure rate, costand breakdown impact over the whole system, must bestudied in order to assess the best solution; whether it isbetter to wait for the failure or to prevent it. In the lattercase the maintenance staff must evaluate whether it is betterto perform periodic checks or use a progressive operatingconditions analysis.It is clear that a good maintenance program must define

different strategies for different machines. Some of thesewill mainly affect the normal operation of the plant, somewill concern relevant safety problems, and others willinvolve high maintenance costs. The overlapping of theseeffects enables us to assign a different priority to every plantcomponent or machine, and to concentrate economic andtechnical efforts on the areas that can produce the bestresults.One relevant IGCC plant feature is the lack of historical

reliability and maintenance costs data (the plant start-up isproposed for March 2000). Initially, the definition of themaintenance plan will be based upon reliability data fromthe literature and on the technical features of the machines.This information will then be updated using the dataacquired during the working life of the plant. The analysissystem has been structured in a rational way so as to keepthe update process as objective as possible. This has beenaccomplished through the use of a charting procedure, usingwell-understood evaluations of different parameters and asimple and clear analysis of corrective interventions. Themaintenance plan developed for the machines of the IGCCplant is based on the well-known FMECA technique [7,8].The analysis results have provided a criticality index forevery machine, allowing the best maintenance policy tobe selected.

4.1. The maintenance strategy adopted by the oil refinerycompany

The internal methodology developed by the company tosolve the maintenance strategy selection problem for thenew IGCC plant is based on a “criticality analysis” whichmay be considered as an extension of the FMECA techni-que. This analysis takes into account the following sixparameters:

• safety;• machine importance for the process;• maintenance costs;• failure frequency;• downtime length;

M. Bevilacqua, M. Braglia / Reliability Engineering and System Safety 70 (2000) 71–83 73

• operating conditions;with an additional evaluation for the

• machine access difficulty.

Note that, the six parameters presented below derivedfrom an accurate pre-analysis to select all of the relevantparameters that can contribute to the machine criticality. Asreported by the maintenance manager, 12 criteria have initi-ally been considered:

a. Safety. Considering the safety of personnel, equipment,the buildings and environment in the event of a failure.b. Machine importance for the process. The importanceof the machine for the correct operation of the plant. Forinstance, the presence of an inter-operational buffer tostock the products can reduce the machine criticalitysince the maintenance intervention could be performedwithout a plant shutdown.c. Spare machine availability.Machines that do not havespares available are the most critical.d. Spare parts availability. The shortage of spare partsincreases the machine criticality and requires a replenish-ment order to be issued after a failure has occurred.e. Maintenance cost. This parameter is based onmanpower and spare parts costs.f. Access difficulty. The maintenance intervention can bedifficult formachines arranged in a compactmanner, placedin a restrict area because they are dangerous, or situated at agreat height (for example, some agitators electric motorsand air-cooler banks). The machine access difficultyincreases the length of downtime and, moreover, increasesthe probability of a failure owing to the fact that inspectionteams cannot easily detect incipient failures.g. Failure frequency. This parameter is linked to the meantime between failures (MTBF) of the machine.h. Downtime length. This parameter is linked to the meantime to repair (MTTR) of the machine.i.Machine type.A higher criticality level must be assignedto the machines which are of more complex construction.These machines are also characterised by higher mainte-nance costs (material and manpower) and longer repairtimes.l. Operating conditions. Operating conditions in thepresence of wear cause a higher degree of machinecriticality.

m. Propagation effect. The propagation effect takes intoaccount the possible consequences of a machine failureon the adjacent equipment (domino effect).n.Production loss cost.The higher the machine importancefor the process, the higher the machine criticality due to aloss of production.

To restrict the complexity (and the costs) of the analysisto be performed, the number of evaluation parameters isreduced by grouping together those that are similar and byremoving the less meaningful ones. An increase in thenumber of parameters does not imply a higher degree ofanalysis accuracy. With a large number of parameters theanalysis becomes much more onerous in terms of datarequired and elaboration time. Besides, the quantitativeevaluation of the factors described is complex and subjectto the risk of incorrect estimates. The following “clusters”were created.The “spare machine availability” mainly affects the unin-

terrupted duration of the production process and can there-fore be linked to the “machine importance for the process”and the “production loss cost”. In terms of spare parts, the“maintenance cost” can include the “machine type” factor,while the manpower contribution to the maintenance costcan be clustered with the “downtime length” attribute.System “safety”, “failure frequency”, “access difficulty”and “operating conditions” are considered to be stand-alone factors by the maintenance staff.For every analysed machine of the new IGCC plant, a

subjective numerical evaluation is given adopting a scalefrom 1 to 100. Finally, the factors taken into considerationare linked together in the following criticality index CI:

CI ! "#S × 1!5$ ! #IP × 2!5$ ! #MC × 2$ ! #FF × 1$

! #DL × 1!5$ ! #OC × 1$% × AD #1$

where S! safety, IP!machine importance for the process,MC!maintenance costs, FF! failure frequency, DL!downtime length, OC! operating conditions, AD!machine access difficulty.In the index, the machine “access difficulty” has been

considered by the management to be an aggravating aspectas far as the equipment criticality is concerned. It is there-fore suitable to evaluate the effect of the machine “accessdifficulty” as an “a posteriori” factor. For this reason withthis approach the machine criticality index has been multi-plied by the machine “access difficulty”.A rational quantification of the seven factors has been

defined and based on a set of tables. In particular, everyrelevant factor is divided into several classes that areassigned a different score (in the range form 1 to 100) totake into account the different criticality levels. For the sakeof brevity, only the evaluation of the “machine importancefor the process” attribute is reported as an example inAppendix A.


Table 1Weight values assigned to the relevant parameters considered in FMECAanalysis

Parameter Weight

Safety 1.5Machine importance for the process 2.5Maintenance costs 2Failure frequency 1Downtime length 1.5Operating conditions 1

The weighted values assigned by the maintenance staff tothe different parameters are shown in Table 1.The weight assigned to safety is not the highest because in

an IGCC plant danger is intrinsic to the process. The oper-ating conditions are weighted equal to one in accordancewith the hypothesis of a correct facility selection as a func-tion of the required service. The breakdown frequency isweighted equal to one in virtue of the fact that failurerates are currently estimated values only.The CI index has been used to classify about 200

machines of the plant (pumps, compressors, air coolers,etc.) into three different groups corresponding to threedifferent maintenance strategies, as shown in Table 2.Note that only corrective, preventive and predictive main-tenance strategies have been taken into account by the refin-ery maintenance management.The main features of the three groups are the following:

• Group 1. A failure of group 1 machines can lead toserious consequences in terms of workers’ safety, plantand environmental damages, production losses, etc.Significant savings can be obtained by reducing the fail-ure frequency and the downtime length. A carefulmaintenance (i.e. predictive) can lead to good levelsof company added-value. In this case, savings inmaintenance investments are not advisable. Thisgroup contains about the 70% of the IGCC machinesexamined.

• Group 2. The damages derived from a failure can beserious but, in general, they do not affect the externalenvironment. A medium cost reduction can beobtained with an effective but expensive mainte-nance. Then an appropriate cost/benefit analysismust be conducted to limit the maintenance invest-ments (i.e. inspection, diagnostic, etc.) for this typeof facilities (about the 25% of the machines). For thisreason a preventive maintenance is preferable to amore expensive predictive policy.

• Group 3. The failures are not relevant. Spare partsare not expensive and, as a consequence, low levelsof savings can be obtained through a reduction ofspare stocks and failure frequencies. With a tightbudget the maintenance investments for these typesof facilities should be reduced, also because theadded-value derived from a maintenance plan isnegligible. The cheapest corrective maintenance is,therefore, the best choice. Group 3 contains 5% ofthe machines.

4.2. Critical analysis of oil company maintenance MCDMmethodology

Some aspects of the criticality index CI proposed andprepared by the maintenance staff are open to criticism.Eq. (1) represents a “strange” modified version of theweighted sum model (WSM), which probably representsthe simplest and still the most widely used MCDM method[2]. But, in this case, there are some weaknesses.

(a) The WSM is based on the “additive utility” supposi-tion [2]. However, the WSM should be used only whenthe decision making criteria can be expressed in identicalunits of measure.(b) The AD factor should be added and not used as amultiplying factor.(c) Dependencies among the seven attributes should becarefully analysed and discussed.(d) The weight values reported in Table 1 are not justifiedin a satisfying manner. The maintenance staff also haveserious doubts about these values, which would suggestthat they have little confidence in the final resultsobtained by the MCDM model. Moreover, no sensibilityanalyses have been conducted to test the robustness of theresults. This fact is probably due to (i) a sensitivity analy-sis is not an easy matter, and (ii) the absence of a softwarepackage supporting this request.

Despite these problems, the classification produced usingthe CI index has made it possible to define three homoge-neous groups of machines. The composition of the clustersconfirms the expectations of the maintenance staff and isconsidered to be quite satisfactory. On the other hand, thedoubts of the maintenance staff mainly concern the main-tenance strategy to adopt for each group of machines. Thisfactor has been used as the starting point for the develop-ment of an AHP approach to assign the “best” maintenancestrategy to each cluster element, taking into account severalpossible aspects.

5. The analytic hierarchy process

The AHP [9–11] is a powerful and flexible multi-criteriadecision making tool for complex problems where bothqualitative and quantitative aspects need to be considered.The AHP helps the analysts to organise the critical aspectsof a problem into a hierarchical structure similar to a familytree. By reducing complex decisions to a series of simplecomparisons and rankings, then synthesising the results, theAHP not only helps the analysts to arrive at the best deci-sion, but also provides a clear rationale for the choicesmade.Briefly, the step-by-step procedure in using AHP is the

following:

1. define decision criteria in the form of a hierarchy ofobjectives. The hierarchy is structured on different levels:


Table 2Maintenance policy selection based on criticality index

Criticality index Maintenance policy

" 395 Predictive290–395 Preventive# 290 Corrective

from the top (i.e. the goal) through intermediate levels(criteria and sub-criteria on which subsequent levelsdepend) to the lowest level (i.e. the alternatives);

2. weight the criteria, sub-criteria and alternatives as a func-tion of their importance for the corresponding element ofthe higher level. For this purpose, AHP uses simple pair-wise comparisons to determine weights and ratings sothat the analyst can concentrate on just two factors atone time. One of the questions which might arise whenusing a pairwise comparison is: how important is the“maintenance policy cost” factor with respect to the“maintenance policy applicability” attribute, in terms ofthe “maintenance policy selection” (i.e. the problemgoal)? The answer may be “equally important”, “moder-ately more important”, etc. The verbal responses are thenquantified and translated into a score via the use ofdiscrete 9-point scales (see Table 3);

3. after a judgement matrix has been developed, a priorityvector to weight the elements of the matrix is calculated.This is the normalised eigenvector of the matrix.

The AHP enables the analyst to evaluate the goodness ofjudgements with the inconsistency ratio IR. The judgementscan be considered acceptable if IR $ 0!1! In cases of incon-sistency, the assessment process for the inconsistent matrixis immediately repeated. An inconsistency ratio of 0.1 ormore may warrant further investigation. For more details onIR, the reader may refer to Saaty [9].After its introduction by Saaty [9], AHP has been widely

used in many applications [12]. Designed to reflect the waypeople actually think, the AHP was developed more than 20years ago and it is one of the most used multi-criteria deci-sion-making techniques. This fact is probably due to someimportant aspects of AHP such as [13]: (1) the possibility tomeasure the consistency in the decision maker’s judgement;(2) it does not, in itself, make decisions but guides theanalyst in his decision making; (3) it is possible to conducta sensitivity analysis; (4) AHP can integrate both

quantitative and qualitative information; and (5) it is wellsupported by commercial software programs. However, it isnecessary to remember some possible problems with AHP(rank reversal, etc.) which are currently debated in the litera-ture [14–16].

6. The hierarchy scheme

In designing the AHP hierarchical tree, the aim is todevelop a general framework that satisfies the needs of theanalysts to solve the selection problem of the best mainte-nance policy. The AHP starts by breaking down a complex,multi-criteria problem into a hierarchy where each levelcomprises a few manageable elements which are thenbroken down into another set of elements. Considering thecritical aspect of this step for the AHP methodology, thestructure has been created following suggestions from themaintenance and production staff of the oil refinery (Fig. 1).The AHP hierarchy developed in this study is a five-level

tree in which the top level represents the main objective ofmaintenance selection and the lowest level comprises thealternative maintenance policies.The evaluation criteria that influence the primary goal are

included at the second level and are related to four differentaspects: damages produced by a failure, applicability of themaintenance policy, added-value created by the policy, andthe cost of the policy. These criteria are then broken downinto several sub-criteria as shown in Fig. 1.The definition of the hierarchy scheme described above

has been developed using a brainstorming process. All thepeople concerned with maintenance problems in the oilrefinery where the IGCC plant is being built have beenincluded in the study. In particular, we have involved themaintenance engineering personnel (who perform the criti-cality analyses and develop the maintenance improvementprocedures), the MON (Maintenance On Site) and MOF(Maintenance Off Site) staff (who manage the maintenanceoperations for the transfer and the mixing of the oil refineryproducts).The hierarchy scheme has been based on the fees due

during the oil refinery maintenance management. In thisway the four criteria of the first level of the hierarchyhave been immediately identified. Possible differences ofopinion expressed by maintenance supervisors have been“smoothed” using the Delphi technique. In this way wehave tried to maximise the advantages of group dynamicsand reduce the negative effects due to the subjectivity ofopinions.The formal tool used during the hierarchy structure defi-

nition was the Interpretive Structural Modelling (IMS)[17,18] approach. IMS is a well-established interactivelearning process for identifying and summarising relation-ships among specific factors of a multi-criteria decision-making problem, and provides an efficient means bywhich a group of decision-makers can impose order on


Table 3Judgement scores in AHP

Judgement Explanation Score

Equally Two attribute contribute equally to the upper-level criteria

1

2Moderately Experience and judgement slightly favour one

attribute over another3

4Strongly Experience and judgement strongly favour one

attribute over another5

6Very strongly An attribute is strongly favoured and its

dominance demonstrated in practice7

8Extremely The evidence favouring one attribute over

another is of the highest possible order ofaffirmation

9

the complexity of the problem. The steps of this methodol-ogy are briefly the following [18]:a. identification of elements which are relevant to thedecision making problem;b. a contextually relevant subordinate relation is chosen;c. a structural self-interaction matrix (SSIM) is developedbased on pairwise comparison of elements;d. SSIM is converted into a reachability matrix and itstransitivity is checked;e. once the transitivity has been achieved, the conversionof an object system into a well-defined matrix model isobtained;

f. the partitioning of the elements and the extractionof the structural model, termed ISM is thenperformed.

The relevant factors defining the damage criteria are iden-tified as loss of production, damages to facilities, to product,to environment, to people, and to company image. Theproduction loss is linked to facility downtime derivedfrom a failure (time required for detection, repair or repla-cement and re-starting) and divided in MTBF and MTTR ina successive hierarchy level. The facility damages aredivided into direct and indirect: the direct damage deals


Fig. 1. The AHP hierarchy scheme adopted.

with the tangible effects of the failure on the machine,the indirect damage takes into account the possibleinfluences (i.e. reduction) of the failure on the workinglife of the plant as a consequence of a “domino effect”on other facilities and instruments. Finally, the environ-mental damage is divided into internal and external tothe plant.For the maintenance policy applicability factor, two

sub-criteria are taken into account: the costs requiredfor the policy implementation and the reliability. Themaintenance costs are divided into hardware (i.e.sensors), software (i.e. a reliability commercial soft-ware), and training costs. The reliability criterionincludes the technical aspect of the maintenance strat-egy adopted in terms of fault detection capability andfacility restoring capability.The added-value criterion deals with the indirect benefit

of a particular maintenance policy. This category includesthe improvements in terms of product quality, safety, andinternal skills (i.e. an overall better knowledge of themachines employed).With respect to the policy cost factor, four sub-

criteria have been considered for the successive hierar-chy level: MTBF, MTTR, saving in the stock of spareparts, and assurance aspects (i.e. possible decreases ininsurance premiums that can be obtained by adopting aparticular maintenance policy). Note that the investmentrequired to implement the policy is not included as sub-criterion. This is due to the fact that the maintenanceinvestment for a single machine is negligible withrespect to the other sub-criteria of the “policy cost”factor. For this reason, the “investment required” attri-bute has been introduced only as sub-criterion of the“policy applicability” factor in the hierarchy.The proposed hierarchy must not be intended as a

general for any process plant application, with somechoices concerning only the particular IGCC planthere analysed. Besides, it is possible to note howsome possible dependencies among the attributes havenot been treated. These structure simplification choicesderive from the necessity to obtain a good trade-off

between a suitable level of detail and a manageablecomplexity of the hierarchy structure for actualindustrial applications.

7. The AHP analysis

Once the hierarchy structure of the maintenance decisionmaking problem has been defined, the priorities of thescheme have been calculated using pairwise comparisonsand the Delphi technique.The three machine groups previously described have

been analysed through the use of the AHP methodology.In this situation five different alternative maintenance poli-cies have been considered. The pairwise judgements used toperform the AHP analysis were stated by the oil refinerymaintenance management that developed the modifiedFMECA analysis shown in Section 4.Table 4 gives an example of the “damages” factor for the

machines of Group 1, with the relative judgements of impor-tance (IR results equal to 0!03 # 0!1$ obtained by adoptingthe quantitative scale showed in Table 3.It is clear that the most important aspect, in terms of

“damages”, is the “plant damage” attribute. This consid-eration is due to the fact that the highest cost derivedfrom an accident is due to production plant damages(direct and indirect, i.e. “domino” effect). Environmen-tal damage is the least important element because of thepresence of modern and sophisticated safety systemsthat guarantee limited consequences for the environ-ment.Fig. 2 shows the global priority indices for each criterion,

sub-criterion and alternative included in the AHP hierarchystructure for a particular Group 1 machine (a 23MW powerair compressor).As can be seen, the overall inconsistency index is equal to

0.046, which is less than the critical value of 0.1. For brev-ity, the partial IR values are not shown in the figure. Never-theless, all the IR values result lower than 0.1.Table 5 shows the final ranking for three machines, each

representative of a different group.


Table 4Pairwise comparison example with respect to “damages” attribute for machines of Group 1. Note: row element is x (or 1/x) times more (or less) important thancolumn element

Production loss Plant damage Product damage Environmental damage People damage Image damage Local Priority

Production loss – 1/7 3 1/5 1/7 1 0.050Plant damage – 9 3 1 9 0.352Product damage – 1/7 1/9 1 0.030Environmental damage – 1/3 7 0.181People damage – 9 0.352Image damage – 0.035

Fig. 2. Global priority indices obtained for a Group 1 facility.

M.Bevilacqua,M

.Braglia/Reliability

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SystemSafety

70(2000)71–83

79

As shown, the AHP results generally confirm the FMECAindications. Once again, for the Group 1 facility the bestmaintenance policy proves to be the predictive one. Forthese machines the most expensive and sophisticated stra-tegies (i.e. predictive and condition-based) are highlypreferable with respect to the others because of the criticalaspect of their failures.The Group 2 machine shows a slight preference for

opportunistic maintenance even if preventive and predictivepolicies should not be neglected.The analysis performed for the machine belonging to

Group 3 shows that corrective and preventive maintenanceare equally appropriate. So, the decision to use correctivemaintenance for this group of machines should be carefullyconsidered. The limited failure consequence in terms ofproduction loss is not sufficient to clearly and immediatelystate that no maintenance action should be performed beforethe failure.The three machines selected for the AHP analysis can be

considered as representative of the corresponding groups. Inother words, they represent more or less the average char-acteristics of the facilities which belong to the same cluster.For this reason, the results obtained for this representativeequipment have been extended to all facilities of the groups.This position has been considered sufficient and satisfactoryby the maintenance staff. In any case, a more detailed analy-sis requiring a new set of pairwise judgements for eachfacility of each group can be always performed. This isprobably the best solution for the “extreme” equipmentcharacterised by the CI values near to the limit of thebelonging cluster.

8. Sensitivity analysis

Although the ranking solution showed a possible scenariowhere, for example, “damages aspects” are clearly the mostimportant criteria for Group 1 machines (see Fig. 2), theAHP solution can change in accordance with shifts inanalyst logic. To explore the response of model solutions(i.e. the solution robustness) to potential shifts in the priorityof maintenance strategies, a series of sensitivity analyses ofcriteria weights can be performed by changing the priority(relative importance) of weights. Each criterion can be char-acterised by an important degree of sensitivity, i.e. the rank-ing of all strategies changes dramatically over the entireweight range [19]. The problem is to check whether a fewchanges in the judgement evaluations can lead to significantmodifications in the priority final ranking. For this reason,sensitivity analysis is used to investigate the sensitivity ofthe alternatives to changes in the priorities of the criteria atthe level immediately below the goal. The analysis proposedemphasises the priorities of the four “first-level” criteria inthe AHP model reported in Fig. 1 and shows how changingthe priority of one criterion affects the priorities of theothers. It is clear that as the priority of one of the criteria

increases, the priorities of the remaining criteria mustdecrease proportionately, and the global priorities of thealternatives must be recalculated. All the results reportedin Tables 6–8 were obtained using the Expertchoice soft-ware, a multi-attribute decision making tool which was usedto support all the AHP applications reported in this paper.For example, from Table 6, one can clearly see the

robustness of the predictive maintenance strategy for themachines that belong to Group 1. This policy remains thebest solution, increasing or decreasing the priorities of eachcriterion. It is only when one increases the priority of the“applicability” factor to an improbable 96% that we obtain achange in the final ranking with the condition-based main-tenance strategy.For the Group 2 machines (Table 7), the final solution is

less stable. Opportunistic and predictive maintenance stra-tegies show some alternations in priority positions. In parti-cular, the maintenance staff must take into consideration“applicability” and “added-value” priority evaluations.There are no situations where catastrophic changes in thefinal ranking are observed.An analysis of the Group 3 machines (Table 8), highlights

more reversals in the final ranking. Corrective, opportunisticand predictive maintenance strategies can all be consideredto be the best solution, depending on which weightingcriteria are used. It can, therefore, be seen that the sensitivityanalysis shows how, in the case study under examination,the maintenance staff should concentrate their attention onthe pairwise evaluation of the four criteria. It is clear that afew changes in the judgement evaluations can lead to modi-fications in the final priority ranking. But it must also beremembered that the importance of corrective maintenancenever goes away. In other words, we have three differentstrategies, all of which can provide the best solution, and thedifferences among them are never of great importance.Summarising the considerations discussed above, one can

affirm that the AHP selections presented in Section 7, whichconcern the best possible maintenance strategies for theIGCC machines, can be accepted with a good degree ofconfidence by the company maintenance staff.We conclude this section with two final considerations.

First, the sensitivity analysis proposed here is only relevantto the priorities of the four “first-level” criteria. Second,because we have changed each attribute weight one at atime, only the “main effects” have been considered. Inother words, “interaction effects” of the changes made totwo or more weights have been ignored. These simplifica-tions have been adopted for the following reasons:

1. the final solution is mainly sensible to changes in thepriorities at the highest level of the hierarchy;

2. the introduction of the interaction effects makes thesensitivity analysis too complex for actual applications.Nevertheless, one should note that the main effects aregenerally the most important aspects in a sensitivityanalysis.


In other words, the simple approach described here seemsto be a good compromise between efficiency and efficacy.

9. Conclusions

The definition of the most appropriate maintenance poli-cies for a large system such as an IGCC plant requires thedevelopment of the appropriate decision support systems.The maintenance plan selection for each component is verycomplex due to the difficulties concerning data collection,the number of factors to be taken into account, their subjec-tivity, the large number of the plant machines (around twohundred in the case under examination) to be considered,and the fact that the plant is still being built.When integrated with a pre-analysis of the facility criti-

cality, the AHP technique has proved to be a valid supportfor the selection of the maintenance strategy. The hierarch-ical structure of the proposed AHP combines many featureswhich are important for the selection of the maintenancepolicy: economic factors, applicability and costs, safety,etc. Also, accepting the fact that there are some differences,the AHP results are not completely different from thosedirectly proposed by oil refinery personnel. This is not asurprise because the same experts performed the twoanalyses. But the AHP approach is characterised by someimportant properties which are considered highly by themaintenance staff:

a. AHP technique makes it possible to approach the deci-sion making problem in a more complete and thoroughway, taking several factors into account. This capacity ismore difficult to obtain when using conventional meth-odologies such as FMECA. It must also be consideredthat the AHP is able to manage a large number of possiblealternatives in an efficient way;b. AHP can integrate both qualitative and quantitativeinformation. With AHP a direct quantitative judgementof the relevant maintenance factors is not necessarilyrequired by the maintenance manager. The pairwisecomparisons are preferred by the manager when severalintangible criteria have to be treated, as is the case withmaintenance selection. In addition, AHP is the onlyknown MCDM model that can measure the consistencyof the decision maker;c. AHP procedure is readily available in decision-makingsoftware packages from several commercial softwaresellers. In particular, these software tools make it possibleto conduct a sensitivity analysis which improves theeffectiveness of the procedure and the stability of deci-sions reached through AHP.

The results and the satisfaction of maintenance manage-ment derived by using the proposed methodology confirmshow AHP can enhance and improve the understanding of thedynamics of a similar complex problem and represents aneffective approach to arrive at decisions.


Table5

AHPfinalrankingforthreemachines,eachonerepresentativeofadifferentgroup

Machinedescription

Predictivemaintenance

Conditionbasedmaintenance

Opportunisticmaintenance

Preventivemaintenance

Correctivemaintenance

Group1

Aircompressor

0.368

0.333

0.136

0.120

0.043

Group2

Centrifugalpump

0.199

0.195

0.219

0.212

0.174

Group3

Centrifugalpump

0.165

0.165

0.219

0.224

0.227


Table 6Observations derived from sensitivity analysis of the criteria priority values with machines of Group 1

Criteria Decreasing Relative priority valuein final solution (%)

Increasing

Damages Predictive maintenance is always the beststrategy

56.6 Predictive maintenance is always the beststrategy

Applicability Predictive maintenance is always the beststrategy

36.8 Predictive maintenance strategy is reachedand overcame by condition-basedmaintenance but only when “applicability”priority is equal to 96%.

Added-value Predictive maintenance is always the beststrategy


Cost Predictive maintenance is always the beststrategy




Increasing

Damages Opportunistic maintenance is always thebest strategy

26.2 Opportunistic maintenance strategy isreached and overcame by predictivemaintenance when “damages” priority isequal to 34.2%.

Applicability Opportunistic maintenance strategy isreached and overcame by predictivemaintenance just when “applicability”priority is reduced to 50.5%.

56.5 Opportunistic maintenance is always thebest strategy

Added-value Opportunistic maintenance is always thebest strategy

5.5 Opportunistic maintenance strategy isreached and overcame by predictivemaintenance when “added-value” priority isequal to 11.5%.

Cost Opportunistic maintenance is always thebest strategy

11.8 Opportunistic maintenance strategy is justreached and overcame by predictivemaintenance when “cost” priority is equalto 19.8%.



Increasing

Damages Corrective maintenance is always the beststrategy

15.1 Corrective maintenance is reached andovercame by opportunistic maintenance justwhen “damages” priority is became to17.1%. Predictive is the new best strategywhen priority is equal to 53.1%

Applicability Corrective maintenance is reached andovercame by opportunistic maintenance justwhen “applicability” priority is reduced to61.5%. Predictive is the new best strategywhen priority is reduced to 47.5%

63.5 Corrective maintenance is always the beststrategy

Added-value Corrective maintenance is always the beststrategy

6.2 Corrective maintenance is reached andovercame by opportunistic maintenancewhen “damages” priority is became to10.2%. Predictive is the new best strategywhen priority is equal to 20.2%

Cost Corrective maintenance is always the beststrategy

11.8 Corrective maintenance is reached andovercame by opportunistic maintenancewhen “damages” priority is became to19.1%. Predictive is the new best strategywhen priority is equal to 33.1%

Acknowledgements

We would like to thank the referees for their constructivecomments that enabled us to improve the quality of thispaper.

Appendix A

An example of evaluation of a factor of Eq. (1) is reportedbelow. The machine importance for the continuity of theIGCC plant operations is evaluated through the analysis ofthe Process Flow Diagrams (PFDs) and Piping and Instru-mentation Diagrams (P&IDs) of the plant, taking intoaccount the availability of spare machines.The scores are assigned in accordance with Table A1,

which is drawn up with the oil refinery maintenance staffand process engineers.Low impact equipment are those items characterised by

minor functions or which operate in circuits where it ispossible to provide the products with intermediate storage.In the latter case, the buffer can provide the plant with thenecessary operating autonomy so that maintenance can beperformed without a plant shut-down. Machines with highor very high impact on the process have also been assigned ahigh score for the availability of a spare part to take intoaccount the possible failure of the spare machine to start.

References

[1] Almeida AT, Bohoris GA. Decision theory in maintenance decision

making. Journal of Quality in Maintenance Engineering1995;1(1):39–45.

[2] Triantaphyllou E, Kovalerchuk B, Mann L, Knapp GM. Determiningthe most important criteria in maintenance decision making. Journalof Quality in Maintenance Engineering 1997;3(1):16–24.

[3] Rausand M. Reliability centered maintenance. Reliability Engineer-ing and System Safety 1998;60:121–32.

[4] Crocker J, Kumar UD. Age-related maintenance versus reliabilitycentered maintenance: a case study on aero-engines. Reliability Engi-neering and System Safety 2000;67:113–8.

[5] Gilchrist W. Modelling failure modes and effects analysis. Interna-tional Journal of Quality and Reliability Management 1993;10(5):16–23.

[6] Del Bravo R, Starace F, Chellini IM, Chiantore PV. Impianto IGCCda 280 MW di Api Energia in Italia. L’energia elettrica1997;74(2):17–24.

[7] Countinho JS. Failure-effect analysis. Transactions NY AcademicScience 1964;26(II):564–84.

[8] Holmberg K, Folkeson A. Operational reliability and systematicmaintenance. London: Elsevier, 1991.

[9] Saaty TL. The analytic hierarchy process. New York: McGraw-Hill,1980.

[10] Saaty TL. Decision making for leaders. Belmont, CA: LifetimeLearning Publications, 1982.

[11] Saaty TL. How to make a decision: the analytic hierarchy process.European Journal of Operational Research 1990;48:9–26.

[12] Vargas LG. An overview of the analytic hierarchy process and itsapplications. European Journal of Operational Research 1990;48:2–8.

[13] Madu CN, Kuci C-H. Optimum information technology for socio-economic development. Information Management and ComputerSecurity 1994;2(1):4–11.

[14] Belton V, Gear T. On a short-coming of Saaty’s method of analytichierarchies. Omega 1983;11(3):228–30.

[15] Dyer JS. Remarks on the analytic hierarchy process. ManagementScience 1990;36(3):259–68.

[16] Saaty TL. An exposition of the AHP in reply to the paper: remarks onthe analytic hierarchy process. Management Science1990;36(3):259–68.

[17] Sage AP. Interpretive structural modelling: methodology for lar. NewYork: McGraw-Hill, 1977.

[18] Mandal A, Deshmukh SG. Vendor selection using interpretive struc-tural modelling (IMS). International Journal of Operations andProduction Management 1993;14(6):52–59.

[19] Min H, Melachrinoudis E. The relocation of a hybrid manufacturing/distribution facility from supply chain perspective: a case study. Inter-national Journal of Management Science 1999;27:75–85.


Table A1Scores for the evaluation of “machine importance for the process” factor

Impact on the process With spare machine Without spare machine

Low 10 50Medium 30 70High 70 90Very high 80 100

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