how to choose between maintenance policies 2

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The analytic hierarchy process applied to maintenance strategy selection

M. Bevilacquaa, M. Bragliab,*

aDipartimento di Ingegneria Industriale, Universitadegli Studi di Parma, Viale delle Scienze, 43100 Parma, ItalybDipartimento di Ingegneria Meccanica, Nucleare e della Produzione, Universitadegli 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 important

Italian 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 (about

200 in total). The machines are clustered in three homogeneous groups after a criticality analysis based on internal procedures of the oil

refinery. With AHP technique, several aspects, which characterise each of the above-mentioned maintenance strategies, are arranged in a

hierarchic structure and evaluated using only a series of pairwise judgements. To improve the effectiveness of the methodology AHP is

coupled 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 inevitable

source of cost. For these companies maintenance operations

have a corrective function and are only executed in emer-gency conditions. Today, this form of intervention is no

longer acceptable because of certain critical elements such

as product quality, plant safety, and the increase in main-

tenance department costs which can represent from 15 to

70% of total production costs.

The managers have to select the best maintenance policy

for each piece of equipment or system from a set of possible

alternatives. For example, corrective, preventive, opportu-

nistic, condition-based and predictive maintenance policies

are considered in this paper.

It is particularly difficult to choose the best mix of main-

tenance policies when this choice is based on preventive

elements, 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 is

being built for an Italian oil company. This plant will

have about 200 facilities (pumps, compressors, air-coolers,

etc.) and the management must decide on the maintenance

approach for the different machines. These decisions will

have significant consequences in the short-medium term for

matters 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 define

the best maintenance strategy to adopt for each machine,

bearing in mind budget constraints. It is not necessary to

identify the best solution from among the alternatives thatthis approach presents. The maintenance manager only

wants to recognise the most critical machines for a pre-

allocation of the budget maintenance resources, without

entering into the details of the actual final choice. This

final choice would, in any case, be impossible because the

plant is not yet operating and, as a consequence, total

knowledge of the reliability aspects of the plant machines

is not yet available. In other words, the problem is not

whether it is better to control the temperature or the vibra-

tion of a certain facility under analysis, but only to decide if

it is better to adopt a condition-based type of maintenance

approach 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 optimal

maintenance frequencies, thresholds for condition-based

intervention, etc.). This level must be postponed until data

from the operating production system becomes available.

Several attributes must be taken into account at this first

level when selecting the type of maintenance. This selection

involves several aspects such as the investment required,

safety and environmental problems, failure costs, reliability

of the policy, Mean Time Between Failure (MTBF) and

Mean Time To Repair (MTTR) of the facility, etc. Several

of these factors are not easy to evaluate because of their

Reliability Engineering and System Safety 70 (2000) 7183

0951-8320/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved.

PII: S0951-8320(00) 00047-8

www.elsevier.com/locate/ress

* Corresponding author. Fax: 39-050-913-040.

E-mail address: [email protected] (M. Braglia).


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intangible and complex nature. Besides, the nature of the

weights of importance that the maintenance staff must give

to these factors during the selection process is highly subjec-

tive. Finally, bearing in mind that the plant is still in the

construction phase, some tangible aspects such as MTBF

and 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 analysed

in 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 task

due to the great number of attributes to be considered, many

of which are intangible. As an aid to the resolution of this

problem, some multi-criteria decision making (MCDM)

approaches are proposed in the literature. Almeida and

Bohoris [1] discuss the application of decision making

theory to maintenance with particular attention to multi-

attribute utility theory. Triantaphyllou et al. [2] suggestthe use of Analytical Hierarchy Process (AHP) considering

only four maintenance criteria: cost, reparability, reliability

and availability. The Reliability Centered Maintenance

(RCM) methodology (see, for example, Ref. [3]) is probably

the most widely used technique. RCM represents a method

for preserving functional integrity and is designed to mini-

mise maintenance costs by balancing the higher cost of

corrective maintenance against the cost of preventive main-

tenance, taking into account the loss of potential life of the

unit 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 criticality

analysis technique (FMECA). This methodology has been

proposed in different possible variants, in terms of relevant

criteria considered and/or risk priority number formulation

[5]. Using this approach, the selection of a maintenance

policy is performed through the analysis of obtained priority

risk number. An example of this approach has also been

followed by our oil company, which has developed its

own methodology internally. This approach makes it

possible to obtain a satisfying criticality clustering of

the 200 facilities into three homogeneous groups. The

problem is to define the best maintenance strategy for

each group.To integrate the internal self-made criticality approach,

this paper presents a multi-attribute decision method based

on 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 maintenance

strategy are arranged in a hierarchic structure and evaluated,

for each facility, through the use of a series of pairwise

judgements. Finally, considering that the maintenance

manager can never be sure about the relative importance

of decision making criteria selected when dealing with

this complex maintenance problem, to improve the AHP

effectiveness the methodology is coupled with a sensitivity

analysis phase.

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

The Integrated Gasification and Combined Cycle(IGCC) plant [6], currently being assembled at The

Falconara Marittima API oil refinery, will make it

possible to transform the oil refinement residuals into

the synthesis gases which will be used as fuel to

produce electricity. The IGCC plan will be placed in

a 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 some

65,000 ton/h of steam will be used inside the oil refinery

for process requirements. The total cost of the project

amounts 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 United

States, is forcing oil refinery firms to reduce the emis-

sions of pollutants from the process plants and reduce

the potential pollution of the refined products. The same

pollution control requirements, mainly a reduction in the

level of nitrogen and sulphur oxides, together with the

increasing need to control operating and investments

costs, is pushing electrical firms to search for more

economic 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 technology

has proved to be a valid solution to the market requirement

of efficient, clean, low consuming and environmentally

orientated production technologies.

The API oil refinery uses a thermal conversion process

and has a production capacity of about 4,000,000 tons of oil

per year (80,000 barrels per day). The production cycle is

typical 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 be

partly converted into the synthesis gases syngas (whichwill be cleaned in the IGCC gasifiers) and partly used to

produce bitumen.

The three main objectives of the oil refinery management

are the following:

1. the elimination of heavy residuals used to produce fuel

oil with high and low sulphur content;

2. the ability to process almost every type of heavy oil with

a high sulphur content;

3. the substitution of the present low efficiency thermoelec-

trical power plant with a more efficient system, with

lower levels of pollutant emissions.

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


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3. Possible alternative maintenance strategies

Five alternative maintenance policies are evaluated in this

case study. Briefly, they are the following.

Corrective maintenance. The main feature of corrective

maintenance is that actions are only performed when a

machine breaks down. There are no interventions until afailure has occurred.

Preventive maintenance. Preventive maintenance is

based on component reliability characteristics. This

data makes it possible to analyse the behaviour of the

element in question and allows the maintenance engineer

to define a periodic maintenance program for the

machine. The preventive maintenance policy tries to

determine a series of checks, replacements and/or

component revisions with a frequency related to the fail-

ure rate. In other words, preventive (periodic) mainte-

nance is effective in overcoming the problems

associated with the wearing of components. It is evidentthat, after a check, it is not always necessary to substitute

the component: maintenance is often sufficient.

Opportunistic maintenance. The possibility of using

opportunistic maintenance is determined by the nearness

or concurrence of control or substitution times for differ-

ent components on the same machine or plant. This type

of maintenance can lead to the whole plant being shut

down at set times to perform all relevant maintenance

interventions at the same time.

Condition-based maintenance. A requisite for the appli-

cation of condition-based maintenance is the availability

of a set of measurements and data acquisition systems to

monitor the machine performance in real time. Thecontinuous survey of working conditions can easily and

clearly point out an abnormal situation (e.g. the exceed-

ing of a controlled parameter threshold level), allowing

the process administrator to punctually perform the

necessary controls and, if necessary, stop the machine

before a failure can occur.

Predictive maintenance. Unlike the condition-based

maintenance policy, in predictive maintenance the

acquired controlled parameters data are analysed to find

a possible temporal trend. This makes it possible to

predict when the controlled quantity value will reach or

exceed the threshold values. The maintenance staff willthen be able to plan when, depending on the operating

conditions, the component substitution or revision is

really unavoidable.

4. The IGCC plant maintenance program definition

An electrical power plant based on IGCC technology is a

very complex facility, with a lot of different machines and

equipment with very different operating conditions. Decid-

ing on the best maintenance policy is not an easy matter,

since the maintenance program must combine technical

requirements with the firms managerial strategy. The

IGCC plant complex configuration requires an optimal

maintenance 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, depending

on the availability request and global maintenance budget.

Every component, in accordance with its failure rate, cost

and breakdown impact over the whole system, must be

studied in order to assess the best solution; whether it is

better to wait for the failure or to prevent it. In the latter

case the maintenance staff must evaluate whether it is better

to perform periodic checks or use a progressive operating

conditions analysis.

It is clear that a good maintenance program must define

different strategies for different machines. Some of these

will mainly affect the normal operation of the plant, some

will concern relevant safety problems, and others will

involve high maintenance costs. The overlapping of theseeffects enables us to assign a different priority to every plant

component or machine, and to concentrate economic and

technical efforts on the areas that can produce the best

results.

One relevant IGCC plant feature is the lack of historical

reliability and maintenance costs data (the plant start-up is

proposed for March 2000). Initially, the definition of the

maintenance plan will be based upon reliability data from

the literature and on the technical features of the machines.

This information will then be updated using the data

acquired during the working life of the plant. The analysis

system has been structured in a rational way so as to keepthe update process as objective as possible. This has been

accomplished through the use of a charting procedure, using

well-understood evaluations of different parameters and a

simple and clear analysis of corrective interventions. The

maintenance plan developed for the machines of the IGCC

plant is based on the well-known FMECA technique [7,8].

The analysis results have provided a criticality index for

every machine, allowing the best maintenance policy to

be selected.

4.1. The maintenance strategy adopted by the oil refinery

company

The internal methodology developed by the company to

solve the maintenance strategy selection problem for the

new IGCC plant is based on a criticality analysis which

may be considered as an extension of the FMECA techni-

que. This analysis takes into account the following six

parameters:

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


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operating conditions;

with an additional evaluation for the

machine access difficulty.

Note that, the six parameters presented below derived

from an accurate pre-analysis to select all of the relevant

parameters that can contribute to the machine criticality. As

reported 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 importance

of the machine for the correct operation of the plant. For

instance, the presence of an inter-operational buffer to

stock the products can reduce the machine criticality

since the maintenance intervention could be performed

without a plant shutdown.

c.Spare machine availability.Machines that do not have

spares 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 on

manpower and spare parts costs.

f. Access difficulty. The maintenance intervention can be

difficult for machinesarranged in a compact manner,placed

in a restrict area because they are dangerous, or situated at a

great height (for example, some agitators electric motors

and air-cooler banks). The machine access difficulty

increases the length of downtime and, moreover, increases

the probability of a failure owing to the fact that inspection

teams 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 mean

time to repair(MTTR) of the machine.

i.Machine type.A higher criticality level must be assigned

to the machines which are of more complex construction.

These machines are also characterised by higher mainte-

nance costs (material and manpower) and longer repair

times.

l. Operating conditions. Operating conditions in the

presence of wear cause a higher degree of machine

criticality.

m. Propagation effect. The propagation effect takes into

account the possible consequences of a machine failure

on the adjacent equipment (domino effect).

n. Production loss cost. The higher the machine importance

for the process, the higher the machine criticality due to a

loss of production.

To restrict the complexity (and the costs) of the analysisto be performed, the number of evaluation parameters is

reduced by grouping together those that are similar and by

removing the less meaningful ones. An increase in the

number of parameters does not imply a higher degree of

analysis accuracy. With a large number of parameters the

analysis becomes much more onerous in terms of data

required and elaboration time. Besides, the quantitative

evaluation of the factors described is complex and subject

to 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 cost

can 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 scale

from 1 to 100. Finally, the factors taken into consideration

are linked together in the following criticality index CI:

CI S 15 IP 25 MC 2 FF 1

DL 15 OC 1 AD 1

where Ssafety, IPmachine importance for the process,

MCmaintenance costs, FFfailure frequency, DL

downtime length, OCoperating conditions, ADmachine access difficulty.

In the index, the machine access difficulty has been

considered by the management to be an aggravating aspect

as 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 with

this 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, every

relevant factor is divided into several classes that are

assigned a different score (in the range form 1 to 100) to

take into account the different criticality levels. For the sake

of brevity, only the evaluation of the machine importance

for the process attribute is reported as an example in

Appendix A.


Table 1

Weight values assigned to the relevant parameters considered in FMECA

analysis

Parameter Weight

Safety 1.5

Machine importance for the process 2.5

Maintenance costs 2Failure frequency 1

Downtime length 1.5

Operating conditions 1


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The weighted values assigned by the maintenance staff to

the 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 accordance

with the hypothesis of a correct facility selection as a func-

tion of the required service. The breakdown frequency is

weighted equal to one in virtue of the fact that failure

rates 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 to

serious consequences in terms of workers safety, plant

and environmental damages, production losses, etc.

Significant savings can be obtained by reducing the fail-

ure frequency and the downtime length. A careful

maintenance (i.e. predictive) can lead to good levelsof company added-value. In this case, savings in

maintenance investments are not advisable. This

group contains about the 70% of the IGCC machines

examined.

Group 2. The damages derived from a failure can be

serious but, in general, they do not affect the external

environment. A medium cost reduction can be

obtained with an effective but expensive mainte-

nance. Then an appropriate cost/benefit analysis

must be conducted to limit the maintenance invest-

ments (i.e. inspection, diagnostic, etc.) for this type

of facilities (about the 25% of the machines). For this

reason a preventive maintenance is preferable to amore expensive predictive policy.

Group 3. The failures are not relevant. Spare parts

are not expensive and, as a consequence, low levels

of savings can be obtained through a reduction of

spare stocks and failure frequencies. With a tight

budget the maintenance investments for these types

of facilities should be reduced, also because the

added-value derived from a maintenance plan is

negligible. The cheapest corrective maintenance is,

therefore, the best choice. Group 3 contains 5% of

the machines.

4.2. Critical analysis of oil company maintenance MCDM

methodology

Some aspects of the criticality index CI proposed and

prepared by the maintenance staff are open to criticism.

Eq. (1) represents a strange modified version of the

weighted sum model (WSM), which probably represents

the 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 when

the decision making criteria can be expressed in identical

units of measure.

(b) The AD factor should be added and not used as a

multiplying factor.

(c) Dependencies among the seven attributes should be

carefully analysed and discussed.

(d) The weight values reported in Table 1 are not justified

in a satisfying manner. The maintenance staff also have

serious doubts about these values, which would suggest

that they have little confidence in the final results

obtained by the MCDM model. Moreover, no sensibility

analyses have been conducted to test the robustness of the

results. This fact is probably due to (i) a sensitivity analy-

sis is not an easy matter, and (ii) the absence of a software

package supporting this request.

Despite these problems, the classification produced using

the CI index has made it possible to define three homoge-

neous groups of machines. The composition of the clusters

confirms the expectations of the maintenance staff and is

considered to be quite satisfactory. On the other hand, the

doubts of the maintenance staff mainly concern the main-

tenance strategy to adopt for each group of machines. This

factor has been used as the starting point for the develop-

ment of an AHP approach to assign the best maintenance

strategy to each cluster element, taking into account several

possible aspects.

5. The analytic hierarchy process

The AHP [911] is a powerful and flexible multi-criteria

decision making tool for complex problems where both

qualitative and quantitative aspects need to be considered.The AHP helps the analysts to organise the critical aspects

of a problem into a hierarchical structure similar to a family

tree. By reducing complex decisions to a series of simple

comparisons and rankings, then synthesising the results, the

AHP not only helps the analysts to arrive at the best deci-

sion, but also provides a clear rationale for the choices

made.

Briefly, the step-by-step procedure in using AHP is the

following:

1. define decision criteria in the form of a hierarchy of

objectives. The hierarchy is structured on different levels:


Table 2

Maintenance policy selection based on criticality index

Criticality index Maintenance policy

395 Predictive

290395 Preventive

290 Corrective


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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 of

the higher level. For this purpose, AHP uses simple pair-

wise comparisons to determine weights and ratings so

that the analyst can concentrate on just two factors at

one time. One of the questions which might arise when

using a pairwise comparison is: how important is the

maintenance policy cost factor with respect to the

maintenance policy applicability attribute, in terms of

the maintenance policy selection (i.e. the problem

goal)? The answer may be equally important, moder-ately more important, etc. The verbal responses are then

quantified and translated into a score via the use of

discrete 9-point scales (see Table 3);

3. after a judgement matrix has been developed, a priority

vector 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 of

judgements with the inconsistency ratio IR. The judgements

can be considered acceptable ifIR 01In cases of incon-

sistency, the assessment process for the inconsistent matrix

is immediately repeated. An inconsistency ratio of 0.1 or

more 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 way

people actually think, the AHP was developed more than 20

years ago and it is one of the most used multi-criteria deci-

sion-making techniques. This fact is probably due to some

important aspects of AHP such as [13]: (1) the possibility to

measure the consistency in the decision makers judgement;

(2) it does not, in itself, make decisions but guides the

analyst in his decision making; (3) it is possible to conduct

a sensitivity analysis; (4) AHP can integrate both

quantitative and qualitative information; and (5) it is well

supported by commercial software programs. However, it is

necessary to remember some possible problems with AHP

(rank reversal, etc.) which are currently debated in the litera-

ture [1416].

6. The hierarchy scheme

In designing the AHP hierarchical tree, the aim is to

develop a general framework that satisfies the needs of the

analysts 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 level

comprises a few manageable elements which are then

broken down into another set of elements. Considering the

critical aspect of this step for the AHP methodology, the

structure has been created following suggestions from the

maintenance and production staff of the oil refinery (Fig. 1).

The AHP hierarchy developed in this study is a five-leveltree in which the top level represents the main objective of

maintenance selection and the lowest level comprises the

alternative maintenance policies.

The evaluation criteria that influence the primary goal are

included at the second level and are related to four different

aspects: damages produced by a failure, applicability of the

maintenance policy, added-value created by the policy, and

the cost of the policy. These criteria are then broken down

into several sub-criteria as shown in Fig. 1.

The definition of the hierarchy scheme described above

has been developed using a brainstorming process. All the

people concerned with maintenance problems in the oilrefinery where the IGCC plant is being built have been

included in the study. In particular, we have involved the

maintenance engineering personnel (who perform the criti-

cality analyses and develop the maintenance improvement

procedures), the MON (Maintenance On Site) and MOF

(Maintenance Off Site) staff (who manage the maintenance

operations for the transfer and the mixing of the oil refinery

products).

The hierarchy scheme has been based on the fees due

during the oil refinery maintenance management. In this

way the four criteria of the first level of the hierarchy

have been immediately identified. Possible differences of

opinion expressed by maintenance supervisors have beensmoothed using the Delphi technique. In this way we

have tried to maximise the advantages of group dynamics

and reduce the negative effects due to the subjectivity of

opinions.

The formal tool used during the hierarchy structure defi-

nition was the Interpretive Structural Modelling (IMS)

[17,18] approach. IMS is a well-established interactive

learning process for identifying and summarising relation-

ships among specific factors of a multi-criteria decision-

making problem, and provides an efficient means by

which a group of decision-makers can impose order on


Table 3

Judgement scores in AHP

Judgement Explanation Score

Equally Two attribute contribute equally to the upper-

level criteria

1

2

Moderately Experience and judgement slightly favour oneattribute over another

3

4

Strongly Experience and judgement strongly favour one

attribute over another

5

6

Very strongly An attribute is strongly favoured and its

dominance demonstrated in practice

7

8

Extremely The evidence favouring one attribute over

another is of the highest possible order of

affirmation

9


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the complexity of the problem. The steps of this methodol-ogy are briefly the following [18]:

a. identification of elements which are relevant to the

decision making problem;

b. a contextually relevant subordinate relation is chosen;

c. a structural self-interaction matrix (SSIM) is developed

based on pairwise comparison of elements;

d. SSIM is converted into a reachability matrix and its

transitivity is checked;

e. once the transitivity has been achieved, the conversion

of an object system into a well-defined matrix model is

obtained;

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

performed.

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. The

production loss is linked to facility downtime derived

from a failure (time required for detection, repair or repla-

cement and re-starting) and divided in MTBF and MTTR in

a successive hierarchy level. The facility damages are

divided into direct and indirect: the direct damage deals


Fig. 1. The AHP hierarchy scheme adopted.


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with the tangible effects of the failure on the machine,

the indirect damage takes into account the possible

influences (i.e. reduction) of the failure on the working

life 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 to

the plant.

For the maintenance policy applicability factor, twosub-criteria are taken into account: the costs required

for the policy implementation and the reliability. The

maintenance costs are divided into hardware (i.e.

sensors), software (i.e. a reliability commercial soft-

ware), and training costs. The reliability criterion

includes the technical aspect of the maintenance strat-

egy adopted in terms of fault detection capability and

facility restoring capability.

The added-value criterion deals with the indirect benefit

of a particular maintenance policy. This category includes

the improvements in terms of product quality, safety, and

internal 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 spare

parts, and assurance aspects (i.e. possible decreases in

insurance premiums that can be obtained by adopting a

particular maintenance policy). Note that the investment

required to implement the policy is not included as sub-

criterion. This is due to the fact that the maintenance

investment for a single machine is negligible with

respect 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 thepolicy applicability factor in the hierarchy.

The proposed hierarchy must not be intended as a

general for any process plant application, with some

choices concerning only the particular IGCC plant

here analysed. Besides, it is possible to note how

some possible dependencies among the attributes have

not been treated. These structure simplification choices

derive from the necessity to obtain a good trade-off

between a suitable level of detail and a manageable

complexity of the hierarchy structure for actual

industrial applications.

7. The AHP analysis

Once the hierarchy structure of the maintenance decisionmaking problem has been defined, the priorities of the

scheme have been calculated using pairwise comparisons

and 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 to

perform the AHP analysis were stated by the oil refinery

maintenance management that developed the modified

FMECA 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 003 01 obtained by adopting

the 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 derived

from 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 the

presence of modern and sophisticated safety systems

that 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 power

air 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 partialIRvalues are not shown in the figure. Never-

theless, all theIR values result lower than 0.1.

Table 5 shows the final ranking for three machines, each

representative of a different group.


Table 4

Pairwise 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 than

column 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 0.050

Plant damage 9 3 1 9 0.352

Product damage 1/7 1/9 1 0.030Environmental damage 1/3 7 0.181

People damage 9 0.352

Image damage 0.035

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


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As shown, the AHP results generally confirm the FMECA

indications. Once again, for the Group 1 facility the best

maintenance policy proves to be the predictive one. For

these machines the most expensive and sophisticated stra-

tegies (i.e. predictive and condition-based) are highly

preferable with respect to the others because of the critical

aspect of their failures.

The Group 2 machine shows a slight preference for

opportunistic maintenance even if preventive and predictive

policies should not be neglected.

The analysis performed for the machine belonging to

Group 3 shows that corrective and preventive maintenance

are equally appropriate. So, the decision to use corrective

maintenance for this group of machines should be carefully

considered. The limited failure consequence in terms of

production loss is not sufficient to clearly and immediately

state that no maintenance action should be performed before

the 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 representative

equipment have been extended to all facilities of the groups.

This position has been considered sufficient and satisfactory

by the maintenance staff. In any case, a more detailed analy-

sis requiring a new set of pairwise judgements for each

facility of each group can be always performed. This is

probably the best solution for the extreme equipment

characterised by the CI values near to the limit of the

belonging cluster.

8. Sensitivity analysis

Although the ranking solution showed a possible scenario

where, for example, damages aspects are clearly the most

important criteria for Group 1 machines (see Fig. 2), the

AHP solution can change in accordance with shifts in

analyst logic. To explore the response of model solutions

(i.e. the solution robustness) to potential shifts in the priority

of maintenance strategies, a series of sensitivity analyses of

criteria 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 entire

weight range [19]. The problem is to check whether a few

changes in the judgement evaluations can lead to significant

modifications in the priority final ranking. For this reason,

sensitivity analysis is used to investigate the sensitivity of

the alternatives to changes in the priorities of the criteria at

the level immediately below the goal. The analysis proposed

emphasises the priorities of the four first-level criteria in

the AHP model reported in Fig. 1 and shows how changing

the priority of one criterion affects the priorities of the

others. It is clear that as the priority of one of the criteria

increases, the priorities of the remaining criteria must

decrease proportionately, and the global priorities of the

alternatives must be recalculated. All the results reported

in Tables 68 were obtained using the Expertchoice soft-

ware, a multi-attribute decision making tool which was used

to 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 the

machines that belong to Group 1. This policy remains the

best solution, increasing or decreasing the priorities of each

criterion. It is only when one increases the priority of the

applicability factor to an improbable 96% that we obtain a

change 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, opportunistic

and predictive maintenance strategies can all be considered

to be the best solution, depending on which weighting

criteria are used. It can, therefore, be seen that the sensitivity

analysis shows how, in the case study under examination,

the maintenance staff should concentrate their attention on

the pairwise evaluation of the four criteria. It is clear that a

few changes in the judgement evaluations can lead to modi-

fications in the final priority ranking. But it must also be

remembered that the importance of corrective maintenancenever goes away. In other words, we have three different

strategies, all of which can provide the best solution, and the

differences among them are never of great importance.

Summarising the considerations discussed above, one can

affirm that the AHP selections presented in Section 7, which

concern the best possible maintenance strategies for the

IGCC machines, can be accepted with a good degree of

confidence by the company maintenance staff.

We conclude this section with two final considerations.

First, the sensitivity analysis proposed here is only relevant

to the priorities of the four first-level criteria. Second,

because we have changed each attribute weight one at a

time, only the main effects have been considered. Inother words, interaction effects of the changes made to

two 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 the

priorities at the highest level of the hierarchy;

2. the introduction of the interaction effects makes the

sensitivity analysis too complex for actual applications.

Nevertheless, one should note that the main effects are

generally the most important aspects in a sensitivity

analysis.



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Table 6

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

Criteria Decreasing Relative priority value

in final solution (%)

Increasing

Damages Predictive maintenance is always the best

strategy

56.6 Predictive maintenance is always the best

strategy

Applicability Predictive maintenance is always the beststrategy

36.8 Predictive maintenance strategy is reachedand overcame by condition-based

maintenance but only when applicability

priority is equal to 96%.

Added-value Predictive maintenance is always the best

strategy


strategy

Cost Predictive maintenance is always the best

strategy


strategy

Table 7




Increasing

Damages Opportunistic maintenance is always the

best strategy

26.2 Opportunistic maintenance strategy is

reached and overcame by predictive

maintenance when damages priority is

equal to 34.2%.

Applicability Opportunistic maintenance strategy is


maintenance just when applicability

priority is reduced to 50.5%.

56.5 Opportunistic maintenance is always the

best strategy

Added-value Opportunistic maintenance is always the

best strategy

5.5 Opportunistic maintenance strategy is


maintenance when added-value priority is

equal to 11.5%.

Cost Opportunistic maintenance is always the

best strategy

11.8 Opportunistic maintenance strategy is just


maintenance when cost priority is equal

to 19.8%.

Table 8




Increasing

Damages Corrective maintenance is always the best

strategy

15.1 Corrective maintenance is reached and

overcame by opportunistic maintenance just

when damages priority is became to

17.1%. Predictive is the new best strategy

when priority is equal to 53.1%

Applicability Corrective maintenance is reached and

overcame by opportunistic maintenance justwhen applicability priority is reduced to


when priority is reduced to 47.5%

63.5 Corrective maintenance is always the best

strategy

Added-value Corrective maintenance is always the best

strategy


overcame by opportunistic maintenance




Cost Corrective maintenance is always the best

strategy


overcame by opportunistic maintenance





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Acknowledgements

We would like to thank the referees for their constructive

comments that enabled us to improve the quality of this

paper.

Appendix A

An example of evaluation of a factor of Eq. (1) is reported

below. The machine importance for the continuity of the

IGCC plant operations is evaluated through the analysis of

the Process Flow Diagrams (PFDs) and Piping and Instru-

mentation Diagrams (P&IDs) of the plant, taking into

account the availability of spare machines.

The scores are assigned in accordance with Table A1,

which is drawn up with the oil refinery maintenance staff

and process engineers.

Low impact equipment are those items characterised by

minor functions or which operate in circuits where it is

possible to provide the products with intermediate storage.

In the latter case, the buffer can provide the plant with the

necessary operating autonomy so that maintenance can be

performed without a plant shut-down. Machines with highor very high impact on the process have also been assigned a

high score for the availability of a spare part to take into

account 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 Engineering

1995;1(1):3945.

[2] Triantaphyllou E, Kovalerchuk B, Mann L, Knapp GM. Determining

the most important criteria in maintenance decision making. Journal

of Quality in Maintenance Engineering 1997;3(1):1624.

[3] Rausand M. Reliability centered maintenance. Reliability Engineer-

ing and System Safety 1998;60:12132.

[4] Crocker J, Kumar UD. Age-related maintenance versus reliability

centered maintenance: a case study on aero-engines. Reliability Engi-neering and System Safety 2000;67:1138.

[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 IGCC

da 280 MW di Api Energia in Italia. Lenergia elettrica

1997;74(2):1724.

[7] Countinho JS. Failure-effect analysis. Transactions NY Academic

Science 1964;26(II):56484.

[8] Holmberg K, Folkeson A. Operational reliability and systematic

maintenance. London: Elsevier, 1991.

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

1980.

[10] Saaty TL. Decision making for leaders. Belmont, CA: Lifetime

Learning Publications, 1982.

[11] Saaty TL. How to make a decision: the analytic hierarchy process.

European Journal of Operational Research 1990;48:926.

[12] Vargas LG. An overview of the analytic hierarchy process and its

applications. European Journal of Operational Research 1990;48:28.

[13] Madu CN, Kuci C-H. Optimum information technology for socio-

economic development. Information Management and Computer

Security 1994;2(1):411.

[14] Belton V, Gear T. On a short-coming of Saatys method of analytic

hierarchies. Omega 1983;11(3):22830.

[15] Dyer JS. Remarks on the analytic hierarchy process. Management

Science 1990;36(3):25968.

[16] Saaty TL. An exposition of the AHP in reply to the paper: remarks on

the analytic hierarchy process. Management Science

1990;36(3):25968.[17] Sage AP. Interpretive structural modelling: methodology for lar. New

York: McGraw-Hill, 1977.

[18] Mandal A, Deshmukh SG. Vendor selection using interpretive struc-

tural modelling (IMS). International Journal of Operations and

Production Management 1993;14(6):5259.

[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;27:7585.


Table A1

Scores for the evaluation of machine importance for the process factor

Impact on the process With spare machine Without spare machine

Low 10 50

Medium 30 70

High 70 90

Very high 80 100

how to choose between maintenance policies 2

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