Lcc book chapter_6_parra_asset_management

81 Draft Versión # 3. Carlos A. Parra M. Email: [email protected] www.confiabilidadoperacional.com

1. Introduction

In this chapter, in the first part, we illustrate a process (Section 2) for built and in-use assets

maintenance management and to characterize maintenance engineering techniques within that

process. This has become a research topic and a fundamental question to reach the effective-

ness and efficiency of maintenance management and to fulfill enterprise objectives [15]. We

Asset Management. The state of the Art in Europe from a Life Cycle Perspective

Van der Lei, Telli; Herder, Paulien; Wijnia, Ype (Eds.)

2012, 2012, XIV, 172 p.

ISBN 978-94-007-2723-6

Chapter 6

Life Cicle Cost Analysis (LCCA) consideration

within the built and in-use assets maintenance

management.

A. Crespo Márquez, C. Parra Márquez**, J.F. Gómez Fernández,

M. López Campos & V. González Díaz

Dept. Industrial Management. University of Seville

School of Engineering, University of Seville, Spain

**Email: [email protected]

Abstract

The chapter presents a generic model for assets maintenance management. This model inte-

grates other models found in the literature for built and in-use assets, and consists of se-

quential management building blocks. More precisely we want to show the reader the im-

portance of selecting an appropriate method when considering the estimation of the non-

reliability cost of an asset. By doing so, we show the impact of maintenance in life cycle

costing and provide arguments to claim about the needs for proper assets maintenance con-

trol.

mailto:[email protected]

http://www.confiabilidadoperacional.com/


82

review a model/process proposed in this chapter tries somehow to integrate other models

found in the literature (see for instance [6,7]) and presents a total of eight sequential manage-

ment building blocks. Each block, as will be discussed, is a key decision area for asset

maintenance and life cycle management.

In the second part of the chapter (Section 3), among referred decision areas and according

to the editorial team of this project, we have selected to explore methods and models that may

be used to do a suitable asset life cycle cost analysis. More precisely we want to show the

reader the importance of selecting an appropriate method when considering the estimation of

the non reliability cost of an asset. By doing so, we somehow show the impact of maintenance

in life cycle costing and provide arguments to claim about the needs for proper assets mainte-

nance control.

2. Characterizing the Maintenance Management Process The maintenance management process can be divided into two parts: the definition of the

strategy, and the strategy implementation. The first part, conditions the success of mainte-

nance in an organization, determines the effectiveness of maintenance. Maintenance effec-

tiveness allows the minimization of the maintenance indirect costs [3] associated with produc-

tion losses and customer dissatisfaction [4], reduces the overall company cost, obtained

because production capacity is available when needed [5].

The second part of the process, the implementation of the strategy will allow us to mini-

mize the maintenance direct cost (labour and other maintenance required resources). Efficien-

cy is acting or producing with minimum waste, expense, or unnecessary effort.

Phase 1:

Definition of the

maintenance

objectives and

KPI’s

Phase 2:

Assets priority

and maintenance

strategy definition

Phase 3:

Immediate

intervention

on high impact

weak points

Phase 4:

Design of

the preventive

maintenance

plans and

resources

Phase 5:

Preventive plan,

schedule

and resources

optimization

Phase 7:

Asset life cycle

analysis

and replacement

optimization

Phase 6:

Maintenance

execution

assessment

and control

Phase 8:

Continuous

Improvement

and new

techniques

utilization

Assessment Efficiency

Effectiveness

Improvement

Figure 1. Maintenance management model (Adapted from [2])

Our model for maintenance management consists of eight sequential management building

blocks, as presented in Figure 1. At the same time, our idea is that there are maintenance engi-

neering tools that may be used to improve each building block decision making process (see

Figure 2).


Phase 1:

Definition of the

maintenance

objectives and

KPI’s

Phase 2:

Assets priority

and maintenance

strategy definition

Phase 3:

Immediate

intervention

on high impact

weak points

Phase 4:

Design of

the preventive

maintenance

plans and

resources

Phase 5:

Preventive plan,

schedule

and resources

optimization

Phase 7:

Asset life cycle

analysis

and replacement

optimization

Phase 6:

Maintenance

execution

assessment

and control

Phase 8:

Continuous

Improvement

and new

techniques

utilization

Assessment Efficiency

Effectiveness

Improvement

Phase 1:

Balance

Score Card

(BSC)

Phase 2:

Criticality

Analysis

(CA)

Phase 3:

Failure Root

Cause Analysis

(FRCA)

Phase 4:

Reliability-

Centred

Maintenance

(RCM)

Phase 5:

Risk―Cost

Optimization

(RCO)

Phase 7:

Life Cycle

Cost Analysis

(LCCA)

Phase 6:

Reliability

Analysis (RA)

& Critical Path

Method

(CPM)

Phase 8:

Total Productive

Maintenance

(TPM),

e-maintenance

Figure 2. Sample of techniques within the maintenance management framework (Adapted from [2])

Phase 1 tries to avoid that the maintenance objectives and strategy could be inconsistent

with the declared overall business strategy [8]. This can indeed be done by introducing the

Balanced Scorecard (BSC) [9]. The BSC is specific for the organization for which it is devel-

oped and allows the creation of key performance indicators (KPIs) for measuring maintenance

management performance which are aligned to the organization’s strategic objectives (See

Figure 3).

Maintenance

planning

and scheduling

Quality Learning

Maintenance

Cost Effectiveness

Maintenance cost (%)

per unit produced (7%)

Data integrity

(95%)

Accomplishment

of criticality analysis

(Every 6 months)

PM

Compliance

(98%)

Maintenance

planning

and scheduling

Quality Learning

Maintenance

Cost Effectiveness

Maintenance cost (%)

per unit produced (7%)

Data integrity

(95%)

Accomplishment

of criticality analysis

(Every 6 months)

PM

Compliance

(98%)

Figure 3. A KPI and its functional indicators (Adapted from [2])

Unlike conventional measures which are control oriented, the Balanced Scorecard puts

overall strategy and vision at the centre and emphasizes on achieving performance targets

[10].

Once the Maintenance Objectives and Strategy are defined, there are a large number of

quantitative and qualitative techniques which attempt to provide a systematic basis for

deciding what assets should have priority within a maintenance management process (Phase



84

2). Most of the quantitative techniques use a variation of a concept known as the

“probability/risk number” (PRN) [11]. In professional risk assessments, risk combines the

probability of an event occurring with the impact that event would cause R=PxC, where P is

probability and C is consequence (Figure 4). Risk assessment techniques can be used to

prioritize assets and to align maintenance actions to business targets at any time.

3

CriticalCritical

SemiSemi--criticalcritical

NonNon--criticalcritical

10 20 30 40 5010 20 30 40 50

ConsequenceConsequence

44

33

22

11

FFrreeqquueennccyy

1 2 1 3

4 2

3

CriticalCritical

SemiSemi--criticalcritical

NonNon--criticalcritical

10 20 30 40 5010 20 30 40 50

ConsequenceConsequence

44

33

22

11

FFrreeqquueennccyy

1 2 1 3

4 2

Figure 4. Generic criticality matrix and assets location

As mentioned above, once there is a certain ranking of assets priority, we have to set up

the strategy to follow with each category of assets. Of course, this strategy will be adjusted

over time, and will consist of a course of action to address specific issues for the emerging

critical items under the new business conditions (see Figure 5).

A

B

C

Ass

et c

ate

gory

Main

tenance

stra

tegy

Sustain – improve

current situation

Ensure certain

equipment availability

levels

Reach optimal reliability,

maintainability and

availability levelsA

B

C

Main

tenance

stra

tegy

Figure 5. Example of maintenance strategy definition for different category assets [2]

An example of detailed maintenance actions for category A assets — where we try to

reach optimal reliability, maintainability and availability levels — could be: 1) Apply FMECA

for critical failure mode analysis; 2) Apply RCM for optimal maintenance task selection; 3)

Standardise maintenance tasks; 4) Analyse design weaknesses and 5) Continue review

FMECA and RCM.

Phase 3 deals with finding and eliminating, if possible, the causes of certain repetitive fail-

ures that take place in high priority items. There are different methods developed to carry out

this weak point analysis, one of the most well known being root-cause failure analysis

(RCFA). This method consists of a series of actions taken to find out why a particular failure

or problem exists and to correct those causes.


Phase 4 is devoted to the design of the preventive maintenance plan for a certain system

and this requires identifying its functions, the way these functions may fail and then establish

a set of applicable and effective preventive maintenance tasks, based on considerations of sys-

tem safety and economy. A formal method to do this is the Reliability Centred Maintenance

(RCM), as in Figure 6.

Figure 6. RCM implementation process

Optimization of maintenance planning and scheduling (Phase 5) can be carried out to en-

hance the effectiveness and efficiency of the maintenance policies resulting from an initial

preventive maintenance plan and program design. Models to optimize maintenance plan and

schedules will vary depending on the time horizon of the analysis [13].

Phase 6 deals with the execution of the maintenance activities ― once designed planned

and scheduled using techniques described for previous building blocks —. This execution has

to be evaluated and deviations controlled to continuously pursue business targets and ap-

proach stretch values for key maintenance performance indicators as selected by the organiza-

tion.

A life cycle cost analysis (Phase 7) calculates the cost of an asset for its entire life span (see

Figure 7). The analysis of a typical asset could include costs for planning, research and devel-

opment, production, operation, maintenance and disposal. A life cycle cost analysis is im-

portant when making decisions about capital equipment (replacement or new acquisition)

[12], it reinforces the importance of locked in costs, such as R&D, and it offers important

benefits. We concentrate on techniques for LCCA in Section 3 of this Chapter.

RCM

team

conformation

Application of

the RCM

logic

Operational

context

definition

and asset

selection

FunctionFunctional

failuresFailure modes

Effect of

failure modes

FMEA

Failure Mode and

Effects Analysis

Tool to answer the first 5

RCM Questions

RCM

Implementation phase

Initial

Phase

Criticality

Analysis

(level?)

Tool to answer

the last 2

RCM Questions

Maintenance

plan

documentation

Final

Phase



86

Corrective Maintenance + Security, Environment, Production =

Non Non ReliabilityReliability CostsCosts = = RiskRisk

Operation + Planned Maintenance Costs.

CAPEX

Capital Costs

OPEX

Operational Costs

Development

costs

Investment

costs

Operation

costs

Time (years)

Acquisition

Construction

Design

Investigation

Remove

Corrective Maintenance + Security, Environment, Production =

Non Non ReliabilityReliability CostsCosts = = RiskRisk

Operation + Planned Maintenance Costs.

CAPEX

Capital Costs

OPEX

Operational Costs

Development

costs

Investment

costs

Operation

costs

Time (years)

Acquisition

Construction

Design

Investigation

Remove

Figure 7. Life cycle cost analysis

Finally, continuous improvement of maintenance management (Phase 8) will be possible

due to the utilization of emerging techniques and technologies in areas that are considered to

be of higher impact as a result of the previous steps of our management process. Regarding

the application of new technologies to maintenance, the “e-maintenance” concept (Figure 8) is

put forward as a component of the e-manufacturing concept [14], which profits from the

emerging information and communication technologies to implement a cooperative and dis-

tributed multi-user environment. E-Maintenance can be defined [10] as a maintenance support

which includes the resources, services and management necessary to enable proactive decision

process execution.

Top Management

Middle Management

Maintenance Dept

Assets /

Information Source

Reports

Reports

Inspections/Complaints

Conventional Maintenance

Top Management

Middle Management

Maintenance Dept

Assets /

Information Source

E-maintenance

Login to

iScada

Precise &

Concise

Information

Top Management

Middle Management

Maintenance Dept

Assets /

Information Source

Reports

Reports

Inspections/Complaints

Conventional Maintenance

Top Management

Middle Management

Maintenance Dept

Assets /

Information Source

Login to

iScada

Precise &

Concise

Information

Figure 8. Implementing e-maintenance (http://www.devicesworld.net)

3. Evaluating the economic impact of the failure in the LCCA Life cycle costing is a well-established methodology that takes into account all costs arising

during the life cycle of the asset. These costs can be classified as the ‘capital expenditure’

(CAPEX) incurred when the asset is purchased and the ‘operating expenditure’ (OPEX) in-

curred throughout the asset’s life. LCCA is a method that can be used, for instance, to evalu-

ate alternative asset options [1], or/and assets maintenance management strategies [2].

For all these potential purposes, a key aspect to introduce in a LCCA is the failure costs. In

order to model that cost we will now introduce the Non-homogeneous Poisson Process model

(NHPP, repairable systems). With the NHPP model we can estimate the frequency of failures

and the impact that could cause the diverse failures in the total cost of ownership of a produc-


tion asset. This section also contains a case of study to illustrate the above mentioned con-

cepts.

3.1. Characterizing the total costs of failures (non reliability) Life cycle cost analysis (LCCA) can be defined [14] as a systematic process of technical-

economical evaluation that considers, in a simultaneous way, economic and reliability aspects

of an asset, quantifying their real impact along its life cycle cost. Reliability is related to op-

erational continuity. We normally say that a production system is "reliable" when it is able to

accomplish its function in a secure and efficient way along its life cycle. Low reliability caus-

es normally high costs, mainly associated to the asset function recovery (direct costs) besides

the corresponding escalated impact in the production process (penalization costs). The totals

costs of non reliability can be then classified as follows ([20], [21] and [22]):

Costs for penalization: Downtime, opportunity losses/deferred production, production

losses (unavailability), operational losses, impact in the quality, impact in security and en-

vironment.

Costs for corrective maintenance: Manpower, direct costs related with the manpower

(own or hired) in the event of a non planned action; and materials and replacement

parts, direct costs related with the consumable parts and the replacements used in the

event of an unplanned action.

3.2. Using NHPP for Reliability Analysis NHPP is a stochastic discrete process where, in its initial formulation, we assume that the

equipment is “as bad as old” (ABAO) operating condition after a repair (this is also referred as

minimal repair in the maintenance modelling literature [24, 25]). In this process the probabil-

ity of occurrence of n failures in any interval [t1, t2] has a Poisson distribution with the mean

[24]:

1

2)(

t

tdtt (1)

Where )(t is the rate of occurrence of failures (ROCOF).

Therefore, according to the Poisson process:

!

)(exp)(

])()(Pr[

2

1

2

1

12n

dttdtt

ntNtN

t

t

nt

t

(2)

Where n = 0, 1, 2,… are the total expected number of failures in the time interval [t1,t2].

Let us represent with )(t the expected number of failures in a time interval [0, t], then

t

dttt0

)()( (3)

One of the most common forms of ROCOF used in reliability analysis of repairable systems

is the Power Law Model ([24] and [25]), that estimates the failure rate as follows: 1

)(

tt (4)

This form comes from the assumption that the inter-arrival times between successive fail-

ures follow a conditional Weibull probability density function, with parameters α and β. The

Weibull distribution is typically used in maintenance area due to its flexibility and applicabil-



88

ity to various failure processes (however, solutions to Gamma and Log-normal distributions

are also possible). As we know by reliability theory, λ(t) is a conditional probability for which

we can consider the following definition (see Figure 10):

Figure 10. Conditional probability of occurrence of failure [26]

)1

(

)(1

)1

(

)(1)(1

)1

(

)1

()()

1(

tR

tR

tR

tRtR

tR

tFtFtTtTP

(5)

where F(t) and R(t) are the probability of failure and the reliability at the respective times.

Assuming a Weibull distribution, Eq. (5) yields:

i

ti

t

itF 1exp1)( (6)

Therefore, the conditional Weibull density function is:

it

it

it

itf 1exp.

1

)( (7)

Now we will use this function in order to obtain the maximum likelihood (ML) estimators

of the parameters of the Power Law model. For the case of the NHPP, different expressions

for the likelihood function may be obtained. We will use expression based on estimations at a

time t after the occurrence of the last failure and before the occurrence of the next failure, see

details on these expressions in [27].


3.2.1. Time terminated NHPP maximum likelihood estimators In the case of time terminated repairable components, the maximum likelihood function L

can be expressed as:

)

2

()()1

(

1

)( tn

tn

i

Ri

tftfn

ii

tfL

(8)

Therefore:

1exp

11

ttL

n

i

iin

i

nttt

2

1

2

1

1

1

exp

(9)

Then the ML estimators for the parameters are calculated. The results are ([24] and [25]):

1

ˆ

n

n

t

(10)

it

ntn

i

n

1

ln

(11)

Where ti is the time at which the ith failure occurs, tn is the total time where the last fail-

ure occurred, and n is the total number of failures. The total expected number of failures in the

time interval [tn, tn+s] by the Weibull cumulative intensity function is [27]:

nt

st

nt

snt

nt

1),( (12)

Where t s is the time after the last failure occurred in the one which needs to be considered

the number of failures and tn is:

n

ii

tn

t

1

(13)

3.3. A NHPP MODEL FOR FAILURE COST ASSESSMENT IN LCCA Our previous NHPP model structure can be used for the quantification of the costs of fail-

ures in the LCCA (cost of non reliability [28]). With this model we propose to assess the im-

pact of main failures on a production system LCC structure by following the next procedure:

1. Identify for each alternative to evaluate the main types of failures. This way for certain

equipment there will be f = 1… F types of failures.

2. Determine for the n (total of failures), the times to failures ft . This information will be

gathered by the designer based on records of failures, databases and/or experience of

maintenance and operations personnel.



90

3. Calculate the Costs for failures fC ($/failure). These costs include: costs of penalization

for production loss and operational impact Cp ($/hour), costs of maintenance corrective

Cc ($/hour) and the mean time to repair MTTR (hours). The expression used to estimate

the fC is shown next:

MTTRCcCpC f )( (14)

4. Define the expected frequency of failures per year ),( snn tt . This frequency is assumed

as a constant value per year for the expected cycle of useful life. The ),( sntnt is calcu-

lated starting from the expression (12). This process is carried out starting from the times

to failures registered ft by failure type (step 2). The parameters and , are set starting

from the following expressions (10) and (11). In the expression (12), st it will be a year (1

year) or equivalent units (8760 hours, 365 days, 12 months, etc.). This time st represents

the value for estimate de frequency of failures per year.

5. Calculate the total costs per failures per year fTCP , generated by the different events of

stops in the production, operations, environment and security, with the following expres-

sion:

f

CF

fsn

tn

tf

TCP

, (15)

The obtained equivalent annual total cost, represents the probable value of money that

will be needed every year to pay the problems of reliability caused by the event of failure,

during the years of expected useful life.

6. Calculate the total costs per failures in present value f

PTCP . Given a yearly value

fTCP , the quantity of money in the present (today) that needs to be saved, to be able to

pay this annuity for the expected number of years of useful life (T), for a discount rate (i).

The expression used to estimate the fPTCP is shown next:

Tii

Ti

fTCP

fPTCP

1

11 (16)

Once this cost is estimated, it is added to the rest of the evaluated costs (investment,

planned maintenance, operations, etc.). Finally, the total cost is calculated in present value for

the selected discount rate and the expected years of useful life. Different results can be ob-

tained, for instance for different assets options or/and maintenance strategy options.

3.4. CASE STUDY The following case study proposes the evaluation of the economic impact of the failures us-

ing the method NHPP. The analysis was developed for the oil company PETRONOX (con-

tractor of Petróleos of Venezuela), located in the field of gas and petroleum Naricual II, in

Monagas, Venezuela. In general terms, it is requires to install a compression system to man-

age a flow average of 20 millions of cubic feet of gas per day. The organization PETRONOX,

evaluates the information of two suppliers of compressors. Next, are shown the data of costs

of: initial investment, operation and maintenance for the two options to evaluate (value esti-

mated by the suppliers, see Table 1):


Option A:

Reciprocant Compressor, 2900-3200 hp, caudal: 20 millions of feet cubic per day

Option B:

Reciprocant Compressor, 2810-3130 hp, caudal: 20 millions of feet cubic per day

Data Option A Option B

I: Investment 1.100.000 $ 900.000 $

OPC: opera-

tionals costs

100.000 $/year 120.000 $/year

PRC: preven-

tive costs

60.000 $/year 40.000 $/year

OVC: overhauls

costs

100.000 $ every 5

years

80.000 $ every 5

years

i: interest 10% 10%

T: expected

useful life

15 years 15 years

Table 1. Economical data

With this information the organization PETRONOX carried out a first economic LCCA and

a comparison made among the two alternatives, in this first evaluation, no failure cost analysis

was considered and results are presented in Table 2:

Results Option A Option B

1) I: Invesment 1.100.000 $ 900.000 $

2) OPC(P): operation-

als costs in present

value

760.607,951 $ 912.729,541 $

3) PRC(P): preventive

costs in present value

456.364,77 $ 304.243,18 $

4) OVC(P): overhauls

costs in present value,

t = 5 years

62.092, 1323 $ 49.673,7058 $



t = 10 years

38.554,3289 $ 30.843,4632 $



t = 15 years

23.939,2049 $ 19.151,3639 $

TLCC(P): Total Life

Cycle Costs in pre-

sent value, i: 10%, T:

15 years (Sum 1…6)

2.441.558,387 $ 2.216.641,254 $

Table 2. Economical results without to evaluate the costs per failures

In Table 2, the oil company doesn't consider the possible costs of failures events. The op-

tion B results to be the best economic alternative (more economic alternative for a lifespan pe-



92

riod of 15 years). There is a difference of approximately: 224.917,133 $ between the two al-

ternatives (this quantity would be the potential saving to select the option B, without consider-

ing the possible costs for failures).

Later on, a proposal consisting on the evaluation of the same figures taking into considera-

tion now the failure costs was made to the organization. It was suggested using a NHPP model

for this evaluation, the total expected number of failures the interval of time [tn, tn+s] is es-

timated by the NHPP stochastic model (Weibull cumulative intensity function) [27]. Next, are

shown the data of costs and times of failures to be used inside the NHPP model (the data of

times to failures f

t were gathered by PETRONOX of two similar compression systems that

operate under very similar conditions in those that will work the compressor to be selected):

Data Option A Option B

Cp ($/hour) 6.000 6.000

Cc ($/hour) 700 400

MTTR (hours) 9 8

ft (months) 5, 7, 3, 7, 2, 4, 3, 5,

8, 9, 2, 4, 6, 3, 4, 2,

4, 3, 8, 9

2, 3, 3, 5, 6, 6, 5,

6, 5, 6, 4, 3, 2, 2,

2, 2, 3, 2, 2, 3, 2,

2, 3, 3

nt (total of

months)

98

82

n (total of fail-

ures)

20 24

Table 3. Failure costs and maintainability/reliability data

With the information of the Table 3, the equation (16) was used to calculate the frequency

of failures per year ),( snn tt . The parameters and of the Distribution of Weibull con-

tained in the equation (16) were calculated from the equations (14) and (15). The total costs

for failures per year f

TCP were calculated from the equations (18) and (19); these costs are

converted to present value f

PTCP with the equation (20). Next, are shown the results of the

frequency of failures and the total costs for failures for year obtained starting from the NHPP

model, for the two evaluated options:



6,97832 6,13985

1,13382 1,22614

),(sn

tn

t

= fail-

ures/year

2,7987 = 2,8 4,3751=4,38

fTCP = $/year 168.840 224.256

fPTCP = $

(i=10%, T=15 years)

1.284.210,46

1.705.708,97

Table 4. Results from NHPP model

Later on, a second LCC economic evaluation was carried out including the results of costs

of failures obtained from the NHPP model. The results are presented in Table 5:


1) I: Invesment 1.100.000 $ 900.000 $

2) OPC(P): operation-

als costs in present val-

ue

760.607,951 $ 912.729,541 $

3) PRC(P): preventives

costs in present value

456.364,77 $ 304.243,18 $



t = 5 years

62.092, 1323 $ 49.673,7058 $



t = 10 years

38.554,3289 $ 30.843,4632 $



t = 15 years

23.939,2049 $ 19.151,3639 $

7)PTCPf: total costs

per failures in present

value

1.284.210,46 $ 1.705.708,97 $

TLCC(P): Total Life

Cycle Costs in present

value, i: 10%, T: 15

years (Sum 1…7)

3.725.768,851 $ 3.922.350,22 $

PTCPf / TLCC(P) = %

(total costs per failures

/ total life cycle costs)

34,46% 43.48%

Table 5. Economical results with the costs per failures



94

In the results of this second evaluation (see Table 5), the total costs for failures are included

in present value PTCPf. Notice that now Option A turns out to be the best economic alterna-

tive, with a difference of approximately: 196.581,368 $ (this quantity would be the potential

saving if selecting the option A instead of B). An important aspect to be considered in this

analysis, is that PTCPf category of cost turns out to be the highest economic factor, with

more weight, inside the process of the two alternatives comparison. Specifically, this category

of costs represents the 43,48% (Option B) and the 34,46% (Option A) of the total LCC of

these two assets (with an interest rate of 10% and a prospective cycle of life of 15 years).

Finally, as per previous results discussion, PETRONOX decided to consider failures cost

analysis in their LCCA. Additionally, the organization PETRONOX decided to develop an in-

ternal procedure allowing the evaluation of reliability opportunity cost, this procedure would

be used in a continuous and obligatory basis every time different options are analyzed inside

the processes of: design, selection, substitution and/or purchase of assets.

3.4.1. Limitations of the NHPP proposed model The analysis of the failure is an important facet in the development of maintenance strategy

in the life cycle cost analysis of the asset. Only by properly understanding the mechanism of

failure, through the modeling of failure data, can a proper maintenance plan and an analysis of

costs be developed [47]. This is normally done by means of probabilistic analysis of the fail-

ure data. From this, conclusions can be reached regarding the effectiveness and efficiency of

preventive replacement (and overhaul) as well as that of predictive maintenance. The optimal

frequency of maintenance can also be established by using well developed optimization mod-

els. These optimize outputs, such as profit, cost and availability. The problem with this ap-

proach is that it assumes that all repairable systems are repaired to the ‘good-as-new’ condi-

tion at each repair occasion. Maintenance practice has learnt, however, that in many cases

equipment slowly degrades even while being properly maintained (including part replacement

and periodic overhaul). The result of this is that failure data sets often display degradation.

This renders conventional probabilistic analysis useless.

The NHPP model has proved to provide good results even for realistic situations with bet-

ter-than-old but worse-than-new repairs [29]. Based on this, and given its conservative nature

and manageable mathematical expressions, the NHPP was selected for this particular work.

The NHPP models can be considered as simple curve-fitting approach that can be easily un-

derstood and implemented by software engineers and developers [47]. It is also this type of

models that have been used by practitioners in most cases. On the other hand, without an in-

depth understanding, the models and analysis are more likely to be misused and further analy-

sis, which could been possible are not carried out. There is a need for more in depth study of

NHPP model and their effectiveness in predicting future failure behaviour. Most of current re-

search focuses on developing more complex models, see other models found in the literature

([48] and [49]). However more research is needed with regard to model selection. When com-

paring models, the focus should be on the prediction rather than fitting as a model can fit the

past data correctly, but has a poor predictive ability. Knafl and Morgan [50] provides some in-

itial discussion on this area.

The model described above has advantages and limitations. In general, the more realistic is

the model, the more complex are the mathematical expression involved. The main strengths

and weakness of this model are summarized next:

Strengths:

It is a useful and quite simple model to represent equipment under aging (deterioration).


Involves relatively simple mathematical expressions.

It is a conservative approach and in most cases provides results very similar to those of

more complex models like Generalized Renewal Process [29].

Weakness:

Is not adequate to simulate repair actions that restore the unit to conditions better than new

or worse than old.

4. Conclusions

The orientation of this chapter is towards maintenance management models, and within

them, to the presentation of techniques to consider LCCA within the process (Phase) of assets

maintenance assessment, control and improvement.

We have shown how the reliability factor and its impact on costs can be critical for LCCA

and may influence in final results produced with this analysis for assets options and/or for

maintenance management strategy alternatives.

Prevision of unexpected failure events and their cost is crucial for correct decision making

and profitability of production process. Improvements of process reliability (quality of the de-

sign, used technology, technical complexity, frequency of failures, costs of preven-

tive/corrective maintenance, maintainability levels and accessibility) may have a great impact

on the total cost of the life cycle of the asset, and on the possible expectations to extend the

useful life of the assets to reasonable costs.

5. Future trends We believe that, within LCCA techniques, there is a potential area of research related to the

optimization of the reliability impact evaluation techniques on LCC. Some interesting trends

that we have identified are as follows:

Stochastic methods see ([30], [31] and [32]). Table 6 shows the stochastic processes used

in reliability investigations of repairable systems, with their possibilities and limits [27].

Advanced maintenance optimization using genetic algorithms see ([33] and [34]).

Monte Carlo simulation techniques see ([35], [36] and [37]).

Advanced Reliability distribution analysis see ([38], [39], [40], [41] and [42]).

Markov simulation methods see ([43], [44], [45] and [46]).

Reliability methods in phase of design see ([51] and [52]).



96

Stochastic pro-

cess

Can be used Back-

ground/

Difficulty

Renewal process

Alternating

renewal process

Markov process

(MP)

Semi-Markov

process (SMP)

Semi-Regenerative

process

Nonregenerative

process

Spare parts provisioning

in the case of arbitrary

failure rates and negligi-

ble replacement or repair

time (Poisson process)

One-item repairable (re-

newable) structure with

arbitrary failure and re-

pair rates

Systems of arbitrary

structure whose elements

have constant failure and

repair rates during the

stay time (sojourn time)

in every state (not neces-

sarily at a state change,

e.g. because of load shar-

ing)

Some systems whose el-

ements have constant or

Erlangian failure rates

(Erlang distributed fail-

ure-free times) and arbi-

trary repair rates

Systems with only one

repair crew, arbitrary

structure, and whose el-

ements have constant

failure rates and arbitrary

repair rates

Systems of arbitrary

structure whose elements

have arbitrary failure and

repair rates

Renewal the-

ory/

Medium

Renewal the-

ory/

Medium

Differential

equations

or integral

equations/

Low

Integral

equations/

Medium

Integral

equations/

High

Partial diff.

eq.; case by

base sol./

High to very

high

Table 6. Stochastic processes used in reliability analysis of repairable systems

Finally, it is not feasible to develop a unique LCCA model, which suits all the require-

ments. However, it is possible to develop more elaborate models to address specific needs

such as a reliability cost-effective asset development.

6. Acknowledgements This research is funded by the Spanish Department of Science and Innovation project

DPI2008-01012 (Modelling e-maintenance policies for the improvement of production sys-

tems dependability and eco-efficiency).


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Corresponding authors

A. Crespo Márquez can be contacted at: [email protected] C. Parra Márquez can be contacted at: [email protected]



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