research article inclusion of management desirability and...

Research ArticleInclusion of Management Desirability and Risk in ServiceFMEA-Based Corrective Action Selection Methodology

Agung Sutrisno,1 Tzong Ru (Jiun Shen) Lee,2 and Hyuck Moo Kwon3

1 Department of Mechanical Engineering, Sam Ratulangi University, Manado 95115, Indonesia2 Department of Marketing, National Chung Hsing University, Taichung 40227, Taiwan3Department of Systems Management and Engineering, Pukyong National University,Busan 608-737, Republic of Korea

Correspondence should be addressed to Agung Sutrisno; [email protected]

Received 29 August 2013; Accepted 3 December 2013; Published 28 January 2014

Academic Editor: Somasundaram Kumanan

Copyright © 2014 Agung Sutrisno et al.This is an open access article distributed under the Creative CommonsAttribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

When selecting competing corrective actions on FMEA, decision makers may have a different desirability to target a certainimprovement goal. Besides, once the corrective actions have been implemented, there may exist a considerable degree of riskdue to the possible uncertainties of the outcome. This paper attempts to present FMEA-based corrective action prioritization,incorporating both management desirability and implementation relevant risk into the model. To reflect team desirability andrisk attitude in estimating the attractiveness of corrective actions, the Derringer desirability and risk aversion factor are used. Anillustrative example is provided to demonstrate the applicability of the proposed model.

1. Introduction

The service FMEA can be effectively used as means for ana-lyzing the risk due to unfavorable business situation. Fol-lowing Seyedhoseini and Hatefi [1], within risk managementframework, corrective action provision is as important as riskquantification. Nevertheless, previous references on improv-ing capability of FMEA in service operation such as [2–6] do not address the issue. Furthermore, improvement ofFMEA-based corrective action methodologies such as [7–12]seems to be still applied to nonservice sectors. Moreover, asGroso et al. [13] stated, it is also very important to considerpossibility of risk occurrence during improvement efforts.But the previous studies ignore the degree of uncertainty onoutcome of strategy implementation, withoutmentioning theimportance on articulation of management desirability.

This paper presents a conceptual model on rankingcorrective action priority, which considers the uncertainty ofimprovement effort and the strategic function of managingrisk in service quality improvement efforts. The model isformulated so that the preference score can be determined onthe basis of team desirability, efforts, impacts, and relevantrisk for each competing corrective action.

The remaining part of the paper is presented according tothe following sections. In Section 2, the desirability functionand other key concepts are presented. Section 3 constructsa new model to estimate attractiveness index of competingcorrective actions based on team desirability, effort, andrisk of implementing corrective action (CA). In Section 4,research methodology and a step by step implementationprocedure are provided with an illustrative example. InSection 5, some discussions are provided with managerialimplications followed by Section 6 conclusion.

2. Desirability Function ofSelecting Corrective Action

In actual application, in attempt to improve, FMEA teamusually has certain desirability as manifestation in selectingcertain corrective action. To articulate such desirability, theuse of various classes of Derringer desirability function canbe utilized to accommodate the situation mentioned above.Along with performance-related desirability, FMEA teamintends to reduce the failure occurrence rate. Nowdenote thatDPCA

𝑖𝑗represents performance-related desirability index of

Hindawi Publishing CorporationJournal of Industrial EngineeringVolume 2014, Article ID 980473, 10 pageshttp://dx.doi.org/10.1155/2014/980473

2 Journal of Industrial Engineering

corrective action 𝑗 for failure mode 𝑖, for 𝑖 = 1, 2, 3, . . . , 𝑘and 𝑗 = 1, 2, . . . , 𝑟

𝑖. And ΔFM

𝑖represents reduction in failure

occurrence rate of failure mode i. Assuming the managementdesirability and reducing the failure occurrence rate are ofthe same importance, and the composite desirability functionCDCA

𝑖𝑗which covers both mentioned above in selecting

specific corrective action is formulated as follows:

CDCA𝑖𝑗= DPCA

𝑖𝑗ΔFM𝑖. (1)

Note that the factor ΔFM𝑖also represents an improve-

ment factor to indicate the effectiveness of a correctiveaction. In attempt to classify various classes of quality-relateddesirability index, references such as [14] can be consulted.

2.1. Quantifying Impact and Effort of Corrective Action. Inattempt to achieve specific goal, FMEA team used resourcesto implement specific CA. From this point of view, allnecessary inputs to accomplish specific improvement activityare called efforts. To simplify the calculation, all aboveeffort variables are usually quantified by monetary metric,the implementation cost. The component of implementingcorrective action costs (IC

𝑖𝑗) and how to estimate them

can be referred to [15, 16]. The impact of an effort canbe defined as the amount of benefit from which a certaincorrective action task will be implemented and representedby usingmonetarymetric such as the net present value (NPV).However, since the outcome of implementing correctiveactions still remains unknown until being implemented,pairwise comparison among some measures of commercialbenefits with the AHP (Analytic Hierarchy Process) can beused. Let WCA

𝑖𝑗represent the weight of estimated benefit of

each corrective action and ICCA𝑖𝑗denote the corresponding

estimated implementing cost; then by deleting assumptionof tangible and intangible benefits, the efficiency of eachcorrective action is formulated as in the following:

AICA𝑖𝑗=𝑊CA𝑖𝑗

ICCA𝑖𝑗

. (2)

Next, since company expects to reach the goal in a limitedimplementing time span, a “lead time” success factors shouldbe considered to appraise competing corrective action. In thisstudy, the lead time of success of corrective action is definedas the estimated time span from initial implementation of acorrective action until an expected benefit can be observed. IfTCA𝑖𝑗represents lead time success factor from implementing

specific CA, then (2) is rewritten as in the following:

AICA𝑖𝑗=𝑊CA𝑖𝑗

ICCA𝑖𝑗TCA𝑖𝑗

. (3)

2.2. Quantifying the Risk of Selecting Corrective Action. Inaddition to considering the impact and effort components,decision makers should also take the risks that may occurin selecting corrective action candidates into consideration[17]. The risk will be considered since uncertainty is anunavoidable factor to implementing strategy [18]. According

to Soderholm andKarim (2010 [19]), the risk in implementingcorrective action may be defined as “the effect of uncertaintyon achieving goal.” The modes of effect might be either thepositive or negative types [20] and their typologies can beconsulted to [21]. In this study, the payoff factors 𝜌CA𝑖𝑗 is usedto represent the surrogate of the benefits or losses gain due tothe existence of risk factor corresponding to CA and it can beestimated as follows:

𝜌CA𝑖𝑗 = P (R)CA+

𝑖𝑗LCA+𝑖𝑗− P (R)CA−

𝑖𝑗LCA−𝑖𝑗. (4)

P(R)CA+/−𝑖𝑗

and LCA+/−𝑖𝑗

represent the probability of riskevent occurrence when selecting a corrective action andrelated estimated economic consequences.

Indeed, determining probability of risk event occurrenceis not easy. Assuming that the explanatory risk variable isavailable, it can be accomplished by using logistic regressionmethodology. However, if it is not available, a dimensionlessordinal scale will become the value of risk impact and occur-rence of risk events. For simplicity in practical application,the use of 1–10 Likert-like scale can be used to represent themagnitude of risk impact and its corresponding occurrencescore. If no historical data available, the basis to determinethe scale can be based on decision makers’ judgment. Theestimated economic losses due to risk event occurrences canbe consulted to [22, 23]. In reverse, somemeasures to estimatethe indication of positive outcomes of expected benefit valuecan be referred to [24]. Next, considering the risk attitude ofFMEA team, the risk aversion factor (RA) should be takeninto consideration and its score can be estimated based on[25].

3. Estimating Preference Score ofCorrective Action

Assuming that each competing CA is timely feasible forimplementation, formulation of a preference score to rankcompeting corrective actions is based on idea that decisionmaker will choose the corrective action with the largestfailure risk, benefit impact, payoff, the least efforts, andshortest lead time success. Therefore, by considering FMEAteam’s desirability, impact, effort, and risk and neglectinginterdependency among CAs, the attractiveness index (AI)as surrogate of decision makers’ preference score in choosingeach competing corrective action can be represented as in thefollowing:

AICA𝑖𝑗=𝑊CA𝑖𝑗RPNFM𝑖CDCA

𝑖𝑗𝜌CA𝑖𝑗

ICCA𝑖𝑗TCA𝑖𝑗RACA

𝑖𝑗

. (5)

The nominator of (5) is the benefit impact componentsof CA; meanwhile the denominator is the effort componentof CA. Larger AICA

𝑖𝑗index represents larger attractiveness

of corresponding CA, and in reverse, lower AICA𝑖𝑗indicated

lower attractiveness of certain CA.

Journal of Industrial Engineering 3

Table 1: Root cause and effect analysis of critical failures from case example (excerpted partly from [28]).

Criticalitypriority Failure mode Effects Possible causes

1Unreliable supply ofgoods/merchandise(RPN = 27.29)

Shortage of goods Poor supplier evaluation and selectionUnreliable supply of goods/merchandise Inappropriate supplier relationship managementLost sales Insufficient inventory of suppliersDecrease customer loyalty Inadequate marketing researchCustomer complaint Lack of upward communicationComplicated job allocation andreplenishment activity Insufficient customer relationship focus

Customer leave Failure to match demand and supply

2Air conditioningmalfunction(RPN = 25.38)

Foods deteriorate Poor electrical power designCustomer complaint Aged air conditioningCustomer leave Fail to adjust the sales floor temperatures

4. Research Methodology

In attempt to demonstrate the applicability of our theo-retical model, a case study is presented. According to Yin(1994 [26]), the typical case study chosen as this studyis aimed at demonstrating application of the new theory,answering the “why” and “how” research questions, andnoticing that the researchers have no control over the objectof study.

4.1. Illustrative Example. In this study, a case study adoptedpartly from [27] which pertains to hypermarket consumers’good selling service is presented as case example. The hypo-thetical goal is to appraise competing CAs by consideringamount of efforts and estimated impact and uncertaintyon implementing CA. Note that all figures used here arehypothetical and are used for illustrative purpose only.

4.2. Implementing Procedure. The implementing procedureto apply our proposed model is described in the followingsections.

4.2.1. Determine Criticality of Service Failure Modes. Refer-ring to Table 1, based on the RPN estimation of partial failuremodes from [27], the first rank of critical failure modes is“Unreliable supply of goods/merchandise” and the secondrank is assigned to “Air conditioning malfunction.” Everyfailure mode has multiple causes and effects. Depending onthe familiarity of FMEA team and deep analysis, variousarrays of Root Cause Analysis as represented by [28] can beused in this stage.

4.2.2. Determine Desirability Index of Potential CorrectiveActions. Upon probable root causes are identifiable, based onbrainstorming, targeted goal and resource capability; desiredperformance specification is then determined. In linewith thegoal in implementing CA, qualitative and quantitative goalsshould exist. For example, effort of “increase supplier fre-quency contact” has qualitative goal to “improve emotionalrelationship with suppliers” and “perform supplier evalua-tion.” This is also proposed to achieve the goal of “reductionin the lead time span of purchased goods/merchandise.”To simplify the calculation, the use of Likert like 1–5 scale

is used to facilitate in quantifying qualitative attributes.The composite desirability index of every potential CA iscalculated between desirability against failure occurrence ratereduction and service performance specification attributes byusing (1). The results are then depicted in Table 2. Amongcompeting CAs, the effort of “Increase frequency of suppliercontacts (CA

12)” and “Fostering inter-personal relationships

with outdoor activities (CA15)” has the largest desirability

index related to the first critical failure mode and “Investingstaff to AC maintenance training (CA

21)” has the largest

desirability related second critical service failure mode.

4.2.3. Estimate the Benefit Score of Potential CorrectiveActions.In attempt to estimate the benefit of specific CA by usingthe AHP, some criteria are used as basis for calculation. Thefirst criterion is the weight of failure attributes from whichservice dimension came from. Then, the second criterion isrelated to the categories of goal to which the CA will betargeted. In this study, it was supposed that the company ofcase example owned its own criteria based on the Likert like1–3–5–7 scoring model. Using such scale as scoring basis,any CAs which aimed to improve sustainability are givenscore 7, product/service quality is scored 5, profitability isscored 3, and 1 is assigned to staff internal growth. Thescore of composite benefit weight is then obtainable bymultiplying the weight of SERVQUAL dimensions and goalof targeted goal as described above. The overall result ofapplying composite scoring model mentioned above is thengiven as in Table 3.

4.2.4. Estimate the Payoff Score of Competing CAs. In attemptto estimate the payoff factor, (4) is used and the scoreon probability of risk event occurrence is supposed to bebased on the FMEA team judgment. The decision treediagram was used to model the potential outcome of riskevents. All theoretical risk factors pertaining to this study aredetermined upon breaking down theCAs and then the resultsare depicted in Table 4.

4.2.5. Estimate Effort Components of Each Corrective Action.Estimation of implementing cost and lead time of suc-cess of a corrective action is based on assumption that


Table2:Desira

bilityindexof

potentialC

Aso

fcasee

xample.

Limit

Root

causeo

ffailure

mod

ePo

tentialcorrectivea

ction

Goal

(benefit)

Current

occurrence

score/targeted

failu

reoccurrence

score

Currentp

erfor-

mance/ta

rgeted

perfo

rmance

Minim

umTarget

Maxim

umWeight

Com

positec

orrective

actio

ndesir

abilityindex

Unreliablesupp

lyof

good

s/merchandise

(FM1)

Poor

supp

lier

evaluatio

nand

selection

Perfo

rmsupp

lier

evaluatio

n(C

A11)

Redu

cetheleadtim

eof

purchasedgood

s

5/3

2days/0.5day

0.5day

27days

30.728

Inapprop

riatesupp

lier

relatio

nshipmanagem

ent

Increase

frequ

ency

ofsupp

lierc

ontacts

(CA12)

Improvep

hysio

logical

relatio

nshipwith

supp

liers

1/4

24

54

2

Insufficientinventory

ofsupp

liers

Find

ingnewcredito

rs(C

A13)

Increase

financial

capabilityto

addnew

supp

liers

2/3

13

48

0.00

0304

Inadequatemarketin

gresearch

Hiring

marketcon

sultant

(CA14)

Increase

understand

ing

onmarketd

ynam

ics

70%/90%

80%

90%

100%

80.00

00305

Lack

ofup

ward

commun

ication

Foste

ringinterpersonal

relatio

nships

with

outdoo

ractivities

(CA15)

Increase

flowof

inner

andou

terc

ompany

commun

ication

70%/100%

70%

100%

100%

52

Insufficientcustomer

relatio

nshipfocus

Dele

gatemarketin

gsta

ffvisitingto

custo

mers

(CA16)

Increase

pleasant

commun

icationqu

ality

with

supp

liers

70%/80%

70%

80%

100%

50.00823

Aircond

ition

ing

Malfunctio

n(FM

2)

Poor

electric

alpo

wer

desig

n

Investingsta

ffto

ACtraining

(CA21)

Increase

preparedness

staff

againstsud

denAC

malfunctio

nin

future

3/1

2/5

24

55

1

Agedairc

onditio

ning

Purchasin

gnewAC

units

(CA22)

Increase

conveniencein

shop

ping

2/5

23

58

0.00

0304

Failto

adjustthes

ales

floor

temperatures

Improvee

mpo

wermento

fop

erationsta

ffon

the

salesfl

oor

(CA23)

Increase

respon

siveness

againstsud

den

inconveniencea

tsho

pflo

or

1/5

15

58

0.20


Table 3: Benefit score of corrective action.

Failure mode Corrective action (CA) Category of targetedgoal/score

Dimensions ofSERVQUAL/score

Composite weightof benefit

Unreliable supply ofgoods/merchandise(FM1)

Performing supplier evaluation (CA11) Product/service quality/5 Reliability/1.50 7.5Improve supplier relationship (CA12) Sustainability/7 Reliability/1.50 10Add adequacy of suppliers (CA13) Sustainability/7 Reliability/1.50 10Improve technique of marketing research(CA14)

Profitability/3 Reliability/1.50 4.5

Facilitate internal upward communication(CA15)

Internal growth/1 Reliability/1.50 1.50

Improve focus on customer relationshipcommunication (CA16)

Sustainability/7 Reliability/1.50 10

Air conditioningmalfunction(FM2)

Train engineering staff on air conditioningmachine maintenance (CA21)

Service quality/3 Tangible/0.80 2.40

Purchase new AC units (CA22) Service quality/3 Tangible/0.80 2.40Improve empowerment of operation staffon the sales floor (CA23)

Staff internal growth/1 Tangible/0.80 0.80

the aforementioned factors are deterministic in terms of theirvalue, and those were assumed obtainable from previousexperience. To simplify the calculation, a Likert-like 1–10scale is used to represent themagnitude of implementing costand lead time score where 1 is assigned to “the least/smallest”categories and 10 is assigned to the “longest/largest” scorecategories. The scale of lead time score can be based on teamdiscretion. The results in estimating the abovementionedcomponents are depicted in Table 5.

4.2.6. Estimate Attractiveness Index of Each Corrective Action.After performing some calculus as described in brief inSection 2, by using (5), the attractiveness index of eachcorrective action in case example is then depicted as inTable 6.

5. Discussions

Driven by observable gaps on improving quality of risk-basedstrategy selection in previous FMEA references, a model forappraising competing improvement strategies is presented.The model gives a theoretical procedure to select correctiveactions by considering FMEA team desirability, amount ofefforts needed, and the risk on implementing correctiveactions. Illustrative example from hypermarket consumerservice is provided to demonstrate the proposed model. Byusing the proposed model, instead of relying only on thefailure risk dimension as represented by the risk prioritynumber (RPN)measures as commonly used by earlier FMEAreferences, management desirability and uncertainty out-come of implementing improvement initiative are consideredat the same time.

Considering time-based competition paradigm, the suc-cess lead time of implementing strategy is incorporated ascomplimentary basis tomeasure time efficiency in appraisingcompeting improvement efforts. Embodiment of lead timesuccess of strategy implementationwill provide supplementalbasis to appraise competing CAs instead of relying on theirfinancial dimension only.Thus, it enables to appraise compet-ing corrective action from both two important dimensions,

financial and time efficiency. Within risk response study,inclusion of time dimension is justified as described by [29].

FMEA takes the risks naturally found in real situation intoconsideration, and the risk aversion factor is incorporatedin the model. In brief, for managerial purpose, the paperprovides a theoretical exemplary step in incorporating teamdesirability and how to deal with uncertainty outcomes whendecision makers attempted to rectify their business problemsbased on competing strategy options. In line with growingstudies onutilizing FMEA in service operations, indeed, thereare many earlier techniques that have been used to overcomethe limitation of using the RPN solely as basis to rankingimprovement efforts. For example, grey relational analysis-based failure risk prioritization has been proposed by [5]to deal with interrelationship of various service dimensionsin a hospital service. Moon et al. (2013) [30] presented theintegration of fuzzy logic and grey theory for ranking criticalfailuremodes at a car dealership service. Some othermethodsfor ranking the risk of failures as basis for selection improve-ment efforts using FMEA methodology can be referred to[31]. What is observed from the previous works has becomethe novelty of our approach compared to earlier studies onincorporating the FMEA team desirability and possibility onrisk occurrence when selecting a corrective action withinframework of impact and effort analysis. Moreover, we alsoprovide an exemplar on how to appraise the weight ofcorrective action by using the AHP tool. To the best of ourknowledge, there are no previous FMEA-based improvementstrategy selection references which include all the aspectsabove.

Despite the benefits for both of theoretical and practicalpurposes, the proposedmodel is certainly not free from limi-tations. Since this is still based on conceptual idea, the modelproposed lacks strong reliability, validity, and generalizationto other service settings. The proven advantage(s) againstconventional FMEA-based CA reprioritization shall be testedin real application followed by appropriate statistical testing.Next, performance-related specification targets are usuallyprobabilistic in nature and those are not accommodated inthe proposed model. In addition, regarding that the goal of


Table4:Estim

ationon

thep

ayoff

scoreo

fcorrectivea

ctions

ofcase

exam

ple.

Correctivea

ction

Payoffcompo

nents

Potentialp

ositive

consequences

Prob

abilityof

successin

achievinggoal

Benefit

category

score

Estim

ated

gain

score

Potentialn

egative

outcom

e

Prob

abilityof

failu

rein

achievinggoal

Loss

category

score

Estim

ated

lossscore

Endeffectcategory

Payoff

score

Perfo

rmingsupp

lier

evaluatio

n(C

A11)

Redu

ctionin

unreliabled

elivery

schedu

le0.8

108

Resistancefrom

supp

liers

0.2

81.6

Dire

ctandop

portun

ityfin

anciallossfor

company

7.4

Improves

upplier

relatio

nship(C

A12)

Possibilityon

increase

inecon

omic

transaction

0.4

72.8

Limitedkn

owledgeto

revealrealsupp

liers’

desire

0.6

84.8

Financialopp

ortunity

loss

−2

Addadequacy

ofsupp

liers(C

A13)

Increase

infin

ancial

oppo

rtun

itygain

0.4

41.6

Financialh

ardship

mom

enttoob

tain

bank

ingloan

0.6

74.2

Opp

ortunityandsale

loss

−2.2

Improvetechn

ique

ofmarketin

gresearch

(CA14)

Opp

ortunitygain

due

towideningmarket

share

0.5

31.5

Non

approvalfro

mtopmanagem

ent

0.5

31.5

Losssale(revenue

risk),degrading

staff

moral

0

Facilitateu

pward

commun

ication(C

A15)

Increase

inworking

prod

uctiv

ity0.7

32.1

Unfavorablecompany

cultu

re0.5

52.5

Staff

prod

uctiv

ityloss−0.4

Improvefocus

oncusto

mer

relatio

nship

commun

ication(C

A16)

Increase

incusto

mers’

order

0.4

52.0

Non

approvalfro

mcompany

owner

0.6

11.6

Saleop

portun

ityloss

0.4

Aircond

ition

ing

malfunctio

n(FM

2)Investsta

ffon

ACTraining

(CA21)

Speedin

alleviating

ACprob

lems

0.8

83.6

UnresolvedAC

malfunctio

nprob

lem

0.2

81.6

Opp

ortunitylosssale

2

Purchase

newAC

units

(CA22)

Ensurin

gsto

reconvenience

0.2

81.6

Non

approvalfro

mtopmanagem

ent

0.8

86.4

Opp

ortunitylosssale−5.8

Empo

wer

available

crew

(CA23)

Improvee

mpathy

againstaggravated

custo

mers

0.7

85.6

Resistancetochange

from

staff

0.3

82.1

Staff

prod

uctiv

ityloss

3.5


Table 5: Estimation on the score of effort components of each corrective action.

Failure mode Corrective action Implementingcost score

Success leadtime score

Risk aversionfactor

Unreliable supply ofgood/merchandise (FM1)

Performing supplier evaluation (CA11) 7 3 0.3

Improve supplier relationship (CA12) 5 5 0.3

Add adequacy of suppliers (CA13) 8 5 0.5

Improve technique of marketing research (CA14) 10 2 0.3

Facilitate intercompany upward communication (CA15) 2 4 0.1

Improve focus on customer relationshipcommunication (CA

16) 4 2 0.3

Air conditioningmalfunction (FM2)

Invest staff on attending AC training (CA21) 5 3 0.2

Purchase new AC equipments (CA22) 6 1 0.1

Empower available crew (CA23) 2 4 0.3

improvement effort may not be fully reached, team tolerabil-ity in achieving improvement goals should be also taken intoconsideration. Another limitation is that since SERVQUAL’sdimension is the basis of this study, following [32], the char-acteristics of SERVQUAL dimensions which are static overtime shall be considered carefully in implementing themodelin practical situation. Ignoring the change of SERVQUALattribution over time is inappropriate since that will increasethe possibility of strategy obsolescence. Another gap thatmust be taken into consideration for practical purposes isthat in real application setting, various competing correctiveactions may have interdependency and such situation is notcovered within the model.

Realizing that FMEA session is a kind of team-orientedactivity, where different persons may have different riskattitudes, and assigning risk aversion score into a singlenumerical value as demonstrated by this study are notappropriate. However, such situation is beyond coverage ofthis study. Lastly, the fuzziness of the scale used in the modelas basis to score the effort variables is not considered informulating the attractiveness index.

5.1. Managerial Implications

5.1.1. Utilization of Derringer Desirability Index in Artic-ulating FMEA Team Desirability. In this study, the Der-ringer desirability index along with failure occurrence ratereduction ratio is used to represent FMEA team desirabilityin alleviating critical failure modes. Utilization of suchindex can facilitate FMEA team to synchronize the targetedimprovement effort. Specific class of desirability index can beused with the targeted improvement effort. For instance, forcorrective action which is intended to reach larger the betterservice performance specification, the corresponding largerthe better (LTB) Derringer desirability class can facilitatesuch targeted goal. By using the Derringer desirability index,FMEA management team can indicate which failure modeand corresponding corrective action’s specification target isgoing to be achieved.

5.1.2. Management of Corrective Action. In practical businesssituation, selection of competing improvement efforts to curbthe root cause of failuremode is usually based on cost-benefit

criterion as company is a kind of profit seeker body and notsolely based on the risk dimension of failure mode (the RPNof failure). In this regard, inclusion of impact and effort analy-sis proposed in this study can facilitate companymanagementwithmore realistic improvement from economic perspective.Moreover, using the composite weight in appraising compet-ing improvement initiative, inclusion on payoff score, andrisk aversion factor, more real improvements are in sight. Itis because in real world application, selecting improvementinitiative is a risky process and the actor who implementssuch initiative is a human being and the level of risk is thusincreased.

6. Conclusion

Despite the importance in risk-based improvement effort,inclusion on teamdesirability and risks in selecting correctiveactions are omitted from previous FMEA references. Inthis paper, a new theoretical model to select competingimprovement strategies based on management desirabilityand risk of strategy selection is proposed. Application of thetheoretical model is demonstrated with case example andmerits and demerits of the model are also discussed.

The proposed model advances service FMEA knowledgeto both academicians and practitioners in the following ways:

(1) providing means on how to integrate FMEA teamdesirability and risks in achieving specific perfor-mance goal when alleviating critical failure mode(s);

(2) providing an easy example on how to determine thepotential corrective actions upon obtaining infor-mation on the critical failure modes, their potentialnegative impacts, and the probable root causes;

(3) presenting an approach on how to consider impactand effort aspect to select competing risk-basedimprovement initiative. In addition, the model pro-vides exemplary on how to appraise the weight of cor-rective action by using certain criterion as facilitatedby the use of decision tool, the AHP;

(4) providing illustrative example on how to identifystrategic corrective action option which correspondsto critical failure modes in the vital service qualitydimensions;


Table6:Attractiv

enessind

exof

CAof

case

exam

ple.

Failu

remod

eCorrectivea

ction

Risk

priority

number

Impactcompo

nent

Effortcom

ponent

Attractiv

enessind

exDesira

bility

index

Weighto

fCA

goal

Payoffscore

ofCA

Implem

entin

gcost

Lead

time

successo

fCA

Risk

aversio

nfactor

Unreliablesupp

lyof

good

s/merchandise

(FM

1)

Perfo

rmsupp

liere

valuation(C

A11)

27.29

0.728

7.57.4

73

0.3

175.01

Improves

upplierrelationship(C

A12)

210

−2

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145.54

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ofsupp

liers(C

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0.00

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9125

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ique

ofmarketin

gresearch

(CA

14)

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40

102

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0

Facilitateintercompany

upward

commun

ication(C

A15)

21.5

0−0.4

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Improvefocus

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mer

relationshipcommun

ication(C

A16)

0.00823

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Aircond

ition

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n(FM

2)

Investstaff

onattend

ingAC

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(CA

21)

25.38

12.40

25

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Purchase

newAC

units

(CA

22)

0.00

0304

2.40

−5.8

61

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−0.178

Empo

wer

availablec

rew(C

A23)

0.20

0.80

3.5

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5.922


(5) showing a simple, step-by-step, and risk-based impro-vement effort selection model which not only consid-ers the risk dimension due to failure modes occur-rence, but also at the same time considers man-agement desirability, risk of implementing specificcorrective action, cost and benefit of corrective action,and also risk attitude of FMEA team.

Regarding that this study is still in theoretical stage,some extendable research directions are viable as avenuefor further investigations. First and foremost, applying theconceptual model in real situation and comparing the actualbenefit between conventional FMEAand the proposedmodelshall be accomplished by future study. Second, inclusionof the value analysis, customers and competitors’ reaction,and strategy flexibility in appraising competing improvementefforts are still unknown in the literature. Considering robust-ness in appraising competing improvement efforts becomes achallenging issue for further investigation.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

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