1.makale

ARTICLE IN PRESS

Robotics and Computer-Integrated Manufacturing 26 (2010) 392–401

Contents lists available at ScienceDirect

Robotics and Computer-Integrated Manufacturing

0736-58

doi:10.1

n Corr

Augusti

E-m

journal homepage: www.elsevier.com/locate/rcim

Conceptual process planning – an improvement approach using QFD, FMEA,and ABC methods

Alaa Hassan a,b,n, Ali Siadat a, Jean-Yves Dantan a, Patrick Martin a

a LCFC Laboratory, Arts et Metiers ParisTech, 4 rue Augustin Fresnel, 57078 Metz, Franceb Mechatronic Engineering Department, HISAT, Barza, Damascus, Syria

a r t i c l e i n f o

Article history:

Received 16 July 2008

Received in revised form

13 November 2009

Accepted 2 December 2009

Keywords:

Process planning

QFD

FMEA

ABC

45/$ - see front matter Published by Elsevier

016/j.rcim.2009.12.002

esponding author at: LCFC Laboratory, Arts

n Fresnel, 57078 Metz, France. Tel.: +33 3873

ail address: [email protected] (A. Ha

a b s t r a c t

Conceptual process planning (CPP) is an important technique for assessing the manufacturability and

estimating the cost of conceptual design in the early product design stage. This paper presents an

approach to develop a quality/cost-based conceptual process planning (QCCPP). This approach aims to

determine key process resources with estimation of manufacturing cost, taking into account the risk

cost associated to the process plan. It can serve as a useful methodology to support the decision making

during the initial planning stage of the product development cycle. Quality function deployment (QFD)

method is used to select the process alternatives by incorporating a capability function for process

elements called a composite process capability index (CCP). The quality characteristics and the process

elements in QFD method have been taken as input to complete process failure mode and effects analysis

(FMEA) table. To estimate manufacturing cost, the proposed approach deploys activity-based costing

(ABC) method. Then, an extended technique of classical FMEA method is employed to estimate the cost

of risks associated to the studied process plan, this technique is called cost-based FMEA. For each

resource combination, the output data is gathered in a selection table that helps for detailed process

planning in order to improve product quality/cost ratio. A case study is presented to illustrate this

approach.

Published by Elsevier Ltd.

1. Introduction

The major manufacturing cost is determined in the early stagesof product development. It is critical to be able to assess qualityand cost as early as possible in the product development cycle.Conceptual process planning (CPP) is an activity for designers toevaluate manufacturability and manufacturing cost in the earlyproduct development stage [1]. This paper proposes an approachto develop a quality/cost-based conceptual process planning(QCCPP). This approach uses QFD (Quality Function Deployment)and FMEA (Failure Mode and Effects Analysis) tools to determinemanufacturing resources with appropriate process capability toproduce product characteristics. Then, it uses ABC (Activity-BasedCosting) method to roughly estimate manufacturing cost. Fig. 1shows the QFD, FMEA, and ABC methods in the productdevelopment process.

A number of different methods have been developed forevaluating the impacts of development process on product qualityand cost. Some researchers focus on the first two phases of

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et Metiers ParisTech, 4 rue

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ssan).

product development, i.e., product planning and parts deploy-ment. For example, Bode and Fung introduce a method forincorporating financial elements into the house of quality in orderto optimize product development resources towards customersatisfaction [2]. Eubanks [3] presents an Advanced FMEA method(AFMEA) applicable in the early stages of design to enhance life-cycle quality of ownership. The process begins by QFD method toidentify customer requirements and relate them to engineeringmetrics and functional requirements responsible for satisfying thecustomer. Chen and Weng [4] apply fuzzy approaches and QFDprocess to determine the required fulfilment levels of designrequirements for achieving the maximum satisfaction degree ofseveral goals in total in the product design stage. Karsak [5]presents a zero–one goal programming methodology that in-cludes importance levels of product technical requirements(PTRs) derived using an analytic network process, cost budget,extendibility level and manufacturability level goals to determinethe PTRs to be considered in designing the product. Otherresearches focus on the later product development phases likeprocess planning. Most of them deal with computer-aided processplanning (CAPP). Culler and Burd [6] present architecture inwhich customer service, CAPP and ABC are incorporated into asingle system, thereby allowing companies to monitor and studyhow expenditures are incurred and which resources are being

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A. Hassan et al. / Robotics and Computer-Integrated Manufacturing 26 (2010) 392–401 393

used by each process. Lau [7] introduces an intelligent computer-integrated system for reliable design feature recognition in orderto achieve automatic process planning. Li [8] applies the geneticalgorithm (GA) to CAPP system to generate optimal or near-optimal process plans based on the criterion chosen. Only a fewefforts give attention to CPP that has significant impacts onmanufacturing quality, cost and lead-time. Feng and Zhang [1]develop a conceptual process planning prototype for the pre-liminary manufacturability assessment of conceptual design inthe early product design stage. It aims at determining manufac-turing processes, selecting resources and equipment and roughlyestimating the manufacturing cost. Chin [9] proposes an approachto carry out the preliminary process planning for quality, in whichthe QFD and the process FMEA are incorporated.

However, more efforts are required to be made to determinekey process alternatives with an adequate process capabilityduring conceptual process planning, to estimate the manufactur-ing cost, and to validate these alternatives before generating thedetailed process plans. The goal of this paper is to propose anapproach, to improve manufacturing process quality and toestimate the manufacturing cost of the product. As shown inFig. 2, the role of the proposed QCCPP approach is to link processdetermining activity to detailed process planning, it is responsiblefor selecting process alternatives (resources) with an adequateprocess capability, process associated risks, and process cost.QCCPP is supported by quality methods and tools, and costestimation methods, particularly QFD and FMEA methods, andABC method. This approach enables designers to optimizemanufacturing process plan concerning with resource

Requirements Definition

Conceptual product design

Detailed product design

Conceptprocess plann

QFD phase I

Product KCs

QFD phase II

Product FMEA

QFD phase III

Design FMEAQF

Pro

Fig. 1. QFD, FMEA, and ABC in the

Fig. 2. The role

determination in order to improve product quality/cost ratio, itcan serve as a useful information system to support decisionmaking in product development. The following sub-paragraphsdescribe briefly the methods used in QCCPP approach.

1.1. Quality function deployment (QFD)

QFD is a quality management technique which is very useful toimprove the product’s quality according to the customer’srequirements. This method begins by analyzing market andcustomer’s needs from a product. Then it translates the desiresof the customer into product design or engineering character-istics, and subsequently into parts characteristics, process plans,and production requirements associated with its manufacture[10, 11]. This is a four-phase process: product planning, partsdeployment, process planning, and production planning [12]. Thisfour-phase approach is accomplished by using a series ofmatrixes, called House Of Quality (HOQ), that guide the productteam’s activities by providing standard documentation duringproduct and process development (Fig. 3). Each phase has amatrix consisting of a horizontal row of ‘‘Whats’’ and a verticalcolumn of ‘‘Hows’’. At each stage, the ‘‘Hows’’ are carried to thenext phase as ‘‘Whats’’ [13].

1.2. Failure mode and effects analysis (FMEA)

FMEA is an important method of preventive quality andreliability assurance. It involves the investigation and assessment

ual ing

Detailed Process planning Manufacturing

Service and Support

D phase IV

cess FMEA

ABC

product development process.

of QCCPP.

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A. Hassan et al. / Robotics and Computer-Integrated Manufacturing 26 (2010) 392–401394

of all causes and effects of all possible failure modes on a system,in the earliest development phases [14]. Along with structuredmethods like QFD, FMEA is a risk management tool that providesdecision guidelines to aid the product development team inachieving a design that provides the most in terms of cost andquality [15]. Basically, FMEA can be classified into two main types[16]: design FMEA which deals with design activities, such asproduct design, machine or tooling design, and process FMEAwhich is used to solve problems due to manufacturing processes.Finally, the team summarizes the analysis in a tabular form called‘‘FMEA table’’.

The traditional FMEA involves ambiguity with the definition ofrisk priority number: the product of occurrence (O), detectiondifficulty (D), and severity (S) subjectively measured in a 1–10range. The three indices used for RPN are ordinal scale variablesthat preserve rank but the distance between the values cannot bemeasured since a distance function does not exist. Thus, the RPNis not meaningful. A cost-based FMEA alleviates this ambiguity byusing the estimated cost of failures [17]. Tarum [18] proposeda new technique called FMERA (Failure Modes, Effects, andFinancial Risk Analysis) that identifies and prioritizes the processpart of potential problems that have the most financial impact onan operation. Alternatives can be evaluated to maximize thefinancial benefits. Adding columns concerning failure costs tostandard FMEA table, a cost-based FMEA table is obtained.

1.3. Activity-Based Costing (ABC)

ABC assumes that cost objects (e.g., products) create the needfor activities, and activities create the need for resources.Accordingly, ABC uses a two-stage procedure to assign resourcecosts to cost objects. In the first stage, costs of resources areallocated to activities to form Activity Cost Pools. These activitiesare allocated in the second stage to cost objects based on these

Fig. 3. Four pha

Fig. 4. The conc

object’s use of the different activities. In order to differentiatebetween the different allocations at the two stages, the first-stageallocation bases are termed ‘‘resource cost drivers’’ and thesecond-stage bases ‘‘activity cost drivers’’ [19, 20]. Fig. 4illustrates the concept of ABC method.

2. QCCPP approach

This section describes the quality/cost-based conceptualprocess planning (QCCPP) approach. QCCPP is a methodology todetermine the process alternatives meeting the quality require-ments and to estimate the manufacturing process cost roughly aswell as the failure cost before knowing the details of new product.The inputs of this approach are: product design and selectedmanufacturing process and the QFD and FMEA analysis resultsfrom design phase. As shown in Fig. 5, the steps for QCCPP processare:Selection of process alternatives using QFD information and aquality measure index, called the composite process capability(CCP), adopted from [9, 21]

�

ses

ept

Process failures analysis using process FMEA;
� Estimation of the manufacturing process cost; � Assessment of non-quality (failure) cost using cost-based
FMEA.

2.1. Selection of process alternatives

In order to achieve the process element level (xj), it is requiredto select appropriate process alternatives (aj) based on partinformation and manufacturing resources. Table 1 is an example

of QFD.

of ABC.

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Fig. 5. The process of QCCPP.

Table 1Example of QFD phase III.

(1) weak (5) medium (9) strong Process elements

Quality characteristic Characteristic weight Machine method (e1) Fixturing mode (e2)

Cone obliquity (q1) 0.35 (v1) 9 (0.9) 1 (0.1)

Perpendicularity between the face and the axis (q2) 0.65 (v2) 9 (0.64) 5 (0.36)

Process elements weight (wj) 0.71 0.29

Process element measure Precision grade Locating error

Process element level (xj) 7 0.02 mm

Correlation coefficient (gj) 1 1.1

Lower bound of process quality level (Lj) 3 0.002

Upper bound of process quality level (Uj) 7 0.2

Process alternatives (aj) Finish turning Final clamping


of QFD during process planning for an axis of a centring system.We use this example to describe the components of the HOQ inphase III. The process quality team translates the qualitycharacteristics (qi) into a set of process elements (ej) and theirtarget levels. For instance, computer numerical control (CNC)finish turning is selected for machining method whose level is the7th precision grade, while final clamping is selected for thefixturing scheme whose locating error is not allowed to be morethan 0.02 mm. The selection of process alternative is just the mostflexible and the most crucial task of process planning for quality.

A process element is a facet that significantly influencesquality characteristics. Process elements generally coverthe machining method, the machine tool, the assembly tool, thefixturing scheme, the tool path mode, the cutting condition, theworkpiece structure, etc. The capability of each process element ismeasured by one or more quality measures or indicators. Forexamples, ‘‘precision level’’ for the machining method, and‘‘deformation’’ and ‘‘locating error’’ for the fixturing scheme arequality measures of process elements.

2.1.1. Estimating the capability of the process element

Based on the process alternative, the capability of each processelement, referred to from now on as an element capability, isdefined as:

Cej ¼ 1þxj�x0

j

Uj�Lj

!gj

; if xj is a large-better index;

Cej ¼ 1�xj�x0

j

Uj�Lj

!gj

; if is a small-better index: j¼ 1;2; . . .;n ð1Þ

whereCej is the capability of ej; x0, xj the standard or benchmark and

the current value of quality value of ej, respectively; gj thecorrelation coefficient, set by empirical machining data, andgj40; Lj, Uj the technical feasible lower and upper bounds of theprocess element (ej) level; n the number of process elements (ej)

The capability index (Ce) of a process element is centered on 1.When the process element level is at a benchmark level, the

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capability of the process element is set as the standard value ‘‘1’’.The more the process element level deviates from the benchmarkvalue (x0), the more the Ce deviates from 1, whereas Ceo1indicates the capability of process element decreases, and Ce41indicates the capability of process element increases. Thestandard process alternative is generally either an enterprisestandard or a straightforward process alternative with which theteam members are very familiar.

2.1.2. Estimating the capability of the quality characteristic

Assuming that the capability of each process element (Cej)affects the quality characteristic (qi) in a linear fashion, theassurance capability of each quality characteristic (Cqi) can beestimated by the following formula:

Cqi ¼Xn

j ¼ 1

wij:Cej; i¼ 1;2; . . .;m ð2Þ

whereCqi is the capability for assuring qi; wij the coefficient of

relationship between qi and ej, andPn

j ¼ 1

wij ¼ 1; m the number of

quality characteristics (qi)Cqi indicates the degree for assuring the quality characteristic

(qi) under a combined process alternative, noted by A=(a1,

a2,y,an), which has a set of quality measures, X=(x1, x2,y,xn).

2.1.3. Estimating the composite process capability (CCP) of all

quality characteristics

The CCP reflects the overall degree of assuring all the qualitycharacteristics. There are two basic approaches to estimate theoverall effect of multiple attributes, namely, the additive fashionand the multiplicative fashion. Owing to every quality character-istic possessing the right to veto on the overall process qualityaccording to the trade-off strategies [22], we adopt the multi-plicative fashion to calculate the CCP as follows:

CCP¼Ymi ¼ 1

ðCqiÞvi ð3Þ

where

vi is relative importance of qi, 0rvir1, andPm

i ¼ 1

vi ¼ 1

In nature, CCP reflects the overall capability level of processalternatives compared with the standard process alternatives. Itcan be easily determined that the CCP of the standard processalternatives, whose quality measures are X0=(x1

0, x20,y,xn

0), isalways equal to 1. As a result, for the process alternatives selectedduring conceptual process planning, a CCP of more than 1indicates that the overall capability level increases and theprobability for assuring all quality characteristics is higher thanthe standard process alternatives. On the contrary, a CCP of lessthan 1 indicates that the overall capability level decreases and theprobability for assuring all quality characteristics is lower thanthe standard process alternatives. Therefore, it should always beexpected that the CCP is higher than one during the conceptualprocess planning.

As shown in Fig. 5, the result of this step is a set of combinedprocess alternatives whose CPP is greater than 1. This resultperforms an input for the FMEA method to continue the QCCPPprocess, and to help the team to make a right decision andcompleting the detailed process plan.

2.2. Analysis of process failures

FMEA is a methodology designed to identify potential failuremodes for a product or process before they occur, to assess therisk associated with each failure mode, to rank the issues in termsof importance and to identify and carry out corrective actions toaddress the most serious concerns [23].

This article deals with process FMEA which is used to analyseprocesses of various potential failures. The focus of standard FMEAis usually on providing quality parts and reducing frequency ofproblems. Severity (S) ratings are usually linked to the ability toprovide quality products to the customer. Occurrence (O) ratingsgives an indication of the frequency of the problem. Detection (D)ratings are an estimation of the effectiveness of problem preven-tion and containment. The Risk Priority Number (RPN) is a productof the Severity, Occurrence, and Detection ratings: SxOxD=RPN.

As shown in Table 2, most quality characteristics in the QFD (qi)should be included in the process FMEA. The process elements inthe QFD (ej) are useful for determining the causes of failures andthe recommended alternative actions [24]. The RPN values arerelated to process alternatives selected in the previous step.

2.3. Estimation of manufacturing cost

Manufacturing process can be decomposed into activities. Inthis paper, ABC method is used to estimate the manufacturingcost taking into account the cost of all activities related to risks.Cost estimating equations are described in the following equa-tions [25,26]:

Cma ¼XN

i ¼ 1

Ciactivity

¼XN

i ¼ 1

CimachiningþCi

load�unloadþCisetupþCi

handling

�

þCiprogramming�testingþCi

overhead

�ð4Þ

Cma is the cost of manufacturing activities. i an index. N the totalnumber of manufacturing activities applied to manufacture an

artifact. Cimachining the machining cost of activity i. Machining

activity is a unit-level activity. Ciload�unload the load and unload cost

of activity i (unit-level activity). Cisetup the setup cost of activity i.

Setup is a batch-level activity. Cihandling the handling cost of activity

i. Handling is a batch-level activity. Ciprogramming�testing the pro-

gramming and testing cost of activity i. Programming-testing is a

product-level activity. Cioverhead the overhead cost of activity i. It is

a facility-level activity.

2.4. Estimation of the cost of failures

To estimate the financial impact of various potential failures,an extended technique, called cost-based FMEA, is used. Based onstandard process FMEA and selected process alternatives fromprevious steps, cost-based FMEA has been used to identify andprioritize the process part of potential problems that have mostfinancial impact. Table 3 shows an extended table which resumesthe cost-based process FMEA analysis for the axis.

Based on existing process FMEA, cost-based process FMEA iscarried out by adding the following steps:

1.
Internal cost per event: internal failure costs are costs thatare caused by products not conforming to requirements orcustomer needs and are found before delivery of products to

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Table 2A process FMEA table.

qi ei

Process Part function Failure mode Cause Effect Detection O S D RPN Alternative action

Turning the cone of

the axis

The cone adapts to

workpiece

The cone obliquity is not

adequate

Dispersion of

machine

Incorrect position of the

workpiece

1 piece/H 6 5 62 150 Measuring tool

realisation

6 5 63 150

90

SPC

Table 3Cost-based process FMEA.

Process Part

function

Failure

mode

Cause Effect Detection O S D RPN Alternative

action

Internal

cost per

event

(h)

External

cost per

event (h)

Events/

Year

Annual

risk (h)

Annual

implementation

cost (h)

New

RPN

New

annual

risk (h)

Turning

the

cone

of the

axis

The cone

adapts to

workpiece

The cone

obliquity

is not

adequate

Dispersion

of machine

Incorrect

position

of the

workpiece

1 piece/H 6 5 6

2

150

60

Measuring

tool

realisation

11 12 210 2315 3000 60 46

6 5 6

3

150

90

SPC 2000 90 46


external customers. Each potential failure event is analyzedto determine the financial risk. The internal cost per eventcan be estimated using the following form:

Cjei ¼ Labour costþMaterial cost ð5Þ

Cjei is the internal cost of event e related to activity j

Labour cost¼ down time� hourly labour cost ð6Þ

Labour cost is the cost of operator work which eliminatesthe failure.Material cost is the cost of component replacement due tofailure. Using ABC method, the cost of manufacturingactivities (Cma) is estimated for the component.

2.
External cost per event: external failure costs are costs thatare caused by deficiencies found after delivery of productsto external customers, which lead to customer dissatisfac-tion. The external cost per event, in our study, is essentiallythe complaints cost:
Cjee ¼ Complaints cost ð7Þ

Cjee external cost of event e related to activity j

Event probability: the probability of failure events, asso-
3. ciated to activity j, can be estimated via prob(O j):prob(O j) is the probability corresponding to the occurrencerank of the risk associated with activity j
Number of events per year¼

probðOjÞ � No: of units per year ð8Þ

The cost of all activities related to risks Cr can be defined as:
4.
Cr ¼XP

j ¼ 1

probðOjÞ probðDjÞ � Cjeiþprobð1�DjÞ � Cj

ee

h i: ð9Þ

prob(Dj) is the probability corresponding to the detectionrank of the risk associated with activity j

Therefore, the annual risk cost ¼No: of units per year � Cr

5.
Implementation cost is the cost of implementing alternativeaction.
For each failure mode, financial risk has been estimated,alternative actions have been identified and their cost has beencalculated. To take into account the risk cost associated tomanufacturing process, manufacturing process cost before alter-native actions implementation could be defined as:

Cm ¼ CmaþCr ð10Þ

Cm is the manufacturing process cost of an artifact.Note that it is not necessary for the alternative actions to

eliminate the risk completely. Therefore, the risk cost must betaken into account even after the implementation of alternativeactions. Manufacturing process cost after alternatives implemen-tation is given by this equation:

Cm0 ¼ CmaþCr0 þCa ð11Þ

Cma is the cost of manufacturing activities after the implementa-tion of alternative actions. This cost may have to be recalculated ifsome of actions modify the manufacturing activities. Cr0 is the costof risk-related activities after alternatives implementation.

Cr0 ¼XP

j ¼ 1

probðO0jÞ probðD0jÞ � Cjeiþprobð1�D0jÞ � Cj

ee

h ið12Þ

prob(O0j) is the probability corresponding to the new occurrencerank of the risk associated with activity j, after the implementa-tion of alternative actions; prob(D0j) is the probability correspond-ing to the new detection rank of the risk associated with activity j,after the implementation of alternative actions. Ca is the cost ofimplemented actions.

3. Discussion

For a manufacturing process plan, we have estimated the riskand manufacturing costs. The modification of this process couldbe translated in terms of selecting alternative resources whichlead for reducing, as much as possible, the risk and manufacturingcosts to achieve an optimal quality/cost ratio. Once the resourcesare selected, the designers have to prioritize the recommended

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alternative actions in order to reduce the most of risks (i.e.improve product quality) and to minimize the cost.

The selection of the ‘‘best’’ alternative depends on a set ofheterogeneous evaluation criteria. Many methods are used forevaluating complex multi-attribute alternatives. For example;analytic hierarchy process (AHP) which is a multi-criteriadecision-making technique proposed by Saaty [27], SPEC method,based on value analysis principle, allows to make decisions amongdesign alternatives on the bases of performance satisfactionindicators [28], and ECOGRAI method which allows for thedefinition of a real customized performance measurement systemwith multi-criteria performance indicators [29].

To facilitate the selection task, we have proposed a decision-making tool adopted from [30]. The team gathers and prioritizesthe evaluation objectives according to the organization policy, andthen it ranks the process alternatives on 1–10 scale. Based onQCCPP results, an alternative selection table is prepared toimprove the team’s decision by addressing the right proportionsof quality and cost issues. This table is presented through a casestudy in the next paragraph.

4. Case study

An example of auxiliary shaft cover of a car engine is presentedin this section to illustrate the implementation of QCCPPapproach. This workpiece comes from foundry workshop andthen NC machining operations aim at ensuring its key qualitycharacteristics. Fig. 6 shows this cover workpiece and its mainquality characteristics. We suppose that the annual rate is600,000 parts, with 2500 parts for each batch. Our objectivehere is to help the team to select a process alternative incompromising among multiple selection criteria. Table 4 lists theprocess alternatives for each process element during the processalternative decision making. The next sub-paragraphs explainQCCPP steps for this workpiece. The quantitative estimates in thisstudy should be considered as illustrative only.

4.1. Estimating the capability of process alternatives

Based on product design and QFD phase I and II, the qualitycharacteristics of the cover are identified in order to achieveits functions. Then the relationships between these quality

Fig. 6. Key operations of the cover workpiece.

characteristics and the process elements are assessed with thequantitative measure of each process element (lower bound,upper bound, and benchmark level). QFD phase III matrix isprepared with this data as shown in Fig. 7. Two alternative groupsare studied in this step, these are AG1=(MT1, FM2, CC2), andAG2=(MT2, FM1, CC1). Finally, the CCP index of each processalternative group is estimated using Eqs. (1–3). As shown in Fig. 7,in spit of the little differences between process element levels foreach group, there is a notable difference between CCP index forAG1, which is equal to 1.456, and for AG2, which is equal to 1.03.Therefore, the overall capability level will be increased to 45.6%with AG1, and to 3% with AG2 compared with the standardprocess alternatives.

4.2. Process failure analysis

The QFD results help the team in identifying what are theproduct quality characteristics and process elements to beanalyzed in FMEA method. The RPN values are related to theprocess alternative group. Performing standard process FMEAanalysis for the cover workpiece, results in the tables shown inFig. 8. Note that the differences between the two analysis are inquantification indices (O and D), and not all the process elementsin QFD matrix are set as failure causes. Using AG1 to manufacturethe cover part needs no alternative actions cause the related RPNsare under the predefined threshold.

4.3. Manufacturing cost estimation

Manufacturing cost is estimated using ABC method. Theactivities that contribute in manufacturing the cover workpieceare firstly identified. For each process alternative group, the userhas to estimate the activity costs using Eq. (4). Fig. 9 shows thetables of manufacturing cost calculation for AG1 and AG2. ForAG1, the estimated manufacturing cost is equal to 5.60 h, and forAG2 is 4.48 h.

4.4. Failure cost estimation

Supposing that downtime to detect the failures is 18 min, andthe hourly labour cost is 12 h/H. The labour cost is estimated usingEq (6):

Labour cost=18 min�12 h/H=3.6 h

The part cost depends on the process alternative used tomanufacture the cover workpiece. Manufacturing cost for eachalternative group is estimated in the previous step:

If AG1 is used, then material cost=5.60 h

If AG2 is used, then material cost=4.48 h

Internal cost for AG1=3.6+5.60=9.20 h

Internal cost for AG2=3.6+4.48=8.08 h

External cost is supposed to be the same for AG1 and AG2. It isequal to 27 h

For AG1, the annual cost of risks before alternative implemen-tation (9):

CrGR1 ¼ 600000 Pð2Þ 1�Pð4Þð Þ � 9:2þPð4Þ � 27ð ÞþPð3Þ 1�Pð3Þð Þð½

�9:2þPð3Þ � 27Þ�

¼ 6000009

1000001�0:31ð Þ � 9:2þ0:31� 27ð Þ

�

þ2:9

100001�0:21ð Þ � 9:2þ0:21� 27ð Þ

�� 3046 euros

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Table 4The process elements and alternatives fo.

Process element Alternative reference Process alternative name Measure Process element level

Machining tool MT1 NC milling machine 1 Precision grade IT8

MT2 NC milling machine 2 Precision grade IT9

Fixturing mode FM1 Outer spoke clamping Locating error 0.06 mm

FM2 Outer support griping Locating error 0.05 mm

Cutting conditions CC1 Cutting force and speed Deformation 0.085 mm

CC2 Cutting force and speed Deformation 0.080 mm

Fig. 7. Results of process alternative capabilities for the cover.

Fig. 8. Process FMEA analysis for two process alternatives: AG1 and AG2.


To calculate the annual cost of risks associated to theworkpiece manufactured by AG2, we use also Eq. (9):

CrGR2 ¼ 600000� Pð5Þ 1�Pð4Þð Þ � 8:08½

þPð4Þ � 27þ 1�Pð3Þð Þ � 8:08þPð3Þ � 27�

¼ 600000�2:9

10001�0:31ð Þ � 8:08þ0:31� 27½

þ 1�0:21ð Þ � 8:08þ0:21� 27�� 49935 euros

For AG2, this cost is the annual risk cost before alternativeimplementation.

For AG2, the implementation cost of the proposed alternativeactions is 1500+785=2285 h

After action implementation, the risk probabilities are reducedso that the annual risk cost will change. The risk cost afteralternative implementation is given by Eq. (12):

Cr0GR2 ¼ 600000� Pð2Þ 1�Pð4Þð Þ � 8:08þPð4Þ � 27þ 1�Pð3Þð Þ½

�8:08þPð3Þ � 27�þ2285

¼ 600000�9

1000001�0:31ð Þ � 8:08þ0:31� 27½

þ 1�0:21ð Þ � 8:08þ0:21� 27�þ2285� 3835 euros

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Fig. 9. Manufacturing cost estimation of the cover for AG1 and AG2.

Fig. 10. Cost-based process FMEA of the cover for AG1 and AG2.

Fig. 11. Process alternative group selection table.


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The results of this step are summarized in Fig. 10.

4.5. Process alternative selection

The final step in QCCPP approach is to make a decisionconcerning the selection of the best process alternative whichcompromises multiple evaluation criteria. The output data of theprecedent steps are gathered in the decision table shown inFig. 11. The team is now able to gather the results in one table, toprioritize the evaluation criteria, and to score the processalternatives against them. It can evaluate the alternative optionsin 1–10 range and then calculate the final score in order toidentify the most suitable alternative which replies to multipleobjectives. This score is not a subjective quantification but itdepends on the numerical data results from the QCCPP approach.As shown in Fig. 11, the final score of process alternative groupsgive the advantage to AG1 (57%), and rank AG2 in the secondplace (43%).

5. Conclusion

In order to improve the effectiveness of conceptual processplanning, this paper proposes a methodology to support the decisionmaking process while taking into account both the quality and costfactors. In this paper, a QCCPP approach is proposed to improvemanufacturing process quality, to estimate manufacturing cost, andto help the team in selecting process alternatives which satisfymultiple selection criteria. QFD technique is used in this approach toassess the process quality and give useful information about thepossible combined resources by incorporating a capability functionfor process elements called a composite process capability index(CCP). On the other hand, ABC method is employed to estimate themanufacturing process cost, and then a cost-based FMEA analysis iscarried out to assess the failure modes due to this manufacturingprocess and to estimate the failure cost. Finally, a selection table isproposed to summarize the results of this methodology and help theteam to select the most suitable combined alternatives towardsquality improvement and cost minimizing.

Further work is required in analyzing the dependence betweenthe failure causes while taking advantage of the common databetween QFD and FMEA. It is also necessary to develop a softwaretool to effectively support this methodology and provide a userinterface to combine all the methods used in it. To develop thisnew tool, an information model is necessary to support the tooldevelopment and the integration of these methods.

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