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Design for quality inagile manufacturing
environment through modifiedorthogonal array-based
experimentationS.R. Devadasan
Department of Production Engineering, PSG College of Technology,Coimbatore, India
S. GoshteeswaranDepartment of Production Engineering, Amrita Institute of Technology,
Coimbatore, India, and
J. GokulachandranDepartment of Mechanical Engineering, Amrita Institute of Technology,
Coimbatore, India
Abstract
Purpose – To provide a modified orthogonal array-based model for enabling the researchers andpractitioners to exploit the technique, “design of experiments” in an agile manufacturing environment.
Design/methodology/approach – The characteristics of Taguchi’s off-line models and agilemanufacturing were studied. A theoretical model of modified orthogonal array-based experimentationwas designed. This model was subjected to implementation study in an Indian pump-manufacturingcompany.
Findings – The model contributed in this paper has shown its feasibility in achieving quality in agilemanufacturing environment.
Research limitations/implications – The authors are residing in an Indian city where themajority of the companies have not adopted agile manufacturing criteria. Hence, it was not possible tocarry out implementation study in an agile manufacturing company. Future researchers shouldexamine the practical validity of the proposed model in agile manufacturing companies.
Practical implications – Since the manufacturing organizations are fast becoming agile, due to thecustomers’ dynamic demands coupled with competition, the traditional quality improvementtechniques are becoming obsolete. The model contributed in this paper is found to be useful inachieving continuous quality improvement in AM environment. Hence the model would be a usefultechnique for today’s practitioners whose activities are increasingly focused towards achieving agilityin manufacturing.
Originality/value – The literature survey covering articles on agile manufacturing indicates that noresearcher or practitioner has contributed a model that would exploit the technique, “design ofexperiments” in an agile manufacturing environment. Hence the proposed model is expected to be ofhigh value for researchers and practitioners to explore the way of achieving continuous qualityimprovement in agile manufacturing environment.
Keywords Agile production, Experimentation, Quality improvement, Delphi method, Taguchi methods
Paper type Research paper
The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at
www.emeraldinsight.com/researchregister www.emeraldinsight.com/1741-038X.htm
JMTM16,6
576
Received August 2003Revised January 2004Accepted March 2004
Journal of Manufacturing TechnologyManagementVol. 16 No. 6, 2005pp. 576-597q Emerald Group Publishing Limited1741-038XDOI 10.1108/17410380510609456
IntroductionDuring the 1970s and 1980s, manufacturing organizations were compelled to focus onenhancing productivity and quality due to the emanation of competitive businesssituation. Of late, it has become so fierce that manufacturing organizations are lookingfor a strategy to tame the situation (Rooks, 2000). It has been widely acknowledged thatthis predicament is due to customers’ requirements, which change very often.Recognizing and imparting them in practice are very much vital for survival of anyorganization. Hence, the manufacturing community has been forced to respond in sucha manner that their production should be low in volumes and high in varieties(Maskell, 2001). Awareness of this state of affairs made some researchers to contributetheir might in this regard and they brought out the paradigm called “agilemanufacturing” (AM) (Hooper et al., 2001). In a nutshell AM is a combination of flexiblemanufacturing system concepts and lean manufacturing principles (Sarkis, 2001) andcalls for quick response to the customers’ demands (Parkinson, 1999). On realizing itsimportance in attaining competence in modern scenario, various forums have beenstarted exclusively for promoting AM principles (Struebing, 1995). As AM paradigm isstill at its nascent stage, significant works are being carried out to make it operationalin today’s organizations (Sharifi and Zhang, 2001). Particularly, the task of researchersis focused towards modifying the existing manufacturing strategies so as to makethem compatible in an AM environment (van Assen et al., 2000, Meredith and Francis,2000). Among all manufacturing strategies, continuous quality improvement comes tothe forefront. At this juncture, the contribution of Taguchi methods towards achievingcontinuous quality improvement is noted with interest. The large volume ofapplications of Taguchi methods reported in literature (Bendell, 1988; Antony andAntony, 2001) signifies their power. Hence it was thought that the exploitation ofTaguchi methods in AM environment would contribute considerably towards theintegration of quality improvement approaches in an AM environment. Taguchi, aJapanese born quality guru, has contributed a number of models, which he hasclassified into off-line and on-line models (Aravindan et al., 1995). As the name implies,off-line models are applied before the actual production starts, whereas on-line modelsare applied during production. This paper reports the research work in whichTaguchi’s off-line model was considered and its role in bringing out agility inmanufacturing companies was examined through subjective and non-subjectiveexperimentation.
Distinguishing features of agile manufacturingA literature overview indicates that the term AM was coined in the early 1990s(Ramasesh et al., 2001). According to one definition:
AM is the science of a business system that integrates management, technology andworkforce making the system flexible enough for a manufacturer to switch over from onecomponent that is being produced to another component that is desired to be produced in acost-effective manner, in a short time within the framework of the system (Chowdiah, 1996).
Fundamentally AM emphasizes upon the design and development of manufacturingsystems which would enable the enterprises to quickly respond to the dynamicdemands of the customers (Hormozi, 2001) without sacrificing the profit margin (Poweret al., 2001). Although various definitions and interpretations of AM are present in
Agilemanufacturing
environment
577
literature, it is very rare to find research papers reporting the distinguishingcharacteristics of AM.
One of the present research requirements is the identification of distinguishingfeatures of AM from that of traditional manufacturing principles. A literature overviewcombined with appropriate interpretations enabled us to identify 20 distinguishedfeatures (van Assen et al., 2000; Bajgoric, 2001; Crocitto and Youssef, 2003; Goldmanet al., 1995; Montgomery and Levine, 1996; Narain et al., 2000; Onuh and Hon, 2001;Pawar and Shaiffi, 2002; Power et al., 2001; Spring and Dalrymple, 2000). The detailsare presented in Table I. As shown, first, AM calls for organizational restructuring byincorporating team-based architectures (Ebrahimpur and Jacob, 2001). Someorganizations have already started progressing in this direction. For example, mostof the software developing companies adopt team management principles to work withthe focused approach for attaining goals specified to teams. This process is facilitatedby instituting closely packed human and information networks, which significantlyimprove both quality and the productivity. Another important criterion is the aspect ofconverting static organization into learning organizations. In traditional organizations,employees after getting initial training become specialists in their area of work. In anAM environment, as processes and products are required to be frequently varied,employees are required to learn new knowledge and skills continuously. For thispurpose the manufacturer should be willing to promote and invest on relevantactivities that would facilitate experimentation and innovations (Lee et al., 2000).Appropriate measures should be taken to tune the employees to become learners ratherthan mere job executors. Another salient point insisted is about the supply chainmanagement (SCM) (Power et al., 2001). Traditional organizations adopt onlysubcontracting procedure whereas in an AM environment, depending upon thecustomer’s dynamic demands, new technologies and methods would have to beincorporated as developing the same within the organization would be quiteuneconomical. Hence, the need arises for employing suppliers as partners. As suitablesuppliers are to be selected amongst many and they have to be managed, SCM assumesspecial significance in an AM environment.
An important point of concern is about the time required for an organization toattain agility (van Assen, 2000). This will vary depending upon various factors. If bothmanagement and employees are enthusiastic, the transformation time needed will bevery less. Likewise if the management has already adopted team managementstructure, the time required will be very much less. Another aspect that concerns themanufacturing community more is the strategies to be adopted in an AM environment.The major manufacturing strategies are quality improvement, productivityenhancement, time management and cost reduction (Murugesh et al., 1997). Of all,quality assumes special importance, as quality has been a major factor in attainingcompetence and sustenance in global market. Meanwhile there is a fear amongmanufacturing community about the increased investment and changing approachesbeing called for in attaining higher level of quality in the emerging AM paradigm. Inorder to overcome this state of affair, it is suggested that the existing quality strategiesmay be adopted in an AM environment and the techniques and tools for attaining themmay be suitably refined (Crocitto and Youssef, 2003). The contribution in this direction,by either theorists or practitioners, most preferably by both will allay the fear ofmanufacturers over the implementation of quality strategies in an AM environment.
JMTM16,6
578
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Table I.Distinguishing featuresof traditional and agile
manufacturingenvironments
Agilemanufacturing
environment
579
Hence, a small piece of research work was carried out for investigating the suitabilityof applying Taguchi’s off-line model in an AM environment. Before discussing thedetails of this work, the salient features of Taguchi’s off-line model are presented in thenext section.
Taguchi’s off-line modelIn recent years, significant efforts are being made in manufacturing arena to reduce thelead-time of design and production (Ebrahimpur and Jacob, 2001). For example,currently it takes more than a year to float an automobile vehicle into market even afterthe successful completion of its design. The main reason attributed to this situation isthat, production cannot be started before the design is subjected to exhaustive testing.In order to test the validity of design, prototype models are constructed and a largenumber of experiments are required to be conducted. This process consumessignificant amount of time and money and requires patience. Taguchi has offeredsolution to overcome this situation (Taguchi, 1989; Ross, 1996). The entire procedureinvolved in conducting experiments (Antony and Antony, 2001) using Taguchi’soff-line model is depicted in Figure 1. As shown, the process starts with the precisedefinition of the problem. Based on the problem definition, the factors that areresponsible for the existence of the problem are identified. Against each factor, thelevels at which they act are to be identified. For a single problem, a number of factorswith at least two levels can be identified. A large number of experiments are requiredto be conducted to identify the best combination of factors and their correspondinglevels. However, it is not viable to conduct all those experiments.
In order to reduce the number of experiments without losing the chance ofidentifying the optimal combination of factors and their corresponding levels, Taguchihas advocated the exploitation of Latin square along with a graphical tool named aslinear graph. The Latin square with its corresponding linear graph is called asorthogonal array (OA). The elements of OAs were developed based on equally likelyprobability of occurring the sample of events from a lot. OAs are denoted by Taguchias L4, L8, L16 etc. Presumably “L” refers to Latin square. The suffixes indicate thenumber of rows of the corresponding OA. The number of rows indicates the number ofexperiments to be conducted. Figure 2 shows L4 orthogonal array. The factors areassigned to the columns according to the specification of linear graph. Columns 1 and 2can exist separately and the column 3 would contain the elements, which would reflectthe combination of the elements of columns 1 and 2. The numbers in the matrixindicate the levels of the corresponding factors.
The design of experiments using OAs is illustrated by considering the L4 OAshown in Figure 2. Experiment 1 is designed by considering level 1 of factor I, level 1 offactor II and level 1 of combination of factors I and II. In cases, where the combinationeffect of factors I and II is not significant, column 3 is ignored. After obtaining theapproval from the management, the experiments are conducted and the results arerecorded. Followed by this, the results are plotted on a graph for initial interpretationand further analysis is made possible by statistical tool namely ANOVA by whichsignificance of factors are ascertained. Further, calculation of “percentagecontribution” is used to verify whether any significant factors are left out. If so, thenthey are traced down and included in a new OA, and further experiments areconducted. If any one of the experiments has shown optimum results, then the
JMTM16,6
580
Figure 1.Implementation
methodology of Taguchi’soff-line quality control
model
Agilemanufacturing
environment
581
corresponding factors and their levels can be implemented in real production. Howeverthis will occur very rarely because OA considered initially, always a low resolution oneand there may be skepticism about the optimality of factors. To overcome this, a highresolution OA has to be considered and experiments have to be conducted according tothis OA. The results of analysis should be utilized to design a modified experiment,which should be conducted with the aim to attain optimality. This is known asconfirmation experiment whose results must further be interpreted and analyzed todetermine whether optimality has been achieved in practice. If it is felt that optimalityis yet to be achieved, then this process should be repeated. Soon after the optimality isachieved in an experiment, then the setup should be implemented in real situation.
A careful analysis would indicate that Taguchi’s off-line model is useful in twoways. First, the time taken in experimentation is greatly reduced. Second, the quality ofresults obtained are good since the model adopts the OAs for conducting experimentswhich represent the accurate sample of the entire domain of experimentation.Interestingly, these two benefits are found to be coinciding with the imperatives of AM.
Taguchi’s off-line model in AMAn overview on the literature indicates that all the research works involving Taguchi’soff-line models have been conducted by considering quantitative features (Antony,2002) such as cutting speed, feed, depth of cut, etc. In the case of an AM environment,experiments are required to be conducted not only in areas involving quantitativefeatures but also in subjective areas. In other words, in an AM environment, the qualityof not only the products but also of the system should be ascertained. This is due to thereason that, the AM system is not static and is dynamic in nature. Each time a newproduct or variant of existing product is to be manufactured according to customers’requirements, the quality of the system to be employed for the purpose is required to beplanned in advance.
A critical study indicates that Taguchi’s off-line model can also be exploited forensuring the quality of the system employed in an AM environment (Starkey et al.,1997). Whenever a new product is to be introduced experiments need to be designedbased on few or all criteria described in Table I. For example if organizational structureis considered as a factor, then semi-vertical structure and full vertical structure arechosen as its levels. An important observation in this regard is the difficulty ofconducting experimentation. Experimentation consisting of organizational structure as
Figure 2.Features of L4 orthogonalarray
JMTM16,6
582
a factor cannot be practically and feasibly conducted since installing two structuressimultaneously or in succession is not possible in practice. Delphi approach can beexploited to overcome this situation. Delphi approach calls for interviewing the expertsusing carefully designed questionnaires and deriving their unbiased opinions. Theresponses are analyzed to determine the optimum results (McCarthy and Atthirawong,2003). Hence, according to the Delphi approach, after designing experiments forenhancing agility by considering critical factors, the executives and employees may beinterviewed to evaluate the success rate of each experiment. In case, the opinionderived from different personnel varied widely, then an exposure program may beorganized and again interviews may be conducted to assess the success rate of thecorresponding experiment. In fact, this approach is a diluted form of brainstormingsessions as insisted by the researchers for conducting experiments based on Taguchi’soff-line method (Antony, 1999, 2001).
In the case of assessing quality of products to be introduced in the AM environment,real time experimentation may be conducted by employing Taguchi’s off-line model.However, even in this case, exploiting new technologies and methods would not be aprudent proposition and hence, real time experimentation is not encouraged. Thereason is that, installing experimental setup requires enormous time and money, bothare critical factors in the AM environment. Hence, in the case of optimizing quality ofproducts in the AM environment too, it is suggested that the Delphi approach may beemployed. Further, the Delphi approach for experimentation should be so structuredthat the analysis of results demand the use of simple statistical tools to avoid confusionand consumption of excessive time and money while identifying the optimal set-ups.On the whole, it is appraised that Taguchi’s off-line model would be a suitabletechnique to be employed in the AM environment, provided its scope should beenlarged from product quality to system quality aspects. Moreover, as narrated in thissection, the conduct of experiments in the AM environment should be modified to theconduct of interviews by adopting the Delphi approach. The exact methodology ofexploiting Taguchi’s off-line model in the AM environment can be understood byreferring to the case study presented in the next section.
Case studyIn order to testify the theoretical confabulations of this research project, interest wasevoked to carry out a case study. For this purpose, the status of AM in variousindustrial sectors of manufacturing was overviewed. Even though the manufacturingcommunity has just begun to hear much about agility, it is interesting to note thatsome industries have already started incorporating certain AM criteria. For example incomputer industry, new models are quite frequently introduced to meet the needs ofcustomers of different nature. However this trend is yet to infiltrate into other areas likethe field of mechanical engineering, where in spite of tremendous technologicaldevelopments (CAD, CAM, Robotics, etc.) having taken place, they are not exploiteddecisively to achieve agility. This has resulted in declined growth of companies,producing traditional products like pumps, compressors etc. One exception to thisstatement is the automobile industry in which new models are brought out in relativelylesser time during the recent times. In this context, it was felt necessary to apply thetheoretical developments to a mechanical engineering industry, which is not yetfamiliar with AM principles. Accordingly, a small-sized company involved in
Agilemanufacturing
environment
583
manufacturing pumps for domestic and agricultural purposes was identified. Thecompany is located in the city of Coimbatore, situated in the southern part of India.Coimbatore city is known for textile and mechanical engineering industries. Besidesfew electronic-based industries also have come up recently. Although a good industrialculture is prevailing in this city, the annual financial turnover of the industry as awhole is only moderate since the majority of the industries are small-sized and veryfew are medium-sized. As the turnover is not very good, these companies delay theirdecision to invest on the latest manufacturing technologies and philosophies. In spite ofthis discouraging situation, of late, few companies have started showing keen interestin adopting new manufacturing technologies and philosophies. The company chosenwas one among them.
The company has three plants and one of them was selected for our study. Thatplant has 75 employees including executives and produces three models of its productnamely submersible, jet, and centrifugal pumps. Out of these, the submersible pump isthe most important one because it contributes maximum profit to the company.Moreover, customer base is fast depleting for the other two types of pumps, whereas itis on increasing trend for the submersible pumps. Hence, it was decided that anysuggestion for improving its manufacturing would be highly useful to the company forits growth. Due to this reason, it was decided to restrict the study to the manufacturingof the submersible pumps. Even though description of the operational features of thesubmersible pump is beyond the scope of this paper, in order to have a clearperspective of the submersible models, the parts and specification of them arepresented in Table II.
In order to infuse agility using Taguchi’s off-line model, two phases of activitieswere required to be carried out. In the first phase, agile product design was considered.The most customer sensitive and important quality related features were given prioritywhile collecting data. These data were used to identify the factors, which would behelpful in imparting agility if a new model were to be designed. The initial analysisindicated that most of the factors are static in nature. This means, the company cannotchange values of these factors, as it is not within its control. For example the operatingvoltage cannot be varied since electric power is supplied at standardized voltage by thegovernment run Power Corporation. Hence, only the variables within the discretion ofthe design engineer were considered as factors for imparting agility in product design.Accordingly four factors, namely: impeller diameter, impeller thickness, speed, andimpeller material were chosen. Apart from this, three more customer sensitive factors,which are not incorporated in existing design, were considered. They were hardness ofimpeller shaft, incorporation of remote control, and provision for sump level controller.After identifying the factors, levels of each factor had to be determined. It was foundout that the existing levels were acceptable to the company since there was noconsiderable quality problem experienced in the existing design. Hence, it was decidedto have level 1 as the existing parameters. However this could be done only in the caseof existing factors and could not be done in the case of newly added factors. The factorsand levels for agile product experimentation are shown in Table III.
On considering the available facilities and time, it was decided to choose L8 OA(Ross, 1996). Seven factors were directly assigned against each column and Table IVwas developed by allowing the factors to take their levels. As mentioned earlier, in anAM environment, long lead-time cannot be tolerated. It was decided to look for other
JMTM16,6
584
Det
ails
Mod
el1
Mod
el2
Mod
el3
Mod
el4
Mod
el5
Mod
el6
Mod
el7
Mod
el8
Cod
eR
F1/
10R
F7/
6R
F4/
8R
F2/
12R
F1/
15R
F7/
9R
F4/
12R
F2/
16P
ower
inh
orse
pow
er(H
P)
0.5
11
11
1.5
1.5
1.5
Sin
gle
or3
ph
ase
11
11
11
11
Vol
tag
e(V
)24
024
024
0,24
024
024
024
024
024
0F
req
uen
cy(H
z)50
5050
5050
5050
50C
urr
ent
inam
per
es6
99
99
1212
12S
pee
din
rev
olu
tion
sp
erm
inu
te(r
pm
)2,
800
2,80
02,
800
2,80
02,
800
2,80
02,
800
2,80
0T
otal
hea
din
met
res
(m)
4827
3250
7240
4867
Dis
char
ge
m3/h
our
16.
63.
62
16.
63.
62
Imp
elle
rty
pe
Rad
ial
Rad
ial
Rad
ial
Rad
ial
Rad
ial
Rad
ial
Rad
ial
Rad
ial
Imp
elle
rm
ater
ial
Nor
yl
Nor
yl
Nor
yl
Nor
yl
Nor
yl
Nor
yl
Nor
yl
Nor
yl
No.
ofst
ages
106
812
159
1216
Effi
cien
cyin
per
cen
tag
e16
2826
2319
2927
20P
rice
inIn
dia
nru
pee
s(R
s)8,
600
10,0
0010
,000
10,0
0010
,000
12,0
0012
,000
12,0
00
Table II.Parts and specification
details of currentlymanufactured models of
submersible pumps
Agilemanufacturing
environment
585
alternatives in place of conducting practical experiments. Hence, initially, there was atendency to prefer computer-based animation and simulation packages. However itwas noted that many simulation packages were developed based on unrealisticassumptions and hence they fail to represent the reality. Their validity in practicalsituations is uncertain. Hence, the Delphi approach of interviewing experts forassessing the results of the experiments was adopted. Since the Delphi approach wasused, randomizing and repeating of experiments were not carried out. Little difficultywas experienced in adopting the Delphi approach, as it was found out that employeesof the company were not trained on latest developments. Even managerial levelemployees did not have sufficient opportunity to keep themselves abreast with thelatest developments. But when informed, the management showed keen interesttowards adopting them. An attempt was made to expose the details of AM to all the tenmanagerial level employees. Out of these, two executives assimilated and showedenthusiasm in associating with this project. Hence these two executives were requestedto respond according to the requirements of the Delphi approach. But, instead ofpreparing a questionnaire, due to its simplicity, the Table IV itself was shown to theexecutives to obtain the responses.
The opinions expressed by the two executives are presented in Table V. One of theauthors personally sat with the executives to derive their opinions and filled the lastthree columns of Table V. This personal appraisal was useful in preventing theexecutives from misinterpreting the details. As shown in Table V, the first experimentcorresponded to the existing manufacturing practice. This experiment was consideredas the basis and both the executives assessed the efficiency improvement or decrementas the result of proposed experiments. Quite interestingly, both anticipated theefficiency improvement in all of the remaining experiments. The difference inefficiency improvement, anticipated by both executives, ranged between only 5 percentand 10 percent. Hence the mean values of anticipated efficiencies proposed by bothexecutives against each experiment were used for subsequent analysis.
The conventional Taguchi’s approach envisages the use of statistical techniques foranalyzing the results of experiments. Since it is a tedious process, it was decided toanalyze the results using a simple two-axis graph to extract optimum parameters byeither extrapolation or interpolation. Accordingly, the graph shown in Figure 3 wasdeveloped. As shown, the efficiencies against each experiment were plotted.Efficiencies anticipated by the executives showed uniform increasing or decreasingtrends. The anticipated efficiencies indicated increasing trend from experiment 1 to 3.Between the experiments 3 and 6, it indicated uniformly intermittent increasing and
LevelsSl. no. Factors Level 1 Level 2
1 Impeller diameter in mm 75 802 Impeller thickness in mm 25 303 Speed in rpm 2,800 3,0004 Material of impeller Noryl Gunmetal5 Hardness in HRC 22 256 Remote control Wire control Cordless7 Sump level controller Wire Ccontrol Cordless
Table III.Factors and levels chosenfor agile productexperimentation
JMTM16,6
586
12
34
56
7F
acto
rs/
exp
t.n
o.Im
pel
ler
dia
.in
mm
Imp
elle
rth
ick
nes
sm
mS
pee
dR
PM
Imp
elle
rm
ater
ial
Har
dn
ess
(HR
C)
Rem
ote
con
trol
Su
mp
lev
elco
ntr
olle
rR
esu
lt(e
ffici
ency
)In
crea
sein
effi
cien
cyR
eact
ion
s
175
252,
800
Nor
yl
22W
ire
con
trol
Wir
eco
ntr
olM
oder
ate
0(r
efer
ence
)–
275
252,
800
Gu
nm
etal
25C
ord
less
Cor
dle
ssL
ow
375
303,
000
Nor
yl
22C
ord
less
Cor
dle
ssH
igh
Mor
ed
isch
arg
e4
7530
3,00
0G
un
met
al25
Wir
eco
ntr
olW
ire
con
trol
Mod
erat
eM
ore
dis
char
ge
580
253,
000
Nor
yl
25W
ire
con
trol
Cor
dle
ssH
igh
Mor
ed
isch
arg
e/h
igh
hea
d/h
igh
spee
d6
8025
3,00
0G
un
met
al22
Cor
dle
ssW
ire
con
trol
Mod
erat
e
780
302,
800
Nor
yl
25C
ord
less
Wir
eco
ntr
olV
ery
hig
hM
ore
dis
char
ge/
hig
hh
ead
880
302,
800
Gu
nm
etal
22W
ire
con
trol
Cor
dle
ssM
oder
ate
Table IV.Design of experiments for
product agility
Agilemanufacturing
environment
587
Fac
tors
/ex
pt.
no.
Imp
elle
rd
ia.
inm
m
Imp
elle
rth
ick
nes
sm
mS
pee
dR
PM
Imp
elle
rm
ater
ial
Har
dn
ess
HR
CR
emot
eco
ntr
olS
um
ple
vel
con
trol
ler
Res
ult
(effi
cien
cy)
Incr
ease
(þ)/
dec
reas
e(2
)in
effi
cien
cyR
eact
ion
s
175
252,
800
Nor
yl
22W
ire
con
trol
Wir
eco
ntr
olM
oder
ate
0(r
efer
ence
)–
275
252,
800
Gu
nm
etal
25C
ord
less
Cor
dle
ssL
owþ
1025
%
375
303,
000
Nor
yl
22C
ord
less
Cor
dle
ssH
igh
þ25
30%
mor
ed
isch
arg
e4
7530
3,00
0G
un
met
al25
Wir
eco
ntr
olW
ire
con
trol
Mod
erat
eþ
1520
%m
ore
dis
char
ge
580
253,
000
Nor
yl
25W
ire
con
trol
Cor
dle
ssH
igh
þ25
50%
mor
ed
isch
arg
e/h
igh
hea
d/h
igh
spee
d6
8025
3,00
0G
un
met
al22
Cor
dle
ssW
ire
con
trol
Mod
erat
eþ
1530
%
780
302,
800
Nor
yl
25C
ord
less
Wir
eco
ntr
olV
ery
hig
hþ
3060
%m
ore
dis
char
ge/
hig
hh
ead
880
302,
800
Gu
nm
etal
22W
ire
con
trol
Cor
dle
ssM
oder
ate
þ15
30%
Table V.Design of experiments forproduct agility(consolidated)
JMTM16,6
588
decreasing trends. From experiment number 6, it increased to a peak towardsexperiment number 7 and then showed decreasing trend towards the eighthexperiment. On the whole, it revealed that experiment number 7 would offer maximumefficiency. However there was a chance that if an experiment is designed byincorporating intermediate levels of non-sensitive factors of the seventh and eighthexperiments, it might still yield better efficiency. During the interviews, the executivesindicated the factors, which are very sensitive from manufacturing point of view.According to them, the levels of these factors cannot be so easily altered. Hence it wasdecided to conduct one confirmation experiment (that is, by interviewing the sameexecutives again) by incorporating revised and modified levels of the non-sensitivefactors. The details of this experiment are shown in Table VI. Both executives wereinterviewed by informing about the details of the ninth experiment. The meananticipated efficiency increase was 35 percent, which is the highest of all theexperiments conducted so far. This confirmed with the graphical extrapolation thatwas carried out theoretically. Moreover the executives anticipated that onincorporation of the factors and levels of confirmation experiment, there will be anincrease of 75 percent increase in discharge volume and delivery head in comparison tothe present factors and levels adopted. Hence, it was inferred that agile product designhas been achieved.
Agile process designThe first phase of the experimentation revealed the possibilities of imparting agility inproducts by refining the existing design by conducting limited number of experimentsusing the Delphi approach. As mentioned in the previous section, it was anticipatedthat adoption of a suitable OA for the purpose of interviewing executives wouldprovide an optimum solution for imparting system agility. However the systembecomes agile only if the agility criteria are implemented in a feasible form. In order tocarry out this task, the second phase of experimentation became a necessity. Hence,theoretically drawn 20 agility criteria (shown in Table I) were examined for thepossibilities of adoption in the company. Out of all, it was found that 11 criteria werefound to be critical and feasible for adoption in the company. The fundamental reasonbehind in selecting these criteria was that, the company has not yet adopted thesecriteria, but it was ready to implement them in stages. However implementation instages would consume considerable amount of time. Hence, it was discernible that a
Figure 3.Graphical analysis of theresults of experiments on
agile product design
Agilemanufacturing
environment
589
12
34
56
7
Fac
tors
exp
t.n
o.
Imp
elle
rd
ia.
inm
m
Imp
elle
rth
ick
nes
sm
mS
pee
dR
PM
Imp
elle
rm
ater
ial
Har
dn
ess
(HR
C)
Rem
ote
con
trol
Su
mp
lev
elco
ntr
olle
rR
esu
lt(e
ffici
ency
)In
crea
sein
effi
cien
cyR
eact
ion
s(l
evel
ofac
cep
tan
ce)
780
302,
800
Nor
yl
25C
ord
less
Wir
eco
ntr
olV
ery
hig
hþ
3060
%m
ore
dis
char
ge/
hig
hh
ead
880
302,
800
Gu
nm
etal
22W
ire
con
trol
Cor
dle
ssM
oder
ate
þ15
30%
9C
onfi
rmat
ion
exp
erim
ent
8030
2,80
0N
ory
l23
Sem
i-p
ower
con
trol
Sem
i-p
ower
con
trol
Bes
tþ
3575
%m
ore
dis
char
ge/
hig
hh
ead
Table VI.Design of experiments forproduct agilityconfirmationexperimentation
JMTM16,6
590
most feasible combination of traditional and agility criteria would serve the immediatepurpose of attaining agility in the system. This will enhance the implementationfeasibility of agile product design achieved during the first stage. The 11 criteria wereconsidered as factors and subsequently their levels were decided. The levels werechosen in such a way that traditional approach was considered as level 1 and agileapproach as level 2 and were denoted by the symbol followed by numerals 1 or 2 as thecase may be. L12 OA was chosen and accordingly. Table VII was formed by assigningthe factors and their levels.
Unlike in the case of agile product design, Table VII was not shown to theexecutives since it was realized that it would be difficult for the executives to imaginethe holistic view of each experiment. In order to overcome this, a questionnairecontaining 12 questions was prepared for each experiment. Both executives were giventhe questionnaire pertaining to experiment 1. The executives were guided whileresponding to the questions. The remaining experiments were conducted byinterviewing the executives by modifying the questionnaire suitability. But the overallformat was not changed. In addition to the questionnaire, each executive was given aresponse sheet and requested to encircle against each question to denote the successrate. Due to lack of space, the questionnaire and the response sheet are not included inthis paper. The encircled numbers were multiplied by a factor 10 to convert them into apercentage of success rate. The mean percentage success rates against all questionswere identified as agility success rate corresponding to that experiment. Apart fromthis, the executives were requested to assess their acceptance rating of eachexperiment. In some cases, it was noted that, in spite of higher efficiencies, executivesexpressed different rates of acceptance for implementing the factors and levels for theexperiments in practice due to various reasons. The efficiencies and acceptance ratingsalong with the mean values are shown in Table VIII.
The results of agile system design experiments are shown in Figure 4 as atwo-dimensional graph. Table VII reveals that experiments 2, 8, and 12 result inmaximum possible efficiencies in obtaining system agility. Hence, the levels ofdifferent factors in these three experiments were analyzed further and the levels withmaximum success efficiency rating were extracted to create a level for confirmationexperiment. The details are presented in Table IX. In this, experiment 13 is theconfirmation experiment. The assignments of levels are described here for factor 1. Inthe case of factor 1, the level 2 was given a high rating by the executives. Hence, in theconfirmation experiment level 2 was assigned under factor 1. Likewise all levels wereallotted and a corresponding questionnaire was prepared. Again each executive wasinterviewed and the final test results were obtained. These results are shown inTable X. The mean agility efficiency of 67.6 percent was anticipated by the executives.
Followed by this, acceptance level was also revealed by them whose mean was 58percent. As these two values were highest of all other experiments, it was inferred thatif the company implements the agility criteria with the levels shown in confirmationexperiment (number 13), then the journey for attaining agility would be smooth andquick.
Concluding remarksAM is also viewed as a set of bridges which are meant for integrating technologies andmanagement philosophies (Sarkis, 2001). Most of the other methods and philosophies
Agilemanufacturing
environment
591
Fac
tors
12
34
56
78
910
11
Ex
pt.
no.
Org
ani-
zati
onst
ruct
ure
Man
ufa
c-tu
rin
gse
t-u
pS
tatu
sof
qu
alit
y
Em
plo
yee
inv
olv
e-m
ent
Cu
stom
erre
spon
se
Des
ign
imp
rov
e-m
ent
Man
ufa
c-tu
rin
gp
lan
nin
g
Su
pp
lych
ain
man
agem
ent
Em
plo
yee
stat
us
Au
tom
atio
nty
pe
ITin
teg
rati
on
1O
1M
1S
1E
1C
1D
1M
P1
SC
M1
ES
1A
1IT
12
O1
M1
S1
E1
C1
D2
MP
2S
CM
2E
S2
A2
IT2
3O
1M
1S
2E
2C
2D
1M
P1
SC
M1
ES
2A
2IT
24
O1
M2
S1
E2
C2
D1
MP
2S
CM
2E
S1
A1
IT2
5O
1M
2S
2E
1C
2D
2M
P1
SC
M2
ES
1A
2IT
16
O1
M2
S2
E2
C1
D2
MP
2S
CM
1E
S2
A1
IT1
7O
2M
1S
2E
2C
1D
1M
P2
SC
M2
ES
1A
2IT
18
O2
M1
S2
E1
C2
D2
MP
2S
CM
1E
S1
A1
IT2
9O
2M
1S
1E
2C
2D
2M
P1
SC
M2
ES
2A
1IT
110
O2
M2
S2
E1
C1
D1
MP
1S
CM
2E
S2
A1
IT2
11O
2M
2S
1E
2C
2D
2M
P1
SC
M1
ES
1A
2IT
212
O2
M2
S1
E1
C1
D1
MP
2S
CM
1E
S2
A2
IT1
Table VII.Design of experiments forsystem agility
JMTM16,6
592
emerged during recent years emphasize either partial or no integration of technologyand management. As the trend of AM spreads very fast and has already captured quitea number of manufacturing companies and research laboratories, it is high time thatthe conventional techniques applied in traditional manufacturing environments arerequired to be examined for their compatibility in AM environments. In this context,the piece of research work reported in this paper was taken up by consideringTaguchi’s off line quality control method in AM environment. Since the authors areresiding in a city with companies wherein the recent research findings take a long timeto penetrate, it was not possible to identify an AM company. Hence a manufacturingcompany, which is following traditional approach, was chosen. Fortunately the
Experiment
Executive1 SR
%
Executive2 SR
%
SRaverage
%
Executive1 AL
%
Executive2 AL
%
ALaverage
% Remarks
1 59.00 55.00 57 50 40 45
2 60.00 59.00 60 60 70 65 High value
3 56.40 52.70 54 60 60 60
4 56.40 54.50 55 50 55 53
5 58.20 55.50 57 60 50 55
6 57.30 52.70 55 40 50 45
7 56.36 52.70 54 40 50 45
8 60.90 55.50 58 50 40 45
9 58.20 55.50 57 50 50 50
10 55.50 51.80 54 60 50 55
11 57.20 57.30 57 50 50 50
12 59.10 56.40 58 60 50 55
Note: SR ¼ success rate; AL ¼ acceptance level
Table VIII.Percentage of efficiencies
and acceptance values
Figure 4.Graphical analysis of theresults of experiments on
system
Agilemanufacturing
environment
593
Fac
tors
12
34
56
78
910
11
Ex
pt.
no.
Org
ani-
zati
onst
ruct
ure
Man
ufa
c-tu
rin
gse
t-u
pS
tatu
sof
qu
alit
yE
mp
loy
eein
vol
vem
ent
Cu
stom
erre
spon
seD
esig
nim
pro
vem
ent
Man
ufa
c-tu
rin
gp
lan
nin
gS
up
ply
chai
nm
anag
emen
tE
mp
loy
eest
atu
sA
uto
mat
ion
typ
eIT
inte
gra
tion
2O
1M
1S
1E
1C
1D
2M
P2
SC
M2
ES
2A
2IT
28
O2
M1
S2
E1
C2
D2
MP
2S
CM
1E
S1
A1
IT2
12O
2M
2S
1E
1C
2D
1M
P2
SC
M1
ES
2A
2IT
113
O2
M1
S1
E1
C2
D2
MP
2S
CM
2E
S1
A2
IT2
Table IX.Confirmation experimentfor organizational agility
JMTM16,6
594
management showed keen interest on AM and the work enjoyed full support of bothmanagement and employees. In spite of this positive factor, the results evolved out ofthis research project are yet to be implemented, as the management is required to makea major decision in this direction. In future it is proposed to insist the management toimplement the results of this project, which will be monitored continuously, andchanges or modifications will be suitably carried out in the levels of factors. Furtherdue to shortage of time, Taguchi’s online quality control methods were not examinedduring this study which is left for future work. On the whole, this work creates animpression that Taguchi methods with suitable refinements will be useful in impartingagility in traditional organizations.
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Executive1 SR
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Executive2 SR
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environment
597