3.a Case Study Application of Six Sigma Methodology in a Small and Medium-sized Manufacturing...

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A case studyApplication of Six Sigma methodology

in a small and medium-sizedmanufacturing enterprise

Prabhakar KaushikMechanical Engineering Department, UIET, MD University, Rohtak, India

Dinesh KhandujaMechanical Engineering Department, NIT, Kurukshetra, India

Kapil MittalMechanical Engineering Department, YIET, Yamunanagar, India, and

Pawan Jaglan

Mechanical Engineering Department, NIT, Kurukshetra, IndiaAbstract

Purpose Six Sigma within small and medium-sized enterprises (SMEs) is rapidly emerging as thenew wave of change in Six Sigma. The purpose of this paper is to review the implications of applyingSix Sigma methodology over the SMEs, taking a specific case of a bicycle chain manufacturing unit.The study could be a paradigm initiative towards high quality products and services at low cost forevery SME.Design/methodology/approach Based on the literature, this paper proposes a process flow chartto present a one-shot picture of the Six Sigma application in a bicycle chain manufacturing unit whichfalls in an SME environment. The methodology adopted is DMAIC methodology of Six Sigma, whichhad been mostly successful so far in large-scale industries. The methodology has been applied toreduce the bush rejection rate (bush is an important component of a cycle chain) by reducing defectsinherent in the processes. The statistical techniques such as two sample t-test and process capability

analysis have been used to establish the process capability before and after the Six Sigma application.Findings This paper is an attempt to justify the highly useful role of quality managementtechniques like Six Sigma for SMEs which are normally presumed to be in the domain of largeindustries. In this paper, Six Sigma methodology has been applied to a small unit manufacturingbicycles chains with dwindling productivity levels. After applying Six Sigma it was found that thechain manufacturing firm can increase its profit by controlling high rejection rate of cycle chain bush.Application of Six Sigma project recommendation brought up the process sigma level to 5.46 from 1.40by reduction in bush diameter variation in the process of bicycle chain bush manufacturing. Thisincrease in sigma level is equivalent to monetary saving of Rs 0.288 million per annum, which is anoteworthy figure for an industry of such level. The success of this study paves the way to furtherextend the Six Sigma application to more such industries working in the SME environment.Originality/value This paper provides documented evidence of Six Sigma implementation in abicycle chain manufacturing unit which has been taken as representative of a small and medium-sizeindustry. The study will yield a great value to academics, consultants, researchers and practitioners of

Six Sigma.Keywords India, Manufacturing industries, Six Sigma, Small to medium-sized enterprises, DMAIC,DPMO

Paper type Case study

1. IntroductionSigma is a Greek letter representing standard deviation or the amount of variation withina given process (McAdam and Lafferty, 2004). According to Harry and Schroeder (2000),Six Sigma is a powerful breakthrough business improvement strategy that enables

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1754-2731.htm

Received August 2008Revised October 2008Accepted December 2008

The TQM JournalVol. 24 No. 1, 2012pp. 4-16r Emerald Group Publishing Limited1754-2731DOI 10.1108/17542731211191186

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companies to use simple and powerful statistical methods for achieving and sustainingoperational excellence. It is a business strategy that allows companies to drasticallyimprove their performance by designing and monitoring everyday businessactivities in ways that minimize waste and resources while increasing customer

satisfaction. Park (2002) described that Six Sigma implies three things: statisticalmeasurement, management strategy and quality culture. It is a measure of howwell a process is performing through statistical measurement of quality level. It is anew management strategy under leadership of the top management that createsquality innovation and total customer satisfaction. It is also a quality culture. Itprovides the way to do things right at the first time and to work smarter by usingdata information. It also provides an atmosphere to solve many critical-to-qualityproblems through team efforts. Statistical representation of Six Sigma describesquantitatively how a process is performing. The goal of Six Sigma is to designprocesses that do what they are supposed to do with very high reliability, ultimatelyproducing very consistent products and services (Coronado and Antony, 2002).The numerical goal of Six Sigma is reducing defects o3.4 parts per million also

known as defects per million opportunities (DPMO), reducing cycle time andreducing costs dramatically which impact the bottom line (Beharaet al., 1995; Goh andXie, 2004).

Although Six Sigma has been implemented with success in many largecorporations, there is still less documented evidence of its implementation insmaller organizations (Harry and Crawford, 2004). Due to growing importance ofsupply chain management issues in global market environment, large firms are heavilydependent on small- to medium-sized enterprises for the provision of high qualityproducts and/or services at low costs. The increasing demand for high qualityproducts and highly capable business processes by large organizations has leftno choice on the SMEs to consider the introduction of Six Sigma business strategy(Keller, 2003).

As small companies are more agile, it is mush easier to buy-in management supportand commitment, as opposed to large organizations. The education and trainingcomponent is much harder for smaller companies. Moreover, small companies do nothave the slack to free up top talented people to engage in training followed byexecution of Six Sigma projects as they are crucial to the day-to-day operations andproblem solving within the company. Being able to link compensation to Six Sigmaimplementation is much easier in small companies compared to a large company(Rowlands, 2004).

2. Research backgroundWessel and Burcher (2004) in their study identify the specific requirements forimplementation of Six Sigma based on a sample of SMEs in Germany. This study also

examines how Six Sigma has to be modified to be applicable and valuable in an SMEenvironment. This is the first study of its kind to be carried out on Six Sigma survey inSMEs.

Burton (2004) proposes alternative Six Sigma deployment models that allow SMEsto implement Six Sigma at a pace where they can digest the methodology and achievebenefits, without significant resource commitment and overhead structure of thetraditional Six Sigma. As a result, SMEs are sometimes able to achieve faster and moreimpressive benefits than their large customers. He also recommends an eight-stepmethodology for successful deployment of Six Sigma within SMEs.

Applicatioof Six Sigm

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Snee and Hoerl (2003) argue that there is nothing inherent in Six Sigma that makes itmore suitable for large companies. They also suggest that the greatest barrier toimplementation in small companies to date has been the way the major Six Sigmatraining providers have structured their offerings. More recently, as more and more sets

of deployment guides and training materials have become available, the pricingstructures have begun to change. Today, it is much easier for SMEs to obtain goodexternal resources without a large up-front payment.

Once an owner of the business (in small firms) is convinced of the advantagesconferred by Six Sigma and visualizes the benefits, it is much easier to implement SixSigma at smaller firms and to realize its benefits (Adams et al., 2003). They suggestthat the initial focus on SMEs can be to reduce quality costs or waste in the system.Effort and investment, as well as results in smaller companies, are more visible withina short time.

Tennant (2001) argues that, in small organizations, if one visible and importantperson is actively against Six Sigma, then this attitude must change or the initiativemust be a non-starter. In other words, in small companies, the top management team

need to be visibly supportive of every aspect of Six Sigma initiative and they mustdemonstrate by their active participation, involvement and by their actions that suchsupport is more than lip service. He also accentuates the point that it is far moreimportant for small- and medium-sized enterprises to ensure that every iota of effort isdirected exactly where it is needed for maximum benefit, so an overall quality strategyplan is vital right from the start of the Six Sigma initiatives. Six Sigma is about overallmanagement strategy, culture and change, and the organization needs to build all ofthis into a sound corporate strategy plan.

Wilson (2004) identifies the following advantages for small-businesses embarkingon Six Sigma initiative:

. Stronger, more intimate relationships with customers.

.

A limited number of sites.. Fewer layers in the management hierarchy.

. Faster and effective internal communication.

. Strong owner influence.

3. About the case studyPresent study was carried out in a SMEs unit manufacturing bi-cycles at Sonipat inHaryana (India). The main product of the unit is manufacturing bi-cycle chain and thecomponents in manufacturing it are plate, pin, bush and roller as shown in Figure 1.

Bush is the main component of chain assembly and is originally in the form of a coil.

The tolerance limit of bush diameter is 5.23-5.27 mm. The initial observations showedvery high rejection rate in manufacturing of bush. The rejection rate of bush was 8.0percent because of high variation in bush diameter. So, there was a great need to reducerejection rate of bush by reducing defects inherent in the processes.

4. Application of Six Sigma DMAIC methodologyFor solving any problem, the methodology adopted must cover all possible causes ofproblem. If the methodology of problem solving is not comprehensive enough, thesolution obtained at completion will not be correct and problem will resurface sooner or

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later. A process flow chart is prepared to proceed in a sequential manner and to presenta one shot picture of the entire methodology, as shown in Figure 2.

In this paper, the high rejection problem of bush was studied in depth and all thefive phases in Six Sigma methodology, i.e. Define, Measure, Analyze, Improve andControl (DMAIC) has been successfully implemented to achieve the existing sigmaquality level from 1.40sto 5.46sas explained below.

4.1 DefineDefine the problem and define what customer requires (Henderson and Evans, 2000).

In define phase, process map and high-level process map a SIPOC (Supplier, Input,Process, Output, Customer) diagram was drawn for bush as shown in Figures 3 and 4.

These diagrams were used to document manufacturing sequence of bush and to

identify the process or product for improvement.

4.2 MeasureIn measure phase, a measurement system analysis is conducted which includes theGauge repeatability and reproducibility (Gauge R&R) studies. The purpose of GaugeR&R study is to ensure that the measurement system is statistically sound. GaugeR&R studies determine how much of the observe process variation is due tomeasurement system variation. Two persons are needed to perform this experiment,which in this case were the operator on line and the investigator. The sample size wasten and two readings were taken on each sample, thereby making a total of 40readings. The gauge used for this experiment was a micrometer.

From the results of Gauge R&R study, repeatability and reproducibility comes out

to be 27.02 percent and 0.00 percent and put the percentage study variation to be 27.02percent, which is o30 percent, indicating that micrometer was correct.

4.3 AnalysisAccording to Kapur and Feng (2005), the analyze phase examine the data collected inorder to generate a prioritized list of source of variation. Many statistical tools are usedto carry out the analysis which are explained as follows.

4.3.1 Process capability analysis. Process capability analysis was performed to findout actual state of the process. Rational sub-grouping was done and 20 samples were

BushInner plate

Inner link

Pin

Outer plate

Outer link

P

Roller

Figure Components of cycle cha


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drawn, in a group of five. Minitab was used to draw process capability analysis curvefor bush diameter as shown in Figure 5.

Z Bench sigma value of process was found to be 1.40 and existing DPMO level ofthe process comes out to be 80,213.07, which is remarkably high and this shows thatthere are lot of opportunities for improvement in the process.

4.3.2 Fishbone diagram. Using process capability analysis, the DPMO level andsigma level of the bush diameter rejection was known. Now it was the time to find outthe causes of bush rejection. Using expert experience and critical analysis of actualprocess, a Fishbone diagram (as shown in Figure 6) was drawn to find out the causes ofmore bush rejections.

Literature survey

Case study of manufacturing industry

Identification of problem

Data collection

Define

Measure

Analysis

Improve

Control

Improvement results

Conclusions

- Identify specific problem- Define customer requirements- Set goals- High-level process map

- Measurement system analysis- Identify variation due to measurementsystem

- Data collection plan

- Draw conclusion from data verification

- Process capability analysis- Determine root causes

- Map cause and effect diagram

- Design of experiment

- Create solution statement

- Implement improvement solutions

- Monitor improvement progress

- Make control charts

- Establish standard measures to maintain performance

DMAIC

methodology

Figure 2.Flow diagram ofmethodology adopted

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4.3.3 Two-sample t-test. In two-sample t-test four important factors were taken forstudy identified from Fishbone diagram. In first case, two-sample t-test was done foroperator skill (unskilled and skilled) by taking the sample size of 50 each for skilledand unskilled operator. In second case, two-sample t-test was done for rod replacement

(after 15 h and after 25 h) by taking the sample size of 50 each for rod replacement after15 and 25 h. In third case, two-samplet-test was done for regrinding of curl tool (after20 h and after 30 h) by taking the sample size of 50 each for regrinding after 20 and30 h. In forth case, two-sample t-test was done for rod-holding mechanism (old andnew) by taking the sample size of 50 each for old and new mechanism.

First case: two-sample t-test for operator skill (skilled and unskilled) is shown inTable I.

Using Minitab, the two-sample t-test shows that as the p-value for bush diametercomes out to be 40.05 therefore operator skill cannot be a factor for bush rejection.

Raw material receipt

Reject

Cuttingand

curling

Inspection

Polishing

Hardening

Reject

Visualinspection

Assembly withinner plate

Ok

OkInspection

Figure Process map for bu

Cycle chainmanufacturing

unit

Bush rejectiondata

Processcapability and

critical analysisof rejection

DecreaseDPMO

Cyclemanufacturingmanagement

Customersatisfaction and

relationship

Six sigmamethodology

Thinking

Flow

High-level process map

Supplier Input Process Output Customer

Figure High-level process m

for bush rejecti


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Second case:two-samplet-test for rod replacement is shown in Table II.Since thep-value for rod replacement comes out to be o0.05 therefore this might be

a factor for bush rejection.Third case:two-samplet-test for curl tool regrinding is shown in Table III.Using Minitab, the two-sample t-test shows that as the p-value for curl tool

regrinding out to be 40.05 therefore this can not be a factor for bush rejection.Forth case:two-samplet-test for rod-holding mechanism is shown in Table IV.Since thep-value for rod-holding mechanism comes out to be o0.05, therefore this

might be a factor for bush rejection.

5.275.265.255.245.23

LSL

Process data

LSLTarget

USLSample meanSample NSD (within)

Potential (within) capability

Z.BenchZ.LSL

Z.USLCpkCCpk

Experiment

within performancePPM < LSLPPM > USL

PPM total

Process capability analysis for bush diameter

5.230005.25000

5.270005.24500

1000.01030

72,614.617598.46

80,213.07

1.401.46

2.430.490.65

Target USL

Figure 5.Process capability

analysis of bush rejectiondata before implementingDMAIC methodology

DMAIC methodology

Skill level of operatorCurl toolregrinding

Rodreplacement

Inconsistency inselecting the point

of checking

Machine Man

Material

Bushrejection

Instrumentcalibration

Inconsistent viewof the scale

Grade

Strip feedingmechanism

Rod-holding

mechanism

Method

Figure 6.Fishbone diagram

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4.4 ImproveIn improve phase, design of experiments was done to find out the optimum conditionsfor the vital few factors found out after the two-samplet-test.

These experiments were conducted to optimize the value of the parameters rod

replacement and rod-holding mechanism. A 2 2 experiment was designed, i.e. anexperiment with two factors each levels. Table V shows the significant vital factors forbush diameter variation.

Rod replacement N Mean SD SE mean

After 15 h 50 5.2490 0.0102 0.0014After 25 h 50 5.23800 0.00990 0.0014

Notes: Sample 1 was taken for rod replacement after 15 h, sample 2 was taken for rod replacementafter 25 h. Differencem(after 15 h)m (after 25 h). Estimate for difference: 0.005000. 95% CI fordifference: (0.001021, 0.008979). t-test of difference 0 (vs not):t-value 2.49. p-value 0.014.df 98. Both use pooled SD 0.0100

Table Two-sample t-test aCI: bush diameter, r

replaceme

Curl tool regrinding N Mean SD SE mean

After 15 h 50 5.24500 0.00678 0.00096After 25 h 50 5.24600 0.00808 0.0011

Notes: Sample 1 was taken for regrinding of curl tool after 20 h, sample 2 was taken for regrinding ofcurl tool after 30 h. Differencem(after 20 h)m(after 30 h). Estimate for difference: 0.001000. 95%CI for difference: (0.003960, 0.001960). t-test of difference 0 (vs not):t-value0.67.

p-value 0.504. df 98. Both use pooled SD 0.0075

Table ITwo-sample t-test a

CI: bush diametcurl tool regrindi

Rod-holding mechanism N Mean SD SE mean

New 50 5.25000 0.00904 0.0013Old 50 5.24500 0.00814 0.0012

Notes: Sample 1 was taken for new rod-holding mechanism, sample 2 was taken for old rod-holdingmechanism. Differencem (new)m (old). Estimate for difference: 0.005000. 95% CI for difference:(0.001586, 0.008414). t-test of difference 0 (vs not): t-value 2.91.p-value 0.005. df 98. Bothuse pooled SD 0.0086

Table ITwo-sample t-test a

CI: bush diametrod-holding mechanis

Operator N Mean SD SE mean

Operator 1 50 5.24600 0.00808 0.0011Operator 2 50 5.24500 0.00814 0.0012

Notes: Sample 1 was taken for unskilled operator, sample 2 was taken for skilled operator.Differencem(operator 1)m (operator 2). Estimate for difference: 0.001000. 95% CI for difference:(0.002220, 0.004220).t-test of difference 0 (vs not):t-value 0.62.p-value 0.539. df 98. Bothuse pooled SD 0.0081

TableTwo-sample t-t

and CI: bush diameoperator sk

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Minitab was used to plot the main effects plot and interaction plot between the vitalfew factors (rod replacement and rod-holding mechanism). Table VI shows thereadings of significant factors at various levels.

The main effect plot (Figure 7) suggests that rod replacement is a minor factor and

rod-holding mechanism is a major factor.Interactions plot (Figure 8) shows that the lines are not parallel to each other so

there are interactions present between the factors. The change in the response meanfrom the low to the high level of rod replacement depends on the level of rod-holdingmechanism.

4.5 ControlIn control phase,X/R control chart was drawn to visualize the presence of assignablecause of variation after implementing the changes in factors proposed by DOE and for

Vital factors Low level High level

Rod replacement 25 h 15 hRod-holding mechanism Old New

Table V.

Significant vital factorsfor bush diametervariation

Sl. No. Rod replacement Rod-holding mechanism Readings bust diameter (mm)

1 After 25 h Old 5.232 After 15 h Old 5.233 After 25 h New 5.244 After 15 h New 5.255 After 25 h Old 5.23

6 After 15 h Old 5.237 After 25 h New 5.248 After 15 h New 5.25

Table VI.Readings of significantfactors at various levels

Meanofbushdiameter

After 15 hAfter 25 h

5.246

5.244

5.242

5.240

5.238

5.236

5.234

5.232

5.230

NewOld

Rod replacement Rod-holding mechanism

Main effect plot for bush diameter

Figure 7.Main effect plot for bushdiameter

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ensuring that the process continues to be in a new path of optimization. A total of100-sample size was taken for drawingX/R chart. TheX/R chart is as shown in Figure 9.

5. Improvement resultsApplication of project recommendation brought up the sigma level to 5.46 with DPMOlevel of 0.02 (an improvement of 80,213.05) as shown in Figure 10, which is equivalentto monitory saving of Rs. 0.288 millions (1 US$Rs. 42 approximately) (see Appendix)which is substantial for a small organization. The chain bush manufacturing processhad been chosen in the bi-cycle chain manufacturing unit because it represents the

general process of a manufacturing unit operating in a SME environment. The success

Rod-holding mechanism

Mean

NewOld

5.250

5.245

5.240

5.235

5.230

Rod replacement

After 25 h

After 15 h

Interation plot for bush diameter

Figure Interactions plot for bu

diame

Sample

Samplemean

2018161412108642

5.27

5.26

5.25

5.24

5.23

X=5.25

UCL=5.26996

LCL=5.23004

Sample

Samplerange

2018161412108642

0.08

0.06

0.04

0.02

0.00

_R=0.03461

UCL=0.07318

LCL=0

X-R chart of bush diameter (mm)-

=

Figure X/R chart for bu

diameter afimproveme

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of Six Sigma application in this case study can definitely encourage the othermanufacturing units to use Six Sigma as a quality tool to reduce the losses in theirprocesses and reap rich benefits from it.

6. ConclusionSix Sigma for SMEs is an emerging topic among many academics and Six Sigmapractitioners over the last two to three years. Very few studies have been reportedabout the successful applications of Six Sigma in SMEs. As small companies are more

agile, it is mush easier to buy-in management support and commitment, as opposed tolarge organizations. The education and training component is much harder for smallercompanies. Moreover, small companies do not have the slack to free up top talentedpeople to engage in training followed by execution of Six Sigma projects as they arecrucial to the day-to-day operations and problem solving within the company. Beingable to link compensation to Six Sigma implementation is much easier in smallcompanies compared to a large company.

This paper is an attempt to justify the highly useful role of management techniqueslike Six Sigma for SMEs, which are normally presumed to be in the domain of largeindustries. Bush diameter variation is found to be a big problem in bi-cycle chainmanufacturing industry. Application of Six Sigma project recommendations broughtup the sigma level to 5.58 and estimated saving from the project after implementationis expected to be around Rs. 0.288 millions per annum which is substantial for anysmall manufacturing industry.

References

Adams, C.W., Gupta, P. and Wilson, C. (2003), Six Sigma Deployment, Butterworth-Heinemann,Burlington, MA.

Behara, R.S., Fontenot, G.F. and Gresham, A. (1995), Customer satisfaction measurementand analysis using Six Sigma,International Journal of Quality & Reliability Management,Vol. 12 No. 3, pp. 9-18.

5.275.265.255.245.23

LSUtegraTLSL

Process data

Sample N

SD (within)

LSL

Target

USL

Sample mean

100

0.00358

5.23000

5.25000

5.27000

5.25000

Potential (within) capability

CCpk

Z.Bench

Z.LSL

Z.USL

Cpk

Experimentwithin performance

PPM < LSL 0.01

PPM > USL 0.01

PPM total 0.02

Process capability of bush diameter (mm)

5.46

5.58

5.58

1.86

1.86

Figure 10.Process capabilityanalysis of bush rejectiondata after improvement

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Burton, T. (2004), Six Sigma for small and medium sized businesses, available at:www.isixsigma.com/library/content/ (accessed June 20, 2008).

Coronado, R. and Antony, J. (2002), Critical success factors for the implementation of Six Sigmaprojects in organization, The TQM Magazine, Vol. 14 No. 2, pp. 92-9.

Goh, T.N. and Xie, M. (2004), Improving on the Six Sigma paradigm, The TQM Magazine,Vol. 16 No. 4, pp. 235-40.

Harry, M. and Crawford, J.D. (2004), Six Sigma for the little guy, Mechanical Engineering,Vol. 126 No. 11, pp. 8-10.

Harry, M.J. and Schroeder, R. (2000), Six Sigma: The Breakthrough Management StrategyRevolutionizing the Worlds Top Corporations, 1st ed., Double Day A Division of RandomHouse Publication, New York, NY.

Henderson, K.M. and Evans, J.R. (2000), Successful implementation of Six Sigma:benchmarking: general electric company, Benchmarking: An International Journal,Vol. 7 No. 4, pp. 260-82.

Kapur, K.C. and Feng, Q. (2005), Integrated optimisation models and strategies for theimprovement of the Six Sigma process,International Journal of Six Sigma and competitiveadvantage, Vol. 1 No. 2, pp. 210-28.

Keller, P. (2003), Does Six Sigma work in smaller companies, available at:www.qualityamerica.com/knowledgecentre/articles/ (accessed July 25, 2008).

McAdam, R. and Lafferty, B. (2004), A multilevel case study critique of Six Sigma: statisticalcontrol or strategic change?, International Journal of Operations & Production

Management, Vol. 24 No. 5, pp. 530-49.

Park, S.H. (2002), Six Sigma for productivity improvement: Korean Business Corporations,Productivity Journal, Vol. 43 No. 2, pp. 173-83.

Rowlands, H. (2004), Implementation issues of Six Sigma in an SME, First InternationalConference on Six Sigma, Glasgow, December 16-17.

Snee, R.D. and Hoerl, R. (2003), Leading Six Sigma, Prentice Hall, Upper Saddle River, NJ.

Tennant, G. (2001), Six Sigma: SPC and TQM in Manufacturing and Services, AshgatePublishing, Aldershot.

Wessel, G. and Burcher, P. (2004), Six Sigma for small and medium-sized enterprises,The TQMMagazine, Vol. 16 No. 4, pp. 264-72.

Wilson, N. (2004), The small company and Six Sigma: advantages of the small business culture,available at: www.sixsigmaforum.com/protected/articles (accessed June 20, 2008).

Appendix

Current PPM 80,213.07

Achieved PPM 0.02Cost/pcRs. 0.06PPM reduction 80,213.05 PPMMonthly production 5,000,000 pcsSaving/month 8,0213.05 5,000,000/106

401,065.25 pcsCost saving/annum 401,065.25 0.06 12

288,766.98 2.88 lacs 0.288 million

Table ACost saving calculation account of reducti

in bush rejection ra

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About the authors

Prabhakar Kaushik is an Assistant Professor in the Department of Mechanical Engineering at

MD University, Rohtak, India. His research interests are in the areas of industrial engineering,

engineering management, Six Sigma and quality control techniques.

Dinesh Khanduja is an Associate Professor in the Department of Mechanical Engineering atNIT, Kurukshetra, India. His research interests are in the areas of industrial engineering, SQC,

Six Sigma, and quality management.

Kapil Mittal is an Assistant Professor in the Department of Mechanical Engineering at YIET,

Yamunanagar, India. His research interests are in the areas of APQP, engineering management,

and quality planning.

Pawan Jaglan is a Research Scholar in the Department of Mechanical Engineering at NIT,

Kurukshetra, India. His research interests are in the areas of SQC, Six Sigma, and quality tools.

To purchase reprints of this article please e-mail: [email protected] visit our web site for further details: www.emeraldinsight.com/reprints

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