An application of Six Sigma methodologyto reduce the engine-overheating problemin an automotive companyJ Antony1*, M Kumar2, and M K Tiwari3

1Six Sigma and Process Improvement Research Centre, Glasgow Caledonian University, Glasgow, UK2Division of Management, Glasgow Caledonian University, Glasgow, UK3Department of Manufacturing Engineering, National Institute of Foundry and Forge Technology, Ranchi, India

The manuscript was received on 5 January 2005 and was accepted after revision for publication on 19 April 2005.

DOI: 10.1243/095440505X32418

Abstract: Six Sigma is a systematic methodology for continuous process quality improvementand for achieving operational excellence. The overstatement that often accompanies thepresentation and adoption of Six Sigma in industry can lead to unrealistic expectations as towhat Six Sigma is truly capable of achieving. This paper deals with the application of SixSigma based methodology in eliminating an engine-overheating problem in an automotivecompany. The DMAIC (define–measure–analyse–improve–control) approach has beenfollowed here to solve an underlying problem of reducing process variation and the associatedhigh defect rate. This paper explores how a foundry can use a systematic and disciplinedapproach to move towards the goal of Six Sigma quality level. The application of the Six Sigmamethodology resulted in a reduction in the jamming problem encountered in the cylinderhead and increased the process capability from 0.49 to 1.28. The application of DMAIC hashad a significant financial impact (saving over $US110 000 per annum.) on the bottom-line ofthe company.

Keywords: Six Sigma, DMAIC, case study, tools and techniques, automotive industry

1 INTRODUCTION

Six Sigma is widely recognized as a methodology thatemploys statistical and non-statistical tools and tech-niques to maximize an organization’s return oninvestment (ROI) through the elimination of defectsin processes. Six Sigma has been on an incrediblerun for over 14 years, producing significant savingsto the bottom-line of many large and small organiza-tions. Motorola invented Six Sigma in the mid 1980sas a powerful business strategy to improve quality.Six Sigma continues to be the best-known approachto process improvement [1]. The original goal of SixSigma was to focus on manufacturing processes;today, however, marketing, purchasing, billing, andinvoicing functions are also involved in Six Sigma

strategies with the aim of continuously reducingdefects throughout the organization’s processes.

The Six Sigma approach starts with a businessstrategy and ends with top-down implementation,having a significant impact on profit if successfullydeployed [2, 3]. It takes users away from ‘intuition-based decisions’ (what we think is wrong), to ‘fact-based decisions’ (what we know is wrong) [4]. Anumber of papers and books have been publishedaddressing the fundamentals of Six Sigma. Topicsinclude: What is Six Sigma [5–7]? Why do we needSix Sigma [8–9]? What makes Six Sigma differentfrom other quality initiatives [1, 10]? Six Sigmadeployment [11, 12]; critical success factors of SixSigma implementation [13]; Six Sigma project selec-tion [14]; and organizational infrastructure requiredfor implementing Six Sigma [1, 12, 15, 16]. For moreinformation on Six Sigma and its methodology,readers are encouraged to refer to section 4. It isimportant to note that a Six Sigma quality level ofperformance should not be the goal for all processes.

CASE STUDY 633

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*Corresponding author: Six Sigma and Process Improvement

Research Centre, Division of Management, Caledonian Business

School, Glasgow Caledonian University, Milton Street Building,

Glasgow G4 0BA, UK. email: j.antony@gcal.ac.uk

at INDIAN INSTITUTE OF TECH on July 1, 2015pib.sagepub.comDownloaded from

A lower Sigma quality level of performance may beacceptable for some processes. For example, a creditcard company had a target that 95 per cent ofcustomers wishing to speak to a customer servicerepresentative must be connected within six rings.The company had established, through a customersurvey, that customers were willing to wait up toseven or eight rings provided they were informed bya recorded voice that a customer service representa-tive would attend to their queries soon. The companyalso found through research that a further reductionto five or less rings would not increase customersatisfaction significantly. In such cases, we do notreally need a Six Sigma process capability. On theother hand, in some processes, even Six Sigma maynot be enough; for example, the level of airlineslanding their aircraft safely needs to be higher thanSix Sigma quality level.Work produced by Haikonen et al. [17] focused on

the manager’s role to adopt and internalize SixSigma philosophy and to give sufficient support tothe implementation process. Furthermore, this workdiscussed the importance of managerial responsibil-ity in developing a measurement system and a real-time reporting system. Motwani et al. [18] used abusiness process change framework to examine thefactors that facilitated or inhibited the success of SixSigma quality efforts at the Dow Chemicals company.Six Sigma implementation at Dow Chemicals resultedin the surpassing of the strategic and financial perfor-mance expectations of the organization [18]. Thecompany has registered an impressive $US1.5 billionof savings since 1999. While Six Sigma methodology isexperiencing widespread adoption among a variety ofbusiness and industries, there is an inherent draw-back of its misapplication if adequately trainedpersonnel – with the correct functional backgrounds– are not available. In this context, academic institu-tions have a pivotal role to play [19]. Antony [20]highlighted the pros and cons of Six Sigma from theviewpoint of an academician. Furthermore, it wasstressed that Six Sigma lacks a theoretical under-pinning and hence it is the responsibility of academi-cians to bridge the gap between the theory andpractice of Six Sigma.This paper deals with a case study performed in an

Indian automobile company with the aim of eliminat-ing an engine-overheating problem using Six Sigmaproblem-solving methodology. The manufacturerwas encountering a problem of engine overheatingdue to water-jacket-passage jamming in one type ofengine manufactured by the company. The cause ofthe jamming in the water jacket of the engine wassand fusion and metal penetration during the castingof the product. The application of the Six Sigmaproblem-solving methodology, DMAIC (define–measure–analyse–improve–control), reduced the

number of defects in the engine cylinder manufac-turing process and thereby improved customersatisfaction and the business profitability of thecompany.

The remainder of this paper is arranged in thefollowing way. In section 2, an introduction tothe case study is presented and the solution to theproblem is explained in terms of the five steps ofthe DMAIC approach. Section 3 presents the conclud-ing remarks and discusses the significance of theproject. Section 4 provides teaching notes on SixSigma so that readers can understand the Six Sigmamethodology and identify the key ingredientsrequired for successful deployment of Six Sigma.

2 CASE STUDY

This case study deals with the reduction of water-jacket-passage jamming in the cylinder head of anautomotive engine. This problem was tackled usinga Six Sigma DMAIC problem-solving methodology.The basic equation of Six Sigma, Y ¼ f ðxÞ, definesthe relationship between a dependent variable, Y ,or outcome of a process, and a set of independentvariables or possible causes that affect the outcome.In the present case study, Y is the high customerdissatisfaction due to the water-jacket-passagejamming problem in the engine cylinder head. SixSigma problem-solving methodology (DMAIC) isrecommended when the cause of the problem isunclear [1]. This project was of highest priority tosenior managers within the company as it wasknown that an effective solution to this problemwould have a significant impact on the bottom-line.Moreover, it was clear to the team members as wellas project champions that elimination of thisproblem would have a colossal impact on customersatisfaction.

The cylinder head is the cast product used toproduce a high-power engine in the range of 15–25HP. Power is developed at the bottom surface ofthe cylinder head and is subjected to high heat andvibration during combustion of the engine. The greycast-iron casting of the head has inlet passages,intricate water-jacket passages for coolant flow, andexhaust passages to discharge the burnt fuels. Thewater jacket is a complex passage for water circula-tion around the cylinder, the valve ports and seats,the combustion chamber, and any other hot partthat requires cooling. The heat of combustion is con-ducted through metal walls to the water in the jacket.The cylinder head is cast by assembling the base core,the water-jacket core, the inlet core, and the exhaustcore into one unit.

Customers repeatedly reported the engine overheat-ing problem to the manufacturer. The overheating

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problem was fundamentally due to water-jacket-passage jamming and was considered to be themajor reason for customer dissatisfaction. The rootcause of this defect was sand fusion and metal pene-tration during casting of the product. These twoparameters were responsible for the water-jacket-passage jamming in the engine cylinder head. Sandfusion is a defect encountered by the foundry shopwhen the molten metal, due to its high temperature,fuses with the sand of themould and the core. Anotherreason for sand fusion may be the low thermalstrength of the sand used for the process of makingcores. Generally, a zircon wash was used to improvethermal strength. Metal penetration is due to porosityremaining in the cores. In thin sections (8mm), slightporosity may result in considerable weakening ofcores and thus the metal may penetrate and jam themould cavity. Metal penetration occurred in 13 loca-tions, depicted in the core drawing in Fig. 1. Locations2, 4, 6, 8, and 12 are most prone to jamming due tosand fusion.

2.1 The define phase

This phase of the DMAIC methodology aims to definethe scope and goals of the improvement project interms of customer requirements and to develop aprocess that delivers these requirements. The firsttask was to develop a project charter to help teammembers clearly understand the scope and bound-aries of the project, project objectives, project dura-tion, resources, roles of team members, estimatedfinancial gains from the project, etc. This creates asense of ownership for the project, it also preventsthe delivery of mixed messages between projectmanagers and team members. The project teammembers included a champion, a black belt, aprocess owner, a green belt, and two graduateengineer trainees.In the define phase of the methodology, team

members ask many questions during the projectcharting session; i.e.: What is wrong in the productionof the cylinder-head casting? Where is the problem?How big is the problem? What is the impact of the

problem? The Six Sigma team ensured that thesepoints had been investigated prior to embarking onthe measure phase.

The goal statement of the project, as defined by theteam members, was the reduction of water-jacket-passage jamming from 0.194 defects per unit (DPU)to 0.029 DPU, which should result in an immensereduction in the cost of poor quality (COPQ). Theteam conducted destructive testing on cylinder-head castings produced on different dates to identifythe root causes of the problem. After performing anumber of brainstorming exercises and using amulti-voting method, the team members arrived atthe conclusion that the defect of jamming was dueto sand fusion and metal penetration. The team alsoconcluded that the root cause of sand fusion andmetal penetration was the porous core. The problemwas occurring in 13 locations on the casting, of which,locations 2, 4, 6, 8, and 12 were more prone tojamming. The impact of the problem was verysevere because it was a main cause of engine over-heating, which led to high warranty failures andcustomer dissatisfaction.

The team focused on the following processes forenhancing customer satisfaction and reducingCOPQ in the foundry:

(a) sand preparation;(b) core-making process;(c) wash preparation and coating.

These processes were selected based on sound engin-eering knowledge and expertise of team memberswith the process, and also taking into account thesteps used in the making of the casting.

2.2 The measure phase

This phase is concerned with selecting one or moreproduct characteristics, mapping the respectiveprocess, making the necessary measurements,recording the results on process control cards, andestablishing a baseline of the process capability orprocess performance. Process mapping provides apicture of the steps needed to create the output orprocess Y . It is a pictorial representation of theprocess, which helps to identify all value- and non-value-added steps, key process inputs (X) and out-puts (Y). Figure 2 shows the process mapping of thecasting process of the engine cylinder head. Thereare three core processes: sand preparation, corepreparation, and wash preparation and coating.

Havingmapped the process, the team proceeded toanalyse the potential causes of defects. Although thistask is carried out in detail during the Analyse phaseof the Six Sigma methodology, the team had to planhow well the X inputs are controlled in the givenprocess. Figure 3 illustrates the cause and effect

Fig. 1 Locations of water-jacket jamming (see text fordetails)

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analysis for the water-jacket-passage jamming prob-lem. The ‘effect’ in this example is the porous core.The output of the cause and effect diagram dependsto a large extent on the quality and creativity of thebrainstorming session.

The cause and effect analysis showed that theprocess variables affecting the porous core weresand leakage, blow pressure, the AFS number of thesand, the gap in the core box, bulk density of thesand, and vent choking. It was also observed by

Fig. 2 Process mapping for cylinder-head core preparation

Fig. 3 Cause and effect analysis of the porous core

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the team members that the contribution of theporous core to water-jacket-passage jamming wasover 80 per cent. Having constructed the cause andeffect diagram, the team then created a cause andeffect matrix. Table 1 is a cause and effect matrixshowing the customers’ needs and the relative impor-tance of the process characteristics that are critical tocustomers.The next step was to define performance standards

according to the customer requirements. The goal ofthese performance standards was to translate custo-mer needs into measurable characteristics. Basedon the specification limits, performance standardsfor each process parameter were established (seeTable 2). Having established the key process param-eters and the critical-to-quality characteristic (depthof porous core), it was essential to establish theaccuracy of the measurement system and the qualityof the data. A data collection plan was established tofocus on the project output and also to carry out thestandard-setting exercise for the same. A Gaugerepeatability and reproducibility (R&R) study wasconducted to identify the sources of variation in themeasurement system and to determine whether themeasurement system was capable or not. It may benecessary to redesign the gauge and perform propermaintenance of instruments when repeatability islarge compared with reproducibility. When reprodu-cibility is large compared with repeatability, thenthe operator needs to be better trained in how touse and read the gauge instrument; a suitable fixturewas needed to help the operators use the gauge moreconsistently. The measurement system was consid-ered acceptable when the measurement system

variability was less than 10 per cent of total processvariability [21]. However, the system may be accepta-ble when the measurement system variability isbetween 10 and 30 per cent; at above 30 per centvariability, the measurement system was not consid-ered acceptable. This study is performed in order tocheck the accuracy of the gauges used for themeasurement of characteristics, as well as to checkthe accuracy of the workers operating the machine.The Gauge R&R study performed on the systemshowed variation of 6.08 per cent, which impliesthat the measurement system is acceptable. Table 3illustrates the results of the Gauge R&R study carriedout during the Measure phase of the project.

The baseline process capability ðCpkÞ was alsoestablished in this phase. The Cpk value based onthe existing process conditions is estimated to be0.49. This clearly indicates that process performanceis poor and clearly needs improvement.

2.3 The analysis phase

The first step in this phase was to gather data from theprocess in order to obtain a better picture of thevalues of the depth of the porous core under differentprocess conditions. Data pertaining to factors affect-ing the response were collected over a period of 36days from different shifts throughout the day (seeTable 4).

In routine foundry production, the casting isshipped to the customer if the mechanical test datasatisfy the requirements of the standard and nojamming occurs in the casting. Data (from Table 4)related to factors affecting the response (depth ofporous core) were analysed to determine not only

Table 1 Cause and effect matrix

Process characteristics

Depth ofporous core

Baumeof wash

Penetrationof wash

Syneresisof wash

Scratch hardnessof wash Importance

Customer needsUniform coating H H L M L 8Proper thickness H H L L M 9Filling of porous core H H L L L 9Weighted requirements 130 130 26 42 44

H, strong relationship¼ 5; M, medium relationship¼ 3; L, weak relationship¼ ?

Table 2 Specification limits for the process parameters andquality characteristics before experimentation

Depth of porous core 0–1.4mmAFS number of core sand 65–70Baume of wash 52–56BeBlow pressure 4–4.5 kg/cm2

Bulk density 1.78–1.95 g/cm3

Fin thickness 0–0.85mmSand leakage 10–30 g/blowVent choking Yes/No

Table 3 Results of Gauge R&R study

Source VariancePercentage contributionof variance

Total Gauge R&R 1.62� 10�3 6.08Repeatability 1.60� 10�3 6.02Reproducibility 1.67� 10�5 0.06Part-to-part 2.50� 10�2 93.92Total variation 2.67� 10�2 100.00

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the relationship between the process parameters andthe response but also to determine the direction ofprocess improvement. Passage jamming tendency inrelation to location is shown in Fig. 4 with the helpof a Pareto chart. The chart was constructed basedon the day-to-day monitoring of manufacturing of

the core and recording passage jamming at differentlocations. Defects occurring at 13 locations werearranged in descending order on the X axis of thePareto chart.

In the Analysis phase, it was important to identifythe possible sources of variation that lead to the

Table 4 Factors affecting response before improvement

Depth ofporouscore

Sandleakage(g/blow)

Blowpressure(kg/cm2)

AFSnumber

Bulkdensity(g/cm3)

Baumeof wash(Be)

Finthickness(mm)

Ventratio

1.7 85 3.8 66.78 1.76 52 0.9 0.133 3331.3 93 3.8 66.78 1.78 52 0.9 0.166 6671.6 82 3.8 66.78 1.76 53 0.7 0.150 0001.8 90 3.8 66.82 1.78 52 0.7 0.166 6671.3 85 3.8 66.82 1.78 51 0.8 0.200 0001.0 80 4.2 66.82 1.78 52 0.8 0.133 3330.7 22 4.0 69.12 1.86 53 0.8 0.333 3331.0 24 4.0 69.12 1.86 54 0.6 0.300 0001.2 18 4.0 69.12 1.86 54 0.8 0.523 8101.0 18 4.0 69.02 1.84 53 0.8 0.714 2861.3 20 4.2 69.02 1.86 53 1.0 0.761 9051.1 25 4.0 69.02 1.86 52 0.6 0.761 9051.0 28 4.0 68.77 1.82 52 0.8 0.190 4761.1 55 3.8 68.77 1.82 51 0.8 0.238 0951.3 35 4.0 68.77 1.82 52 1.0 0.190 4760.8 12 4.0 67.14 1.82 50 0.7 0.714 2861.3 12 4.0 67.14 1.82 51 0.6 0.714 2861.3 18 3.8 67.14 1.82 50 0.7 0.738 0951.0 20 4.0 68.72 1.84 52 0.6 0.571 4291.2 18 3.8 68.72 1.84 51 0.5 0.666 6671.0 22 3.8 68.72 1.84 52 1.1 0.333 3331.2 13 4.2 69.02 1.84 54 1.0 0.222 2221.2 24 3.8 69.02 1.84 54 1.1 0.222 2220.8 25 4.4 69.02 1.84 54 0.9 0.333 3331.3 18 3.8 68.77 1.80 50 0.8 0.666 6671.0 10 4.2 68.77 1.80 52 1.1 0.333 3331.5 25 4.2 68.77 1.80 52 0.9 0.388 8891.5 75 4.2 70.08 1.74 53 0.6 0.166 6671.7 82 4.0 69.02 1.74 54 1.1 0.111 1111.7 82 3.8 69.02 1.74 54 1.0 0.166 6670.8 13 4.2 68.72 1.80 53 0.9 0.400 0001.1 30 4.0 68.72 1.82 53 0.9 0.320 0000.9 15 4.2 68.72 1.82 53 0.8 0.440 0001.3 35 3.8 69.12 1.82 53 0.8 0.360 0001.2 30 3.8 69.12 1.82 54 0.9 0.320 0001.2 24 4.0 69.12 1.82 54 1.1 0.320 000

Fig. 4 Pareto chart to show location-wise jamming tendency

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jamming problem. Moreover, it was also important tounderstand the causes for the poor process capability.The aim of the project team was to enhance theprocess capability by reducing variation in theprocess.At this point, it is imperative to identify the param-

eters that are significant to the process so that theycan be brought under statistical control. Theimprovement goal of the project was defined statisti-cally through benchmarking the engine-manufac-turing process with an automobile company locatedin Brazil. This automobile company in Brazil hasdeployed Six Sigma in each of its engine-manufac-turing processes and no final testing of the engine isdone after it has been assembled. A simple regressionanalysis is performed to determine the significance ofprocess parameters. It is concluded from the regres-sion analysis that the variables with P values lessthan 0.05 and 0.01 are statistically significant forfurther study. Table 5 shows that sand leakage, bulkdensity, and vent choking ratio are the parametersthat need to be further optimized and controlled.The optimization of these parameters will yield anoptimum response (i.e. depth of porous core).

2.4 The improve phase

In this phase, it was decided to perform a designedexperiment with the three process parameters (sandleakage, bulk density, and vent choking ratio) identi-fied from the analysis phase.

1. Sand leakage. Sand was coming out of the corebox when the door half and the ram half wereclosely packed due to misalignment of the twohalves. The two halves of the core box wereproperly aligned and packing was provided toseal any opening still remaining. This parametercan be varied from 10 to 30 g/blow.

2. Bulk density. The desirable bulk density of coresand is between 1.78 and 1.95 g/cm3. Bulk densitywas increased by adding iron oxide and mill scale.The iron oxide composition was increased to 3 percent to achieve the desired bulk density and to

improve the high-temperature thermal propertiesof the core sand. Mill scale addition of 1.5 percent also helped to increase the density andthermal conductivity of the sand.

3. Vent choking. This was one of the serious prob-lems posed by the foundry men. The resin usedfor the core sand blocked the opening of ventby sticking to it during the curing process. Thisresulted in the production of a porous corebecause the pressurized air from the core shootercould not escape out of the core box. Out of 52vents in both halves of the core box, only 30vents were not blocked. Thinner (alcohol) wasapplied to clean the vents. The cleaning frequencywas increased from once a week to twice a dayduring the shift break.

The following steps were taken to control otherprocess parameters during the experiment.

1. AFS number. Proper care was taken during sieveanalysis of the sand so that only sand grains ofAFS number 65–70 reached the hopper of theshell core machine.

2. Blow pressure. The pressure gauge was checkedto maintain the blow pressure in the range of4–4.5 kg/cm2.

3. Baume of wash. The wash was regularly checkedto maintain its value according to the specificationstandards (52–56Be).

4. Fin thickness. Correct maintenance of the shellcore machine brought the fin thickness in therange of 0–0.85mm.

Design of experiments (DoE) was conducted usingthe three process parameters identified from theprevious phase. Each process parameter was studiedat two levels in order to keep the size of the experimentto a minimum, as well as to meet time and cost con-straints. A 23 full factorial design was chosen so thatboth main effects and interaction effects among theparameters could be investigated. In order to havesufficient degrees of freedom for studying both maineffects and interaction effects, each trial conditionwas replicated twice [22]. Table 6 illustrates the resultsof the experiment with average depth of the porouscore as the variable of interest. The average depth ofthe porous core before the experiment was 1.25mm.As the objective of the experiment was to minimizethe depth of the porous core, the first objective ofthe analysis was to determine the effect of the processparameters and to understand the presence of anyinteractions, if present. Figures 5 and 6, respectively,illustrate the main-effects plot and the interactionsplot. In order to determine the statistical significanceof both main and interaction effects, it was decided toconstruct a normal probability plot of effects (seeFig. 7). Figure 7 indicates that only the main effects

Table 5 Results of regression analysis for process param-eters

Process parameters P

Sand leakage 0.002�

AFS number of core sand 0.414Baume of wash 0.060Blow pressure 0.155Bulk density 0.003�

Fin thickness 0.104Vent choking 0.001�

�Significance of process parameters at 1 and 5 per cent significancelevels.

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were statistically significant at 10 per cent significancelevel. None of the interactions were statistically signif-icant although the interaction plot (Fig. 6) suggestedthat there was a slight interaction between vent

choking ratio and sand leakage. The main-effectsplot (Fig. 5) indicated that the optimum levels of theprocess parameters for minimizing the depth of theporous core are: bulk density, high level (1.95 g/cm3);

Table 6 Results of 23 full factorial experiment

Sandleakage

Bulkdensity

Ventchoking

Depth of porous core (in mm) Average depthof porous core

(g/blow) (g/cc) ratio Replication 1 Replication 2 (mm)

10 1.78 0 0.75 0.65 0.710 1.95 0 0.6 0.6 0.610 1.78 1 0.8 1.0 0.910 1.95 1 0.85 0.75 0.830 1.78 0 0.90 1.0 0.9530 1.95 0 0.8 0.9 0.8530 1.78 1 0.9 1.1 1.030 1.95 1 0.9 1.0 0.95

Fig. 5 Main-effects plot for the depth of the porous core

Fig. 6 Interactions plot showing the interactions among the process parameters

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vent choking ratio, low level (No); sand leakage, lowlevel (10 g/blow). Confirmation trials were carriedout using the optimal settings and the average depthof the porous core was computed to be 0.80mm.Moreover, the process variability was significantlyreduced as well. The process capability ðCpkÞ hadimproved from 0.49 to 1.28. This clearly indicated asignificant improvement in process performance.

2.5 The control phase

The real challenge of Six Sigma methodology is not inmaking improvements to the process but in sustain-ing the optimized results. This requires standardiza-tion and constant monitoring and control of theoptimized process. An extensive training programfor the process-related personnel was conductedwithin the company where the case study was per-formed. It is well known that real improvement will

only come from the shop floor. Process sheets andcontrol charts were made so that the operator couldtake preventive action before the critical processparameters and critical performance characteristicswent outside the control limits. A complete databasewas prepared to sustain the improved result. Propermonitoring of the process helped to detect andcorrect out-of-control signals before they resulted incustomer dissatisfaction. Normal silica or chromitesand was mixed with shell sand of AFS number 70–75 to achieve better results. Zircon mixed withmagnetite was used as a wash substitute to increasethe refractoriness of the wash. Implementation ofthe aforementioned suggestions resulted in furtherimprovements in process capability and processyield.

Run charts for the depth of the porous core wereconstructed (Figs 8 and 9) before and after improve-ments were made to the process. The purpose of

Fig. 7 Normal probability of effects for the experiment

Fig. 8 Run chart for the porous core before improvement.LSL, lower specification limit; USL, upper specifica-tion limit

Fig. 9 Run chart for depth of porous core after improve-ment

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the run charts here was to analyse variability in theporous core around its mean value. Figure 9 (afterthe improvement phase) shows that all the pointsare within the specifications and that variability inthe porous core had been reduced significantly.

2.6 Key results and financial savings generatedfrom the project

The production department manufactured 100 000units of cylinder head to cater for the needs of custo-mers. Production was carried out in two shifts perday, with 8 hours per shift, and there are six workingdays per week. The cylinder-head manufacturinginvolved a series of sequential steps as follows:

(a) making of the core in a core shooter machine;(b) finishing of the core;(c) coating of the finished core with a wash;(d) drying the wash coating by passing through a

heating section;(e) assembling the core in an automated casting

section for metal casting of the cylinder head;(f) removing the unwanted part from the casting by

passing through a fettling section;(g) cleaning and polishing the cylinder-head casting;(h) testing the quality of the casting in the inspec-

tion/testing unit.

After achieving the optimal condition and main-taining it, the team carried out a cost–benefit analysisof the whole project. The costs incurred by the com-pany in manufacturing the cylinder head is dividedinto four categories: labour cost, raw material cost,operating expenses, and other overhead costs. Allthese costs were incurred in manufacturing theproduct as it passed through the various processingstages. A break down of all these costs for the manu-facture of a single unit as it passes through the eightsections is given in Table 7. As the raw material isprocessed through various machines, some value isadded at every stage of production. Therefore, adefect coming at any stage of production is consid-ered as a loss to the organization. However, theimpact of these defects on the organization increases

according to the stage at which they are identified: ateach stage some value is added to the product andeach value-adding activity has an associated cost.The impact of defects in the form of delay reschedule,rework, delivery delay, and more inspection alsoincreases with the increase in the number of stagesat which the defects are identified.

The savings generated from the Six Sigma projectwere divided into two parts: first, savings related toraw materials and secondly savings from othersources such as labour costs, operating costs, andother overhead costs. The details of the cost savingsfrom raw materials are listed in Table 8 and theydemonstrate the power of Six Sigma as a strategyfor achieving significant financial savings to thebottom-line of the organization. This would alsohave helped the senior managers of the company toappreciate the power of Six Sigma.

Table 9 gives the details of the savings generatedfrom other sources (including labour costs, operatingcosts, and other overhead costs) by the organizationwhen the defect was detected at a particular stage.The cost savings from the other sources were$US11 471.60. It can be deduced from Table 9 thatthe percentage of defects arising in each stage hasdecreased considerably. Thus, there is an improve-ment of $US111 524.93 in the total profit, in monetaryterms, for the company after Six Sigma implementa-tion. This profit in monetary terms is attributed tosavings generated from raw material and othersources.

Table 7 Total cost (excluding raw material cost) incurred in manufacturing a unit product

Manufacturing stepsLabour costper unit

Operating costper unit

Overhead costper unit Total cost

Core shooter machine 0.72 10.75 3.25Surface finishing of core 0.60 2.50 1.20Wash coating 0.60 3.25 0.75Heating section – 5.40 1.30Casting unit 0.89 12.54 4.50Fettling unit – 8.25 2.35Cleaning and polishing 0.72 11.32 2.80Inspection/testing unit 0.96 5.75 2.20

4.49 59.76 18.35 82.6

Table 8 Details of cost savings generated from rawmaterial (shell sand)

Cylinder head casting required 100 000/yearWater-jacket core required 100 000/yearCore rejection before project 0.194DPUCore rejection after project 0.029DPU

So core rejection reduced by 85%85 000 cores saved per year

Shell sand required for 85 000 cores 336 000 kgShell sand cost Rs 13.40/kgTotal cost saved Rs 450 2400 or $US100 053.33

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Table 10 presents the key results of the study show-ing the key metrics used. These metrics clearly indi-cate the performance improvements achieved afterimplementation of Six Sigma methodology. The datashown in Table 10 were collected over a period of 4days from different shifts.DPU and yield were calculated as follows

DPU ¼ Number of defects found/number ofunits processed ð1Þ

Yield ¼ e�DPU ð2ÞIt was observed that porosity in the water-jacket

core reduced drastically. The process capability ofthe system was increased from the previous value of0.49 to 1.28, showing tremendous improvement inthe production system.

3 CONCLUSION

Six Sigma has been widely publicized in recent yearsas a powerful methodology to combat quality-relatedproblems and to achieve customer satisfaction. It hasbeen considered as a strategic approach to improvebusiness profitability and to achieve operationalexcellence through effective application of bothstatistical and non-statistical tools/techniques. Theapplication of Six Sigma is growing almost every day

moving from the manufacturing industry to service,transactional, administrative, R&D, sales and market-ing, healthcare, and software-development indus-tries. This paper presents a case study of enginecylinder-head production from a leading automotivecompany in India, showing how the effective intro-duction and implementation of a Six Sigma programcan lead to breakthroughs in profitability.

In the face of overwhelming claims about themerits and power of Six Sigma, the fact remains thatSix Sigma is most effective when an organizationalready has a firm idea of what forms of productsand services are in alignment with the organization’sgoals and customer expectations. It is suited toproblems where output can be readily measured.This paper explores how a foundry department tooksteps to move towards the goal of achieving SixSigma quality level. The application of DMAICmethodology was extremely valuable in reducingthe engine cylinder jamming problem; the defectrate per unit was reduced from 0.194 to 0.029DPU;moreover, the capability of the process was signifi-cantly improved from 0.49 to 1.28. The estimatedsavings generated from this project were around$US111 524.93. The project team members includeda champion, a black belt, a process owner, a greenbelt and two graduate engineer trainees. This projectwas targeted to enhance customer satisfaction byreducing water-jacket-passage jamming in the

Table 9 Breakdown of cost savings at each stage of production

Manufacturing stepsLabour(Rs/unit)

Operatingcost(Rs/unit)

Overheadcost(Rs/unit)

Totalcost(Rs/unit)

% defectbeforeSix Sigma

% defectafterSix Sigma

AnnualimpactbeforeSix Sigma(Rs)

AnnualimpactafterSix Sigma(Rs)

Savings afterSix Sigmaimplementation(Rs)

Core shooter machine 0.72 10.75 3.25 14.72 7.0 0.029 103 040 426.88 102 613.12Surface finishing ofcore

1.32 13.25 4.45 19.02 0.2 0.012 3804 22.82 3781.18

Wash coating 1.92 16.50 5.20 23.62 0.1 0.007 2362 165.34 2196.66Heating section – 21.90 6.50 28.40 3.0 0.008 85 200 227.20 84 972.80Casting unit 2.81 34.44 11.00 48.25 4.0 0.010 193 000 482.50 192 517.50Fettling unit 2.81 42.69 13.35 58.85 0.7 0.0087 41 195 523.765 40 671.23Cleaning and polishing 3.53 54.01 16.15 73.69 1.0 0.0031 73 690 228.44 73 461.56Inspection/testingunit

4.49 59.76 18.35 82.60 0.2 0.0062 16 520 512.12 16 007.88

Total savings¼Rs 516 221.93or $US11471.60

Table 10 Comparison before and after improvement based on key metrics

Key metrics usedDepth of porous corebefore improvement

Depth of porous coreafter improvement

Defect rate 0.194DPU 0.029DPUYield 82% 97.14%Capability indices 0.49 1.28Process mean 1.202mm 0.843mmProcess standard deviation 0.277mm 0.137

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cylinder head of an automotive engine. The results ofthis project provide greater stimulus for the widerapplication of Six Sigma methodology across thecompany in the future.

4 TEACHING NOTE

Six Sigma is a measure of process performance and aprocess operating at 6-Sigma quality has a defect rateof 3.4 parts-per-million opportunities [23]. In manyinstances a Six Sigma process is regarded as worldclass. The average performance of most processestoday is in the range of 3–4 Sigma. The Six Sigmameasure of process capability assumes that theprocess mean may shift over the long term by asmuch as 1.5 Sigma, despite our best efforts to controlit. In the Six Sigma process, 3.4 defects per millionopportunities (DPMO) is obtained by assuming thespecification limits are six standard deviations awayfrom the process target value and that the processmay shift by as much as 1.5 Sigma. The 3.4 DPMOvalue is the area under the normal curve beyond6� 1:5 ¼ 4:5 Sigma. Similarly the 66 807 DPMO forthe 3-Sigma process is the area under the normalcurve beyond 3� 1:5 ¼ 1:5 Sigma.The process capability indices Cp and Cpk are used

as the vehicles to characterize the produced processquality. Six Sigma is achieved when the productspecifications are at �6� (where � is the standarddeviation of the process) and when the processwidth is half the specification band. The processpotential ðCpÞ for a Six Sigma process would be 2(when the process is centred) and the actual processperformance ðCpkÞ would be 1.5 (when there is 1:5�shift in the process mean). Considerable researchhas been carried put on the understanding andcorrect interpretation of the process capabilityindices for stable and unknown trend pattern pro-cesses [24–28]. The Taguchi method of experimentaldesign has been extensively used in reducing processvariability and hence improving the processcapability. A number of authors have reported onthe application of the Taguchi approach to experi-mental design for improving product quality andenhancing process performance [29–33].The five-step process of Six Sigma starts with the

define phase. The define phase involves identifyinga project’s critical-to-quality (CTQ) characteristicsdriven by the voice of the customer (VOC), followedby developing a team charter and finally defining ahigh-level process map connecting the customer tothe process and identifying the key inputs andrequirements.In the measurement phase, the team identifies the

key internal processes that influence CTQ character-istics and measures the defects currently generated

relative to those processes. The project’s CTQ charac-teristics are selected with the help of fishbone dia-grams, quality function deployment (QFD), Paretocharts, etc. A Gauge R&R study is conducted tocheck whether themeasurement system is acceptableor not.

The analyse phase consists mainly of three steps:establishing process capability with the help ofcapability indices, defining performance objectivesby the team benchmarking, and identifying thesources of variation by performing analysis of var-iance (ANOVA) tests and hypothesis testing. Basedon the above information, the root causes of defectsand their impact on the business/process can beidentified.

The improvement phase helps the team to confirmthe key variables and quantifies their effects on theCTQ characteristics. Statistical tools such as fishbonediagrams, box plots, linear regression, hypothesistesting, mistake proofing, etc. are used for processimprovement. Techniques such as DoE, fractionalfactorial, response surface methodology, and theTaguchi method are the advanced techniques usedfor more complex process. By implementing theoptimum parameters into practices, the performanceis improved and financial goals are reached.

In the last step of the DMAIC process, i.e. thecontrol phase, the improved data on significantfactors, as identified from the experimental design,are monitored and the whole process is well docu-mented to ensure that improvements are maintainedbeyond the completion of the project. The five-stepSix Sigmamethodology (DMAIC) is depicted in Fig. 10.

For the successful implementation of Six Sigmaprojects, certain factors or ingredients should be inplace. The success of Six Sigma is linked to a set ofcross-functional metrics which lead to significantimprovements in customer satisfaction and bottom-line benefits. The approach involves identifying pro-jects that target the customer’s concerns and havethe potential for significant payback. This is accom-plished by putting the quality and process improve-ment tools into the hands of a large number ofmanagers, engineers, and operators throughout theorganization.

The application of Six Sigma requires top-manage-ment involvement and provision of appropriateresources and training [34]. Senior managers withinthe organization must be taught the principles ofSix Sigma to enable the restructuring of the businessorganization and to change their attitude towardsthis more disciplined approach. Continuous supportand uncompromising commitment from top man-agement are indispensable for the successful intro-duction and development of Six Sigma programs[9]. Adjustments to the culture of the organizationand a change in the attitudes of the employees are

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again very important at the introduction stage of SixSigma in any organization.Employees have to be motivated and willing to

accept responsibility for the quality of their ownwork. Implementation of a Six Sigma programrequires the right mindset and attitude in thepeople working at all levels within the organization[35]. The people within the company must be madeaware of the changes. The result obtained by imple-mentation of the program must be made public andthe result should not only be related to success storiesbut should also admit and communicate anystumbling blocks encountered. These measures willhelp other projects in the pipeline to avoid thesame mistakes and to learn from the mistakes.There is also a need for an effective organizational

infrastructure to be in place to support the introduc-tion of Six Sigma and the development program. Anextensive training is provided to all professionals –such as project champions, black belts, and masterblack belts – so that a clear vision is developed ofhow Six Sigma can help the organization meet itsbusiness goals. The belt system ensures that everyonein the organization is speaking the same language inorder to make the execution of the project mucheasier throughout the organization.A considerable proportion of Six Sigma training

involves learning the principles behind the meth-odology; this takes the form of projects conductedin phases, generally recognized as define–measure–analyze–improve–control (DMAIC). It is not just theDMAIC methodology which makes the applicationof Six Sigma successful in organizations; rather it is

the collection of tools and techniques that areintegrated into DMAIC in a sequential and rigorousmanner that makes the Six Sigma applicationsuccessful. Moreover, DMAIC creates a sense ofurgency by emphasizing rapid project completion in3–6 months.

Six Sigma builds on improvement methods thathave been shown to be effective and integrates thehuman and process elements of process improve-ment. The human elements of process improvementmay include teamwork, customer focus, and culturechange. The process elements of process improve-ment include understanding the types of processvariation, process capability indices, process stability,and DOE to reduce process variation and henceimprove process performance.

Six Sigma is based on a scientific method whichutilizes statistical thinking. Statistical thinking, there-fore, is fundamental to the methodology of Six Sigma,which is action oriented, focusing on processes anddefect reduction through variation reduction andquality/process improvement goals. The core princi-ples of statistical thinking and the role of manage-ment in statistical thinking for improving businessperformance will continue to grow in importance inthe forthcoming years.

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Fig. 10 The five-step methodology of Six Sigma

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