International Journal of Lean Six Sigma Application of Six Sigma methodology in a small-scale foundry industry E.V. Gijo Shreeranga Bhat N.A. Jnanesh

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Application of Six Sigma methodology in a small-scale foundry industry E.V. Gijo

Six Sigma in small scale industry 193

SQC & OR Unit, Indian Statistical Institute, Bangalore, India

Shreeranga Bhat Downloaded by SELCUK UNIVERSITY At 06:47 15 January 2015 (PT)

Department of Mechanical Engineering, St. Joseph Engineering College, Mangalore, India, and

Received 11 September 2013 Revised 11 September 2013 Accepted 16 September 2013

N.A. Jnanesh Department of Mechanical Engineering, K.V.G. College of Engineering, Sullia, India Abstract Purpose – The purpose of this article is to illustrate how the Six Sigma methodology was applied to a small-scale foundry industry to reduce the rejections and rework in one of its processes. Design/methodology/approach – The research reported in this paper is based on a case study carried out in an industry using the Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) approach and its application in improving the leaf spring manufacturing process of a foundry shop. Findings – The root causes for the problem of rejection and rework were identified through data-based analysis at different stages in the project. The process parameters were optimized and measures for sustainability of the results were incorporated in the process. As a result of this study, the overall rejection was reduced from 48.33 to 0.79 per cent, which was a remarkable achievement for this small-scale industry. This was leading to improvement in on-time delivery to the customer. The finance department of the company estimated the annualized savings due to the reduction in rejection and was to the tune of USD8,000 per year. Research limitations/implications – The paper is based on a single case study executed in a company, and hence, there is limitation in generalizing the specific results from the study. However, the approach adopted and the learning from this study can be generalized. Originality/value – This article illustrates the step-by-step application of Six Sigma DMAIC methodology in a small-scale foundry industry to solve an age-old problem in the organization. Thus, this article will be helpful for those professionals who are interested in implementing Six Sigma to such industries. Keywords Six Sigma, DMAIC, ANOVA, Taguchi method Paper type Case study

1. Introduction Six Sigma has been successfully implemented worldwide for ⬎ 25 years, producing significant savings to the bottom-line of many large and small organizations (Treichler, 2005). It is a disciplined, systematic and data-driven approach to process improvement that targets the near-elimination of defects from every product, process and transaction (Firka, 2010; Montgomery, 2010). This approach has been widely used to improve

International Journal of Lean Six Sigma Vol. 5 No. 2, 2014 pp. 193-211 © Emerald Group Publishing Limited 2040-4166 DOI 10.1108/IJLSS-09-2013-0052

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performances and reduce costs for several industrial fields (Kaushik et al., 2012). The aim of such a methodology is not only to improve the process but also to ensure that gains made are sustained (Snee, 2004). The application of Six Sigma is growing and moving from the manufacturing field to encompass all business operations, such as services, transactions, administration, Research & Development (R&D), sales and marketing and especially to those areas that directly affect the customer (Hahn et al., 2000). It starts with a business strategy and ends with top-down implementation, with a significant impact on profit if successfully deployed (Breyfogle, 2003). As a project-driven management approach, the range of Six Sigma applications is also growing from reduction of defects in an organization’s processes, products and services to become a business strategy that focuses on improving understanding of customer requirements, business productivity and financial performance (Kwak and Anbari, 2006; Heavey and Murphy, 2012). Even though implementation of Six Sigma started in the manufacturing industry, nowadays it is being implemented in a variety of fields including low-volume complex environment to small- and medium-sized enterprises for addressing different types of business- and process-related problems (Tjahjono et al., 2010; Julien and Holmshaw, 2012; Deshmukh and Chavan, 2012). There has been a lot of further research carried out in various aspects of Six Sigma implementation, including comparison of this method with various other initiatives and integrating the Six Sigma methodology with other initiatives (Chiarini, 2011; Jeyaraman and Teo, 2010; Lagrosen et al., 2011; Leon et al., 2012). Define-Measure-Analyze-Improve-Control (DMAIC) framework of Six Sigma methodology has been well established as a benchmarking tool for process improvement and customer satisfaction (Chen et al., 2005; Snee, 2010). DMAIC process improvement cycle begins with the definition of a problem, followed by the measurement of baseline performance. Suitable target variables are then identified and mapped against the potential predictors by appropriate analytical tools to determine improved process settings, and finally, the improved process performance is sustained by imposing effective control plans. The Six Sigma DMAIC framework utilizes various statistical and non-statistical tools and techniques to eliminate process variation in a disciplined fashion (Gijo et al., 2011a, 2011b). This study is an initiative to demonstrate the application of the Six Sigma DMAIC methodology in a small-scale foundry industry. The remaining part of this article presents the details of the case study in Section 2, followed by lessons learned in Section 3 and concluding remarks and future research directions in Section 4. 2. Case study in foundry This company where the case study was performed started its operations in the year 1996 as a private limited company. It was set up as a small-scale industrial unit for manufacture and sale of automobile leaf springs, with an initial capacity of 2,000 tons per annum. The capacity has been gradually increased, and as of date, the unit has a capacity of 7,000 tons per annum of multi-leaf springs and 2,000 tons per annum of parabolic springs. The sales, which were to the extent of USD100,000 during the first year, have steadily increased to a business turnover of around USD1.5 million with a manpower of around 100 people. There was no formal quality improvement initiative implemented in the organization. There were isolated events of kaizen and lean

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methodology implementation in some of its processes in the past. Very small improvements in the processes were achieved through these initiatives. This article illustrates a case study to address the problems of rejection and rework faced by the organization during the manufacture of automobile leaf springs (Figure 1). The leaf springs are commonly used for suspension in wheeled vehicles and are designed to withstand varying stress and vibrations during vehicle movement due to different road conditions. A leaf spring can either be attached directly to the frame at both ends of the vehicle or attached directly at one end, usually the front, with the other end attached through a shackle – a short swinging arm. The leaf springs are expected to absorb the vertical vibrations and impacts due to road unevenness by means of variations in the spring deflection so that the potential energy is stored in spring as strain energy and then released slowly (Kumar and Vijayarangan, 2007a). Hardness is one of the most important characteristics which is maintained during manufacturing of leaf springs (Dhoshi et al., 2011; Kumar and Vijayarangan, 2007b). The leaf springs with high hardness will make the material brittle, which will result in breakage of the spring, whereas low hardness will not take up specified loads, creating vibrations. Thus, it is very important to manufacture the product within the specified hardness limits. Any variation in hardness beyond the specified limits led to rejection of the product. The company follows the quench hardening process for the manufacturing of leaf springs, which is as follows. The material is heated to a certain temperature, and then rapidly cooled to achieve the specified hardness. This cooling cycle varies depending on the material properties of the product. This process produces a harder material either by surface hardening or by through-hardening process, depending on the rate at which the material is cooled. The material is then tempered to reduce the brittleness that might have increased during the quench hardening process (Callister, 2003). The company had an ever-increasing problem of rework and rejections in the hardening process, with an approximate rejection of 48.33 per cent during the past six months, as the hardness of the manufactured spring was crossing desired specification limits. This situation has not only increased the manpower, material and other overhead costs for manufacturing, but also created the fear of losing business due to lack of on-time delivery of products to the customers. Usage of technical knowledge and kaizen improvements were not able to identify the root causes of the problem. Hence, to address all these problems, the company decided to apply the Six Sigma DMAIC approach in this

Six Sigma in small scale industry 195

Figure 1. Picture of leaf spring

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Table I. Roles and responsibilities of project team

process, as the root causes of the problem were unknown. The remaining part of this section illustrates various activities carried out at different phases of the DMAIC methodology. 2.1 The define phase The purpose of the define phase in a Six Sigma project is to define a project with all necessary details including the objectives, scope, team composition, schedule, etc. (Gijo and Scaria, 2010; Sharma and Chetiya, 2010). This is the first activity in a Six Sigma project. If the project selection and goal-setting activity are not done properly, it can lead to failure of the project (Breyfogle, 2005). It is also equally important to identify the right people in the team, including the champion, master black belt (MBB), the team leader and team members, for successful completion of the project (Breyfogle, 2005). The details of roles and responsibilities of the team for successful completion of this project are summarized in Table I. Hence, after a detailed discussion at various levels in the management, a project charter was drawn with all details of the project. The charter thus prepared is presented in Table II. This project charter forms the basis of all future actions and decisions in the project. The project team, in this case, included the general manager of the company as champion, a black belt as leader of the project, four green belts (GB) (manager – production, engineer – production, supervisor – production and engineer – quality control) and two operators from the process. The team also prepared a detailed flowchart of the leaf spring manufacturing process, which is presented in Figure 2.

Before the project

Roles and responsibilities During the project

SL. No. Team members

Designation

1

Project champion General manager

2

Project leader

3

Team members

Provide ongoing support for implementation Ensure monitoring of results Capture budgetary savings Ensure that project Manage team members Black belt Review purpose of documentation is the project statement Lead meetings completed and Coordinate with champion lessons learned are communications within Draft rest of the captured the team and outside charter Update dashboard Keep records of various Finalize team Monitor activities during the members implementation of project the new solutions Lead team’s work Monitor results of the project Carry out responsibilities Use improved Production engineer Learn necessary methods skills and methods of assigned during team Quality control Six Sigma including meetings engineer Production manager the usage of Minitab Help in data collection Assist in piloting of the software Production solutions supervisor Contribute knowledge and expertise Identify project goals Select team members Draft purpose statement

Provide direction and guidance Review team progress Remove road blocks Control time

After the project

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Project title: Reducing rejection in the hardening process of automobile leaf spring manufacturing Background and reason for selecting the project: Hardness of the material used for automobile leaf spring manufacturing is crossing the required specification of 245-265 BHN, leading to an approximate rejection of 48.33 percentage of the products manufactured in the past six months. This is increasing the material and labor cost in the company and thus affecting profitability and on-time delivery Aim of the project: To reduce the rejection of hardening process from 48.33 per cent to ⬍ 5 per cent Project champion General manager Project leader Black belt Team members Engineer – production, engineer – quality control Manager – production, supervisor – production Operator – Shift I, operator – Shift II Characteristics of product/process output and its measure CTQ Measure and specification Defect definition Hardness 245-265 BHN Hardness crossing 245-265 BHN Expected benefits Reduction in rejection and rework as a result of reduced variation in hardness. This will help the organization to improve the on-time delivery of products to its customers Schedule Define: one week Measure: one week Analyze: two weeks Improve: two weeks Control: four weeks

To have a better understanding of the process and have good clarity in the scope of the project, the team performed a Supplier-Input-Process-Output-Customer (SIPOC) analysis (Table III). This SIPOC provides the clarity regarding the process boundaries, the customers for the process outputs and the suppliers for the process inputs (Gijo and Scaria, 2010). After understanding the process and the problem in detail, the team discussed the objective of the project in detail. The need of the project is to reduce the rejection percentage of the process, which is at 48.33 per cent for the past six months. These rejections are due to high variation in hardness. Whenever the hardness values are falling beyond the specification limits of 245-265 BHN, the product is rejected. Hence, if the variation in hardness is reduced, the rejections and rework could be eliminated. Thus, the team decided to focus on hardness for further improvement, which is defined as the critical to quality (CTQ) characteristic for the project. The specification limits for this hardness measurement were 245-265 BHN. These details are also included in the project charter presented in Table II. 2.2 The measure phase The goal of the measure phase of the DMAIC project is to gather information from the existing process to evaluate the baseline status of the process. This entails the following key tasks: identifying the characteristics were data to be collected, studying the accuracy of the measurement system, collecting and recording the data and establishing a baseline performance of the process (Gijo et al., 2011a). The next step in the measure phase is to evaluate adequacy of the measurement system used for collection of data. Hence, the team decided to conduct a measurement system analysis (MSA) for the measurement system used for recording the data on

Six Sigma in small scale industry 197

Table II. Project charter

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Figure 2. Process flowchart of leaf spring manufacturing

Supplier

Input

Shearing department Heating shop Quenching shop

Material

Process

Output

Customer

Hardening process

Hardened leaf spring production report

The Hardness Inspector General Manager

Furnaces Reservoir Quenching oil Hardening Process

Table III. Supplier-Input-ProcessOutput-Customer

Heang Process

Cambering

Quenching

Inspecon

hardness. The instrument used for measuring hardness is a “Brinell Hardness Testing Machine” with least count of 0.001 mm. Now, to conduct the MSA study, ten specimens and two operators were selected. Each operator measured each specimen four times and the hardness values were recorded (AIAG, 2002; Montgomery, 2002). These data were further analyzed to study the repeatability and reproducibility variation using Minitab statistical software. From the output of the Minitab analysis presented in Table IV, it was observed that the total gauge repeatability and reproducibility (total GR&R)values

Source

Standard deviation

Study variation (per cent)

0.35934 0.33116 0.13949 4.72234 4.73599

7.59 6.99 2.95 99.71 100

Total gauge R & R Repeatability Reproducibility Part-to-part Total variation

Six Sigma in small scale industry 199

Table IV. Results of gauge repeatability and reproducibility (R and R) study of hardness measurement

Probability Plot of Hardness Normal

99.9

Mean StDev N AD P-Value

99 95 90

Percent

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for hardness were 7.59 per cent. Because this value is within the acceptable limit of 30 per cent, the team concluded that the measurement system can be used for further study (AIAG, 2002). Then a detailed data collection plan was prepared with sample size, type of sampling with stratification factors like operator, shift, etc. As per the plan, the data were collected on hardness from the process. These data were tested for normality by the “Anderson – Darling normality test” with the help of Minitab software. From the Minitab software output, the p-value of the test was found to be ⬍ 0.05 (refer to Figure 3), which leads to the conclusion that the data are from a population that is not normal (Montgomery and Runger, 2007). The data were further tested for all known distributions with the help of Minitab software, but could not identify any distribution fitting to these data. Even after Box-Cox and Johnson transformations, the data were not transformed to normality (Montgomery and Runger, 2007). Because none of the distributions were fitting to these data, the “observed parts per million (PPM) total” of 270,000 from Figure 4 was considered as an estimate of baseline performance of the process (Gijo et al., 2011a).

256.6 14.14 100 4.161 <0.005

80 70 60 50 40 30 20 10 5 1 0.1

200

220

240

260 Hardness

280

300

Figure 3. Normality test of hardness

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LSL

Overall Capability Pp 0.24 P P L 0.27 P P U 0.20 P pk 0.20 Cpm *

230

Figure 4. Process capability analysis of hardness

USL

P rocess Data LSL 245 Target * USL 265 Sample Mean 256.625 Sample N 100 StDev(Overall) 14.1404

Observed P erformance P P M < LSL 60000.00 P P M > USL 210000.00 P P M Total 270000.00

240

250

260

270

280

290

300

Exp. Overall P erformance P P M < LSL 205505.85 P P M > USL 276832.87 P P M Total 482338.72

2.3 The analyze phase The aim of the analyze phase in a Six Sigma project is to identify the potential causes for the process problem being studied and then select the root causes with the help of data and their analysis (Oliya et al., 2012). Once a list of potential causes has been generated, the next step is to plan for validation of these causes based on the data collected from the process. Lot of innovative thinking and discussions are required to identify the potential causes for a problem (Roth and Franchetti, 2010). A brainstorming session was planned and conducted by the team with the involvement of all the concerned personnel of the process, and a list of potential causes for variation in CTQ was generated. A cause-and-effect diagram was drawn based on these causes, which is presented in Figure 5. All the causes listed in the cause-and-effect diagram in Figure 5 are to be validated based on data to identify the root causes. Hence, it is necessary to explore the type of data possible to collect on each of these causes and plan for an appropriate analysis to make meaningful conclusions about the potential causes. The team had a detailed discussion in this regard with the MBB and the operators and supervisors of the process. Based on this discussion, a plan was prepared with details of data on each cause and the type of validation required. The summary of these details are presented in Table V. As per the plan given in Table V, few of the causes are to be validated by process observation or “Gemba” and the remaining causes to be validated based on various statistical analyses (Womack, 2011). Few cases where the Gemba analysis was used to identify root causes are explained in the following paragraph. The summary of results of all these validations is also presented in Table V. Process parameters like reservoir volumes, quenching time, oil temperature, type of quenching oil, etc., were fixed during the

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Figure 5. Cause-and-effect diagram

establishment of the process based on the trial-and-error method. Hence, it was decided to conduct a design of experiment (DOE) at the improvement phase for these parameters, so that an optimum process setting can be identified. Some of the Gemba observations during this study were as follows (Womack, 2011). Profiles of the operators and time and motion study for a period of one week revealed that the operators are having adequate skill and experience to carry out the process. It was also observed that the work load and rest times are followed as per stipulated government norms. Repeatability and reproducibility of the measurement system were confirmed with the MSA study during the measure phase. Room temperature and humidity were checked every hour for one week and it was confirmed that both are within the desired specification limits. Material composition was confirmed from the past month purchase history, as one piece in every batch was checked by the purchase officer before lot acceptance. Mechanical and chemical properties were verified with microstructure study by taking two pieces per day for a period of one week. The conclusions from these validations are also included in Table V. 2.4 Improve phase During the improve phase of the project, solutions for the selected root causes are to be identified and implemented to observe the results. As per the decision of the team in the analyze phase, a DOE was planned and conducted during this phase to identify the optimum settings for the process parameters. After a detailed discussion, the parameters selected for experimentation were quenching time, oil temperature, type of quenching oil and reservoir volume. During the brainstorming session, the team felt that the interaction of “quenching time” with “reservoir volume”, “oil temperature” and “type of quenching oil” can have a significant impact on hardness. Hence, these interactions were also considered for further study. The response of the experiment was decided as hardness measured on the component with the hardness tester. Because the relationship

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Sl. No.

Causes

Specification/desired status

Observation

Remarks

1

Reservoir volume

Small (1.5 ⫻ 2 ⫻ 1) m3– Medium (1.75 ⫻ 2.25 ⫻ 1) m3– Large (2 ⫻ 2.5 ⫻ 1) m3

To be studied by DOE

2

Variation in quenching time

(4-6) minutes

3

Changes in oil temperature

(35-45)°C

4

Lack of skill

Operator must be at least semiskilled

5

Heavy work load

No more than 48 hours duty/ week, with weekly holiday

6

Lack of time for rest

Half an hour rest time for every five hours

7

R&R

8

Variation in the room temperature

Percentage variation in GR&R ⬍ 30 per cent Should be less than 35°C

9

Changes in humidity

40-60 per cent RH

10

Variation in composition

Should be of same composition

11

Different types of quenching oil Changes in the mechanical properties Changes in the chemical properties

No specification available

Medium reservoir was used were space not sufficient Variation beyond specification limits was observed Variation beyond specification limits was observed Skill of the operator is adequate Work load is distributed as per plan Rest times are allotted as per industrial norms Within the acceptable limit Except the morning time, it was observed as constant No significant variation Constant, as it was checked before purchase Slow cooling rate

12

Table V. Cause validation details

13

To be studied by DOE

To be studied by DOE

Not a root cause Not a root cause Not a root cause Not a root cause Not a root cause

Not a root cause Not a root cause

Martensite structure

No variation found

To be studied by DOE Not a root cause

Properties related to Martensite structure

No variation found

Not a root cause

between these variables and the hardness is not established as linear, it was decided to experiment all these factors at three levels (Taguchi, 1988; Ross, 1996). The factors and their selected levels are presented in Table VI. For conducting a full factorial experiment with four factors, each at three levels, it required to conduct a total of 81 experiments. Conducting 81 experiments was very costly and time consuming for the organization. Hence, it was decided to adopt orthogonal arrays for conducting the fractional factorial experiments. Estimation of the effects of four factors each at three levels and three interactions requires 18 degrees of freedom (df), requiring minimum of 19 experiments

Sl. No. 1 2 3

4

Factor

1

Level 2

Quenching time (in minutes) Oil temperature (in °C) Quenching oil (quench-o-meter rating, in s) Reservoir volume (in m3)

4

5

3 *6

35 *Fast (9)

40 Medium (13)

*45 Slow (17)

Small (1.5 ⫻ 2 ⫻ 1)

*Medium (1.75 ⫻ 2.25 ⫻ 1)

Large (2 ⫻ 2.5 ⫻ 1)

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Note: * Existing level

(Wu and Hamada, 2011). The nearest three-level orthogonal array (OA) for designing this experiment is L27(313). The design layout (Table VII) for the experiment was prepared by allocating the factors and level to the L27(313) OA. The experimental sequence given in the design layout was randomized and experimentation was done and the hardness values were recorded. The significance of the main effects and interactions on hardness to be studied based on the experimental data. Because the variation in the output characteristic (hardness) is to be studied and reduced for this process, Taguchi’s signal-to-noise (S/N) ratio concept was utilized for analyzing the data. Because hardness is a nominal-the-best type of ˉ is the ˉ 2/s2), where Y characteristic, the S/N ratio formula used for analysis was 10log (Y average and s is the standard deviation for each experiment (Taguchi, 1988). The S/N ratio values were calculated for all the 27 experiments, and these S/N ratio values were subjected to ANOVA to identify the significant factors and interactions (Montgomery and Runger, 2007). The ANOVA table thus obtained is presented in Table VIII. From the ANOVA table, it was found that the p-values for factors “quenching time” and “reservoir volume” and the interaction of “quenching time” with “quenching oil” and “oil temperature” were found to be less than 0.05, leading to the conclusion that these factors and interactions significantly affect the hardness. To identify the optimum level for these factors, the main effect and interaction plots for the S/N ratio values were prepared and are presented in Figures 6 and 7 (Gijo et al., 2011a, Gijo and Scaria, 2012). The best level for any factor corresponds to the level with the highest S/N ratio (Phadke, 1989; Wu and Hamada, 2011). The optimum factor level combination thus identified is presented in Table IX. Finally, these optimum results were implemented after preparing an implementation plan with responsibility and target date. The results were observed after successful implementation of the solutions for a period of one week. The data on hardness were recorded during implementation. This data were analyzed to understand the level of improvement in the process characteristics. The ppm level of hardness was found to be 7,924 (The details are presented in Figure 8.) The overall rejection percentage was reduced from 48.33 to 0.79, which was very significant for the process. The individual chart plotted for comparing the process data before and after the project shows significant reduction in variation in hardness after the project (Figure 9).

Six Sigma in small scale industry 203 Table VI. Factors and their levels for experimentation

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Table VII. The design layout for experimentation

Table VIII. ANOVA table (Minitab output)

Experiment number 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Quenching time

Oil temperature

4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6

35 35 35 40 40 40 45 45 45 35 35 35 40 40 40 45 45 45 35 35 35 40 40 40 45 45 45

Quenching oil

Reservoir volume

Fast Medium Slow Fast Medium Slow Fast Medium Slow Fast Medium Slow Fast Medium Slow Fast Medium Slow Fast Medium Slow Fast Medium Slow Fast Medium Slow

Small Medium Large Medium Large Small Large Small Medium Small Medium Large Medium Large Small Large Small Medium Small Medium Large Medium Large Small Large Small Medium

Source

DF

SS

MS

F

p-value

Quenching time Oil temperature Quenching oil Reservoir volume Quenching time ⫻ oil temperature Quenching time ⫻ quenching oil Quenching time ⫻ reservoir volume Error Total

2 2 2 2 4 4 4 6 26

699.91 56.94 20.12 249.96 289.03 304.23 54.84 90.37 1765.40

349.96 28.47 10.06 124.98 72.26 76.06 13.71 15.06

23.24 1.89 0.67 8.30 4.80 5.05 0.91

0.001* 0.231 0.547 0.019* 0.044* 0.040* 0.514

Note: * Significant at 5 per cent level of significance

2.5 Control phase Having identified the root causes, implemented the solutions for them and observed the results, the project team moved on to the control phase of the project. The critical components of this phase include standardization and documentation of the improved

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Figure 6. Main effects plot for S/N ratio

Interaction Plot (data means) for SN ratios 35

40

45

Small

Medium

Large 50

40

Quenching Time

30

50

40

Oil Temperature

30

50

40

Quenching Oil

Quenching Time 4 5 6 Oil Temperature 35 40 45 Quenching Oil Fast Medium Slow

30 50 40

Reservoir Volume

30 4

5

6

Fast

Medium

Reserv oir Volume Small Medium Large

Slow

Signal-to-noise: Nominal is best (10*Log(Ybar**2/s**2))

process and creating a plan for monitoring the process (Gijo et al., 2011a). To ensure that the proposed methods of improvement are sustained, the team implemented a set of control mechanisms. The process is standardized and is documented in quality management system documents (Karthi et al., 2011). Also, a process flowchart was prepared and displayed at the shop floor with details of process specifications. This display helps everyone to understand the process details and also act as a knowledge management system. Check sheets were prepared for data recording and control charts were made to monitor the process so that the operator can take timely action before the critical process parameters and performance characteristics go out of limits. The check

Figure 7. Interaction plot for S/N ratio

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sheet is prepared by taking all parameters of the process into account, and the operator records the readings from the process during the usual production. This activity was found to be very useful in keeping track of all the process data for analysis at a later stage, in case of any problem. A periodic review of these results was planned to ensure sustainability of the achieved results. An “individual-chart” for hardness was introduced for monitoring the process, along with an out-of-control action plan (OCAP) (Grant and Leavenworth, 2000). This OCAP helps the operator to initiate action in the process in case of assignable causes. It is also necessary to make sure that all the employees are aware of the improvement actions implemented in the process. Hence, a one-day awareness training program was arranged for all the employees about the Six Sigma methodology. 3. Lessons learned Much has been observed in the company regarding the deployment of improvement initiatives and the associated Six Sigma methodology. The learning from this initiative are summarized for implementing future improvement activities effectively. Key lessons learned from the case study focus around the leadership activities, involvement of people in improvement initiatives, data collection and subsequent data-based cause validation. Generally, critical success factors (CSFs) such as management commitment, leadership and training are very crucial for the success of the Six Sigma projects

Table IX. Optimum factor level combination from the experiment

Figure 8. Process capability analysis of hardness

Sl. No

Parameters

Optimum level after DOE

1 2 3 4

Reservoir volume Quenching time Oil temperature Quenching oil

Large (2 ⫻ 2.5 ⫻ 1) m3 Five minutes 40° C Medium (13 seconds)

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Figure 9. I-Chart for hardness

(Laureani and Antony, 2012). The same are observed during different phases of case study. Leadership has been proven to be an extremely important factor for Six Sigma implementation in many automotive and its supporting industries (Habidin and Yusof, 2013). During the define, analyze and improve phases of the study, it was observed that all hurdles in executing the study were cleared by strong leadership at middle management. Thus, it was identified that improvement initiatives require strong leadership support not only at the higher level but also in the middle level of the organization. Most of the time, people at lower levels in small traditional organizations have a fear of job security. Quite often they think that if improvement projects are carried out, and cycle time and rework in the process are reduced, that may lead to reduction of head count and loss of job opportunity in the organization. Thus, giving awareness training in the Six Sigma methodology to the lower-level people in the organization about the focus of this improvement initiative will help them to understand the purpose of this methodology and drive away any fear about the end result. This will create a sense of urgency for improvement projects at lower levels of the organization itself. The main enabler for Six Sigma implementation is the top management commitment that can promote an effective company-wide training to let all the employees be involved in the project (Tjahjono et al., 2010). One of the reasons for success of this study was the strong support from the champion. The champion was keen to implement the Six Sigma methodology for addressing process problems in the organization. Especially during the DOE, champion’s strong support helped the team to draw meaningful conclusions. This shows that knowledge about the Six Sigma methodology is essential at every level of the organization for successful implementation. Organizational learning integrated with DMAIC methodology can provide a useful framework for successful Six Sigma implementation. This can be an aid in strengthening the “voice of the customer”, the measurement capabilities as well as the effectiveness and the efficiency of the processes (Lagrosen et al., 2011; AL-Najem et al., 2013). Training imparted during the measure phase on technical details of the process under study, data collection plans and some of

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the key tools of the Six Sigma approach were the backbones in achieving the project goal. Involving the people from all levels in the organization in the development of the data collection process and sharing the inferences from data analysis with them greatly helped in getting support for collecting the data. Quite often the shop-floor workers are not aware of the technical details of the process they are working with. Proper training on the technical details will help them to do process-based thinking, recognize variation in the critical characteristics and focus on breakthrough improvement in performance. During the improvement phase, support from all levels of the organization is required for successful implementation of the solutions. 4. Concluding remarks and future research directions The goal of this case study was to demonstrate the application of the Six Sigma methodology in a small-scale foundry industry for improving the hardening process. It has been observed that successful implementation of the Six Sigma methodology can be helpful in creating a better process that is consistent, sustainable and cost-effective that can meet the increasing demands of the customers. As a result of this study, the overall rejection percentage was reduced from 48.33 to 0.79. This result motivated the management to implement the Six Sigma methodology for all improvement initiatives in the organization. For this purpose, the management decided to arrange in-house Six Sigma green-belt and black-belt training for the people in the organization. The organization is able to realize significant benefit from the project. The company has invested around USD1,100 for the project toward training and other activities. This, in turn, resulted in a financial saving of USD8,000 per year due to the reduction of rejection in the process. This substantial benefit, in turn, resulted in reduced material scrap rate and decreased lead time of the process. The Six Sigma initiatives developed a culture toward continuous improvement and data-based thinking throughout the organization. The company has identified few areas for improvement through the Six Sigma methodology. These include reduction of raw material inventory, reducing lead time in raw material procurement, reducing customer complaint resolution time, etc. For executing these projects, few candidates for black belt (BB) and green belt (GB) were identified. It was also decided by the management that all the new products hereafter will be designed by using the design for Six Sigma (DFSS) methodology. For this purpose, one engineer was trained in DFSS methodology. Thus, after observing success in the first project, the management prepared a very ambitious plan for implementing Six Sigma in the organization. Management commitment, leadership and trainings are important CSFs in successful Six Sigma implementation (Coronado and Antony, 2002). During the study it was confirmed that these CSFs are important, even in a small-scale industry, where there is a lack of awareness about the Six Sigma methodology. Because this is a single case study, the possibility of generalizing the findings to other contexts remains uncertain. Further research on CSFs of six sigma methodology in small-scale industries, especially in rural areas, would be beneficial for developing countries. References AIAG (2002), Measurement Systems Analysis, Reference Manual, 3rd ed., Automotive Industry Action Group, Southfield, MI.

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AL-Najem, M., Dhakal, H., Labib, A. and Bennett, N. (2013), “Lean readiness level within Kuwaiti manufacturing industries”, International Journal of Lean Six Sigma, Vol. 4 No. 3, pp. 280-320. Breyfogle, F.W. (2003), Implementing Six Sigma: Smarter Solutions Using Statistical Methods, John Wiley, New York, NY. Breyfogle, F.W. (2005), “21 Common problems (and what to do about them)”, Six Sigma Forum Magazine, Vol. 4 No. 4, pp. 35-37. Callister, W.D. (2003), Material Science and Engineering: An Introduction, 6th ed., John Wiley and Sons, pp. 321-323, 365-366. Chen, S.C., Chen, K.S. and Hsia, T.C. (2005), “Promoting customer satisfactions by applying six sigma: an example from the automobile industry”, The Quality Management Journal, Vol. 12 No. 4, pp. 21-33. Chiarini, A. (2011), “Japanese total quality control, TQM, Deming’s system of profound knowledge, BPR, Lean and Six Sigma: comparison and discussion”, International Journal of Lean Six Sigma, Vol. 2 No. 4, pp. 332-355. Coronado, R.B. and Antony, J. (2002), “Critical success factors for the successful implementation of six sigma projects in organizations”, The TQM Magazine, Vol. 14 No. 2, pp. 92-99. Deshmukh, S.V. and Chavan, A. (2012), “Six Sigma and SMEs: a critical review of literature”, International Journal of Lean Six Sigma, Vol. 3 No. 2, pp. 157-167. Dhoshi, N.P., Ingole, N.K. and Gulhane, U.D. (2011), “Analysis and modification of leaf spring of tractor trailer using analytical and finite element method”, International Journal of Modern Engineering Research (IJMER), Vol. 1 No. 2, pp. 719-722. Firka, D. (2010), “Six Sigma: an evolutionary analysis through case studies”, The TQM Journal, Vol. 22 No. 4, pp. 423-434. Gijo, E.V. and Scaria, J. (2010), “Reducing rejection and rework by application of Six Sigma methodology in manufacturing process”, International Journal of Six Sigma and Competitive Advantage, Vol. 6 Nos. 1/2, pp. 77-90. Gijo, E.V. and Scaria, J. (2011a), “Application of Taguchi method to optimise the characteristics of green sand in a foundry”, International Journal of Business Excellence, Vol. 4 No. 2, pp. 191-201. Gijo, E.V. and Scaria, J. (2012), “Product design by application of Taguchi’s robust engineering using computer simulation”, International Journal of Computer Integrated Manufacturing, Vol. 25 No. 9, pp. 761-773. Gijo, E.V., Scaria, J. and Antony, J. (2011b), “Application of Six Sigma methodology to reduce defects of a grinding process”, Quality and Reliability Engineering International, Vol. 27 No. 8, pp. 1221-1234. Grant, E.L. and Leavenworth, R.S. (2000), Statistical Quality Control, 7th ed., Tata McGraw-Hill, New Delhi. Habidin, N.F. and Yusof, S.M. (2013), “Critical success factors of Lean Six Sigma for the Malaysian automotive industry”, International Journal of Lean Six Sigma, Vol. 4 No. 1, pp. 60-82. Hahn, G.J., Doganaksoy, N. and Hoerl, R. (2000), “The evolution of Six Sigma”, Quality Engineering, Vol. 12 No. 3, pp. 317-326. Heavey, C. and Murphy, E. (2012), “Integrating the balanced scorecard with Six Sigma”, The TQM Journal, Vol. 24 No. 2, pp. 108-122.

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Jeyaraman, K. and Teo, L.K. (2010), “A conceptual framework for critical success factors of lean Six Sigma: implementation on the performance of electronic manufacturing service industry”, International Journal of Lean Six Sigma, Vol. 1 No. 3, pp. 191-215. Julien, D. and Holmshaw, P. (2012), “Six Sigma in a low volume and complex environment”, International Journal of Lean Six Sigma, Vol. 3 No. 1, pp. 28-44. Karthi, S., Devadasan, S.R. and Murugesh, R. (2011), “Integration of Lean Six-Sigma with ISO 9001:2008 standard”, International Journal of Lean Six Sigma, Vol. 2 No. 4, pp. 309-331. Kaushik, P., Khanduja, D., Mittal, K. and Jaglan, P. (2012), “A case study: application of Six Sigma methodology in a small and medium-sized manufacturing enterprise”, The TQM Journal, Vol. 24 No. 1, pp. 4-16. Kumar, M.S. and Vijayarangan, S (2007a), “Analytical and experimental studies on fatigue life prediction of steel and composite multi-leaf spring for light passenger vehicles using life data analysis”, Materials Science, Vol. 13 No. 2, pp. 141-146. Kumar, M.S. and Vijayarangan, S (2007b), “Static analysis and fatigue life prediction of steel and composite leaf spring for light passenger vehicles”, Journal of Scientific and Industrial Research, Vol. 26 No. 2, pp. 128-134. Kwak, Y.H. and Anbari, F.T. (2006), “Benefits, obstacles and future of Six Sigma approach”, Technovation, Vol. 26 Nos. 5/6, pp. 708-715. Lagrosen, Y., Chebl, R. and Tuesta, M.R. (2011), “Organisational learning and Six Sigma deployment readiness evaluation: a case study”, International Journal of Lean Six Sigma, Vol. 2 No. 1, pp. 23-40. Laureani, A. and Antony, J. (2012), “Critical success factors for the effective implementation of Lean Sigma”, International Journal of Lean Six Sigma, Vol. 3 No. 4, pp. 274-283. Leon, H.C.M., Perez, M.D.C.T., Farris, J.A. and Beruvides, M.G. (2012), “Integrating Six Sigma tools using team-learning processes”, International Journal of Lean Six Sigma, Vol. 3 No. 2, pp. 133-156. Montgomery, D.C. (2002), Introduction to Statistical Quality Control, 4th ed., John Wiley, New York, NY. Montgomery, D.C. (2010), “A modern framework for achieving enterprise excellence”, International Journal of Lean Six Sigma, Vol. 1 No. 1, pp. 56-65. Montgomery, D.C. and Runger, G.C. (2007), Applied Statistics and Probability for Engineers, 4th ed., John Wiley & Sons, Inc, London. Oliya, E., Owlia, M.S., Shahrokh, Z.D. and Olfat, L. (2012), “Improving marketing process using Six Sigma techniques (case of Saman Bank)”, International Journal of Lean Six Sigma, Vol. 3 No. 1, pp. 59-73. Phadke, M.S. (1989), Quality Engineering using Robust Design, Prentice Hall, NJ. Ross, P.J. (1996), Taguchi Techniques for Quality Engineering, McGraw-Hill, New York, NY. Roth, N. and Franchetti, M. (2010), “Process improvement for printing operations through the DMAIC Lean Six Sigma approach: a case study from Northwest Ohio, USA”, International Journal of Lean Six Sigma, Vol. 1 No. 2, pp. 119-133. Sharma, S. and Chetiya, A.R. (2010), “Six Sigma project selection: an analysis of responsible factors”, International Journal of Lean Six Sigma, Vol. 1 No. 4, pp. 280-292. Snee, R.D. (2004), “Six Sigma: the evolution of 100 years of business improvement methodology”, International Journal of Six Sigma and Competitive Advantage, Vol. 1 No. 1, pp. 4-20. Snee, R.D. (2010), “Lean Six Sigma-getting better all the time”, International Journal of Lean Six Sigma, Vol. 1 No. 1, pp. 9-29.

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Taguchi, G. (1988), Systems of Experimental Design, Volume 1 and 2, UNIPUB and American Supplier Institute, New York, NY. Tjahjono, B., Ball, P, Vitanov, V.I., Scorzafave, C., Nogueira, J., Calleja, J., Minguet, M., Narasimha, L., Rivas, A., Srivastava, A., Srivastava, S. and Yadav, A. (2010), “Six Sigma: a literature review”, International Journal of Lean Six Sigma, Vol. 1 No. 3, pp. 216-233. Treichler, D.H. (2005), The Six Sigma Path to Leadership, Pearson Education, Delhi. Womack, J. (2011), GEMBA Walk, Lean Enterprise Institute, Inc, Cambridge, MA. Wu, C.F.J. and Hamada, M. (2011), Experiments-Planning, Analysis, and Parameter Design Optimization, John Wiley, New York, NY. About the authors E.V. Gijo is a faculty member in the Statistical Quality Control (SQC) and Operations Research Unit of the Indian Statistical Institute, Bangalore, India. He holds a master’s degree in Statistics and another master’s degree in Quality, Reliability and Operations Research. He is an active consultant in the fields of Six Sigma, quality management, reliability, Taguchi methods, time-series analysis and allied topics in a variety of industries. He is a certified MBB and Trainer in Six Sigma and a qualified assessor for ISO-9001 and ISO-14001 systems. He has published more than 20 papers in reputed international journals and is a regular reviewer for six international journals in statistics and quality management. He also teaches in the academic programs of the Institute. E.V. Gijo is the corresponding author and can be contacted at: [email protected] Shreeranga Bhat is a faculty member at the Department of Mechanical Engineering of St. Joseph Engineering College, Mangalore, India. He holds a bachelor’s degree in Mechanical Engineering and a master’s degree in Engineering Management from Manipal Institute of Technology, Manipal. He is a certified black belt in Six Sigma from the Indian Statistical Institute, Bangalore. His areas of interest include lean manufacturing, Six Sigma and design of experiments. N.A. Jnanesh is currently working as the Principal of KVG College of Engineering, Sullia, Karnataka, India. He completed his BE degree from Mysore University, ME degree from Karnataka University, Dharwad, and PhD from Mangalore University. His research topic was application of total quality management (TQM) in technical education with special reference to curriculum development. His areas of interest are TQM, Six Sigma, statistical quality control (SQC), production management and operation management. He has more than 23 years of experience in the domains of teaching and administration. He was a member of several bodies of universities and visited different countries and presented several papers at national and international conferences and seminars. Currently he is guiding three students for their doctoral degree in Visvesvaraya Technological University, Belgaum.

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