Page 1

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN 2249-6890 Vol. 3, Issue 4, Oct 2013, 11-22 © TJPRC Pvt. Ltd.


M.Tech Scholar, Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, India


Professor, Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology, Durg, Chhattisgarh, India

ABSTRACT This paper discusses the implementation of Six-sigma methodology in reducing defectives in a cylinder liner manufacturing industry. The Six-sigma DMAIC (define– measure – analyze –improve – control) approach has been used to achieve this result. This paper explains the step-by-step approach of Six-sigma implementation in this manufacturing process for improving quality level. This resulted in reduction of rejection, and thus, reduced the Defect per Million Output (DPMO) from 66900 to 6050.This had resulted in increasing the sigma level from 2.91 to 4.43, without any huge capital investment. TPM is implemented which results in increase of Overall Equipment Efficiency (OEE). During this study, data is collected on all possible causes and was analyzed and thereby conclusions were made. Implementation of Six-sigma methodology and TPM has resulted in large financial savings for the firm.

KEYWORDS: Six-Sigma, DMAIC, Process Capability, Fishbone Diagram, SIPOC Diagram, Pareto Chart, Process Yield, Overall Equipment Efficiency (OEE), Total Productive Maintenance (TPM)

INTRODUCTION The fast changing economic conditions such as global competition, declining profit margin, customer demand for high quality product, product variety and reliable deliveries had a major impact on manufacturing industries. To respond to these needs various industrial engineering and quality management strategies such as Total Productive maintenance (TPM), Total Quality Management, Kaizen, JIT manufacturing,, Enterprise Resource Planning, Business Process Reengineering, Lean manufacturing have been developed. A new paradigm in this area of manufacturing strategies is Six Sigma. The Six Sigma approach has been increasingly adopted worldwide in the manufacturing sector in order to enhance productivity and quality performance and to make the process robust to quality variations. Six-sigma is a disciplined, systematic, data-driven approach to process improvement adopted by organizations world over. Motorola introduced the concept of six-sigma in the mid-1980s as a powerful business strategy to improve quality. Six-sigma continues to be the best-known approach for process improvement. Six Sigma is a business performance improvement strategy that aims to reduce the number of mistakes/defects to as low as 3.4 occasions per million opportunities. Sigma is a measure of “variation about the average” in a process which could be in manufacturing or service industry. Six Sigma improvement drive is the latest and most effective technique in the quality engineering and management spectrum. It enables organizations to make substantial improvements in their bottom line by designing and monitoring everyday business activities in ways which minimizes all types of wastes and Non Value Added (NVA) activities and maximizes customer satisfaction. While all the quality improvement drives are useful in their own ways, they


Nilmani Sahu & Sridhar

often fail to make breakthrough improvements in bottom line and quality. Voelkel, J.G.(2002) contents that Six Sigma blends correct management, financial and methodological elements to make improvement in process and products in ways that surpass other approaches. Mostly led by practitioners, Six Sigma has acquired a strong perspective stance with practices often being advocated as universally applicable. Six Sigma has a major impact on the quality management approach, while still based in the fundamental methods & tools of traditional quality management (Goh & Xie2004) Six Sigma is a strategic initiative to boost profitability, increase market share and improve customer satisfaction through statistical tools that can lead to breakthrough quantum gains in quality; Mike Harry and Schroeder (2000). Six Sigma is a new paradigm of management innovation for company’s survival in this twenty first century, which implies three things: Statistical Measurement, Management Strategy and Quality Culture. Six Sigma is a business improvement strategy used to improve profitability, to drive out waste, to reduce quality costs & improve the effectiveness and efficiency of all operational processes that meet or exceed customers’ needs & expectations Antony & Banuelas (2001). Tomkins (1997) defines Six Sigma as a program aimed at the near elimination of defects from every product, process and transaction. Snee (2004) defines Six Sigma as a business improvement approach that seeks to find and eliminate causes of mistakes or defects in business processes by focusing on process outputs that are of critical importance to customers. Kuei and Madu (2003) define Six Sigma as: Six Sigma quality means meeting the very specific goal provided by the 6σ metric and Management by enhancing process capabilities for Six Sigma quality. Mdhdiuz zaman and Sujit kumar (2013) discusses the implementation of Six-sigma methodology in reducing rejection in a welding electrode manufacturing industry. Sushil kumar and Prajapathi (2011) presented DMAIC based Six Sigma approach implemented to optimize the processes parameters of a foundry for the defect reduction. Rajeshkumar and Sambhe (2012) in their paper focus on a case of provoked mid-sized auto ancillary unit consisting of 350-400 employee and employed Six Sigma methodologies to elevate towards the dream of Six Sigma quality level. Sokovic. et al (2006) Systematic application of Six Sigma DMAIC tools and methodology results with several achievements such are reduction of tools expenses, cost of poor quality and labour expenses.Adan Valles et al (2009) presents a Six Sigma project conducted at a semiconductor company dedicated to the manufacture of circuit cartridges for inkjet printers and shown the improvement in reduction in the electrical failures of around 50%. Tushar Desai and. Shrivastava (2008) deals with an application of Six Sigma DMAIC(Define–MeasureAnalyze-Improve-Control) methodology in an industry which provides a framework to identify, quantify and eliminate sources of variation in an operational process in question, to optimize the operation variables, improve and sustain performance. Dalgobind Mahto and Anjani Kumar(2008) In their paper, root-cause identification methodology was adopted to eliminate the dimensional defects in cutting operation in CNC oxy flame cutting machine and a rejection has been reduced from 11.87% to 1.92% on an average. Six sigma, Total productive maintenance are inter linked to achieve productivity and Quality excellence. Process improvement through Six sigma improves Quality rating and TPM results in improvement in Overall Equipment Efficiency. In this paper an attempt is made to improve process yield by improving Overall Equipment Effectiveness and Six-sigma Level. It gives reduction in defects per million.

CASE STUDY A study was conducted in a firm which is a leading manufacturer o f cylinder liner for automotives. The firm i s accredited with ISO 9002 quality standards. The company has more than 200 employees. Major customers of the company in four wheeler segments are Ford, Telco, Fiat, Maruti, General Motors etc., and in two wheeler segments are Bajaj Auto, Kinetic Motors, LML, Yamaha, Hero Motors etc. As part of recent management change, the plant


Six Sigma Implementation Using DMAIC Approach-A Case Study in a Cylinder Liner Manufacturing Firm

has initiated a company-wide quality improvement strategy. The firm’s principal product is a cast iron cylinder liner (or sleeve) that is inserted into the aluminum block produced by the engine manufacturer. Given the reliance of the liner company on this single class of products, it needs to respond quickly to the ever-increasing expectations of the customer. In fact, word has it, that the engine manufacturer soon plans to announce new, more stringent specifications for the liner. The firm has a vision to implement concepts of Six sigma, TPM, TQM, Kaizen, JIT, Lean manufacturing to achieve quality excellence. Given this background, an attempt is made to implement six sigma concept for process improvement.

THE DMAIC SIX SIGMA METHODOLOGY The DMAIC methodology follows the phases: define measure, analyze, improve and control. (Antony & Banuelas 2004). Although PDCA could be used for process improvement, to give a new thrust Six Sigma was introduced with a modified model i.e. DMAIC. The methodology is revealed phase wise (Figure 1) which is depicted in A, B, C, D and E and is implemented for this Project.

Figure 1: The Dmaic Methodology (Pyzdek, 2003) Table 1: Process Yield of Cylinder Liners and OEE Month March 2012 April 2012 May 2012 June 2012


Process Yield 42.01% 42.3 % 43.1% 43.3 %

OEE 0.40 0.41 0.405 0.41

Process yield in percentage

42 40

Process yield in percentage

Figure 2: Process Yield in Percentage in Different Months


Nilmani Sahu & Sridhar

Define Phase This phase determines the objectives & scope of the project, collect information on the process and the customers, and specify the deliverables to customers (internal & external). Problem Description The operational process concerned is machining operations. Table 1 presents cylinder liner process yield and Overall Equipment Efficiency (OEE) as reviewed for the last four months. The problem encountered in the manufacture of cylinder liners is the large number of rejection of the units after manufacturing. The occurrence of rejection of cylinder liners was due to non-confirmance of inner diameter, outer diameter, coller width, Groove Diameter , Shoulder Ovality with respect to the required standard specifications. Due to improper maintenance percentage of machines availability and utilization are low. Cylinder liners process yield is low because of poor utilization of the machine and poor Quality. Pareto chart illustrates this in Figure 2. It was decided to improve this process yield. Table 2 presents the team charter for the project. Process Mapping The process mapping with Supply-Input-Process-Output-Customer (SIPOC) provides a picture of the steps needed to create the output of the process. Figure 3shows the SIPOC diagram. Identifying Key Quality Characteristics (QCH) The diameter of the cylinder liners is a key QCH. The upper specification limit (USL) is 103.492 mm, and the lower specification limit (LSL) is 103.466 mm.(figure 4). The other Key Quality Characteristics are Groove Diameter, Shoulder Ovality, and Collar width. Table 3 shows Specifications of cylinder liner. Table 2: Project Team Charter


Six Sigma Implementation Using DMAIC Approach-A Case Study in a Cylinder Liner Manufacturing Firm

Figure 3: SIPOC Diagram

Figure 4: Drawing of Cylinder Liner Table 3: Specifications of Cylinder Liner Parameter Surface roughness (Âľm) Inner diameter Outer diameter Coller width Under cut diameter Coller diameter Length

Upper Specification Limit 1.92 104.036 106.994 8.045 106.87 111.98 201.16

Lower Specification Limit 1.88 104.023 106.958 8.056 106.83 111.92 201.12

Table 4: Operations in the Process and Measuring Parameters Operation 1 2 3 4 5

Description Rough turning, boring, parting off. Fine turning, grooving, collar width formation. Rough grinding Fine boring Internal dia. Chamfering

Measuring Parameters Total length

Gauges Used Vernier callipers

Inner diameter

Bore gauge

Collar width Outer diameter Inner diameter

Flange micrometer Flange micrometer Bore gauge


Nilmani Sahu & Sridhar

6 7 8

Fine grinding Rough honing Fine honing

Table 4: Contd., Concentricity Outer diameter Collar diameter

Dial gauge Micrometer Micrometer

Table 5: Percentage Utilization of the Machines, Quality Rating and Overall Equipment Effectiveness Parameter Machines Availability time in percentage Machines idle time in Percentage Percentage utilization of the machines (Performance) Quantity planed (units) Quantity produced (units) Quantity rejected (units) Qty accepted (units) Quality Rating Overall Equipment Effectiveness

Value 79 32 68 18000 12320 2620 9700 0.79 0.79x0.68x0.79 =0.42

Measure Phase This phase is concerned with selecting appropriate product characteristics, studying the measurement system, making necessary measurements, recording the data, and establishing a baseline of the process capability or sigma level for the process. Table 4 shows Operations in the process, measuring parameters and gauges used. Current Process Capability A vital part of an overall quality improving program is process capability analysis by which the capability of a process can be measured and assessed. The process capability index CP enjoys a broad base f acceptance in the industry. The CP is obtained from CP = (USL - LSL) / 6 σ; The standard deviation is estimated by σ = R¯/ d2; Where, d2 is constant related to sample size, while R¯ is CL value in R chart. Here, σ = 3.41.The estimators of CPL, CPU and CPK are expressed by CPL = (x¯ - LSL) / 3 σ; CPU = (USL - x¯) /3 σ; CPK = min (CPL, CPU); CP value greater than 1 means that the process uses up less than 100 percent of the specification band, i.e. relatively less non conforming points will be observed. Whereas, CP value less than 1, means the process uses up more than the specification band.CPK value is less than CP value, means that the process is off centred, but capable, and has to be confirmed with more no. of samples. Whereas, CPK value less than zero means that the entire process mean lies outside the specifications, hence, the process is incapable.. As per calculation, the values obtained are CP = 0. 5138, CPL = 0. 1035, CPU = 0. 0919, and CPK = 0. 0919. It can be seen that the process uses up more than the specification band. It can also be deciphered that the process is off-centered, but capable. From the measurement phase it is observed that Current Sigma Level is 2.91 and defects per million are 66900.

Six Sigma Implementation Using DMAIC Approach-A Case Study in a Cylinder Liner Manufacturing Firm


Overall Equipment Effectiveness Table 5 shows computation of Overall Equipment Effectiveness in the month of sept’2012. The findings are showing the need for implementing Total Productive Maintenances to improve Overall Equipment Effectiveness.(Nilmani and Sridhar 2013). Analyse Phase The objective of analyse phase in this study is to identify the root causes that creates the dimensional variation of the cylinder liners. This phase describes the potential causes identified which have the maximum impact on the low process yield, causes for low Overall Equipment Effectiveness. Pareto Chart Analysis Data analysis was carried out in this phase to find the reasons for rejection and reworking of cylinder liners. It arises due to defects viz., diameter variation, poor surface finish, eccentricity and variation in collar width. Pareto analysis on the various types of defects is shown in Figure 5. In the diagram X-axis represents causes and Y-axis represents percentage of occurrence. It is found that inner diameter variation caused the major portion in rejection of the cylinder liners. Due to poor quality and low utilization of the machines Overall Equipment Effectiveness is not satisfactory. Figure 6 shows Pareto diagram illustrating the reasons for low utilization of the machines.

Figure 5: Pareto Diagram Illustrating the Causes for Poor Quality of the Cylinder Liners

Figure 6: Pareto Diagram Illustrating the Reasons for Low Utilization of the Machines Fishbone (Ishikawa) Diagram Analysis The tool that is used for the analysis of the causes of variation in the specifications of the cylinder liners is the Cause-and-Effect diagram or fishbone diagram. A cause-and-effect diagram for process yield presents a chain of causes &


Nilmani Sahu & Sridhar

effects, sorts out causes & organizes relationship between variables. The cause-and-effect diagram prepared for the 22 initial probable causes identified can be viewed in Figure 7. This phase aims at adjusting the process mean on target. Process mean can be adjusted on target by improving the factors that have significant effects on the mean. The DPMO of the process was found to be 1666.67 and the corresponding sigma level was calculated to be 4.43.The process capability of the key Quality characteristics is shown in the Table 6.

Figure 7: Cause and Effect Diagram for Out of Specifications of Cylinder Liner Quality Concern

Figure 8: Mean and Range Charts Showing Boring Process is Out of Control

Figure 9: Mean and Range Charts Showing Boring Process is in Control after Eliminating Assignable Causes


Six Sigma Implementation Using DMAIC Approach-A Case Study in a Cylinder Liner Manufacturing Firm

Table 6: Process Capability of the Key Quality Characteristics Process Capability Cp Cpk1 Cpk2 Cpk

Groove Diameter 0.006 1.578 1.683 1.473 1.473

Shoulder Ovality 0.003 1.521 1.545 1.636 1.545

Collar Width 0.002 1.66 1.33 1.53 1.53

Improve Phase Improve Process During improvement phase statistical p ro cess control (SPC) is used as a monitoring tool. The objective of SPC w a s to control the variations in the process reduce the rejections and improve the process capability. To illustrate Figure 8 represents liner specifications are out of control after boring process and Figure 9 represents liner specifications are in control limits control after rectification of assignable causes. (Nilmani and Sridhar 2012) Brainstorming Session In this phase detailed discussions and brainstorming sessions were carried out. Solutions were identified for all root causes. Process Capability after Improvement This phase aims at adjusting the process mean on target. Process mean can be adjusted on target by improving the factors that have significant effects on the mean. The DPMO of the process was found to be 1666.67 and the corresponding sigma level was calculated to be 4.43.The process capability of the key Quality characteristics is shown in the Table 6. Pareto Chart after Improvement After implementation of the solutions, the reasons for rejection were analyzed with the Pareto chart. The Pareto chart after improvement is shown in Figure 8. Improvement in Overall Equipment Effectiveness As shown in the Pareto chart figure 6, the reasons for low machine utilization were analysed and Total Productive Maintenance was initiated to improve Overall Equipment Effectiveness.The improvement in availability, utilization, Quality Rating and Overall Equipment Effectiveness (OEE) are given in Table 5. (Nilmani and Sridhar 2012)

Figure 10: Pareto Chart after Improvement in the Process


Nilmani Sahu & Sridhar

Table 7: Results after Implementation of TPM (April 2013) Parameter Total unavailable time in percentage Availability of the machine in percentage Percentage of the machine idle time Percentage utilization of the machine (Performance) Quantity planed (units) Quantity produced (units) Quantity rejected (units) Qty accepted (units) Quality Rating Overall Equipment Effectiveness(OEE)

Value 11 89 24 76 18000 13920 990 12930 0.93 0.89x0.76x0.93 =0.63

E. Control Phase This is about holding the gains which have been achieved by the project team. Implementing all improvement measures during the improve phase, periodic reviews of various solutions and strict adherence on the process yield is carried out. The Project team members executed strategic controls by an ongoing process of reviewing the goals and progress of the targets. The team met periodically and reviewed the progress of improvement measures and their impacts on the overall business goals. The real challenge of Six Sigma implementation is not in making improvements in the process but in sustaining the achieved results. In this phase, the process control charts and Pareto charts are regularly utilized for monitoring diameter readings. Visible Results The implementation of the various tools and brainstorming sessions has resulted in the improvement of the manufacturing process, and also on the firm as a whole. Table 8 shows results after improvement and control. The comparison of sigma level before and after undertaking the study is depicted in Figure 11.

Figure 11: Comparison of Sigma Level before and after Undertaking the Study


Six Sigma Implementation Using DMAIC Approach-A Case Study in a Cylinder Liner Manufacturing Firm

Table 8: Results after Improvement and Control Parameter Process yield Overall Equipment Effectiveness(OEE) Six-sigma Level Defects per million Process capability Index

Before Improvement 44%

After Improvement 90%



2.91 66900

4.43 6050



CONCLUSIONS Detailed analysis has been performed to rectify the problems of rejection of cylinder liners due to the variations in Quality characteristics of the manufactured units. TPM is implemented to improve the utilization of machines. Analysis is carried out with the help of tools like Pareto analysis, process capability analysis and fish-bone diagram. The process Sigma level through Six Sigma DMAIC methodology was found to be approaching 4.43Sigma from 2.91, while the process yield was increased to 90% from a very low figure of 44%. This Six Sigma improvement methodology viz.DMAIC project shows thatthe performance of the firm is increased to a better level as regards to: enhancement in customers’ (both internal and external) satisfaction, adherence of delivery schedules, development of specific methods to redesign and reorganize a process with a view to reduce or eliminate errors, defects; development of more efficient, capable, reliable and consistent manufacturing process and more better overall process performance, creation of continuous improvement.


Adan Valles, Jaime Sanchez, Salvador Noriega and Berenice Gómez Nunez(2009) Implementation of Six Sigma in a Manufacturing Process- A Case Study international journal of industrial engineering 16(3),171-181, 2009.


Antony, J. & Banuelas, R. (2001) “Six Sigma: A Business Strategy for Manufacturing Organizations”, Manufacturing Engineering, Vol. 8,No. 3, pp. 119-121.


Banuelas, R. & Antony, J. (2004) “Six Sigma or design for Six Sigma? “, The TQM Magazine, Vol. 2 No. 3, pp. 29-33.


Dalgobind Mahto; Anjani Kumar(2008) Application of root cause analysis in improvement of product quality and productivity, Journal of Industrial Engineering and management JIEM, 2008 – 01(02):16-53 - ISSN: 2013-0953




& Schroeder,

R. (2000)


Sigma: TheBreakthroughManagementStrategyevolutionizing

TheWorld’sTop Corporations, Doubleday, New York,. 6.

Goh, T. & Xie, M. (2004) “Improving on the Six Sigma Paradigm”, The TQM Magazine, Vol. 16 No. 4, pp. 235-240.


Kuei, C. & Madu, C. (2003) “Customer-Centric Six Sigma Quality & Reliability Management”, International Journal of Quality & Reliability Management, Vol. 20 No. 8, pp. 954-964.


Mdhdiuz zaman,Sujit kumar pattanayak study of feasibility of six sigma implementation in a manufacturing industry; a case study-international journal of mechanical and industrial engineering(IJMIE)ISSNno.2231477,vol3,issue1,2013


Nilmani Sahu & Sridhar


NilmaniSahu,Dr.sridhar(2012)Quality Improvement Using Statistical Process Control A Case study in a cylinder liner manufacturing unit, Proceedings of National conference on “Emerging Trends in Mechanical Engineering"(ETME12), on 29-30 November, 2012,organized by Technocrats Institute of Technology, Bhopal (M.P.)

10. NilmaniSahu, Dr.sridhar(2013) Methodology of Evaluating the Overall Equipment Effectiveness in a cylinder liner











Technology, (TJPRC), Volume 3, Issue 3. 11. Rajesh kumar,U, Sambhe sixsigma practice for quality improvement- a case study of Indian auto ancillary unit IOSR journal of Mechanical and Civil Engg.(IOSR-JMCE) ISSN2278-1684 vol4,issue4, 2012. 12. Snee, R. (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. 13. Sokovic M, D. Pavletic, E. Krulcic (2006) Six Sigma process improvements in automotive parts production, Journal of Achievements in Materials and Manufacturing Engineering, Vol19,Issue1,November 2006 14. Sushil kumar,ps Satsangi, Prajapathi six sigma an excellent tool for process improvement-a case study international journal of scientific and engineering research, vol2,issue9,september2011 15. Tomkins, R. (1997) “GE Beats Expected 13% Rise”, Financial Times, pp. 22, October 10. 16. Thomas Pyzdek (2003) “Six Sigma Infrastructure”, Quality Digest. 17. Tushar N. Desai and Dr. R. L. Shrivastava (2008) Six Sigma — A New Direction to Quality and Productivity Management(2008), Proceedings of the World Congress on Engineering and Computer Science 2008, WCECS 2008, October 22 - 24, 2008, San Francisco, USA. 18. Voelkel, J.G. (2002) “Something’s missing — An Education in Statistical Methods will Make Employees more Valuable to Six Sigma Corporations”, Quality Progress, pp. 98-101.

2 six sigma implementation full  

This paper discusses the implementation of Six-sigma methodology in reducing defectives in a cylinder liner manufacturing industry. The Six-...

Read more
Read more
Similar to
Popular now
Just for you