SIXSIGMA IMPLEMENTATION USING DMAIC APPROACH-A CASE …. Six sigma implementation.full.pdf · are Bajaj Auto, Kinetic Motors, LML, Yamaha, Hero Motors etc. As part of recent management

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.

SIXSIGMA IMPLEMENTATION USING DMAIC APPROACH-A CASE STUDY IN A

CYLINDER LINER MANUFACTURING FIRM

NILMANI SAHU1 & SRIDHAR

2

1M.Tech Scholar, Department of Mechanical Engineering, Chhatrapati Shivaji Institute of Technology,

Durg, Chhattisgarh, India

2Professor, 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

12 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–Measure-

Analyze-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 13 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 Process Yield OEE

March 2012 42.01% 0.40

April 2012 42.3 % 0.41

May 2012 43.1% 0.405

June 2012 43.3 % 0.41

40

42

44

Process yield in percentage

Process yield in percentage

Figure 2: Process Yield in Percentage in Different Months


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


Figure 3: SIPOC Diagram

Figure 4: Drawing of Cylinder Liner

Table 3: Specifications of Cylinder Liner

Parameter Upper Specification Limit Lower Specification Limit

Surface roughness (µm) 1.92 1.88

Inner diameter 104.036 104.023

Outer diameter 106.994 106.958

Coller width 8.045 8.056

Under cut diameter 106.87 106.83

Coller diameter 111.98 111.92

Length 201.16 201.12

Table 4: Operations in the Process and Measuring Parameters

Operation Description Measuring Parameters Gauges Used

1 Rough turning, boring, parting off. Total length Vernier callipers

2 Fine turning, grooving, collar

width formation. Inner diameter Bore gauge

3 Rough grinding Collar width Flange micrometer

4 Fine boring Outer diameter Flange micrometer

5 Internal dia. Chamfering Inner diameter Bore gauge


Table 4: Contd.,

6 Fine grinding Concentricity Dial gauge

7 Rough honing Outer diameter Micrometer

8 Fine honing Collar diameter Micrometer

Table 5: Percentage Utilization of the Machines, Quality Rating and Overall Equipment Effectiveness

Parameter Value

Machines Availability time in percentage 79

Machines idle time in Percentage 32

Percentage utilization of the machines

(Performance) 68

Quantity planed (units) 18000

Quantity produced (units) 12320

Quantity rejected (units) 2620

Qty accepted (units) 9700

Quality Rating 0.79

Overall Equipment Effectiveness 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.


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 &


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


Table 6: Process Capability of the Key Quality Characteristics

Process

Capability

Groove

Diameter

Shoulder

Ovality

Collar

Width

0.006 0.003 0.002

Cp 1.578 1.521 1.66

Cpk1 1.683 1.545 1.33

Cpk2 1.473 1.636 1.53

Cpk 1.473 1.545 1.53

Improve Phase

Improve Process

During improvement phase statistical process 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


Table 7: Results after Implementation of TPM (April 2013)

Parameter Value

Total unavailable time in

percentage 11

Availability of the machine in

percentage 89

Percentage of the machine idle

time 24

Percentage utilization of the

machine (Performance) 76

Quantity planed (units) 18000

Quantity produced (units) 13920

Quantity rejected (units) 990

Qty accepted (units) 12930

Quality Rating 0.93

Overall Equipment

Effectiveness(OEE)

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


Table 8: Results after Improvement and Control

Parameter Before

Improvement

After

Improvement

Process yield 44% 90%

Overall Equipment

Effectiveness(OEE) 42% 63%

Six-sigma Level 2.91 4.43

Defects per million 66900 6050

Process capability

Index 0.51 1.33

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.

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