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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
14 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 15 Cylinder Liner Manufacturing Firm
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
16 Nilmani Sahu & Sridhar
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.
Six Sigma Implementation Using DMAIC Approach-A Case Study in a 17 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 &
18 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 19 Cylinder Liner Manufacturing Firm
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
20 Nilmani Sahu & Sridhar
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
Six Sigma Implementation Using DMAIC Approach-A Case Study in a 21 Cylinder Liner Manufacturing Firm
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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