performance improvement through six sigma methodologies a...

International Journal of Engineering Technology, Management and Applied Sciences

www.ijetmas.com September 2016, Volume 4, Issue 9, ISSN 2349-4476

49 Dr. N. Venkateswaran

Performance Improvement Through Six Sigma Methodologies

– A Plastic Firm Case Approach

Dr. N. Venkateswaran

Professor, Panimalar Engineering College, Chennai

Abstract

Six Sigma is a set of techniques and tools for process improvement. The term Six Sigma originated from terminology

associated with manufacturing, specifically terms associated with statistical modeling of manufacturing processes. The

study was adopted at Plastic firm. The basic purpose of undertaken this study is to explore the effectiveness of using six

sigma technique while manufacturing and to explore the environment that hinder the implementation of Six sigma

production principles of material flows and also the objectives of the study to identify and analyze the root cause of

problems occurred during the manufacturing process and find the effective remedial measures for eliminating the root

cause of the problems of production using six sigma methodologies at the firm. The research design adopted for this

study was Experimental research design. The study make use of secondary data which was collected from the company’s

production reports for a period of 6 months (July’2015 to December’2015) and also study collected literature review

from journals, internet and magazines. The collected data was analyzed and interpreted by making use of tools like Why-

Why analysis, Fishbone diagram, Pareto analysis, Trend analysis, Value stream mapping and Histogram. The major

findings from this study are lengthy times taken for manufacturing process and lack of knowledge among operators and

in-charges. The study also recommended identifying the likely causes of the problems, taking preventive action and

planning contingent action. Finally research was concluded by reducing non-value adding hours in industry to increase

the efficiency of production.

Keyword: Value Stream mapping, Process Capability, On-time delivery, Variability

1.0 INTRODUCTION

In the existing competitive world every customer seeks best and customized products from suppliers in the

stipulated time with low cost. In this current economic situation all sectors are shaken by global meltdown and

crisis. Six-Sigma is a relatively newer concept than Total Quality Management but not exactly its

replacement. The main focus of Total quality management is to maintain existing quality standards whereas

Six Sigma primarily focuses on making small necessary changes in the processes and systems to ensure high

quality. TQM is less visible now than in the early 1990s due to problems including lack of integration,

leadership apathy, a fuzzy concept , unclear quality goals and a failure to break down internal barriers‖ and

conclude that Six Sigma can overcome these deficiencies , stating that Six Sigma„s expansion heralds

a ̳rebirth„ of the quality movement . This has led industries to take hard decisions like cutting off production

owing to the lowered demand. According to Harry and Schroeder (2000), “Six Sigma is not merely a

management concept, but a business process that allows companies to greatly improve their minimal

performance criteria by designing and monitoring everyday business activities in ways that minimize waste

and resources while increasing customer satisfaction.” Hence improving productivity, quality, on time

delivery with reduced costs became the need of the hour. So to become competitive focus should be on cost

reduction to minute details and building quality and to gain agility focus should be given on removing

manufacturing constraints and making manufacturing process smoother. The focus of Six Sigma is not on

counting the defects in processes, but the number of opportunities within a process that could result in defects

so that causes of quality problems can be eliminated before they are transformed into defects (Antony, 2006).

Many companies follow Six Sigma Methodologies for improving their performance by reducing wastage. Six-

Sigma is a set of techniques and tools for process improvement. It seeks to improve the quality of process

outputs by identifying and removing the causes of defects (errors) and minimizing

variability in manufacturing and business processes. It uses a set of quality management methods,

mainly empirical, statistical methods, and creates a special infrastructure of people within the organization

("Champions", "Black Belts", "Green Belts", "Yellow Belts", etc.) who are experts in these methods.

http://en.wikipedia.org/wiki/Statistical_dispersion

http://en.wikipedia.org/wiki/Manufacturing

http://en.wikipedia.org/wiki/Business_process

http://en.wikipedia.org/wiki/Quality_management

http://en.wikipedia.org/wiki/Empirical

http://en.wikipedia.org/wiki/Statistics




For the term “Six Sigma” there appears to be little consensus on its definition. Proposing an emergent

definition of Six Sigma based on a grounded theory approach, Schroeder et al. (2008) concluded that Six

Sigma offers a new structure that promotes both control and exploration in improvement efforts. They

asserted that academics need to develop a deeper and richer knowledge of Six Sigma so that they do not over

hype or quickly dismiss it.

The maturity of a manufacturing process can be described by a sigma rating indicating its yield or the

percentage of defect-free products it creates. A six sigma process is one in which 99.99966% of all

opportunities to produce some feature of a part are statistically expected to be free of defects (3.4 defective

features per million opportunities) this defect level corresponds to only a 4.5 sigma level.

1.1 NEED FOR THE STUDY

Due to the growth potential of the manufacturing industry and increasing pressure from markets it is important

to find methods that would make manufacturing production more efficient. Such a method could be six sigma

productions that have been widely adopted in recent years. The purpose of the study is to explore the

effectiveness of using six sigma techniques during manufacturing process and also to explore the environment

which hinders their implementation methodologies during material flows.

1.2 OBJECTIVES OF THE STUDY

To understand the Six Sigma methodology, tool, and techniques

To understand the level of integration of various functions in company‟s six sigma principles.

To identify and analyze the root cause of problems occurred during the manufacturing process within the

firm.

To find the effective remedial measures for eliminating the root cause of the problems of production using

six sigma methodologies

To find the effectiveness of Six Sigma implementation at within the organization

To give suggestions to improve the six sigma manufacturing operations in the organization.

1.3 LITERATURE REVIEW

Six Sigma is defined as “a well-established approach that seeks to identify and eliminate defects, mistakes or

failures in business processes or systems by focusing on those process performance characteristics that are of

critical importance to customers” (Antony, 2008). Six Sigma is a statistical methodology that aims to reduce

variation in any process (Chakravorty and Shah, 2012; Naslund, 2008), reduce costs in manufacturing and

services, make savings to the bottom line, increase customer satisfaction (Drohomeretski et al., 2013; Shah et

al., 2008; Manville et al., 2012; Naslund, 2008), measure defects, improve product quality, and reduce defects

to 3.4 parts per million opportunities in an organization (Lee and Wei, 2009; Chen and Lyu, 2009). These are

done through powerful analytical and statistical tools and techniques such as Quality Function Deployment

(QFD), Failure Mode and Effect Analysis (FMEA), Statistical Process Control (SPC), Design of Experiments

(DOE), Analysis of Variance (ANOVA), Kano Model, etc. (Bhuiyan et al., 2006).

A review of case studies has identified many reasons for organizations to implement an Lean Six Sigma (LSS)

strategy in the new millennium: for example, to improve their business performance and operational

efficiency, especially in the growth of global markets, to improve product quality (Vinodh et al., 2012), reduce

production costs and enhance customer satisfaction (Chen and Lyu, 2009). More recently, LSS comprises the

implementation of DMAIC methodology with a mix of appropriate tools from the Lean toolkit and Six Sigma

at each step of DMAIC (Kumar et al., 2006; Vinodh et al., 2011). Moreover, the role of DMAIC in LSS is as a

framework and a solid base for successful implementation (Chakravorty and Shah, 2012). Pickrell et al.

(2005) argued that LSS uses the Six Sigma framework as a platform for initiatives in conjunction with Lean

principles and tools.

As a result of ideas about the integration of LSS (Lean Six Sigma) and the interest in LSS by organizations,

researchers have the interest to publish more papers on LSS to try to come up with a comprehensive approach

to achieve CI. For instance, a number of academics have developed an integrated strategy such as the

strategies that were developed by Thomas et al. (2008), Snee and Hoerl (2007), Pepper and Spedding (2010)




and so on. Other researchers have developed a framework for the successful integration of LSS, such as Salah

et al. (2010), Alsmadi and Khan (2010) and Kumar et al. (2006). The benefits and the critical success factors

of applying LSS in parallel are also noted in many case study papers in both the manufacturing and the service

sector (Akbulut-Bailey et al., 2012; Pickrell et al., 2005; Hardeman and Goethals, 2011).

However, the term LSS was first introduced into literature around 2000 LSS teaching was established in 2003

as part of the evolution of Six Sigma (Timans et al., 2012). Since that time, there has been a noticeable

increase in LSS popularity and deployment in the industrial world (Shah et al., 2008), especially in large

organizations in the west such as Motorola, Honeywell, General Electric and many others (Timans et al.,

2012; Laureani and Antony, 2012) and in some small- and medium-sized manufacturing enterprises (SMEs)

(Kumar et al., 2006). LSS was defined by Snee (2010) as “a business strategy and methodology that increases

process performance resulting in enhanced customer satisfaction and improved bottom line results.” LSS

methodology aims to improve capability in an organization, reduce production costs (Lee and Wei, 2009;

Chen and Lyu, 2009) and maximize the value for shareholders by improving quality (Laureani and Antony,

2012).

However, not all organizations have gained real benefits from LSS as unsuccessful implementation rendered it

ineffective. In addition, there are many gaps that need to be addressed in LSS literature such as benefits,

motivation factors, challenges and limitations (Pepper and Spedding, 2010; Laureani and Antony, 2011).

Hence, the purpose of this paper is to address such gaps within LSS that are most important within the

manufacturing sector and allow them to achieve the most benefits from this strategy, as well as to identify the

gaps and give recommendations for future research.

1.4 RESEARCH GAP

The present study analyzes the gap in application of six sigma methodologies for continuous

production/process sector with a focus on the plastics industry. The goal of this research is to investigate how

six sigma tools can be adapted from the discrete to the continuous manufacturing environment, and to evaluate

their benefits on a specific application instance. Although the process and discrete industry share several

common characteristics, there are areas where they are very different. Both manufacturing settings have

overlap, but at the extreme, each has its unique characteristics.

This research attempts to identify the gap that exists between discrete and continuous manufacturing where six

sigma techniques from the discrete side are directly applicable. Value stream mapping is first used to map the

current state and then to identify the sources of waste and lean tools to eliminate these wastes. The future state

of the map is then developed for a system with six sigma tools applied to it. To quantify the benefits gained

from using lean tools and techniques in the value stream mapping, a detailed simulation model is developed

and a designed experiment is used to analyze the outputs.

1.5 RESEARCH METHODOLOGY

Research Design: Experimental

Data Source: Secondary data collected from the company‟s production reports.

Study Period: 6 Months – July‟2015 to December‟2015

Tools for Data Analysis:

Why-Why analysis

Fish Bone Diagram

Histogram

Pareto Analysis

Value Stream Mapping

Histogram

1.6 DATA ANALYSIS AND INTERPRETATION

To analyze the root cause of problems occurred during the manufacturing process

Problem 1: Excessive chips and non-uniform edges found on the products.




Reason: Cutting Torch found faulty.

Action Taken: Using Why-Why Analysis we are detecting the root cause and giving a remedial measure to

take corrective action.

1.6.1 TABLE SHOWING ROOT CAUSE DETECTION ON THE CUTTING TORCH BY WHY-WHY

ANALYSIS

Why-Why Analysis Answer Finding and Action

Why Excessive chips and non-

uniform edges occurred

Machine has some problem Machine is running

smoothly

Why machine giving faulty edges Due to pressure, Moisture

content in gases, or Movement

of machine / Torch

Clean and Check Nozzles

size, Check Moisture, and

operation of Torch

Why torch components are not

OK

Lifting gear box Mounting was

loose

Fastening bolts and nuts

were not present

Why Fastening bolts and nuts

were not present

Four drill hole were not

matching

Fixed in only two bolts

Why not replaced The part was not available Take corrective and

preventive action

Interpretation: All kinds of cutting were done through Dissolved Acetylene and Oxygen. Gas is supplied to

the machine by longitudinal hoses. The cutting quality depends on the accurate selection of gases and accurate

positioning of cutting torch. The sensing mechanism maintains the distance between nozzle tip to plate, which

has been recommended by the machine supplier to keep it 10 to 15 mm. It should not be below 10 mm. The

rack and spindle sleeve must be greased every 100 operating hours to reduce this error.

Problem 2: Sensing feeler most frequently do not maintain the recommended distance as per machine

specification.

Reason: It has been observed that the machine was too dirty.

Action Taken: Training has been arranged on CNC Oxy Flame Cutting Machine, by the company for the in-

charges and the operators on recommendation.

1.6.2 CHART SHOWING CAUSE EFFECT DIAGRAM FOR DEPICTING SENSING FEELER FAILURE




Interpretation: It has been observed that the machine was too dirty and due to this the sensing feeler most

frequently do not maintain the recommended distance as per machine specification, the lifting gear box of the

single torch was to be mounted in a cast aluminium box to keep the device light. The clutch on the top of the

box prevented the lifting shaft from being bent. It was interesting to note that, the mounting of the cast

aluminium box was not proper. Apart from the above timely cleaning of nozzle was not a regular practice.

Problem 3: Lengthy times for manufacturing Thermo Flasks

Reason: Reasons are due to more non-value added hours in production.

Action Taken: Value Streamed the production process and traced out the Value added and non-value added

time consumed in each process and by each resource.

1.6.3 TABLE SHOWING VALUE STREAM MAPPING BEFORE SIX SIGMA IMPLEMENTATION-PRODUCTION PHASE OF THERMO FLASK

S.

No

Operation in

Sequence

Machine-wise Activity

Description

Resources Involved Time

Taken

(Minutes)

Category (Value

Added(VA))/Non-Value

Added(NVA))

1 Raw Material

handling

Handling from stock yard to

Cortina M/c

1 Helper, EOT Crane,

1 Operator

25 NVA

2 Data conversion DNC to CNC Cortina Machine 1 Engineer, 1

computer

10 NVA

3 CNC Cutting At Cortina Machine 1 Operator 35 VA

4 Material Removal &

shifting

Cortina Machine 2 helper, 1 Crane, 1

inspector

55 NVA

5 Material Preparation Manual Grinding 2 Operators,2

grinding machines

25 NVA

6 Inspection Manual Grinding 1 Inspector 20 NVA

7

Seggregation &

Shifting

Material preparation,

Bending,Assembly or Machining

1 Operator, 1 Helper,

fork lifts trolleys,

Crane

30 NVA

8 Assembly Collection of prepared material for

assembly

1 Operator, 2 Helper,

Fixtures, Gauges

40 VA

9 Inspection Assembly 1 Inspector 20 NVA

10 Loading & setting at

Manipulator

For welding EOT Crane, 1

Operator

15 NVA

11 Welding MIG welding 1 Operator, CO2 Gas,

Welding Machine

60 VA

12 Inspection UT machine 1 Inspector 25 NVA

13 Unloading & shifting

to machinery

Unloading by EOT Crane 1 Helper, Fork lift 20 NVA

14 Setting at VTL For machining 1 Helper, EOT Crane,

1 Operator

20 NVA

15 Base Machining Fixture & special tool 1 Operator 50 VA

16 Inspection Vernier, Jig 1 Inspector 20 NVA

17 Unloading & Shifting

to Boring

Fork lift, EOT Crane 1 Helper, 1 EOT

Crane, Operator

20 NVA

18 Setting at Horizontal

Boring

For machining 1 Helper, EOT

Crane, 1 Operator

50 NVA

19 Boring Ø 90±1., Ø 80±1 1 Operator 20 VA

20 Inspection Vernier, Jig 1 Inspector 20 NVA


to Drilling & Tapping

Fork lift, EOT Crane 1 Helper 20 NVA

22 Setting at Radial Drill For Drilling 1 Helper, EOT Crane,

1 Operator

75 VA

23 Drilling & Tapping Ø 15.5+0.2, Ø 20+0.3 1 Operator 25 NVA

24 Inspection Gauge, Tap 1 Inspector 15 NVA


to cleaning

Fork lift, EOT Crane 1 Helper, Fork lift,

EOT Crane

20 NVA

26 Cleaning & surface

treatment

Phosphating & Rust oil 1 Operator, 1 helper 25 NVA

27 Inspection Visually 1 Inspector 10 NVA

Note: EOT – Electric Overhead Traveling




1.6.4 CHART SHOWING VALUE STREAM MAPPING BEFORE SIX SIGMA

IMPLEMENTATION-PRODUCTION PHASE OF THERMO FLASK

Interpretation: From the above value stream chart it is interpreted that total time elapsed for manufacturing

thermo flask is 12.83 hours. Before six sigma implementation the firm has more non-value added category

(8.16 hours) which makes the firm to have longer lead time for completing the finished product. The

management continuously carry out to identify the reasons for non-value added hours.

Problem 4: Leakage in Insulated Bottles

Reason: Leakage, Dirt & Miscellaneous

Action Taken: Pareto Analysis conducted to depict the Defect Percentage and immediate action taken

1.6.5 TABLE SHOWING THE DEFECTS OCCURRED IN INSULATED BOTTLES

Type of defects Number of Defects % of Defects Cumulative %

Leaking 4495 64.5 64.5

Miscellaneous 1686 24.19 88.69

Dirty 788 11.31 100

Total 6969 100

1.6.6 PARETO CHART OF DEFECTS OCCURRED IN INSULATED BOTTLES

Interpretation: Pareto analysis was carried out to identify the utmost occurring defects and prioritize the

most critical problem which was required to be tackled. The collected data was generated in the form of a

Pareto chart.




1.6.7 TABLE SHOWING TRAINING GIVEN TO MOULD HANDLERS & OPERATORS

Year Lean Tools Job Training To Mold handlers

Jun-15 2 2 0

Jul-15 4 4 1

Aug-15 5 7 0

Sep-15 6 3 0

Oct-15 2 8 0

Nov-15 0 2 0

Dec-15 1 3 1

1.6.8 CHART SHOWING TRAINING GIVEN TO MOULD HANDLERS & OPERATORS

Interpretation: After implementation of proper maintenance schedule and giving training to the operators

and maintenance persons, data were collected to estimate the improvement in quality in terms of rejection.

The trend analysis of 6 month has been plotted and presented in the above trend chart. It broadly highlights as

accepted or rejected. It is clear from the above analysis that rejection has almost negligible due to attaining the

skill within a very short time after the training.

1.6.9 TABLE SHOWING DEFECTS IN PLASTIC MOULDS RECTIFIED AFTER SIX SIGMA TRAINING

Days Total Item Cut Accepted Rejected

1 155 145 10

2 178 170 8

3 165 160 5

4 170 167 3

5 188 184 4

6 190 188 2

7 196 194 2

8 191 189 2

9 198 196 2

10 195 195 0

11 200 197 2

12 198 197 1

13 201 201 0

14 197 196 1




1.6.10 CHART SHOWING DEFECTS IN PLASTIC MOULDS RECTIFIED AFTER SIX SIGMA

TRAINING

Interpretation: Why-Why analysis was conducted as a root cause analysis measure for cutting torch.

Remedial measure was planned in the form of training to the operators and in-charges. After implementation

of proper maintenance schedule and giving training to the operators and maintenance persons, data were

collected to estimate the improvement in quality in terms of rejection. The trend analysis of 14 days during

January 2016 has been plotted and presented in the above trend chart. It broadly highlights as accepted or

rejected. It is clear from the above analysis that rejection has almost negligible due to attaining the skill within

a very short time after the training.

1.6.11 TABLE SHOWING RESULTS OF THERMO FLASKS BEFORE AND AFTER THE SIX

SIGMA IMPLEMENTATION

Status Before

Implementation After Implementation Change

(Hrs) Times in Minutes Total Time % Total Time %

Value Adding 280 36.36 400 76.63 120

Non-Value Adding 490 63.64 122 23.37 368

Total 770 100 522 100

1.6.12 CHART SHOWING HISTOGRAM RESULTS OF THERMO FLASKS BEFORE AND AFTER

THE SIX SIGMA IMPLEMENTATION




Interpretation: The histogram above is the resultant of Value Stream Analysis. The effectiveness of six

sigma implementation is clearly shown here. This chart visually shows the increase in value added hours and

decreases in non-value added hours post adoption of six sigma tools.

1.6.13 TABLE SHOWING THE DEFECTS OF INSULATED BOTTLES BEFORE AND AFTER SIX

SIGMA ADOPTION

Type of defects % of Defects Before

Implementation

% of Defects After

Implementation

Leaking 64.5 8.38

Miscellaneous 24.19 3.88

Dirty 11.31 2.44

1.6.14 CHART HISTOGRAM SHOWING THE DEFECTS OF INSULATED BOTTLES BEFORE

AND AFTER SIX SIGMA ADOPTION

Interpretation: The histogram above shows the comparison of results of defect rectification of leakage in

insulated bottles manufactured. The problem was shown using Pareto analysis and the occurred problems are

listed as leakage, miscellaneous and dirt accumulation in raw material mixing. This was successfully reduced

using six sigma analysis. The above chart clearly shows the reduction in defects considerably after

implementation of six sigma tools.

1.7 FINDINGS FROM THE STUDY

Excessive chips and non-uniform edges found on the products due to cutting torch fault

Sensing feeler most frequently do not maintain the recommended distance as per machine specification

because the machine was too dirty

Lengthy times taken for manufacturing Thermo Flasks due to more non-value added hours in production

There was a leakage found in most insulated bottles produced due to Leakage, Dirt & other reasons

Defects in plastic moulds made many rejections in goods due to the lack of knowledge among operator &

in-charges

There was an improvement in accepted rate of materials after root cause analysis of cutting torch

There was an increase in the speed of thermo flask production after value stream mapping

The results of defect rectification on Insulated Bottles before and after the Six Sigma Implementation




1.8 RECOMMENDATIONS

It is recommended that design review is intended for all levels of product design activity (e.g., component,

subsystem and complete system programs) and supporting manufacturing engineering activity.

Requirements and targets have been allocated and cascaded to subsystems and components.

Six-Sigma contributes to team involvement but care must be taken on the selection of project leaders and

to adapt Six Sigma to the organization, both on the program implementation and as the program

progresses over time.

Situation appraisal to be made to identify concerns, set priorities, and plan the next steps.

Problem analysis should to precisely describe the problem, identify and evaluate the causes and confirm

the true cause.

Decision analysis to be made to clarify purpose, evaluates alternatives, and assesses the risks of each

option and to make a final decision.

Potential problem analysis is being identified for safety degradation that might be introduced by the

corrective action

Identify the likely causes of the problems, take preventive action and plan contingent action.

1.9 CONCLUSION

In the industrial world, Six Sigma is a business mentally for perfecting a system and all of its components. In

addition, this perfection is continuously strived for, in an attempt to ensure excellence as a priority within an

organization or corporation as a whole. This incorporates much more than process improvement on a

production level. This involves creating cost-effective processes throughout an organization and maintaining

their quality indefinitely. More importantly, Six Sigma is a means of increasing a corporations overall net

worth and profit margins by effectively, honestly, and thoroughly measuring all of the activities in the

corporation. This includes not all process improvements for the product or services, but also organizational

improvements among employees in all departments.

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