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An Application of Lean Six Sigma To Improve

The Assembly Operations At A

Wireless Mobile Manufacturing Company

David Woo, Holly Wong

A thesis submitted in partial fulfillment of the requirements for the degree of

Bachelor of Applied Science in Industrial Engineering

Supervisor: Viliam Makis

Department of Mechanical and Industrial Engineering University of Toronto

March 2007

Thesis: An Application of Lean Six Sigma to Improve the Assembly Operations at a Wireless Mobile Manufacturing Company Page 1

Abstract

This thesis project is the focus of our final year MIE496Y1Y thesis course. The purpose of the thesis course is to allow students to pursue an area of technical interest. Our group, composed of two Industrial Engineering students, is interested in the Lean Six Sigma approach and has decided to work on a Lean Six Sigma project at a wireless mobile manufacturing company. Lean Six Sigma is an overall quality improvement approach combining and capitalizing the strengths of Six Sigma and Lean Management improvement programs. In completing this thesis project, our goal is to acquire extensive knowledge on the approach and apply it to improve the quality of an assembly operation process of a wireless mobile manufacturing company facility. This thesis report will document the work that we have accomplished in the past eight-and-a-half months on this project. The emphasis is placed in 3 areas:

Familiarization with the Lean Six Sigma methodology

Critical analysis of two published case studies of Lean Six Sigma application

Our case work on the implementation of the methodology in improving the assembly operations at the wireless mobile manufacturing company

We have made use of our developed knowledge from our research and literature review in carrying out the case project on the wireless mobile manufacturing company. The objective is to minimize the number of process defects and optimize the efficiency of the assembly operation. The DMAIC (Define-Measure-Analyze-Improve-Control) approach was followed to identify the causes of this problem, measure the process capability, analyze the potential causes of demoted quality, implement improvements and control the process such that long-term improvements can be sustained. We have proposed a capable process model with minimal process variation. Lean tools and techniques will be employed to achieve the efficient use of resources and to minimize non-value added activities in the process.


Acknowledgements

We would like to thank the Department of Mechanical and Industrial Engineering for giving us this valuable and memorable experience. We would especially like to express our gratitude for Professor Viliam Makis for his advice, guidance and patience. This thesis project could not have been completed without him. Last but not least, we would like to thank the wireless mobile manufacturing company for their cooperation.


Table of Contents

Acknowledgements .................................................................................................................... 2 List of Figures ............................................................................................................................ 5 List of Tables ............................................................................................................................. 6 List of Abbreviations ................................................................................................................... 7 1. Introduction ............................................................................................................................ 8 2. Literature Review ..................................................................................................................10

2.1 Six Sigma Defined ...........................................................................................................10 2.1.1. The DMAIC Cycle ....................................................................................................12 2.1.2. Six Sigma Tools and Techniques ............................................................................13

2.2 Lean Management Defined .............................................................................................15 2.2.1 Lean Tools and Techniques .....................................................................................16 2.2.2 The Toyota Way .......................................................................................................19

2.3 Lean Six Sigma Defined ..................................................................................................21 2.4 Case Study Review 1 by Holly Wong: An Application of Six Sigma Methodology to Reduce the Engine Overheating Problem in an Automotive Company ..................................22

2.4.1 The Define Phase .....................................................................................................23 2.4.1.1 Analysis of the Define Phase .............................................................................24

2.4.2 The Measure Phase .................................................................................................25 2.4.2.1 Analysis of the Measure Phase..........................................................................27

2.4.3 The Analysis Phase ..................................................................................................28 2.4.3.1 Analysis of the Analysis Phase ..........................................................................29

2.4.4 The Improve Phase ..................................................................................................31 2.4.4.1 Analysis of the Improve Phase...........................................................................32

2.4.5 The Control Phase....................................................................................................34 2.4.5.1 Analysis of the Control Phase ............................................................................35

2.4.6 Summary and Knowledge Applicable to Company Case Study ................................36 2.5 Case Study Review 2 by David Woo: Reduction of Yarn Packing Defect Using Six Sigma Methods: A Case Study ........................................................................................................37

2.5.1 Case Overview and Summary of Findings ................................................................37 2.5.2 Additional Supplementary Analysis and Findings .....................................................38 2.5.3 Application on Company Case Study .......................................................................41

3. Lean Six Sigma Application: A Case Study ...........................................................................43 3.1 Company Background .....................................................................................................43 3.2 Lean Six Sigma Implementation: the DMAIC Framework ................................................44

3.2.1 The Define Phase .....................................................................................................44 3.2.1.1 Value Stream Mapping ......................................................................................45 3.2.1.2 Findings: Identification of Scope and Problem Area ...........................................49

3.2.2 The Measure Phase .................................................................................................50 3.2.2.1 Defect Data and Analysis ...................................................................................50 3.2.2.2 Cycle Time and Takt Time Analysis ...................................................................53

3.2.3 The Analyze Phase ..................................................................................................54 3.2.3.1 Cause-and-Effect Analysis .................................................................................54 3.2.3.2 Statistical Interpretation and Analysis ................................................................56

3.2.3.2.1 Manufacturing Process Stability ..................................................................57 3.2.3.2.1.1 p-Chart Analysis on Process Defects ...................................................57

3.2.3.2.2 Analysis of Variance of Operators and Supplier lots ....................................59 3.2.3.2.2.1 Inapplicability of Control Charts ............................................................59


3.2.3.2.2.2 Analysis of Variance (ANOVA) .............................................................59 3.2.3.2.2.3 One-Way ANOVA – Inspection Operators ............................................61 3.2.3.2.2.4 One-Way ANOVA – Supplier Lots ........................................................64

3.2.4 The Improve Phase ..................................................................................................66 3.2.4.1 Section I: Long-Term Philosophy .......................................................................67 3.2.4.2 Section II: The Right Process Will Produce the Right Results ............................68 3.2.4.3 Section III: Add Value to the Organization by Developing People and Partners .79 3.2.4.4 Section IV: Continuously Solving Root Problems Drives Organizational Learning ......................................................................................................................................83

3.2.5 The Control Phase....................................................................................................87 4. Conclusion ............................................................................................................................88 5. References ...........................................................................................................................89 Appendix A: Data for Case Study 1 – Analysis Phase ...............................................................91 Appendix B: Data for Case Study 1 – the Improve Phase ....................................................... 102 Appendix C: Support Materials for Case Study 2…………………………………………………..106 Appendix D: Data for Variance Analysis on Supplier Capability………………………………….110 Appendix E: Case Study 1 Paper: An Application of Six Sigma Methodology to Reduce the Engine-Overheating Problem in an Automotive Company………………………………………..113 Appendix F: Case Study 2 Paper: Reduction of Yarn Packing Defects Using Six Sigma Methods: A Case Study……………………………………………………………………………….128


List of Figures

Figure 1: Sigma variation shown on normal curve. (From Itil & ITSM World, 2003 ) .......................................... 10

Figure 2: A Process Tends to Shift 1.5 Sigma Units (Arnheiter, 2005) ........................................................... 11

Figure 3: The “4P” Model of the Toyota Way ............................................................................................ 21

Figure 4: Process Map for Cylinder-Head Core Preparation ........................................................................ 25

Figure 5: Cause and Effect Diagram for Porous Core ................................................................................. 26

Figure 6: Residual Analysis for the Sand Leakage Factor ........................................................................... 31

Figure 7: Normal Probability Plot of Effects .............................................................................................. 33

Figure 8: Interactions Plot Displaying the Interactions Among Process Parameters .......................................... 33

Figure 9: Main-Effects Plot for the Depth of the Porous Core ....................................................................... 34

Figure 10: Run Chart for the Depth of the Porous Core Before Improvement .................................................. 35

Figure 11: Run Chart for the Depth of the Porous Core After Improvement ..................................................... 36

Figure 12: Product-Quantity Analysis by Product Group ............................................................................. 46

Figure 13: Product-Revenue Analysis by Product Group ............................................................................ 46

Figure 14: Product-Defect Rate Analysis by Product Group ......................................................................... 47

Figure 15: Current State Value Stream Map of Product E Process. ............................................................... 48

Figure 16. Overall Yield Graph for Product E ............................................................................................ 51

Figure 17: Defect Origin Breakdown for Product E .................................................................................... 51

Figure 18: Process Defect Origin Breakdown for Product E ......................................................................... 52

Figure 19: Component Defect Origin Breakdown for Product E .................................................................... 52

Figure 20: Comparing the cycles times and Takt time of the assembly steps for Product E ................................ 53

Figure 21: Cause-and-Effect Diagram for Manufacturing Defect ................................................................... 55

Figure 22: Cause-and-Effect Diagram for Process Efficiency ....................................................................... 55

Figure 23: p-Chart for Sample Fraction Nonconforming .............................................................................. 58

Figure 24: Normality Probability Plot and Residual Plot of Operator Measurements .......................................... 62

Figure 25: Box Plot of Measurements by Operator .................................................................................... 63

Figure 26: Plot of residuals versus factor levels ........................................................................................ 63

Figure 27: Box Plot of Measurements Between Supplier Lots ...................................................................... 65

Figure 29: Push Versus Pull System (Regani, 2004) .................................................................................. 72

Figure 30: Traditional, unleveled production Figure 31: Traditional, unleveled production .............................. 74


List of Tables

Table 1: The DMAIC Methodology and the Steps Included in each Phase ..................................................... 12

Table 2: Combining key Lean and Six Sigma principles .............................................................................. 22

Table 3: Factor Levels for Factorial Design .............................................................................................. 32

Table 4: Data Table for Testing of Variance ............................................................................................. 40

Table 5: Data Table for Process Defects ................................................................................................ 58

Table 6: Housing Length Observations from 5 Operators ............................................................................ 61

Table 7: Sample Housing Length Measurement from 9 Lots ........................................................................ 65

Table 8: Toyota Team Roles and Responsibilities ..................................................................................... 82

Table 9: Raw Data Collected for the Analysis Phase .................................................................................. 91

Table 10: Data for 23 Full Factorial Design ............................................................................................. 102


List of Abbreviations

5S 5 components: seiri (sort), seiton (set in order), seiso (shine), sieketsu, (standardize), shitsuke (sustain)

ANOVA Analysis of Variance

C/O Changeover time

C/T Cycle time

CEO Chief Executive Officer

CFT Cross Functional Test

COPQ Cost of Poor Quality

Cp Process capability Index

Cpk Minimum capability index

DMAIC Design, Measure, Analyze, Improve, Control

DoE Design of Experiment

DPMO Defects per million opportunities

DPU Defects per unit

FIFO First In First Out

IVSMP Improved Value Stream Mapping Procedure

JIT Just-In-Time

LCL Lower Control Limit

LMD Length Measuring Device

LSL Lower Specification Limit

MTO Make To Order

NCCPM Nonconforming Parts Per Million

PQ$ Product Quantity Revenue

R&R Repeatability and Reproducibility

RMA Repair Maintenance

SMT Surface Mounted Technology

SPC Statistical Process Control

TFO Two-For-One twisting

TPM Total Productive Maintenance

TPS Toyota Production System

TQM Total Quality Management

UCL Upper Control Limit

USL Upper Specification Limit

VSM Value Stream Mapping

WIP Work In Progress


1. Introduction

Today, organizations strive for an improved level of process capability and manufacturing

quality to achieve the bottom-line objectives of generating a profitable margin and sustainable

competitiveness and share in the market. Lean Six Sigma is a quality improvement strategy

that helps companies achieve these results. The purpose of the thesis is to provide an insightful

research and examination of the methodology, centering on its implementation and its

application to a real business process. The main portion of this work will be dedicated to a case

study analysis of the assembly process at a wireless mobile manufacturing company. Based on

a variety of literature and case study review, a Lean Six Sigma model will be developed and

implemented at the facility. The result of the thesis will be a proposal detailing the customized

implementation framework for the company, along with the benefits derivable from the

application of the methodology.

To satisfy a growing demand and expectation from customers while coping with increasing

product complexity and limited resources, companies must improve on a continuous basis. As

products become more complex, the number of components at the sub-assembly level

becomes increasingly large, leading to a higher probability of defective assembly; as a result,

there is a drive for superior component quality. Moreover, processes need to have a greater

capacity and efficiency to provide a greater throughput to meet customer requirement.

Continuous improvement tools and techniques are introduced to address these issues, allowing

the manufacturing of superior quality products with efficient processes. The Lean Six Sigma

methodology is one of them. Since its introduction, the methodology has become widely

popular among many industries. Despite its popularity and success rate in numerous cases,

there are misconceptions about the methodology that foster some companies‟ reluctance in

accepting and adapting it. Because of the allocation of substantial resources and time required

for implementation, and the risk of interrupting regular business operations, some companies

are hesitant to implement the methodology. Focusing negatively on the short-term disturbances

caused by the implementation of Lean Six Sigma, these companies are blind to the long-term

benefits of the methodology.

This thesis project focuses on the approach and application of the Lean Six Sigma

methodology. Time has been devoted on literature review and studying a variety of cases to


develop a thorough understanding of the methodology. The knowledge acquired has been

applied continuously throughout the course for the work, which includes the development of the

Lean Six Sigma model for the case study. It is hoped that the thesis can provide a clearer

understanding of the methodology and resolve some misconceptions that companies may have.

This report will begin with the literature review section. Some background knowledge we have

acquired on the Lean Six Sigma will be given, along with a description of some of the tools and

techniques used in the papers that will be applicable for the case project on the wireless mobile

company. Attention will then be directed towards the company case study. We will provide a

background introduction of the company, outlining the problems of our focus, as well as the

goals to be achieved for the thesis. Following the introduction is the implementation

methodology for the case, describing in detail the work that has been done in every stage of the

DMAIC approach in the implementation of Lean Six Sigma. The Data Analysis section will

include a discussion summarizing the results found in the case. Our analysis and our

improvement recommendations for the assembly process will be proposed. A list of the

research materials and some of the referenced graphs/tables will be given in Appendix D at the

end of the report.


2. Literature Review To gain a thorough understanding of the Lean Six Sigma methodology, each student studied

many articles on the topic. In addition, each student focused on the examination and critical

analysis of a published case study on its application. Our extensive review of literature on Lean

Six Sigma prepared us for the application of the methodology on our case at the wireless mobile

manufacturing company. The following sections begin with a detailed definition of Lean Six

Sigma, as understood from our study of literature. Then, each student will give an overview and

critical analysis of the chosen case study. The section will be concluded by each student‟s

summary of the knowledge acquired from the case studies, as well as a description of how it

can be applied in the case study.

To understand Lean Six Sigma, one must first understand Lean Management and Six Sigma,

since Lean Six Sigma is the integration of the Lean Management and Six Sigma systems. The

following describes the key concepts of each system, and the tools and techniques employed in

the implementation of each system. After the “building blocks” of Lean Six Sigma are

examined, the value and method of the implementation of Lean Six Sigma will be discussed.

2.1 Six Sigma Defined

Statistically, the term sigma represents the standard deviation, the variation around the process

mean. The objective of Six Sigma is to achieve a quality level of at most 3.4 defects per million

opportunities (DPMO). Six Sigma means that there are 6 standard deviations from the process

mean to the specification limits when a normally distributed process is centered (See Figure 1).

Figure 1: Sigma variation shown on normal curve. (From Itil & ITSM World, 2003 )


In the original definition of Six Sigma, it was assumed that a process could shift 1.5 sigmas

without detection. Therefore, a 1.5-sigma drift margin was built into the standard definition of

Six Sigma. If a Six-Sigma process shifts 1.5 sigma units from the process mean to either side,

the final products would be 99.97% defect free, having 3.4 DPMO (See Figure 2).

Figure 2: A Process Tends to Shift 1.5 Sigma Units (Arnheiter, 2005)

However, over the past few years, Six Sigma has evolved to be more than a simple statistical

definition. Although the Six-Sigma metric of reducing defects to only a few parts per million for a

processes still applies, Six Sigma has become a complex quality improvement philosophy and

approach. It is an overall long-term decision-making business strategy, incorporating a quality

management philosophy as well as a systematic methodology that aims to measure defects,

reduce variation and improve the quality of products, processes and services (Arnheiter, 2005).

The Six Sigma strategy originates from two sources: total quality management (TQM) and the

Six Sigma metric mentioned above, invented by Motorola Corporation in the mid 1980s. TQM

distributes the responsibility of quality management to everyone in an organization. In other

words, everyone, not only the quality control personnel, contributes to the quality of goods and

services. Also, TQM places emphasis on focus on customer satisfaction and significant training

in statistics and roots cause analysis methods needed for problem solving. These problem

solving methods are performed by employing the “Magnificent Seven” tools of quality: control

charts, histograms, check sheets, scatter plots, cause-and-effect diagrams, flowcharts and

Pareto charts. These concepts and tools are adopted by the Six Sigma strategy (Antony, 2005).


2.1.1. The DMAIC Cycle

Nowadays, Six Sigma represents the strategy combing the Six Sigma statistical measure and

TQM. The approach used in Six Sigma to solve problems is the DMAIC cycle, which stands for

Define, Measure, Analyze, Improve and Control, the steps taken to attain Six Sigma quality

management. The DMAIC problem-solving methodology is particularly useful when (Banuelas,

2005):

1) the cause of the problem is unknown or unclear,

2) the potential of significant savings exist, and

3) the project can be done in 4-6 months.

The DMAIC methodology integrates statistical process control (SPC) tools and techniques,

including the “Magnificent Seven”, to solve problems and achieve continuous quality

improvement in a disciplined fashion (Kumar, 2006). Table 1 lists the steps included in the

phases of the DMAIC mythology (Banuelas, 2005):

Table 1: The DMAIC Methodology and the Steps Included in each Phase

Phase Steps Included

1. Define

Define the scope and boundaries of the project

Define defects

Define team charter to identify process definition, critical-to-quality parameters, benefit impact, key milestone activities with dates, support required and core team members

Estimate the impact of the project in monetary terms

2. Measure

Map process and identify process inputs and outputs

Establish baseline process capability

Establish measurement system capability

Conduct cause and effect analysis

Establish data collection plan

3. Analyze

Gather data

Identify possible sources of variation that causes problem

Perform cause and effect analysis to identify parameters that most significantly affect the process

Select critical-to-quality parameters to improve

4. Improve

Screen potential causes that affect process

Perform design of experiment to identify optimal setting of process parameters to eliminate problem

Discover variable relationships


Establish operating tolerances

5. Control Plot control charts to establish new process capability

Develop a control plan to sustain improved quality

With the Six Sigma overall strategy, an organization can not only achieve near perfect quality

using the DMAIC methodology, but also attain superior availability, reliability, delivery

performance, and after-market service (Arnheiter, 2005). All of these factors contribute to

customer satisfaction. To ensure the effectiveness of the Six Sigma philosophy within an

organization, formal training programs must be put in place and supported by management.

2.1.2. Six Sigma Tools and Techniques

The Six Sigma toolkit includes basic statistical process control (SPC) tools, called the

“Magnificent Seven”. These tools are employed in various stages of the DMAIC cycle. The

objectives of employing SPC tools are to bring the process in control and to reduce variations

due to special causes. SPC tools are widely used by industry for problem solving. After

discussing the “Magnificent Seven” on-line processing monitoring tools, the off-line techniques

Regression Analysis, Hypothesis Testing, and Analysis of Variance (ANOVA) will also be

discussed.

Run Chart (Check Sheet)

A run chart keeps track of process measurements over time. It is used for a rough check of the

process stability, and it is particularly useful in identifying changes in the process mean and

standard deviation. When looking at run charts, one pays attention to huge jumps in

measurements, patterns that occur over time (e.g. whether the measurements show an

increasing trend), and an increase in variance. A check sheet is similar to a run chart, but it is

used to keep record of equipment over time.

Histogram

A histogram is a graphical display of measurement frequencies. It is used to identify the shape

and location of the distribution of measurements, but the process must be in control for the

identification of distribution to be accurate. A histogram shows the proportion of measurements

that fall into each bin. The number and range of bins are determined by the constructor of the


histogram. The mean and variability of the process can be easily seen on the histogram. If the

specification limits are shown, the histogram can display the process capability.

Pareto Diagram

A Pareto diagram is similar to a histogram, but the bins show attribute data instead of

measurement ranges. Also, the values plotted are arranged in descending order. This is due to

Pareto‟s Principle, which states that a small number of causes contribute to the majority of

problem. The aim of the Pareto Chart is to identify these causes, so they can be eliminated

later.

Cause and Effect Diagram

A cause and effect diagram is used to identify and analyze a problem in a team setting. Teams

brainstorm to generate categories such as materials, machines, personnel, environment, etc.

Within each category, the team identifies causes that contribute to the effect (the problem). A

cause and effect diagram visually displays these causes, and help the team to locate the most

significant causes that lead to the problem.

Scatter Diagram

A scatter diagram is used to investigate the relation between the two quality characteristics on

the x and y axes, e.g. whether x values increase as y values increase. However, note that

correlation does not imply causality, e.g. one cannot conclude that an increase an x causes and

increase in y, even if x values increase as y values increase. To investigate causal

relationships, one can use the Design of Experiments (DoE).

Control Chart

The control chart is similar to a run chart, but it plots measurements over time on a chart with

control limits. The objective of a control chart is to quickly identify the occurrences of special

causes. When an occurrence is indicated by the chart, e.g. if a measurement falls outside of

the control limits, then the process is stopped and the cause is investigated. The cause is

identified, eliminated, and the process is improved. One also looks for patterns on the control

chart. If a pattern exists, it may be an indication that the process is unstable. There are many

types of control charts (to name a few – X-bar, R-bar, S, I, MR) that are used for different

circumstances.


2.2 Lean Management Defined

Lean Management is a philosophy emphasizing on the reduction of wastes. The elimination of

waste leads to improved quality, and decreased production time and cost. In comparison, Lean

Management is the more process-driven quality control initiative, while Six Sigma is more data-

driven. Lean Management encompasses the following key principles (Kumar, 2006):

Pull processing: production is triggered only when there is demand from the

customer end, not pushed by the production end

Perfect first-time quality: strive for zero defects, locating defects and controlling

variances at the source

Waste minimization: minimize all non-value added activities that do not add value,

maximize efficient usage of resources

Continuous improvement: continuously reduce costs, improve quality, increase

productivity and encourage information sharing an overall strategy

Flexibility: systems can react to changing demands to produce a greater diversity of

products in small batches quickly

Supplier relationship management: a long term relationship with suppliers is created

and maintained through collaborative risk sharing, cost sharing and information

sharing arrangements. There is no disconnection between the parties.

The concept of Lean Management stemmed from the Toyota production system, a

manufacturing philosophy pioneered by Japanese engineers at Toyota. However, it is arguable

that the root of the Lean Management system is not the Japanese system, but Henry Ford‟s

system that advocated high throughput and low inventories, as well as the practice of short-

cycle manufacturing at early Ford assembly plants. Ford invented the system to eliminate

quality problems caused by high production volumes, large batch sizes, and long non-value

added queue times between operations – characteristics of the traditional US “batch-and-

queue” production system (Arnheiter, 2005).

In contrast, Lean Management encourages small batch sizes and the “pull” strategy, which

means that nothing is made until there is a customer demand. This is called the make-to-order

(MTO) approach (Arnheiter, 2005). With this approach, waste is eliminated and non value-

added activities are cut down. The reduced cycle time with this approach ensures that defects

are discovered quickly.


Similar to Six Sigma, Lean Management is also an overall strategy. At every chance, Lean

Management strives to reduce variability. A lean organization attempts to reduce demand

variability, manufacturing variability and supplier variability. For example, when a Lean

organization reduces manufacturing variability, it not only considers variability in the product

quality parameters, but also in task operation times, such as machine downtime, employee

absenteeism and operator skill level.

2.2.1 Lean Tools and Techniques

The Lean Management toolkit includes a number of tools, techniques and practices that enable

and facilitate the implementation of Lean. The SPC tools and techniques used in Six Sigma are

also part of the Lean Management toolkit. Lean tools and techniques include Five-Why

analysis, 5S practice, Just-in-time (JIT) production, Kanban, value stream mapping, Total

productive maintenance (TPM), Production flow balancing, and many more (Kumar, 2006).

Five-Why Analysis

The Five-Why Analysis supports kaizen (continuous improvement), a key principle of Lean

Management. This technique requires employees to pose the question “Why?” five times every

time a problem is encountered. The employee should go to a deeper, more detailed level with

each “Why?” and become closer to locating the root cause of the problem. The Five-Why

Analysis is critical because countermeasures can only be identified when the root cause of the

problem is understood.

Below is an illustration of 5 Why Analysis:

(1) The company is missing due dates – why?

(2) Products have long lead times – why?

(3) The company does not have enough manufacturing capacity – why?

(4) Setup times are long – why?

(5) Product changeover is time consuming.


After this analysis, the company can locate the root cause of the problem, and can rectify the

problem by reducing the changeover time between products, thereby increasing manufacturing

capacity.

5S Practice

5S is a lean tool that facilitates teamwork. The five S‟s stand for sort, stabilize, shine,

standardize, and sustain – a series of activities for eliminating wastes that lead to errors, defects

and injuries (Liker, 2003).

Sort Sort through items and keep only what is needed while disposing of what is not Straighten There is a place for everything and everything should in its place. Shine The cleaning process exposes abnormal and pre-failure conditions that could

hurt quality or cause machine failure. Standardize Develop systems and procedures to maintain and monitor the first three S‟s

(Sort, Straighten, Shine). Sustain Maintaining a stabilized workplace is an ongoing process of continuous

improvement, so regular management audits should be employed to stay disciplined.

5S practice supports a smooth production flow, an essential characteristic of a lean system. It

also helps to make problems visible.

To enforce 5S practice, workers have to be equipped with the necessary education and training;

they should also be encouraged with rewards to give them incentive to properly maintain and

continuously improve operating procedures and the workplace environment.

Just-in-Time (JIT) Production

Just-in-Time is a set of principles, tools, and techniques aimed to eliminate wastes by producing

“only the necessary products, at the necessary time and in the necessary quantity” (Regani,

2004). JIT encourages the organization to produce and deliver products in small quantities, with

short lead times, to meet specific customer needs. With JIT, the company does not build up

excessive inventory, resulting in significant reduction of inventory costs. Also, the company is

more flexible to changing customer demands.


JIT is based on the „pull‟ system of manufacturing. With the pull system, production is initiated

with customer demand. In other words, there would be no production if there were no demand.

The „pull‟ system is contrasted against the „push‟ system, where the company pushes

0production to its highest potential, producing as much as possible, often leading to excessive

inventory of work-in-process (WIP) as well as finished goods. With the pull system, customer

demand pulls the workflow, and processes in the assembly line begin when parts are delivered

by the previous process in the line. Theoretically, if JIT is successfully implemented throughout

the organization, the company would have no need for storage as WIP and finished goods

inventory would be completely eliminated.

Kanban

A Kanban means „signboard‟ in Japanese. Kanban‟s definition has broadened to signify a tool

used to effectively control the flow and the quantity of parts in production. In the Kanban

system, when an operator needs parts for a certain process of the production line, he writes the

details of the parts needed and quantity needed on the Kanban. He then takes this card to the

preceding process to withdraw the amount needed (Regani, 2004).

Value Stream Mapping

A value stream is the set of activities that contributes value to the customer. Value stream

mapping is a method for showing the material and information flow in diagram form. The value

stream map captures process, material flows, and information flows of a given product family

and helps to identify waste in the system. Activities that do not contribute to value in the

customers‟ eyes are waste and should be eliminated.

Total Productive Maintenance (TPM)

Total Productive Maintenance (TPM) is a set of tools that gives workers a high degree of

autonomy and responsibility in improving productivity. Every worker leans how to clean,

inspect, and maintain equipment. Workers are trained to analyze information flow and

processes such that they can see waste and solve problems at the root cause. With an

effective method of maintaining the plant and equipments, productivity is increased, and so are

employee morale and job satisfaction.


Production Flow Balancing

Production Flow Balancing is a technique used to level out the workload of a production line. It

discourages rushing production in batches to meet deadlines, and encourages producing a

variety of products in small batches on a consistent basis. This eliminates overburden to people

and equipment. Workers have more time to focus on continuous improvement. It also helps to

reduce huge inventory buildup of one or a few particular products.

2.2.2 The Toyota Way

It is believed that the Toyota Company is the inventor of “lean production” (also known as the

“Toyota Production System”). Utilizing the Toyota Production System (TPS), Toyota has

consistently manufactured automobiles with superior quality. To be a truly lean organization,

the company must go beyond the surface tools and techniques of Lean and adopt a Toyota-

style culture of quality. There were two aspects to TPS – the „hard‟ or technical part focusing on

manufacturing systems like JIT and Kanban, and the „soft‟ or people related part emphasizing

respect for every human in relation to the company, such as workers and suppliers. Moreover,

all employees must be committed to learning continuously and creating a Lean enterprise.

Below is a summary of the 14 Toyota Way Principles, the pillars of the successful Toyota

Production System (Liker, 2003):

Principle 1: Base your manage decisions on a long-term philosophy, even at the expense of

short-term financial goals.

Principle 2: Create continuous process flow to bring problems to the surface.

Principle 3: Use “pull” system to avoid overproduction.

Principle 4: Level out the workload.

Principle 5: Build a culture of stopping to fix problems, to get quality right the first time.

Principle 6: Standardized tasks are the foundation for continuous improvement and employee

empowerment.

Principle 7: Use visual control so no problems are hidden.

Principle 8: Use only reliable, thoroughly tested technology that serves your people and

technology


Principle 9: Grow leaders who thoroughly understand the work, live the philosophy, and teach it

to others.

Principle 10: Develop exceptional people and teams who follow your company‟s philosophy.

Principle 11: Respect your extended network of partners and suppliers by challenging them and

helping them improve.

Principle 12: Go and see for yourself to thoroughly understand the situation.

Principle 13: Make decisions slowly by consensus, thoroughly considering all options;

implement decisions rapidly.

Principle 14: Become a learning organization through relentless reflections and continuous

improvement.

Figure 3 shows the “4P‟ model of the Toyota Way. The 14 guiding principles are divided into

four categories: Philosophy, Process, People and Partners and Problem Solving. To be a true

lean organization, a company must strive for success in all four categories. Many companies

consider themselves lean enterprises, but in reality, they have only implemented basic lean

tools to attain a lean process. They have not cultivated a lean culture in the company. For

long-term results, companies have to look beyond merely achieving a lean process; they have

to instill lean thinking in every employee‟s mind. The “4P” model and the 14 Principles guides us

through the transformation into a lean enterprise and help us realize the enormous benefits a

true lean company can enjoy.


Figure 3: The “4P” Model of the Toyota Way

2.3 Lean Six Sigma Defined

Lean Six Sigma is an approach combining and capitalizing the strengths of the Six Sigma and

Lean Management improvement programs. It has been claimed that companies that practice

either lean management or Six Sigma exclusively would reach a point of diminishing returns.

After the initial problem solving and process re-engineering efforts, systems show significant

improvement, but further improvements are not easily realized (Arnheiter, 2005).

The effectiveness of Lean Sigma stems from integrating the two popular process improvement

methodologies to create a single, coordinated initiative. Lean and Six Sigma methods

complement and reinforce each other. In comparison to applying the two methodologies

separately, improvements are identified and implemented more rapidly, process variations are

more controlled and cost reductions are greater. The data-driven aspect of Six Sigma pushes

the organization to make efficient use of data in decision-making and problem solving, using the

structured DMAIC methodology that promotes a scientific approach to quality. The resulting

higher process capability leads to substantial cost reduction. To complement a high quality

production process, a company should also provide high quality service. Some Lean

organizations are characterized by the produce-to-order strategy. With this strategy, a company

starts small-batch productions only when there is a customer order and avoids unnecessary


long production runs; hence, manufacturing lead times are reduced. This means that the

products are delivered to the customer more quickly. Other Lean organizations adopt the

produce-to-stock strategy. These companies also provide better service through Lean

Management because they decrease their horizon of their forecasts and replenish their stocks

more often, thereby reducing leads times and inventory costs, and increasing the company‟s

profit and inventory turnover rate (Arnheiter, 2005).

Because of its practicality and effectiveness, the Lean Sigma approach is widely popular among

various manufacturing and service industries. Table 2 summarizes the key Lean and Six Sigma

principles embodied by the Lean Six Sigma approach (Arnheiter, 2005):

Table 2: Combining key Lean and Six Sigma principles

Lean Principles Six Sigma Principles

There is an overriding philosophy that aims to eliminate non-value added operations

Data-driven methodologies are emphasized. Changes are based on quantitative analysis rather than intuition.

Incentive systems encourage global optimization instead of local optimization.

Variation of quality characteristics is minimized using structured methodologies.

Decisions are made according to their relative impact on the customer

A company-wide and highly structured education and training program is designed and implemented.

2.4 Case Study Review 1 by Holly Wong: An Application of Six Sigma Methodology to Reduce the Engine Overheating Problem in an Automotive Company

The purpose of reviewing the case study is to gain insight on the approach and application of

the Six Sigma methodology and apply this knowledge in solving the problems at the wireless

mobile manufacturing company. With this purpose in mind, the following section will explore the

key steps taken to reduce the problem in the analyzed case study, the tools and techniques

employed, and the acquired knowledge that will be useful in performing our case study with the

company.

In the reviewed case study, an automotive company eliminated an engine-overheating problem

using the DMAIC approach. The engine-overheating problem was caused by a water-jacket-

passage jamming problem in the engine cylinder head. The problem required immediate

attention because it caused high level of dissatisfaction among the company‟s customers.


Solving this problem would not only bring about a significant impact on the company‟s profit, but

would also increase customer satisfaction tremendously. The DMAIC methodology was

effective choice in solving the engine-overheating problem because the methodology is

recommended when (Banuelas, 2005):

1) the cause of the problem is unknown or unclear,

2) the potential of significant savings exist, and

3) the project can be done in 4-6 months.

The first two conditions are known to be true for this project. As for the third condition, it cannot

be guaranteed that any project is done in 4-6 months, but given the severity of the problem, the

company would like the project to be completed as soon as possible. From reviewing the case

study, one can see that 4-6 months is a reasonable time frame for solving the engine-

overheating problem. The DMAIC methodology provides a structured, effective framework that

guides the company to accomplish this goal.

DMAIC stands for Define-Measure-Analyze-Improve-Control, the five phases undergone in

problem solving. The work done in each of the phases will be described, and an analysis will be

provided based on the student‟s research and statistical analysis results. Minitab is the

software used for statistical analysis. At the end of the case study analysis, a summary on what

is learned from the case study and how it can be linked to our project will be given.

2.4.1 The Define Phase

In the define phase, the company defined the scope and goals of the improvement project,

translated customer requirements into actions to be taken, and developed a process plan to

initiate these actions. The project‟s ultimate goal is to improve customer satisfaction and

increase the company‟s profit. To achieve this goal, the company must solve the engine-

overheating problem. Since it was determined that the water-jacket-jamming defect was the

cause of the engine-overheating problem, it was chosen to be the performance measure

parameter. The team defined the goal statement of the project to be the reduction of water-

jacket-jamming from 0.194 defects per unit (DPU) to 0.029 DPU. Achieving this goal would help

the company achieve substantial reduction in the cost of poor quality (COPQ).


After several brainstorming sessions, the team identified the two main parameters causing the

water-jacket-passage-jamming defects – namely, sand fusion and metal penetration during

casting of the product. With careful investigation, the team found that the root cause of sand

fusion and metal penetration as the porous core of the engine cylinder head. The depth of

porous core was therefore identified as the critical-to-quality characteristic.

2.4.1.1 Analysis of the Define Phase

Although this case was an application of Six Sigma, not Lean Six Sigma, the team effectively

utilized the five-why analysis – a lean tool – in the define phase to locate the root causes of the

problem and narrow the scope of their investigation. They started with the voice of the

customer and ended with some process parameters to which they should direct attention:

(1) The customer is complaining about the engine over-heating problem – why?

(2) The water-jacket-passage jamming problem causes overheating – why?

(3) Sand fusion and metal penetrations cause the defect of jamming – why?

(4) The porous core of the engine cylinder-head allows sand fusion and metal penetrations

– why?

The last „why‟ would be answered in the subsequent measure phase, where the team

constructs a cause and effect analysis to find the process variables affecting the porous core.

Although the team successfully defined the performance measure parameter and critical-to-

quality characteristic of the improvement project, these tasks would have been done more

effectively if process mapping had be done early in the define phase, after the scope and goals

have been defined. The team chose to perform process mapping in the succeeding measure

phase, but it would have been valuable for them to perform it in the define phase. Process

mapping would give the team a clearer understanding of the cylinder-head core preparation.

Although most team members might have thorough knowledge of the process from experience,

it would be beneficial to map the process on paper, to ensure that every member had the same

understanding of the process, and any questions would be answered before the project

continued. Process mapping also ensured that all stages of the process were examined, and


no potential problem areas were neglected. It was a crucial first step that must be done before

any improvements can be made.

2.4.2 The Measure Phase

In the measure phase, the team established the process capability or process performance by

selecting one or more product characteristics to monitor, mapping the process, taking

measurements and tracking results on process control cards. From the process map, the team

identified all value- and non value-added steps, key process inputs and outputs. Using the

process map shown in Figure 4, the team began the analysis of the potential causes of defects.

Although this task was performed in detail in the analysis phase, the team needed to narrow

down the number of potential causes so they could focus on measuring the significant

parameters that caused the porous core, and ultimately, the water-jacket-jamming defects and

the engine overheating. The critical-to-quality characteristic was set to be the depth of the

porous core.

Figure 4: Process Map for Cylinder-Head Core Preparation


To evaluate the causes of the porous core, the team used cause and effect analysis. One of

the tools used in cause and effect analysis was a cause an effect diagram. The team identified

categories such as measurements, machines, materials, etc, and brainstormed to identify

causes within each category that led to the effect of the porous core. The cause and effect

diagram constructed is shown on Figure 5.

After further analysis, the team found that the potential parameters affecting the porous core

were:

1) Sand leakage,

2) Blow pressure,

3) The AFS number of sand,

4) The gap in the core box,

5) Bulk density of the sand, and

6) Vent choking.

Porous Core

Environment

Measurements Material

Machines

Personnel

Know ledge

Experience

O perators Working on

Temperature of C ore BoxV ent C hoking Ratio

Sand Leakage

F ineness of IronF ineness of Mill Scale

Bulk DensityBaume of Wash

Slide C aliper

Baume Meter

A FS Number of Sand

C leanliness of BoxTemperature of Work

C leanliness of Tank

Cause and Effect Diagram

Figure 5: Cause and Effect Diagram for Porous Core


Specification limits were then set for these process parameters. These limits were dictated by

the performance standards required by the customer. Having set the specification limits, the

next step was to establish the accuracy of the measurement system and the quality of the data.

This was achieved by conducting a Gauge repeatability and reproducibility (R&R) study. The

purpose of this study was to determine the capability of the measurement system and to identify

the sources of variation in the measurement system. If the measurement system was not

capable, the variations would have to be controlled before any measurements could be taken

with confidence. A data collection plan was set out to gather data for the selected process

parameters. The last step taken in the phase was to establish a baseline process capability –

the Cpk value was estimated to be 0.49.

2.4.2.1 Analysis of the Measure Phase

There was no explanation as to why the depth of the porous core was chosen as the CTQ to

monitor (we do not know why the depth affects the porosity of the core), so it was the impossible

to evaluate the validity of measuring the depth of the porous core as the response of varying the

process parameters listed above. One can only assume that the deeper the porous core was,

the more susceptible the water-jacket-passage was to sand fusion and metal penetration,

leading to passage jamming and engine overheating.

Because no quantitative data was given on customer requirements, it was not possible to

deduce the accuracy of the specification limits set for the process parameters. There were also

no details given on the Gauge R&R study data collection plan, so no analysis can be performed

on these parts of the measure phase. However, the measurement system was considered

acceptable if the variability was between 10 and 30 percent, and the variability for the system in

the case study was 6.08 percent, implying that the measurement system was not just

acceptable, but satisfactory.

In terms of process capability, one can calculate the Cpk value based on the data given in the

case. The data is given in Appendix A. In the case study, the data was not shown until the

analyze phase, and since the Cpk value was calculated in the measure phase, it was unclear

whether the Cpk value was derived from the set of data given. Nonetheless, the following shows

the calculation of the Cpk value for the given set of data:


From the case study, the specification limits are: LSL = 0mm, USL = 1.4mm. From the sample

data, μ = 1.2056, σ= 0.2756,

Cpk = min { (μ- LSL) / 3σ , (USL - μ) / 3σ}

= min { (1.2056 – 0) / 3(0.2756) , (1.4 – 1.2056) / 3(0.2756)}

= min {1.46, 0.24}

= 0.24

This calculated Cpk is even lower than the Cpk value given in the case study (0.49). By industry

standard, a capable process has a Cpk value of greater than 1.33. If the automotive company‟s

Cpk value was 0.24, this means that the company‟s process performance was poor and needed

vast improvement.

Unfortunately, no description was given about the process parameters and no details were

stated on how they affect the depth of the porous core, so no analysis can be performed on the

identification of the process parameters.

2.4.3 The Analysis Phase

The analysis phase was started by gathering data from the process for analysis. Data was

collected randomly over the course of 36 days, during different shifts throughout the day. For

every data point, the factors measured were the depth of porous core (mm), sand leakage

(g/blow), blow pressure (kg/cm2), AFS number, bulk density (g/cm3), baume of wash (Be), fin

thickness (mm) and vent ratio – the depth of the porous core was the response, all of the others

were previously determined as the critical factors affecting the quality of the product.

The company used a Pareto chart to identify the locations at which water-passage-jamming

defects occurred most frequently. To reduce the defects at these locations, the team had to

enhance the process capability by reducing variability in the process. Regression analysis was

performed to find the parameters that have the most effect on the process. The study stated

that variables with P values less than 0.01 were statistically significant for further analysis; these

parameters were found to be sand leakage, bulk density and vent choking ratio.


2.4.3.1 Analysis of the Analysis Phase

Using the data collected, analysis can be performed using the Minitab statistical software.

Shown in Appendix A are the descriptive statistics and regression analysis done on Minitab for

each factor affecting response before improvement.

To understand the causes for the poor process capability, it is first required to identify the

parameters that significantly affect the process. Regression analysis is used to pinpoint these

parameters. Referencing Probability & Statistics for Engineers & Scientists (Walpole, 2002), the

simple linear regression model is as follows:

In our case, the Yi values are the depth of porous core values given in the data table (which can

be found in Appendix A) and the xi values are the measurements for the factor in consideration,

e.g. sand leakage. A regression line can be plotted for each factor. The estimated or fitted

regression line is:

A fitted regression model can be constructed for each factor. The estimated or fitted regression

model is:

Yi = α + βxi + εij i = 1,2,…,a

Yi is the response related to the independent variable x in the equation. α is the unknown intercept.

β is the slope parameter. εi is the random variable, or residual, assumed to be normally distributed with E(ε) = 0 and Var(ε) = σ2

^

Ŷi = â + βxi i=1,…,n ^ Ŷ, â, β are the estimators for the parameters defined in the simple linear regression model.

ŷ = a + bx

ŷ is the fitted value. a is an estimate for the regression coefficient α, the intercept. b is an estimate for the regression coefficient β, the slope parameter. x is the independent variable


With the fitted simple linear regression model in Equation 3, one can test for the significance of

regression with the hypothesis testing shown below:

From Minitab, the following regression analysis result for the sand leakage factor is obtained (α

= 0.01, n = 36):

From this result, we can conclude the following:

1) The regression equation is ŷ = 0.962 + 0.00642x, where ŷ is the depth of porous core

value and x is the independent variable for sand leakage,

2) The test statistic F is 25.74, which is greater than F 0.01, 1, 34 ≈ 7.45, therefore the null

hypothesis is rejected and regression is found to be significant. This conclusion is

confirmed by the p-value, which is P(F1, 34 > F*) = 0.

Regression Analysis: depth porous core versus sand leakage The regression equation is

depth porous core = 0.962 + 0.00642 sand leakage

Predictor Coef SE Coef T P

Constant 0.96249 0.05943 16.19 0.000

sand leakage 0.006420 0.001266 5.07 0.000

S = 0.210976 R-Sq = 43.1% R-Sq(adj) = 41.4%

Analysis of Variance

Source DF SS MS F P

Regression 1 1.1455 1.1455 25.74 0.000

Residual Error 34 1.5134 0.0445

Total 35 2.6589

Unusual Observations

depth

sand porous

Obs leakage core Fit SE Fit Residual St Resid

6 80.0 1.0000 1.4761 0.0639 -0.4761 -2.37R

R denotes an observation with a large standardized residual.

Test H0: Regression is not significant (β = 0)

H1: Regression is significant (β ≠ 0)

If F>F α, 1, n-2, reject H0 and conclude that regression is significant.


Normality of the response measurements are confirmed with the normal probability plot in

Figure 10:

Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.61.41.21.0

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.40.20.0-0.2-0.4

8

6

4

2

0

Observation Order

Re

sid

ua

l

35302520151051

0.50

0.25

0.00

-0.25

-0.50

Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

Histogram of the Residuals Residuals Versus the Order of the Data

Residual Plots for depth porous core

Figure 6: Residual Analysis for the Sand Leakage Factor

The regression analysis is done in the same fashion for all other factors. The parameters with

significant regression are sand leakage, bulk density, and vent choking, which means that these

factors significantly affect the response of the depth of the porous core. These are consistent

with the parameters identified by the team in the case study.

2.4.4 The Improve Phase

A 23 full factorial Design of Experiment (DoE) was carried out using the three process

parameters identified from the analyze phase to determine which combination of factor settings

was optimal for improving production quality. Each process parameter was studied at two levels

– please refer to Table 3 for the factor levels. Each trial condition was replicated twice. The

team wanted to study the main effects as well as interaction effects among the parameters. The

objective of the improvement phase was to minimize the depth of the porous core.


Table 3: Factor Levels for Factorial Design

Factor Low Level High Level

Sand Leakage 10 g/blow 30 g/blow

Bulk Density 1.78 g/cm3 1.95 g/cm3

Vent Choking 0 (no vent choking) 1 (vent choking exists)

In the case study, the team found that only the main effects were statistically significant at 10

percent significance level – none of the interactions were statistically significant. It was

concluded that the optimum levels of process parameters to minimize the depth of the porous

core were: high level bulk density (1.95 g/cm3); low level vent choking level (No vent choking);

low level sand leakage (10 g/blow). Running trials at this optimal setting, the team found a

significant improvement on the process capability, including:

The process capability value (Cpk) improved from 0.49 to 1.28.

The average depth of porous core was reduced to 0.80mm from 1.21mm

The process variability was significantly reduced

2.4.4.1 Analysis of the Improve Phase

Using Minitab, a 23 full factorial Design of Experiment (DoE) was performed using the three

significant process parameters identified from regression analysis. They were bulk density, vent

choking ratio and sand leakage. The response was depth of porous core (mm) and the data

was given in the case study. The raw data and full results for the factorial experiment are

shown on Appendix B. This DoE in Minitab was designed to simulate the same experiment

done by the team in the case study (2 levels for each factor, 2 replications for each setting).

Please see Table 3 for the factor levels used in the DoE.

The normal probability plot of effects in Figure 7 shows that only the main effects A, B and C are

statistically significant at 10 percent significance level. The interactions plot in Figure 8 shows

that there is a slight interaction between the sand leakage and vent choking factors; other than

that, there appears to be no interaction between the process parameters. From the main effects

plot shown in Figure 9, we can conclude that the optimal setting for minimizing the depth of

porous core is: low level sand leakage (10 g/blow), high level bulk density (1.95 g/cc) and low

level vent choking (no vent choking). These findings are consistent with the team‟s conclusion

in the case study.


Standardized Effect

Pe

rce

nt

543210-1-2-3

99

95

90

80

70

60

50

40

30

20

10

5

1

Factor Name

A Sand leakage

B Bulk density

C V ent choking ratio

Effect Type

Not Significant

Significant

C

B

A

Normal Probability Plot of the Standardized Effects(response is Depth of porous core observatio, Alpha = .10)

Figure 7: Normal Probability Plot of Effects

Sand Leakage (g/blow)Sand Leakage (g/blow)

Bulk Density (g/cc)Bulk Density (g/cc)

Vent Choking RatioVent Choking Ratio

1.951.78 10

0.9

0.8

0.7

0.9

0.8

0.7

Sand

Leakage

(g/blow)

10

30

Bulk

Density

(g/cc)

1.78

1.95

Interaction Plot (data means) for Average Depth of Porous Core(mm

Figure 8: Interactions Plot Displaying the Interactions Among Process Parameters


Me

an

of

Av

era

ge

De

pth

of

Po

rou

s C

ore

(mm

3010

0.95

0.90

0.85

0.80

0.75

1.951.78

10

0.95

0.90

0.85

0.80

0.75

Sand Leakage (g/blow) Bulk Density (g/cc)

Vent Choking Ratio

Main Effects Plot (data means) for Average Depth of Porous Core(mm

Figure 9: Main-Effects Plot for the Depth of the Porous Core

2.4.5 The Control Phase

Full value can only be obtained from the improvements if the optimized results are sustainable.

The company made a list of changes to ensure that improvements will remain long-term:

- Standardization of tasks in the process

- Constant monitoring and control of the improved process

- Conducting an extensive training program for the process-related personnel

- Visually displaying the process parameters on process sheets and control charts so that

operators could take preventive measure before problems occur

After implementing these changes, the company achieved sustainable improvements in the

process. The company used run charts to monitor the improved process and ensure the

process stays in control. All data points plotted on the run chart after the improvements are

implemented fall within the specification limits.


2.4.5.1 Analysis of the Control Phase

Comparing the run charts for the depth of the porous core before (Figure 10) and after

improvements (Figure 11), one can confirm that the company was successful in attaining

sustainable improvements in the process. From the case study, all points are within

specification limits, variations are reduced, and process capability is improved significantly.

Observation

de

pth

po

rou

s c

ore

35302520151051

1.8

1.6

1.4

1.2

1.0

0.8

0.6

Number of runs about median:

0.94141

14

Expected number of runs: 18.50000

Longest run about median: 6

Approx P-Value for Clustering: 0.05859

Approx P-Value for Mixtures:

Number of runs up or down:

0.29431

25


Longest run up or down: 3

Approx P-Value for Trends: 0.70569

Approx P-Value for Oscillation:

Run Chart for the Depth of the Porous Core Before Improvement

Figure 10: Run Chart for the Depth of the Porous Core Before Improvement


Observation

de

pth

of

po

rou

s c

ore

aft

er

87654321

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

0.0

Number of runs about median:

0.73168

4


Longest run about median: 4

Approx P-Value for Clustering: 0.26832

Approx P-Value for Mixtures:

Number of runs up or down:

0.02827

7


Longest run up or down: 1

Approx P-Value for Trends: 0.97173

Approx P-Value for Oscillation:

Run Chart for the Depth of the Porous Core After Improvement

Figure 11: Run Chart for the Depth of the Porous Core After Improvement

2.4.6 Summary and Knowledge Applicable to Company Case Study

From reviewing and analyzing the case study of the application of Six Sigma to reduce the

engine-overheating problem, valuable knowledge was acquired and served to be useful when

applied to our project at the wireless mobile manufacturing company. The DMAIC methodology

was recommended for our case study for the same reasons it was recommended for the

automotive company‟s problem. We had aimed to follow the steps taken in each phase of the

DMAIC cycle. However, replicating every step was infeasible due to our time and resource

limitation, nature of our project, and data availability. For example, as we had limited time for

site visits, we could not conduct a Gauge R&R study for the measurement phase. Ideally, the

objective of our project would be the same as the team‟s, that is, to eliminate the problem in

order to improve customer satisfaction and increase company revenue. But, again, due to time

limitation and the academic nature of our project, our objective could not be as extensive. Our

objective was therefore to propose improvements to the wireless mobile manufacturing

company‟s management and the company would hold the decision to implement the

improvements after the conclusion of our thesis project.


2.5 Case Study Review 2 by David Woo: Reduction of Yarn Packing Defect Using Six Sigma Methods: A Case Study

The paper by Mukhopadhyay and Ray was presented as an utter illustration of the Six Sigma

methodology and its relative application on a real business case problem. As of the intended

initiative for quality improvements of Six Sigma, the case followed in accordance to the

associated framework of the methodology – the DMAIC model (Define – Measure – Analyze –

Improve – Control) in addressing to the quality problem as identified in the case. In seeing the

use of a number of statistical tools for the definition and analysis of the problem within the study,

the case gave a resourceful foundation with respect to some of the statistical approaches and

methods that were seen to be applicable for the thesis work on the company case study. With

that taken into account, this Literature Review section provided an overview of the case on the

Indian textile company for its attempt in resolving the quality problem in its yarn manufacturing

process. An introduction to the background and the respective problem would be described in

the beginning section of this Review. The section following then extended to some additional

analysis work, supplement to the major findings as computed from the paper. The Review

would close up with a summary of acquired knowledge from the case, providing a view of its

application to the actual thesis work.

2.5.1 Case Overview and Summary of Findings

The context for this case study was based upon an Indian textile company of yarn

manufacturing. Characterized as a mid-sized operation, the company was in the midst of

encountering a major weight variation quality problem of its yarn cone product. The impact of

which was rather significant, resulted in a considerable number of customer complaints and a

substantial packing rejects rate. The intended goal for this case was therefore set to correct the

problem by the application of Six Sigma methods. Using weight as the standard of measure,

preliminary data was collected at the beginning phases in the case and 5 areas were identified

to be the probable contributing variation parameters that constituted the cause of the weight

variation problem. These 5 probable contributors were, namely,

1. Capability of process and machineries

2. Empty yarn cheese weight

3. Yarn count


4. Moisture content, and

5. Yarn length, with the associated capability of the Length Measuring Device (LMD)

The assembly winding and Two-for-one twisting (TFO) were the process areas that were seen

to be the most probable physical source of where the problem originated from, raw data of the 5

parameters was thus collected at these two process stages.

With the problem defined and raw data collected, the case study proceeds to the Measure and

Analysis stage of the DMAIC model in two phases. The Measure and Analysis portion of the

paper gave assessment on each of the variation parameters through application and

computation of a number of hypothesis testing and capability analysis. It was concluded from

Phase I of the Analysis in that the yarn count, capability of process, moisture content and empty

yarn cheese weight were not the contributors to the problem, based on that these factors were

proved to have only a minimal impact in causing the weight variation of the end product. The

focus of interest had then been directed to examining the yarn length in Phase II of the Analysis.

A regression model was found to be well fitted with the data. The conclusion from which

provided that 74.5% of the variability of the gross cheese weight was due to this variability in

yarn length. Yarn length was therefore determined to be the major parameter that contributed

to the significant weight variation problem. The case study ended with a summary of

improvements measures that was in addressing to yarn length variation root cause, along with

some of the achievements resulted from implementation of which.

2.5.2 Additional Supplementary Analysis and Findings

The previous section captured a number of summarized findings as identified from the work of

the author, providing an adequate overview of the problem and the respective Six Sigma

improvement application. Aimed to develop a more comprehensive and in-depth

understanding with the application and implication of the methodology, supplementary analysis

was done on the case. A number of statistical tools that were introduced in the paper would be

used for in providing some of the validation calculations and additional computations to some of

the findings as identified from the paper. In giving a more in-depth examination on the case,

additional analysis was done revolving on 3 areas of core interest:


(i) Validation on the Significance of Contribution of Yarn Count Moisture Content to

Gross Weight

(ii) Testing for Equality of Mean and Variance for TFO (Two-for-one twister) stage and

(iii) Extension to the Gage Repeatability of Length Measuring Machine

Each of which would further be discussed in more detail as follow.

(i) Validation of the Significance of Count and Moisture Content to Gross Weight

Analysis Phase I in the case focused on the analysis and testing of the variation parameters in

determining ones that contribute to the weight variation problem. The capability of the

machineries and empty cheese weight were found to be of minimal significance to the gross

weight variation. With respect to the yarn count and moisture content parameters, author had

simply indicated regression analysis was done and that the analysis failed to establish the

parameters as significant contributory factors. With that taken into account, regression analysis

was done here as an extension to which, aiming to achieve the validation of the finding.

The analysis was done on the 3 main yarn genre count that are of most affected from the weight

variation problem. Data was inputted into MINITAB and Regression Analysis was performed

on each of the 3 genres. The residual plot and regression data was shown in Appendix B in the

back of the Report.

The results computed for yarn of count 3/20sP appeared to concur with the claim of the case

study, as can be seen from the relatively small coefficient compared to that of the constant in

the regression formula. The respective p-value of 0.754 and 0.872 for the two parameters also

suggested that the hypothesis of the parameter posing no effect could be rejected and

disregarded. The result was further supported by the minimal 0.5% R-Sq and 0% R-Sq value

computed. On the other hand, the respective results computed for yarn of count 4/12sP or

2/42sP however suggested that the Actual Yarn Count and MC% might in fact be the significant

contributors to the variation in the gross weight. In interpreting the p and R-Sq value computed

for each of the parameters for the two yarn genre, the effect of Moisture Content appeared to

rather significant for the 4/12sP yarn genre; whereas both Moisture Content and Yarn Count

contributed a substantial effect on the 2/42sP genre.

The Residual Plots for the Gross Cheese Weight for all 3 yarn count genre had also been

generated from MINITAB. The randomness distribution of data displayed from the Residual


Versus Fitted Value and Order of Data plots, along with the linearity shown in the normal

probability plot of the residuals give a good indication that the model adequacy for all 3

regression models are of relatively good fit.

(ii) Testing for Equality of Mean and Variance for TFO stage

The author conducted test on various variation parameters in attempting to derive the

contributors to the weight variation. A mean and variance testing for the winding process was

conducted in Analysis Phase 1 in the case. Result of which indicated that the capability of the

machineries are of minimal significance to the weight variation of the yarn corn. Similar kind of

testing was, however, not been performed for the Two-For-One Twister process. The second

part of this supplementary analysis would therefore take on this aspect and would perform on

the TFO stage testing with respect to the Equality of Mean, Equality of Variances and Equality

of Mean Comparison of Two Sides. The associated calculations are computed and are shown

in Table 4.

Table 4: Data Table for Testing of Variance

Testing for Equality of Mean

Machine No. Count n Mean StDev Min Max LSL T USL t CALC tα/2,n-1 .-tα/2,n-1< tCALC < tα/2,n-1 p value α

2 3/12sP 10 4.635 0.058 4.55 4.7 4.35 4.5 4.65 7.36 2.2622 no 0 0.05

5 4/12sP 10 4.55 0.1027 4.45 4.75 4.35 4.5 4.65 1.54 2.2622 yes 0.16 0.05

7 4/12sP 10 4.605 0.0926 4.5 4.75 4.35 4.5 4.65 3.58 2.2622 no 0.0059 0.05

8 4/12sP 10 4.09 1.078 1.05 4.6 4.35 4.5 4.65 -1.2 2.2622 yes 0.26 0.05

11 2/42sP 10 2.086 0.102 1.82 2.16 2.05 2.1 2.2 -0.43 2.2622 yes 0.67 0.05

22 3/20sP 10 2.136 0.0324 2.08 2.18 2.05 2.1 2.2 3.52 2.2622 no 0.0066 0.05

25 3/20sP 10 1.982 0.2595 1.52 2.18 2.05 2.1 2.2 -1.44 2.2622 yes 0.18 0.05

27 3/20sP 10 2.116 0.02459 2.08 2.14 2.05 2.1 2.2 2.06 2.2622 yes 0.07 0.05

33 2/42sP 20 2.109 0.04128 2.02 2.18 2.05 2.1 2.2 0.97 2.093 yes 0.34 0.05

34 2/42sP 10 2.118 0.0512 2.06 2.18 2.05 2.1 2.2 1.11 2.2622 yes 0.29 0.05

51 2/42sP 10 2.146 0.0481 2.04 2.2 2.05 2.1 2.2 3.02 2.2622 no 0.014 0.05

60 2/42sP 10 2.12 0.0566 2.04 2.24 2.05 2.1 2.2 1.12 2.2622 yes 0.29 0.05

64 2/42sP 20 1.068 0.03458 0.98 1.14 0.925 1 1.075 8.79 2.093 no 0 0.05

65 2/42sP 10 1.104 0.02797 1.08 1.16 0.925 1 1.075 11.76 2.2622 no 0 0.05

66 2/42sP 10 1.098 0.0476 1.04 1.18 0.925 1 1.075 6.52 2.2622 no 0.0001 0.05

70 2/42sP 10 1.076 0.01265 1.06 1.1 0.925 1 1.075 19 2.2622 no 0 0.05

71 2/42sP 10 1.084 0.02459 1.04 1.1 0.925 1 1.075 10.8 2.2622 no 0 0.05

72 2/42sP 10 1.08 0.0432 1.02 1.12 0.925 1 1.075 5.86 2.2622 no 0.0002 0.05

if yes, running on target

if no, running out of target

Testing for Equality of Variances

Machine No. Count n1 n2 s1 s2 v1 v2 F Fα/2,9,9 F1-α/2,9,9 Fα/2,9,9 < F < F1-α/2,9,9

33 2/42sP 10 10 0.06 0.02 0.00 0.00 5.49 4.026 0.2484 NO

64 2/42sP 10 10 0.04 0.03 0.00 0.00 1.91 4.026 0.2484 YES

no, means that different

variance

Testing for Quality of Means of Two Sides

Machine No. Count x1 x2 n1 n2 t CALC tα/2,n1+n2-

2 .-tα/2,n-1< tCALC < tα/2,n-1 p value α

33 2/42sP 2.106 2.112 10 10 -0.32 2.1009 yes 0.76 0.05

64 2/42sP 1.074 1.062 10 10 0.77 2.1009 yes 0.45 0.05


Based on the interpretation of the figures, the following findings can be concluded:

1) 10/18 of the machine are shown to have t-value fall out of range, indicating that

the process is in fact running out of target. That values to 56% of the samples.

2) The F value computed in the Testing for Equality of Variance for Machine 33 falls

out of range, which means that the two sides of the machine appear to be

running with a relatively notable variance difference.

3) The Comparison of the Means of Two sides do, however, seem to be running

quite stable, sides of both machines appear to have similar mean figures.

The findings do give a further reinforcement to the significance of the weight variation problem.

As computed, 56% of the sample data appear to be running out of target. One of the two

sample group of data also seem to be running with a notable difference in variance. The need

for facilitating Six Sigma and other improvement measures should therefore be necessary and

demand immediate attention.

(iii) Extension to the Gage Repeatability of Length Measuring Machine

Given in the problem had been shown only the R-chart for repeat measurement of yarn length

by the Length Measuring Device. In determining further the capability of the Length Measuring

Machine, the I-MR Charts for the 4/12sP and 2/42sP yarn count genre were plotted and can be

referred to in the Appendix B. The interpretation from which in fact coincided with the

conclusion from the analysis, in that the Machine was proved to be incapable. Despite the fact

that all data points of the measurement fitted within the LCL/UCL range, both I-MR Charts

showed that there were more than 6 data points running consecutively on either side of the

mean, giving a strong indication in that the process might be out of control.

2.5.3 Application on Company Case Study

Seeing a practical and complete use of the Six Sigma methodology and framework for the case,

the study served as an extremely useful example and illustration for an effective application of

the methodology and the significant benefits of which in addressing to a quality problem of a

company. The use of a number of statistical tools gave a resourceful foundation with respect to

some of the statistical approaches and methods that would be applicable for our thesis work.

Pareto charts were a strong indicator to the source and origin of problem area. Hypothesis


testing, in conjunction with the use of regression analysis, supported the validation of test data

and provided further support to the identification of a problem, with respect to specifically the

variability and co-linearity of the process. And the use of control charts specified if a process or

quality of production was in control or not. These statistical tools are seen applicable to the

company case study, in that the application of them would provide great usefulness to the

analysis work for the case.


3. Lean Six Sigma Application: A Case Study

The main portion of the thesis is dedicated to a case study centering on the application of the

Lean Six Sigma quality improvement approach on a real business process. The case study is

undertaken at a wireless mobile device company, wherein the overall objective is to improve the

quality and efficiency of the manufacturing process. Supplemented with a number of Lean

Management principles, the DMAIC problem solving methodology is used as the main

framework in executing the Lean Six Sigma implementation. Improvement opportunities and

measures are proposed in the conclusion, aiming to attain significant enhancements on the

quality of the product and performance of the process.

The following sections of the thesis present the case in detail. An introduction to the company

will be given, providing an overview of the manufacturing process steps, along with the

motivation behind which Lean Six Sigma is proposed. The next section will describe the steps

undertaken in the DMAIC framework. The action taken and the critical findings and analysis

from each of the 5 phases will be detailed. The conclusion will discuss some of the difficulties

encountered during the course of the work as well as the effectiveness of the proposed Lean

Six Sigma framework.

3.1 Company Background

We have conducted our case study analysis on a wireless mobile device company during the

eight months‟ time for the thesis. The company is the designer and manufacturer of a range of

wireless communication handheld devices. Supporting a production volume of approximately 3

million devices annually, the company‟s manufacturing facility runs 5 production lines, 24 hours

a day, 7 days a week, with 4 crew rotations. The manufacturing process is divided into 5 core

areas:

1. Incoming 2. Surface Mount Technology 3. Unit Assembly 4. Packaging 5. Repair Maintenance (RMA) (SMT)

Although the rapidly growing demand and a fast-paced introduction of new product models over

the years have been beneficial for company‟s business, they have also generated some

problems for the company‟s manufacturing process. These problems have resulted in reduced


productivity and capability of the process. Thus, the company‟s objective is to continuously

improve its manufacturing process to reduce these problems, which include:

A high level of Work In Progress (WIP) in Production and Repair Service

A long lead time in process, and

A lack of responsiveness in the identification and resolution of process issues.

To achieve this objective, the company has started adopting the Lean Six Sigma practice in

recent years and a number of small-scale projects have been launched to target specific areas

in the manufacturing process. The intent of the projects is to apply Lean Six Sigma tools and

techniques to eliminate „waste‟, in order to achieve an reduction in lead time and cost, and an

improvement in the quality of the process and product. For our thesis, we will focus on

implementing Lean Six Sigma on the assembly operation process.

3.2 Lean Six Sigma Implementation: the DMAIC Framework

As proposed, the DMAIC problem solving methodology has our main framework in executing

the implementation of Lean Six Sigma at the company. Divided into 5 main phases (Define,

Measure, Analyze, Improve and Control), the methodology offers a well-defined and structured

approach for implementation. A variety of Lean-based tools and principles have been employed

in conjunction with the framework, effectively supplementing the work of identifying problem

areas in the earlier phases and the proposal of improvement measures in the concluding phase.

3.2.1 The Define Phase

The first phase of the DMAIC methodology is the Define phase. In this phase, one aims to

define the scope and objective of the project work. Because we were unfamiliar with the

manufacturing operation at the start of the project, we decided to apply the Value Stream

Mapping technique to map out the process and identify the main problems of the process.

Value Stream Mapping is a popular Lean Production tool. For our case, it proved to be

particularly useful for obtaining a big picture of the process; it also revealed general problem

areas associated with the manufacturing process of the company.


3.2.1.1 Value Stream Mapping

Value Stream Mapping (VSM) is a Lean Production development tool that involves the analysis

of the „value stream‟ of the process. Activities in the process are classified as either value-

added or non-value added. The initiative of the technique is “to eliminate all the wasteful steps

such that flow can be introduced in the remaining „value-added‟ processes.” For our present

case, we have referenced the Improved Value Stream Mapping Procedure (IVSMP) (Braglia,

Carmignani and Zammori, 2006) in performing the value stream mapping on the wireless mobile

device manufacturing process. The IVSMP is an interactive procedure based on a set of seven

steps, listed as follows:

1. Select a product family

2. Identify machine sharing

3. Identify the main value stream

4. Map the critical path

5. Identify and analyze wastes

6. Map the future state for the critical/sub-critical path

7. Identify the new critical path and iterate the process

Using these 7 steps as our guideline, a Product Quantity Revenue Analysis (PQ$ Analysis) is

conducted as the first part of the Value Stream Mapping process. The PQ$ Analysis defines the

scope for the project work by identifying the specific product family group that most significantly

affects the operation of the company in terms of production volume and selling revenue. After

acquiring a better understanding of the current state operations, it was discovered that high

defect rate was a major issue; therefore, defect rate was included as additional parameter in our

analysis. Data was collected on the 5 product groups produced by the company and PQ$

Analysis charts were generated (please refer to Figures 12 and 13). Due to confidentiality

reasons, the identity of the product groups is protected and the groups are represented by

Product A, B, C, D and E.


Product-Quantity Analysis

25%

48%

71%

92%

100%

0

6000

12000

18000

24000

Product C Product E Product B Product D Product A

Product Group

We

ek

ly V

olu

me

0%

20%

40%

60%

80%

100%

Perc

en

tag

e

Figure 12: Product-Quantity Analysis by Product Group

Product-Revenue Analysis

26%

53%

74%

94%100%

0

1800000

3600000

5400000

7200000

Product E Product C Product D Product B Product A

Product Group

Re

ve

nu

e

0%

20%

40%

60%

80%

100%

Perc

en

tag

e

Figure 13: Product-Revenue Analysis by Product Group


Product-Defect Rate Analysis

1830 (35%)

622 (13%)

1394 (25%)

462 (9%)

23 (1%)

0% 20% 40% 60% 80% 100%

Product E

Product D

Product C

Product B

Product A

Pro

du

ctG

rou

p

Percentage

Avg # Defects (Weekly)

Figure 14: Product-Defect Rate Analysis by Product Group

According to Braglia, the underlying logic of PQ$ analysis is that high-volume products are

responsible for the largest part of non-value-added costs such as: material handling, WIP,

queuing, and other operational costs. From our analysis, Product E was found to be one of the

highest volume and revenue-generating product group. From Figure 14, one can see that it

also had the highest defect rate out of the 5 groups. From the PQ$ analysis, we concluded that

we would focus the remaining of our thesis on the manufacturing operation of Product E and

aim to improve the process of this product group.

The next step of Value Stream Mapping was to develop the Current State Value Stream Map for

Product E. The Value Stream Map captured the entire manufacturing process, beginning with

the surface mounting operation of the circuit board and ending with the software configuration of

the mobile device units. The map can be referred to in Figure 15. The Value Stream Map

illustrated the physical movement of inventory and information flow. The process cycle time

(C/T), changeover time (C/O), and yield for each process step was computed and listed on the

map. The process map was developed based on our timing results and observations made on-

site at the company manufacturing facility. Interactions with the Continuous Improvement

Manufacturing group of the company and workers at the Product E production line workstations

also provided us with valuable information.


SM

T S

IDE

1

1

C/T

= 1

3 s

ec/u

nit

C/O

= 2

,70

0 s

ec

Ba

tch

= 2

40

Yie

ld =

97

%I

13

se

c

C/T

= 9

se

c/u

nit

9 s

ec

Insp

ectio

n

SID

E 1

1

SM

T S

IDE

2

1

C/T

= 1

3 s

ec/u

nit

C/O

= 2

,70

0 s

ec

Ba

tch

= 2

40

Yie

ld =

97%

I

13

se

c

C/T

= 9

se

c/u

nit

9 s

ec

Insp

ectio

n

SID

E 2

1

BL

T 3 te

st

he

ad

s

C/T

= 8

3 s

ec/u

nit

Yie

ld =

90%

83

se

c

De

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g

2

C/T

= 6

00

se

c/u

nit

C/T

= 3

00

se

c/u

nit

Re

pa

ir In

Pro

gre

ss:

7 u

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hift

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pe

ratio

n.

Op

era

tor

is o

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ted

to

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n in

occa

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na

l b

asis

2,6

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se

c1

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c

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da

ys

30

0 s

ec

60

0 s

ec

4-5

da

ys

4-5

da

ys

Asse

mb

lyO

pe

ratio

n 1

C/T

= 9

7 s

ec/u

nit

1I

Asse

mb

lyO

pe

ratio

n 2

C/T

= 9

7 s

ec/u

nit

1

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ce

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pe

ratio

n Y

ield

= 9

1.5

%

Co

mp

on

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ield

= 8

7%

Inte

ractive

Te

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C/T

= 1

12

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ld =

81.5

%

4 t

est

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kt

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1+

Figure 15: Current State Value Stream Map of Product E Process.


3.2.1.2 Findings: Identification of Scope and Problem Area

A comprehensive understanding of the process was acquired upon completion of the Value

Stream Map. In the process of developing the map, a list of findings, major areas of focus and

concern were identified and summarized as follows:

The process flow was found to be unevenly distributed with respect to the cycle time of

process steps, ranging from 13 seconds to 112 seconds.

There was a high number of Work-In-Progress level (2-3 hours of inventory) in between

steps.

The non value-added time of the manufacturing process was computed to be 4.76

hours, compared to 10.78 minutes of value-added operations.

The yield of the process gradually decreased from process to process. Starting with a

97% yield at the SMT process, operations at the back end suffered a significant drop –

for example, Interactive and Cross Functional Check only had a 81% yield.

These findings narrowed the focus for our case and helped to define and pinpoint the problems

that the company faced.

Based on the observations of the uneven process flow and the high number of WIP level, the

wireless mobile device manufacturing process was found to be inefficient, accounting for the

large proportion of time dominated by non-value added activities. A declining yield within the

process and a substantial level of defect rate from the Product-Defect Rate Analysis also

indicated that manufacturing operation had much room for improvement. The inefficiency of the

process and the unacceptable quality of the products imposed a negative effect on the

company‟s objectives in reducing cost and maximizing financial return. Thus, from this analysis,

our objectives for the case were set clearly. The goals were to achieve an even, leveled-out

flow as well as to improve the yield of the manufacturing process – by eliminating waste

activities and minimizing the number of defects.


3.2.2 The Measure Phase

The second phase of the DMAIC cycle is the Measure phase. The development of the process

map and the problem definition from the Define phase enabled us to proceed to the Measure

phase of the DMAIC framework. We have identified from the Define phase that a large number

of defects and process inefficiency were our problem areas to focus on for the case. In the

Measure phase, our goal was to validate and further develop the identified problem areas, as

well as to gain a deeper understanding of the problems. To achieve this goal, we collected

historical raw data and conducted further analysis. The set of data served important as it laid

the foundation for the work that was to be conducted in the later stages of the DMAIC

framework, when the problem origin would be identified and the required improvement

measures would be proposed.

3.2.2.1 Defect Data and Analysis

To tackle the defect rate problem, 25 weeks of defect figures for Product E were collected. After

a thorough examination of the data, we compiled an overall yield graph and the defect origin

breakdown Pareto chart for the process, as shown in Figures 16 and 17. The manufacturing

process of Product E produced, on average, 2,000 defects per week, accounting for a 30%

defect rate and a Defects Per Million Opportunities (DPMO) of 600 - 700. Relative to the 90%

target process yield set and an optimal 6-sigma quality standard of 3.4 Non-Conforming Parts

Per Million (NCCPM), the poor yield and the high defect rate level gave an indication of the

incapability and ineffectiveness of the company‟s operation. Based on the Pareto chart, a

significant portion of the defect origin was composed of process and component defects. In

developing a more comprehensive understanding and insight into these problem areas, Pareto

charts for the process defects and the component defects were constructed (Figure 18 and 19).

Missing Flow Checks and damaged parts or components are the major contributors to process

defects whereas the majority of component defects were mostly cosmetically related.


Figure 16. Overall Yield Graph for Product E

Defect Origin BreakDown - Product E

20032

117569306

4064

500

43.9%

69.6%

90.0%

98.9% 100.0%

0

15000

30000

45000

60000

TEST COMPONENT PROCESS SMT OPERATOR

RELATED

Defect Origin

Un

its

0.0%

25.0%

50.0%

75.0%

100.0%

Pe

rce

nta

ge

Figure 17: Defect Origin Breakdown for Product E


Process Defect Origin BreakDown - Product E

4340

20431621

717 585

46.6%

68.6%

86.0%

93.7%

100.0%

0

3000

6000

9000

12000

Damaged

Components

Contaminated

Component

Failed

Flow Checks

Housing

Mismatch / Gaps

Missing

Components

Defect Origin

Un

its

0.0%

25.0%

50.0%

75.0%

100.0%

Pe

rce

nta

ge

Figure 18: Process Defect Origin Breakdown for Product E

Component Defect Origin BreakDown - Product E

7030

3104

1094

295 233

59.8%

86.2%

95.5%98.0% 100.0%

0

4000

8000

12000

16000

Defective Lens Defective

Top/Bottom Housing

Defective SMT

Components

Defective LCD Defective Keypad

Defect Origin

Un

its

0.0%

25.0%

50.0%

75.0%

100.0%

Pe

rce

nta

ge

Figure 19: Component Defect Origin Breakdown for Product E


3.2.2.2 Cycle Time and Takt Time Analysis

The cycle time is the time it takes for one unit of product to go through the manufacturing

process. The Takt time is the rate of customer demand. Ideally, for a lean process, the cycle

time of each step should be equal to the Takt time – in other words, the product should be

produced at the rate that the customer is demanding the product. Producing faster than the Takt

time creates overproduction; while producing slower than the Takt time leads to bottlenecks.

The Takt time should be used to determine the rate of production.

Figure 20 below shows the cycle times of each step in the assembly operation process. The

Takt time was found to be 96 seconds. On Figure 20, one can see the discrepancies between

the cycle times and Takt time for each step in the process. The earlier steps‟ cycle times were

found to be much shorter than the Takt time, while the later steps‟ cycle times were longer than

the Takt time. These discrepancies led to the build-up of WIP inventory.

Product E Assembly Operations Cycle Time

139

139

83

97 97

110 112

104

0

20

40

60

80

100

120

SMT

(Side 1)

Inspection

(Side 1)

SMT

(Side 2)

Inspection

(Side 2)

BLT Assembly

Op. 1

Assembly

Op. 2

Assembly

Op. 3

Interactive

Test & CFT

Softw are

Config.

Process Steps

Cycle

Tim

e [

sec]

Takt Time = 96 sec

Product E Assembly Operations Cycle Time

139

139

83

97 97

110 112

104

0

20

40

60

80

100

120

SMT

(Side 1)

Inspection

(Side 1)

SMT

(Side 2)

Inspection

(Side 2)

BLT Assembly

Op. 1

Assembly

Op. 2

Assembly

Op. 3

Interactive

Test & CFT

Softw are

Config.

Process Steps

Cycle

Tim

e [

sec]

Takt Time = 96 sec

Figure 20: Comparing the cycles times and Takt time of the assembly steps for Product E


3.2.3 The Analyze Phase

The third phase of the DMAIC cycle is the Analyze phase. After we gathered preliminary data,

refined the scope of our problem and established the process capability from the Measure

phase, we proceeded to the Analyze phase to locate the root causes of the problems. In the

Analyze phase, we gathered more data, identified possible sources that caused the problem,

and performed extensive analysis to determine the parameters that contributed most

significantly to the process and product quality. The goal was to select critical-to-quality

parameters to consider for improvement in the subsequent Improve phase. For our case, the

majority of the analysis has been facilitated by the application of Statistical Process Control

(SPC). SPC is, as defined by Montgomery, “a powerful collection of problem-solving tools

useful in achieving process stability and improving capability through the reduction of variability.”

A number of SPC tools, including the Cause-and-Effect Diagram and the Control Chart, will be

used throughout the Analyze Phase to identify, validate and determine the effects of the

potential causes to the problems.

3.2.3.1 Cause-and-Effect Analysis

With limited knowledge of the mobile device manufacturing process, it was decided that we

should begin our analysis with the development of a cause-and-effect diagram. The cause-and-

effect diagram is a common SPC tool used to visually display potential causes. Reference was

made to Introduction to Statistical Quality Control and the 7-step methodology of How to

Construct a Cause-and-Effect Diagram was followed. The diagrams have been developed

based on our observations at on-site visits and discussions with groups of experienced

individuals from the company. The Cause-and-Effect diagrams for Manufacturing Defects and

Process Inefficiency are illustrated in Figure 21 and Figure 22 respectively. In conducting the

analysis, 5 main categories were created, including Machine/Equipment, Raw

Material/Component, Methods, Measurements and Personnel. Generated under each of the 5

categories were causes that contributed to the problems.


Manufacturing Defects

Maintainance

Machine Capability (SMT)

Equipment Storage

Lot Variability

Operator Variability

Faulty measuring gauges

Specification Limit

Supplier Process Capability

Workload / Shift Scheduling

Inadequate Supervision

Insufficient Training

Line Configuration

Environment Tidiness

Unclear Operating Procedure

Materials Identification

Materials Handling / Storage

Imbalance Line Flow

Methods

Personnel

Raw Material/Component

Measurements

Machine/Equipment

Environment

Manufacturing Defects Cause-and-Effect Diagram

Figure 21: Cause-and-Effect Diagram for Manufacturing Defect

high WIP Level)(Non Value Added Activities,Process Inefficiency

Batch Production

Inflexible Changeover

inspection standard

Non-standardized

Communcation to Supplier

Delayed Feedback

Feedback

Ineffective Problem

Minimal Communcation

Task Unevenly Distributed

Inbalance Workload

Procedure not updated in timely manner

Production Scheduling

Line Configuration

Unclear Operating Procedure

Materials Identif ication

Materials Handling / Storage

Imbalance Line Flow

Methods

Personnel

Raw Material/Component

Measurements

Machine/Equipment

Environment

Process Inefficiency Cause-and-Effect Diagram

Figure 22: Cause-and-Effect Diagram for Process Efficiency


The Cause-and-Effect Diagrams gave a useful preliminary diagnosis of the causes in the two

problem areas. In general, imbalanced line flow and uneven workload distribution were the

most notable causes that contributed to the high defect level and inefficiency of the process.

The inability to achieve a balanced and smooth production flow, as revealed by the huge cycle

time variation and high WIP level, led to other problems in the process, like overburdening

operators and machines.

Ineffective handling and storage of materials and work in progress components were significant

causes that contributed to process problems such as missed flow checks and missing or

damaged components. These causes, considered as „wastes‟ in a lean process, are closely

related to the manufacturing practices, layout design of the facility and process design. By

applying Lean Principles, these wastes can be significantly reduced. These Lean improvement

measures will be discussed in detail in the Improve phase of the case.

We also identified a number of causes that revolved around personnel performance and raw

material / supplier capability. To generate a conclusion on the most significant causes in these

areas, it was necessary to collect and examine data related to these causes. The second part

of the analysis thus directs attention to statistical analysis on the identified causes in these

areas. The analysis was based on set of historical yield/defect data, as well as housing length

inspection data collected. We hoped to determine the stability of the process, as well as the

effects that supplier capability and operators variability impose on the process.

3.2.3.2 Statistical Interpretation and Analysis

The cause-and-effect diagrams revealed a set of potential causes that contributed to the high

defect level and process inefficiency of the company. However, according to Arnheiter and

Maleyeff in The Integration of Lean Management and Six Sigma, a Lean Six Sigma organization

should “stress data-driven methodologies in all decision making, so that changes are based on

scientific rather than ad hoc studies.” Extensive statistical analysis was therefore conducted on

the data collected on various aspects of the manufacturing process. The analysis provided a

more quantitative approach and perspective of the problem. The analysis focused on 2 core

areas, addressing the manufacturing stability and factor effects on the process. The p-Chart

and Analysis of Variance (ANOVA) technique were the tools used in conducting the analysis.


3.2.3.2.1 Manufacturing Process Stability

A prerequisite for employing any kind of improvement or enhancement tool is a stable and in-

control process. For achieving this, the control chart is one of the most widely-used tools in the

industry for providing an on-line process-monitoring mechanism. As a graphical display of

specific process parameters, the control chart shows the occurrences of process variability and

instability. The information from control charts is also useful for determining the capability of the

process for the hypothesis testing technique.

For our case, we have made use of a specific type of control chart called the p-chart in

evaluating the stability of the process. Constructed with the collected historical yield/defect data

for Product E, the p-chart features a plot of the fraction nonconforming. The fraction

nonconforming refers to the ratio of the number of defectives to the total number of processed

units. Since production volume varied in each monitored time period, we have taken variable

sample size into account in developing the p-chart.

3.2.3.2.1.1 p-Chart Analysis on Process Defects

The data table for the Process Defects and the respective p-chart are illustrated in Table 5 and

Figure 23. The p-chart reveals that 16 out of the 25 sample data points are out of the 3-sigma

control limits. Without additional data available, we were unable to perform further investigation

on why the process was out of control. Despite this, one can see that the process had

considerable fluctuations in its fraction nonconforming and it was instable as almost ¾ of the

data points fell outside the 3-sigma control limits. Because the operators were responsible for

identifying defects (by rejecting lots in a lot sampling process), the instability of the process led

to the suggestion that the variability of observations in between operators led to the observation

of an instable process. This became the focus of our analysis for the next section. Our findings

indicated that the company lacked the controllability and consistency in its operation.


Week i Sample

Size, ni

# of Nonconforming,

Di

Sample Fraction

Nonconforming,

pi^

= Di / ni

Standard Deviation,

p^

= [ p̄ ( 1-p̄ ) / ni ]

½

LCL,

p̄ - 3 p^

UCL,

p̄ +

3 p^

p̄ = Σ Di / Σ ni = 0.347863

Wk 29 1 4100 1394 0.3400 0.0074 0.3255 0.3702 Wk 30 2 4285 1478 0.3449 0.0073 0.3260 0.3697 Wk 31 3 4367 1476 0.3380 0.0072 0.3262 0.3695 Wk 32 4 4430 1431 0.3230 0.0072 0.3264 0.3693 Wk 33 5 6091 1876 0.3080 0.0061 0.3296 0.3662 Wk 34 6 6962 2402 0.3450 0.0057 0.3307 0.3650 Wk 35 7 5140 1563 0.3041 0.0066 0.3279 0.3678 Wk 36 8 5538 1600 0.2889 0.0064 0.3287 0.3671 Wk 37 9 6824 2286 0.3350 0.0058 0.3306 0.3652 Wk 38 10 5870 1984 0.3380 0.0062 0.3292 0.3665 Wk 39 11 5111 1988 0.3890 0.0067 0.3279 0.3678 Wk 40 12 4907 1565 0.3189 0.0068 0.3275 0.3683 Wk 41 13 4983 1540 0.3091 0.0067 0.3276 0.3681 Wk 42 14 4992 1483 0.2971 0.0067 0.3276 0.3681 Wk 43 15 4851 1771 0.3651 0.0068 0.3273 0.3684 Wk 44 16 5483 1870 0.3411 0.0064 0.3286 0.3672 Wk 45 17 6478 2423 0.3740 0.0059 0.3301 0.3656 Wk 46 18 5635 2417 0.4290 0.0063 0.3288 0.3669 Wk 47 19 5455 2395 0.4390 0.0064 0.3285 0.3672 Wk 48 20 4396 1780 0.4049 0.0072 0.3263 0.3694 Wk 49 21 5071 2125 0.4190 0.0067 0.3278 0.3679 Wk 50 22 5044 1942 0.3850 0.0067 0.3277 0.3680 Wk 51 23 5978 1764 0.2951 0.0062 0.3294 0.3663 Wk 52 24 4483 1690 0.3770 0.0071 0.3265 0.3692 Wk 53 25 4783 1416 0.2960 0.0069 0.3272 0.3685

Σ ni =

131,257 Σ Di = 45,659

Table 5: Data Table for Process Defects

Sample

Sa

mp

le F

ract

ion

No

nco

nfo

rmin

g

24222018161412108642

0.450

0.425

0.400

0.375

0.350

0.325

0.300

_P=0.3479

UCL=0.3685

LCL=0.3272

p-Chart of # Nonconforming

Figure 23: p-Chart for Sample Fraction Nonconforming


3.2.3.2.2 Analysis of Variance of Operators and Supplier lots

As recalled from the Lean Six Sigma definition, one of the tenets of the approach is to reduce

variations in processes. Variations of a process stem from various sources that include out-of-

control machines, operator errors, or defective raw materials, and the cumulative effects of all

these variations result in an unacceptable level of process performance. We have identified

from the measure phase that there was a substantial level of component defects and we

proposed that the variability of the operators‟ inspection standards and lot variability from

suppliers were the two most probable causes that led to the defects. We made use of ANOVA

to validate the two potential causes and to evaluate the effects the variability had on the process

and part components. The analysis was done using the housing length raw data collected on

sample inspections by operators. The raw data can be found in Appendix C.

3.2.3.2.2.1 Inapplicability of Control Charts

The application of control charts was initially proposed for supplier process capability, defect

and nonconformance analyses. However, after further review and consultation with our

supervisor, we found that the use of control charts was unsuitable, due to the loss of the time

effect. The control chart is an on-line monitoring technique that plots process output samples

collected over a regular sampling interval. Even though the collected housing length data from

the supplier lots were inspected in a First-In-First-Out (FIFO) method, there was no knowledge

of the time order in which the supplier produced the lots. With the loss of the time effect, the

control chart is not a proper tool to be used in conducting process capability assessment in our

case.

3.2.3.2.2.2 Analysis of Variance (ANOVA)

The purpose of ANOVA is to evaluate the consistency/variability in between different levels

(treatments) of a factor of interest. Individual operators and supplier lots were the factors

considered in our case. The analysis was based on observations made in each level of the

factor. The observations can be represented in a linear statistical model:


yij = μ + ζi + εij where i = 1,2,…,a j = 1,2,…,n yij = the score of the j observations in the i level μ = the mean of all levels (the whole population) ζi = the parameter associated with the effect of the i level εij = the sum of all other effect, also known as error effect Assumption:

1. Error is normally distributed and independently distributed with mean zero and variance σ2

The variability of the different levels of the factor is then computed by testing the equality of the

population means through hypothesis testing. The hypothesis tested is represented as:

H0: ζ1 = ζ2 = … = ζa = 0

H1: ζ1 ≠ 0 for at least one i

If the null hypothesis is true, the levels of the factor of interest can be concluded as consistent

and changes in between levels have no effect on the mean response; whereas if the hypothesis

is rejected, it can be deduced that the factor has a significant effect on the response.

The fundamental technique for the testing is a partitioning of the total sum of squares related to

the effects in the model used. With the sum of squares, the test statistics F0 is computed. For

F0 > Fα, a-1, a(n-1), H0 would be rejected. The formula for the Sum of Squares and the test statistics

F0 are summarized as follows:

SSTotal = Σ Σ yij2 – ytotal

2 / N SSTreatment = Σ yi total

2 / n - ytotal2 / N

SSE = SSTotal - SSTreatment

F0 = [ SSTreatment / (a-1) ] / [ SSE / a(n-1) ]

For our case, ANOVA was conducted on the variability of the operators in conducting parts

inspection as well as the lot variability from the supplier.


3.2.3.2.2.3 One-Way ANOVA – Inspection Operators

Component defects were detected and recorded by operators during the assembly process,

thus the variability of operators‟ methods of conducting the inspection may pose a significant

effect on the process defect rate. Please refer to the sample observations collected for 5

different operators in Table 4.

Observations

1 2 3 4 5 6 7 8 9 10 Totals Averages

Operator 1 109.94 109.94 109.97 109.92 109.91 109.88 109.8 109.99 109.89 110.11 1,099.35 109.94

Operator 2 109.92 109.89 110.06 109.91 109.92 110.04 110.01 110.05 110.08 110.04 1,099.92 109.99

Operator 3 109.93 110.02 109.88 109.9 109.95 110.19 110.12 110.13 110.2 110.15 1,100.47 110.05

Operator 4 110.14 110.13 110.15 110.17 110.1 110.07 110.05 110.05 110.06 110.15 1,101.07 110.11

Operator 5 109.99 109.98 110 110.02 110.01 109.78 109.95 109.82 109.8 109.8 1,099.15 109.92

5,499.96 110

Table 6: Housing Length Observations from 5 Operators

We have conducted ANOVA with the Minitab software package and the result is presented in

below. Since Fo = 7.89 > F0.01,4,45 = 3.76, the null hypothesis H0: ζ1 = 0 is rejected. Thus, it can

be concluded that there was a substantial variability in between operators in conducting the

inspection; the variability did have an effect on the mean measurement of the housing lengths.

This result suggests that the inspection training of the company might be ineffective or some

operators lack experience in taking measurements, based on the substantial variability shown in

between the inspection of the operators, which can be clearly seen in the box plot shown in

Figure 25.

One-way Analysis of Variance Analysis of Variance for Measurement

Source DF SS MS F P

Operator 4 0.25169 0.06292 7.89 0.000

Error 45 0.35868 0.00797

Total 49 0.61037

Individual 95% CIs For Mean

Based on Pooled StDev

Level N Mean StDev -----+---------+---------+---------+-

Operator 10 109.935 0.081 (-----*----)

Operator 10 109.992 0.073 (----*-----)

Operator 10 110.047 0.125 (-----*----)

Operator 10 110.107 0.046 (-----*----)

Operator 10 109.915 0.101 (----*-----)

-----+---------+---------+---------+-

Pooled StDev = 0.089 109.90 110.00 110.10 110.20


The Normal and Residual plots on Figure 24 confirm the normality assumption for operator

measurements.

Residual

Pe

rce

nt

0.20.10.0-0.1-0.2

99

90

50

10

1

Fitted Value

Re

sid

ua

l

110.10110.05110.00109.95109.90

0.2

0.1

0.0

-0.1

-0.2

Residual

Fre

qu

en

cy

0.160.080.00-0.08-0.16

10.0

7.5

5.0

2.5

0.0

Observation Order

Re

sid

ua

l

50454035302520151051

0.2

0.1

0.0

-0.1

-0.2



Residual Plots for Measurements

Figure 24: Normality Probability Plot and Residual Plot of Operator Measurements

Both the box plot (Figure 25) and the residuals versus factor plot (Figure 26) reveal that

Operator 1 and 3 are subject to relatively a considerable degree of variability, suggesting more

training and standardization may be needed for operators in order to maintain a more consistent

level of performance.


Operator

Me

asu

rem

en

ts

Operator 5Operator 4Operator 3Operator 2Operator 1

110.2

110.1

110.0

109.9

109.8

Boxplot of Measurements by Operator

Figure 25: Box Plot of Measurements by Operator

Operator

Re

sid

ua

l

54321

0.2

0.1

0.0

-0.1

-0.2

Residuals Versus Factor (Operator)

Figure 26: Plot of residuals versus factor levels


3.2.3.2.2.4 One-Way ANOVA – Supplier Lots

The ANOVA for supplier lots was performed in the same manner as the ANOVA done on

operator measurements. We have deduced from the above analysis that there was a

substantial effect from the variability in between operators. To provide a more precise

assessment of the variability effect of the supplier lots, the data used for this part of the analysis

was limited to the measurements taken by one specific operator (we chose the operator who

took most measurements), so the effect of operator variability was eliminated. The raw data is

presented in Table 8. From the Normality and Residual plots, the data is shown to comply with

the normality assumption of the model. The ANOVA result is shown below:

The test statistics Fo was computed and Fo = 2.84 < F0.01,8,36 = 3.05. Contrary to the previous

case, we were unable to reject the null hypothesis of H0: ζ1 = 0, suggesting that there was no

significant effect from the variability between the supplier lots. One can see from the box plot

shown on Figure 27 that the variability between supplier lots were not as substantial as

variability between operators inspection results (please see the box plot in Figure 25).

However, the analysis did not provide an indication of the supplier process capability. Thus, it

could be assumed that the operators‟ variability in taking measurements was the most likely

cause for the substantial defect level.

One-way Analysis of Variance Source DF SS MS F P

Lot 8 0.03392 0.00424 2.84 0.015

Error 36 0.05380 0.00149

Total 44 0.08772

S = 0.03866 R-Sq = 38.67% R-Sq(adj) = 25.04%

Individual 95% CIs For Mean Based on Pooled

StDev

Level N Mean StDev -+---------+---------+---------+--------

1 5 110.138 0.026 (-------*--------)

2 5 110.102 0.040 (-------*--------)

3 5 110.144 0.046 (--------*--------)

4 5 110.084 0.042 (--------*--------)

5 5 110.132 0.055 (--------*--------)

6 5 110.090 0.028 (-------*--------)

7 5 110.072 0.030 (--------*--------)

8 5 110.074 0.028 (-------*--------)

9 5 110.076 0.042 (--------*--------)

-+---------+---------+---------+--------

110.040 110.080 110.120 110.160

Pooled StDev = 0.039


Lot 1 Lot 2

110.14 110.13 110.15 110.17 110.10 110.05 110.07 110.14 110.13 110.12

Lot 3 Lot 4

110.11 110.18 110.17 110.18 110.08 110.08 110.06 110.03 110.12 110.13

Lot 5 Lot 6

110.10 110.16 110.18 110.17 110.05 110.11 110.11 110.11 110.05 110.07

Lot 7 Lot 8

110.06 110.12 110.08 110.06 110.04 110.06 110.12 110.08 110.05 110.06

Lot 9

110.07 110.05 110.05 110.06 110.15

Table 7: Sample Housing Length Measurement from 9 Lots

Sample

Ho

usin

g L

en

gth

[m

m]

987654321

110.20

110.15

110.10

110.05

Boxplot for Lot

Figure 27: Box Plot of Measurements Between Supplier Lots


3.2.4 The Improve Phase

The fourth phase of the DMAIC methodology is the Improve phase. In this phase, we will

suggest improvements for management to implement. Normally, the improve phase consists

the following activities: screening causes that affect the process, identifying optimal setting of

process parameters, discovering variable relationship, and establishing revised operating

tolerances (Banuelas, 2005). However, due to the time constraint and academic nature of our

project, the scope of our thesis project does not include the actual implementation of the

improvements; we will only propose a list of improvements as guidelines for the company to

follow.

From the analyze phase, we have found the process parameters that most significantly affect

quality. They are:

Imbalanced line flow

Uneven workload distribution

Ineffective handling and storage of materials and work in progress components

Personnel performance

Raw material / supplier capability

From Figure 17 in section 5.2.2.1, one can see that the most defects were found at the Board

Level Test – the defect being the failure of the circuit board within the device. These defects

accounted for 43.9% of the defect origins for Product E. The production of the circuit boards

was outsourced, and the boards arrived at the company as incoming parts. Similarly, the

production of other components contained in the device were also outsourced and supplied to

the company as incoming parts. The defects found in these components formed the second

highest defect category, making up 25.7% of the defect origins. Together, these two categories

of defects accounted for 69.6% of the total number of defects. This high percentage suggests

that the quality of incoming parts must be drastically improved. To achieve this, the process

parameter of raw material/supplier capability should be examined. The company should

establish a closer relationship with suppliers to ensure the excellence in quality of incoming

parts.

Additionally, one can note from Figure 17 that the third highest defect origin category is process

defects. These include missed Cross Functional Tests (CFTs) and missing components in the


devices. These errors are not unavoidable and can be eliminated or reduced with

improvements in the process parameters of imbalanced line flow, uneven workload distribution,

ineffective handling and storage of materials and work in progress components, and personnel

performance.

The 14 Lean Management principles of the Toyota Way, as aforementioned in Section 4.2.2,

provide a framework for establishing a close relationship with suppliers and improving the

process. Therefore, we will refer to these principles as we propose improvements to the

company.

3.2.4.1 Section I: Long-Term Philosophy

The first „P‟ in the “4P” model (please refer to Section 4.2.2 for the model) stands for

„Philosophy‟. There is only one principle that belongs to this section; nevertheless, it is a crucial

one and it must be followed before one proceeds to the other principles. The first principle sets

the foundation for lean transformation. If a company does not adopt a long-term philosophy,

implementation efforts will only be superficial and will be abandoned when difficulties are

encountered.

Principle 1: Base Management Decisions on a Long-Term Philosophy, Even at the

Expense of Short-Term Financial Goals

In an interview with a manager at the wireless mobile manufacturing company, we were told that

the underlying cause of the quality problems was the company‟s overzealous drive for

production. Demand was strong for the company‟s products – there were booming demands in

terms of volume and novelty. The company was, of course, pleased that demand was strong

and did not want to miss the opportunity to achieve great financial gains. In an effort to

generate enough supply to meet this demand, management had chosen to place emphasis on

the production volume, not quality. Because of this, the quality of the products suffered, leading

to a high number of defects, a massive buildup of defective products waiting to be repaired, and

a huge scrap rate as non-repaired obsolete models were discarded when new models were

introduced. As one can see, the company‟s management decision was the heart of the

problem. To fix this problem at the root, the company has to follow Toyota‟s footsteps and


implement Principle 1 of the Toyota Way: base management decisions on a long-term

philosophy, even at the expense of short-term financial goals.

What should the long-term philosophy be? It could only be beneficial to consult the mission

statement of Toyota Motor Manufacturing North America. The mission has three parts (Liker,

2003):

1. Contribute to the economic growth of the country in which it is located (external

stakeholders).

2. Contribute to the stability and well being of team members (internal stakeholders).

3. Contribute to the overall growth of Toyota.

The mission points out that the Toyota‟s ultimate goal is to contribute to society; without this

contribution, it cannot contribute to its external or internal stakeholders. The desire to foster

growth in society gives the company motivation to make superior products. Toyota workers are

encouraged to contribute to Toyota, to grow and learn, and to build lasting relationships with

customers. Moreover, Toyota feels responsible to achieve stable, long-term growth, and mutual

benefits for its stakeholders and partners.

To achieve drastic improvements in product and process quality, the wireless mobile

manufacturing company must first adopt a long-term philosophy like Toyota‟s. This

philosophical sense of purpose should supersede any short-term decision-making, even at the

expense of short-term financial goals (Liker, 2003). Then, all management decisions should be

driven by the philosophy. The company should not be focusing on producing an enormous

volume of products to meet demand, but rather, being responsible to society, customers,

employees and stakeholders. With this mindset, the benefits of implementing lean tools will be

long lasting; superior-quality products will be made, waste will be eliminated, and employees will

be empowered. Transforming into a lean organization is not a quick and easy task. Only with a

long-term philosophy would the company persevere and succeed with the transformation.

3.2.4.2 Section II: The Right Process Will Produce the Right Results

The second „P‟ of the “4P Model” stands for „Process‟. There are seven principles in this section

to guide companies in the redesign of work processes to achieve high value-added, continuous

flow. Toyota Production System (TPS) tools are introduced in this section, guiding companies


to adopt Toyota‟s unique approach to manufacturing excellent products with an efficient

process.

Principle 2: Create Continuous Process Flow to Bring Problems to the Surface

A high work-in-progress inventory level can be observed at the wireless mobile manufacturing

company. This is because operators are instructed to perform their tasks as quickly as

possible. Since some operations require more time than others, there is a buildup of WIP at the

more time-consuming operations as parts arrive at a rate quicker than the production rate. Also,

there is an accumulation of WIP at the repair station since there are more defected products

than the repair station staff can handle. Eventually, some of these defected products remain

unfixed, and are tossed out because the model becomes obsolete with the introduction of new

models. Much waste is created.

Waste elimination is the ultimate goal of lean manufacturing. Seven types of non-value-adding

wastes were identified by Toyota (Liker, 2003):

1. Overproduction: producing items when there is no demand, which generates more

wastes such as excess inventory, overstaffing, transportation, and storage costs

2. Waiting: workers being idle due to a lack of smoothness in production flow

3. Unnecessary transport: transporting WIP long distances around the facility, or moving

finished goods into storage areas

4. Overprocessing: taking unnecessary steps to process products or parts, e.g. repairing

defective products is an example of overprocessing

5. Excess inventory: having excess raw material, WIP, or finished goods is a waste of

space, and holds up the company‟s capital; also, with inventory as buffer stock,

employees do not feed the immediate urge to solve problems to produce products to

meet demand

6. Unnecessary movement: employees are wasting effort to create unnecessary movement

to perform operations

7. Defects: producing defective parts not only lead to unnecessary inspection and rework, it

also damages the company‟s relationship with the customer if they products are shipped

The batch-and-queue process at the wireless mobile manufacturing company has resulted in

many of these wastes. One can observe all of the seven wastes from the company‟s


manufacturing process. Work processes must be redesigned to eliminate wastes, such that

only value-added activities are included in the process. To do this, the company must

understand the concept of „flow‟.

Flow means minimizing the time of transforming raw materials to finished goods, with no waiting

time in between operations. This will lead to the highest quality, lowest cost, and shortest

delivery time. Flow also enforces the implementation of lean tools and philosophies such as

preventative maintenance and built-in quality (Liker, 2003). In the effort of creating seamless

flow from operation to operation, achieving flow – both material and information flow – exposes

problems that demand immediate solutions. The problems have to be fixed before operations

can be continued; the process will shut down if they are not fixed right away. In traditional mass

production processes, problems are hidden because it is assumed that a process takes days or

even weeks to complete. By the time the problems are discovered, large batches of defects

products or parts are already made. In contrast, a lean process only takes a few hours to

accomplish the same result. Problems surface more quickly, and are dealt with before any

more defected products are processed. This is why lean companies achieve better product

quality with flow.

To achieve flow, the company should follow this process: when the customer places an order,

obtain the raw materials needed just for the customer‟s order from the suppliers (the customer

order is a trigger to start production; if there is no demand, production should not be initiated).

The raw materials then flow immediately to the plant, where workers assemble the order, and

the completed order flows immediately to the customer. Throughout this process, parts can

continuously being moved. Nothing should be idle or waiting to be processed.

Flow requires reorganizing the layout of assembly lines at the company. U-shaped lean

manufacturing cells can be put in place to enforce flow. Cellular manufacturing (also known as

group technology) groups together products or parts with similar design and manufacturing

processes. Each cell is a combination of an assembly line (layout by product) and a job shop

(layout by process), achieving the high efficiency of an assembly line and the ability to produce

a high variety of products as a job shop. With cellular manufacturing, parts need not be

transported from department to department for processing. The entire manufacturing process is

performed within the cell. The waste of unnecessary transportation is eliminated. A U-shaped


line like the one shown in Figure 21 will link processes and people together such that problems

are detected immediately.

Principe 3: Use “Pull” System to Avoid Overproduction

The wireless handheld manufacturing company places emphasis on producing huge volumes of

products to meet high demand. This has led to overproduction, which is considered the

„fundamental‟ waste as it leads to the other types of wastes (Liker, 2003). In our case, it has led

to:

1. Waiting (products waiting to be processed or repaired)

2. Unnecessary transport (transporting defected parts to the repair department, and back to

the assembly line or packing after repair)

3. Overprocessing (repairing and re-testing are examples of overprocessing)

4. Excess inventory (excess WIP inventory is observed at bottleneck assembly and repair

stations)

5. Unnecessary movement (workers have to physically transport parts to and from the

repair station, causing unnecessary movement)

SM

T

Bo

ard

Test

Pa

ckin

g

Testin

g

Assembly 1

Assembly 2

Assembly 3

Assembly 4

Figure 28: U-Shaped Lean Manufacturing Cell


6. Defects (there is a high defect rate, based on our observations and data provided by the

company)

These wastes are results of a „push‟ system, where the company initiates production according

to demand forecasts, pushes production to the maximum capacity, and stores excess products

that are not shipped to the customer as inventory. To eliminate these wastes, the company

should adopt a „pull‟ system. The „pull‟ system follows the principle of just-in-time

manufacturing: “provide the downline customers with what they want, when they want it, and in

the amount they want” (Liker, 2003). Material is replenished as it is consumed. Figure 29

shows the difference between the push and pull systems.

Figure 29: Push Versus Pull System (Regani, 2004)

The ultimate form of pull is one-piece flow, where only one work piece moves between

operations in a cell at a time. There is absolutely no WIP inventory in a one-piece flow cell.

However, if implementing one-piece flow is not realistic for the company, we suggest that the

company minimize work in process and finished-goods inventory by stocking only small

amounts of the products; the company can restock frequently in small batches based on the

amount shipped to the customer. Toyota invented the Kanban system to facilitate this pull

system. „Kanban‟ is a Japanese word for card, but in general, it means a signal to say parts

have been consumed and need to be replenished. The Kanban contains detailed information

regarding the parts and amount required. Kanbans can be used internally between operation

stations, as well as externally between the company and its suppliers. With a Kanban system in

place, the company would not need complex systems for inventory tracking.

Raw Materials

Parts Manufacture Assembly Product

Demand Assembly

Parts Supply

Raw Materials

PUSH SYSTEM

PULL SYSTEM


Principle 4: Level Out the Workload (Heijunka)

In the measure phase of the DMAIC cycle, we found that most process defects come from

missed cross-functional tests and missing components in devices. The cause for these defects

is that in the attempt to drive production to its maximum capacity, the company has

overburdened its workers. To avoid this, the company should follow Principle 4 to level out the

workload for its employees.

According to Toyota, eliminating waste is just one-third of the equation for making lean

successful. Eliminating overburden to people and equipment and eliminating unevenness in the

production schedule are just as important. The Toyota Way encourages the elimination of the

three M‟s – Muda, Muri, and Mura. Muda means waste, the seven types of wastes defined in

Principle 2. Muri is overburdening people or equipment. Overburdening people causes safety

and quality problems, while overburdening equipment results in breakdowns and defects. Mura

is the unevenness in production schedule. In traditional manufacturing systems like the wireless

mobile manufacturing company, production schedule is planned according to demand and

production volume can fluctuate dramatically. There are times when the demand is high and

the company has to produce more than its staff or equipment can handle; on the other hand,

when demand is low, its staff or equipment would be idle (Liker, 2003). Mura is therefore

related to muri as people and machines are overburdened when there is a push for production.

Mura is also related to muda as the company must be equipped with the personnel, equipment

and materials for the highest level of production, although they are not in use most of the time.

During the times when demand is not at its peak, the resources are waiting to be used – they

are considered wastes.

To eliminate mura, which would in turn eliminate muri and muda, Toyota invented the concept of

heijunka, which translates to “leveling out the workload”. The idea is to “work like the tortoise,

not the hare.” As explained by Taiichi Ohno, the inventor of TPS: “The slower but consistent

tortoise causes less waste and is much more desirable than the speedy hare that races ahead

and then stops occasionally to doze. The Toyota Production System can be realized only when

all the workers become tortoises.” (Ohno, 1988)

Heijunka levels the production volume and product mix. To adopt heijunka, the company should

consolidate customer orders over a period of time and level them out such that the same


volume and product mix are produced each day. Traditional production is unleveled, where

product A is produced in large batches, then changeover occurs, product B is produced in large

batches, and so on. Figure 30 shows a diagram of an unleveled process. Large batches of

each product are produced before changing over to the next product.

Figure 30: Traditional, unleveled production Figure 31: Traditional, unleveled production (Liker, 2003) (Liker, 2003)

Instead of running an unleveled production schedule, the company should opt of a leveled,

mixed-model production, like the one shown in Figure 31. Here, a small batch of every product

mix is built every day. With a leveled schedule, the company is flexible to changing customer

demands – for example, if the customer decides that he wants a large-sized product on

Monday, the company will have the product ready. The use of labour and machines is

balanced, reducing the stress on employees and equipment, so employees are less likely to

make mistakes and equipment is less likely to break down. Also, the demand is smoother for

upstream processes and the plant‟s suppliers, allowing the company to obtain raw materials or

parts from its upstream processes and suppliers more quickly and economically, as the

suppliers do not have to store as much inventory as well (Liker, 2003).

One thing to keep in mind is that a leveled, mixed-model production would only be effective if

the company can achieve quick, inexpensive changeovers. Easy retooling is something that the

company has to accomplish through redesign of work processes and/or equipment, and through


sufficient employee training. We suggest that the company spend some time and effort into

accomplishing effortless changeovers, as it is a prerequisite for heijunka, which is needed to

achieve the lean benefits of continuous flow.

Principle 5: Build a Culture of Stopping to Fix Problems, to Get Quality Right the First

Time

Much waste has been created in the wireless mobile manufacturing company because quality

was not right the first time. When defects are discovered, waiting, unnecessary transport, over

processing, excess inventory, and unnecessary movement are created to fix the defects. All of

these wastes can be eliminated if the defects are avoided in the first place.

To do this, the company should encourage workers to stop the process to build in quality. It

may appear counter-intuitive, but lean experts do not consider running production 100% of the

time as a good result because they believe every manufacturing process has problems; if the

process is not shutting down, this means that the problems are hidden. On the contrary, if

problems are revealed and the process is shut down, the problems can be solved and the

process will produce better-quality products more effectively. They believe that the production

lines need to be stopped if continuous improvement was desired.

To achieve this result, the company needs a method to detect defects, so production is

automatically stopped before the product moves on to other operations and more resources are

spent on a defective product. One way to do this is to use “Andon cords” (Andon is a signal for

help with solving quality problems). These are cords on which operators can pull and stop the

assembly line when a defect is detected. Every member on the line would then contribute to fix

the problem, so production can resume as quickly as possible. Stopping the line to fix the

problem and preventing the defect from propagating downstream is more effective and less

costly than having to inspect and repair quality problems after the entire manufacturing process.

Since a lean company keeps very little inventory, there is even more emphasis to get quality

right the first time, as the company has little buffer stock to meet customer demands in case of

quality problems. Each operator feels an immediate sense of responsibility to ensure quality at

each station; he does not mask the problems and continue to produce. By revealing problems

and fixing them at the source, unnecessary waste is avoided and productivity increases


drastically. We suggest that the company put in an Andon system and educate operators so

that they are encouraged to spot and fix problems. Another suggestion is examine the work

process and product design to see if error-proofing can be built in, making it impossible to

commit an error, or easier to detect a mistake.

Principle 6: Standardized Tasks Are the Foundation for Continuous Improvement and

Employee Empowerment

From the analyze phase, we found that operators were not trained to measure incoming parts in

a consistent, standardized way. The measured lengths appeared to be significantly different

from operator to operator, making it difficult to determine the congruency of the incoming lots.

This suggests that the operators‟ experience or education in taking measurements vary

considerably. This suggests that standardized procedures have to be in place before quality

improvement efforts can be implemented.

Standardization is the basis for continuous improvement and quality as it stabilizes the process

and ensures that workers possess the fundamental skills to produce the product. It is

analogous to learning basic swimming strokes before being able to train to swim faster.

Improving a process is not possible until it is standardized. For an unstable process, a shift

would only be considered normal and would not be detected as a variation. The process must

be standardized and stabilized before continuous improvements can be made.

Additionally, standardized work facilitates building in quality. The Six Sigma standard of close-

to-zero defects can only be achieved through standardized work. A standard work sheet is

used to keep track of the standardized process, listing every detailed step of the process. If a

worker is found to be continuously producing defective products, one can compare the worker‟s

tasks to those on the standard work sheet. Any variations can be noted and reported to the

worker to avoid further defects. If the worker followed the standard work sheet and defects still

occur, then the standardized tasks have to be revised to avoid the errors.

We suggest that the company create standard work sheets for every process. Employees can

be trained (or re-trained) using the standard work sheets. The standard work sheet can be

posted such that team leaders can check for discrepancies between the employees‟

performance and the standard tasks.


For standardization to be an enabler of employee empowerment and not a impediment to

employee creativity, he standard work sheets should be specific enough to be useful guides, but

general enough to allow flexibility, creativity and innovation (Liker, 2003). It should be clear that

employees are encouraged to challenge, not blindly follow the standard procedures.

Principle 7: Use Visual Control So No Problems Are Hidden

During our visit at the company‟s manufacturing plant, we noticed that tools were scattered

everywhere, and pieces of paper were strewn over the work surfaces. We observed that

operators had to look for tools and sometimes parts to perform their tasks. We also saw piles of

WIP inventory waiting at workstations, and heaps of defective products waiting to be fixed at the

repair stations. In a disorganized workplace like this, work cannot be performed effectively, and

problems are not apparent to the observer. It is hard to tell where items are supposed to be

placed and which tasks are standardized. With the problems hidden, no one bothers to deal

with them until the problem becomes so severe that it affects daily operations. By then, more

time and cost will have to be spent on fixing the problem.

To avoid this, the company should follow Principle 7 and use visual controls so not problems are

hidden. The first step to take is to clean up the workplace so the visual controls can be seen

clearly. A lean tool that the company can utilize is the 5S system. The five S‟s originally

represent the Japanese words seiri, seiton, seiso, seiketsu, and shitsuke, which respectively

translate to sort, straighten, shine, standardize and sustain. The 5S system effectively

eliminates wastes that lead to errors, defects, and injuries in the workplace (Liker, 2003).

Please refer to Section 5.2.2 for a more detailed description of the 5S System. Figure 32

summarizes the 5S System.

After cleaning up the workplace with the 5S, we suggest that the company use simple visual

control systems to help operators determine whether they are following standard tasks or

deviating from them. Visual control is “any communication device used in the work environment

that tells us at a glance how work should be done and whether it is deviating from the standard.”

It provides a medium for workers to find out how well they are performing their jobs. It contains

information that supports value added flow, such as where items belong, how many items

belong there, what the standard procedure is for a task, the status of work in process, and many

other types of information critical to the flow of work activities (Liker, 2003).


The company should design visuals control such that anyone who looks at a process, machine

or operator can immediately tell whether there is any deviation from the standard work. It is

therefore imperative for the company to first accomplish the objectives of Principle 6 in

achieving standardized work; if not, the information portrayed in visual controls would be

useless. Visual controls also aid in keeping track of inventory levels, as well as managing

projects. In general, visual controls help to foster a transparent and waste-free workplace,

where all variances are exposed and information is displayed clearly. In fact, we have already

discussed some forms of visual controls – lean tools such as Kanban, the one-piece-flow cell,

and andon are all examples of visual controls as they bring problems to the surface.

Liker provided two other suggestions to keep processes simple and visible:

Avoid using a computer screen when it moves the worker‟s focus away from the workplace.

Reduce reports to one piece of paper whenever possible, even for your most important

financial decisions

We believe that it will be beneficial for the company to consider these suggestions.

Figure 32: The 5S System (Liker, 2003)

Sort Clear out rarely

used items

Straighten Organize and

label everything

Shine Keep

everything clean

Standardize Create rules to sustain the first 3 S‟s

Sustain Use regular

management audits to stay disciplined

Eliminate

Waste


Principle 8: Use only reliable, thoroughly tested technology that serves your people and

processes

We are unfamiliar with the technology used on-site at the wireless mobile manufacturing

company, so it would be inappropriate to comment on whether the technology used is reliable

and thoroughly tested. However, given that the company is part of the high-tech wireless

communication device industry, it is reasonable to assume that the company would be likely to

adopt new, cutting-edge technology to support its operations. With this in mind, we would

advise the company to follow Principle 8 and only adopt new technology that harmoniously

supports employees, processes and company values.

Technology should always be used to support people, never to replace them. It is wise to

standardize and stabilize a process manually before adding new technology to improve the

process. If the new technology is not reliable or difficult to standardize, then flow would be

disrupted. The automated process would be worse than the pre-automated manual one.

Hence, it is crucial for the company to thoroughly test new technologies before implementing it.

Through testing, any technology that might disrupt stability, reliability, and predictability should

be rejected or modified. However, this does not mean that the company should not look into

new technology at all. The company is still encouraged to adopt new technologies to improve

workflow processes, as long as it is prudent in choosing them and adapting them appropriately.

3.2.4.3 Section III: Add Value to the Organization by Developing People and Partners

The third „P‟ in the “4P” model stands for “People and Partners”. There are three principles in

this section that document how Toyota respects, challenges, and grows its people and partners.

Many companies neglect this aspect of the Toyota Way as they expend all their efforts on

achieving a lean process. They do not realize that people and partners are just as important as

the process. People are drivers of the lean process and the problem solvers. Without them,

none of the other “P‟s” in the model can be achieved. They must therefore be properly

educated and cultivated to become leaders in the lean initiative.


Principle 9: Grow Leaders Who Thoroughly Understand the Work, Live the Philosophy

and Teach It to Others

A common practice in today‟s business world is to „purchase‟ CEOs from other companies

(based on their accomplishments) in hopes of giving the company a fresh start, in the sense that

a new CEO would lead the company in a new direction. When severe problems appear at a

company, it is not uncommon for the company to replace its CEO, who is trusted to „resuscitate‟

the company. The Toyota Way does not encourage this practice. Instead, it preaches growing

leaders from within. Only when a leader is grown from within would he fully adopt the

philosophy and teach it to others. This leader makes sound management decisions based on

the long-term philosophy and propagates the philosophy across the organization. This leader

would not abandon the philosophy when faced with severe difficulties. This leader is the

epitome, a role model of the company‟s philosophy and way of doing business. Moreover, a

leader promoted from within understands the daily operations of the workplace in greater depth

than a leader purchased from outside the organization. This understanding is the foundation for

making important business decisions that affects day-to-day operations.

Fortunately, the wireless mobile manufacturing company has been growing its leader from

within. Its leader founded the company 23 years ago and still remains the CEO of the company.

We can only suggest that the leader put forth a Toyota-like philosophy that centers around

contributing to society, customers and the economy, make this philosophy known and evident to

the whole enterprise, and promote this philosophy by living it. We also suggest that the

company supports “promoting from within” when making hiring decisions for its managerial

positions.

Principle 10: Develop Exceptional People and Teams Who Follow Your Company’s

Philosophy

The company structure at the wireless mobile manufacturing company does not promote

teamwork. The company is divided into departments such as manufacturing, production,

quality, sales and marketing, and maintenance. There are no teams within each department.

TPS experts would frown upon this organization structure. Principle 10 of the Toyota Way

mandates the use of cross-functional teams to improve quality and productivity and enhance


flow by solving difficult technical problems. Empowerment is achieved when people use the

company‟s tools collaboratively to improve the company (Liker, 2003).

We propose that the wireless mobile manufacturing company adopt a structure that encourages

teamwork throughout the organization. While each individual performs value-added work, the

teams coordinate the work, motivate, and learn from each other. Teams brainstorm together to

come up with innovative ideas. These teams will be cross-functional, so each individual team is

capable of producing products without relying on other teams. The boundaries among

departments are eliminated. Team members are more knowledgeable about the whole process

from obtaining raw materials to shipping the product to the customer. Because of this, each

team member feels a bigger sense of ownership for the product, and is motivated to

continuously improve the product. Kaizen is better accomplished through teamwork.

Furthermore, team members constantly remind one another of sharing and living out the

company values and beliefs. This strengthens a strong, stable culture at the company.

There exists a unique organization structure at Toyota. Employees are divided into many teams

with four to eight team members, each with a team leader. Team members perform production

operations according to standard work sheets, solve problems and improve continuously. The

team leader has worked on the production line previously, but does not perform a manual job

unless a team member is absent. There is an additional role called the group leader, who is

responsible for leading three to four teams. Group leaders’ responsibilities include those that

are normally performed by specialty support functions like human resources, engineering, and

quality. The responsibilities for each team role are summarized in Table 9.

The Toyota‟s team structure has proven to be a powerful and effective one that drives problem-

solving and continuous improvement. The wireless mobile manufacturing company is

encouraged to follow a team structure like Toyota‟s.


Team Member

- Perform work to current standard - Maintain 5S in his work area - Perform routine minor maintenance - Look for continuous improvement opportunities - Support for problem-solving small group activities

Team Leader

- Process start-up and control - Meet production goals - Respond to andon calls by team members - Confirm quality – routine checks - Cover absenteeism - Training and cross-training - Work orders for quick maintenance - Insure standardized work is followed - Facilitate small group activities - On-going continuous improvement projects - Insure parts/materials are supplied to processes

Group Leader

- Manpower / vacation scheduling - Monthly production planning - Administrative: policy, attendance, corrective actions - Team morale - Confirm routine quality and team leader checks - Shift to shift coordination - Process trials (changes in process) - Team member development and cross-training - Report / track daily production results - Cost reduction activities - Process improvement projects: productivity, quality, ergonomics, etc. - Coordinate major maintenance - Coordinate support from outside groups - Coordinate work with up-stream and down-stream processes - Group safety performance - Help cover team leader absence - Coordinate activities around major model changes

Table 8: Toyota Team Roles and Responsibilities

Principle 11: Respect Your Extended Network of Partners and Suppliers by Challenging

Them and Helping Them Improve

From the measure phase, we found that component defects account for 25.7% of the defects for

a product. From the analyze phase, we determined that the quality of incoming parts from

suppliers is inconsistent. These facts strongly suggest that the company‟s suppliers have to

improve their quality standards. The Toyota Way encourages companies to treat their suppliers

and partners with respect, help them grow and develop, so that they can rise to a superior


standard. Such a relationship is mutually beneficial for the company and its suppliers and

partners.

To achieve this long lasting, rewarding relationship with its suppliers, the wireless mobile

manufacturing company is encouraged to treat its suppliers and partners as an extension of its

business, not as outsiders isolated from the business. This way, the company and its suppliers

and partners work towards a common goal. The company should choose its suppliers and

partners carefully. Once they are chosen, the company will educate them about lean principles.

A company cannot be a true lean enterprise unless its suppliers are also lean, since its

suppliers have to be as capable as its own plants at delivering excellent-quality products just in

time. Otherwise, flow would not be continuous between the supplier and the company. An

added advantage of educating partners and suppliers about lean principle is that the company

can hone its lean management skills in the process.

3.2.4.4 Section IV: Continuously Solving Root Problems Drives Organizational Learning

The fourth and last „P‟ of the “4P model” is for “Problem Solving”. The three principles in this

section encourage people to strive for continuous improvement and learning. People are

considered the centre of the TPS because operations can only attain stability through

continuous improvement driven by people. People have to be trained to detect waste and solve

problems at the root cause by repeatedly asking why the problem occurs.

Principle 12: Go and See for Yourself to Thoroughly Understand the Situation (Genchi

Genbutsu)

At the wireless mobile manufacturing company, we asked a manager what he would do if a

problem was reported. He replied that he would inform the maintenance department and hope

that they fix the problem as soon as possible. A Toyota manager would have taken a

completely different course of action. Toyota managers are trained to adopt genchi genbutsu,

which is interpreted as ”going to the place to see the actual situation for understanding” (Liker,

2003). When notified of a problem, any Toyota employee would first go to the actual location of

the problem and confirm the facts for himself. He would not theorize based on what others told

him or what information systems showed. After he sees the problems with his own eyes, he can

apply the Five-Why Analysis, which prompts him to ask the question “Why?” five times when the


problem is encountered. The employee advances to a deeper, more detailed level with each

“Why?” and become closer to locating the root cause of the problem.

Even when there are no reported problems, The Toyota Way encourages employees, especially

high-level managers and executives, to go on the plant floor and just watch the process. While

observing, the employee should figure out for himself what exactly was happening – he should

question, analyze and evaluate.

For our case, understanding the process in depth would greatly boost the company‟s Lean Six

Sigma initiative. The company collected much data in the attempt of analyzing it with statistical

software like Minitab, and much analysis is generated, but one can only make full use of the

result when it can be placed in context. If our group had more time, we would certainly spend it

on deeply observing the plant floor operations. We encourage employees at the company to go

and see for themselves so they could completely understand the operations.

Principle 13: Make Decisions Slowly by Consensus, Thoroughly Considering All Options;

Implement Rapidly

Senior management makes all executive decisions at the wireless mobile manufacturing

company. No input is taken from operators, even though they are the most affected by the

decisions, as they run the day-to-day operations of the facility. The decision making process at

the company is the opposite as the one preached by the Toyota Way: at the wireless mobile

manufacturing company, decisions are made and announced quickly, but they are not always

implemented rapidly since not everyone agrees with the decisions.

According to a manager at the company, when given a project to implement in a year, the

company would typically spend a few months planning, after which they would begin

implementation. However, problems arise after implementation, and more time has to be spent

on fixing the problems. Therefore, the company often has to extend project deadlines past the

one-year mark. On the other hand, if Toyota is given the same project, it will spend nine to ten

months in the planning phase, after which a small test pilot will be performed; if the pilot is

successful, the full implementation is performed at the end of the year, with no post-

implementation problems at all. We would advise the company to abide by the same project


management and decision making models, as less time and cost would be required to

implement the project right the first time.

The reason why Toyota needs nine to ten months for planning is because Toyota does not limit

itself by going in one not direction; it thoroughly considers all alternatives. The Toyota decision-

making process includes five steps (Liker, 2003):

1. Finding out what is really going on, employing genchi genbutsu.

2. Understanding underlying causes that explain surface appearances – asking “Why?” five

times (Five-Why Analysis).

3. Broadly considering alternative solutions and developing a detailed rationale for the

preferred solution.

4. Building consensus within the team, including Toyota employees and outside partners.

5. Using very efficient communication vehicles to do steps one through four, preferably on

one sheet of paper (using effective visual control).

The company should consider following this process for its own decision making, and also

consider the opinions of all employees while making the decision, since the solution will affect

everyone to a certain degree.

Principle 14: Become a Learning Organization Through Continuous Improvement

(Kaizen) and Relentless Reflection (Hansei)

The last of the 14 principles focuses on cultivating a perpetual learning organization. To

achieve this goal, the concepts of kaizen and hansei must be understood by every employee.

Kaizen is a continuous improvement process. This part of the principle brings together many

principles that we have previously discussed. It re-emphasizes establishing standard and stable

processes so that one can use continuous improvement tools to determine the root cause of

inefficiencies and to apply countermeasures. A low-inventory process design is also re-

emphasized, as wastes can only been observed when they are not masked by inventory. When

wastes are exposed, kaizen can be employed to eliminate them. The Five-Why analysis is re-

visited as a tool for getting to the root cause of the problem. Only when the root cause is

located and eliminated can the process truly be improved. All other efforts are only superficial,

and do not completely remove the problem.


Hansei is Japanese word that roughly means “reflection”. Hansei is a mindset, an attitude that

encourages reflection. However, at Toyota, it is more than just a philosophical belief system, it

is a practical tool that encourages people to reflect after doing something, like finishing a

project, to identify all the shortcomings of the completed project and potential improvements for

the future. Countermeasures can be developed to avoid making the same mistakes again.

Kaizen and hansei go hand in hand because one cannot correct his mistakes and make

improvements unless he did some reflection to realize these mistakes. The company is

encouraged to educate its employees about kaizen and hansei, so employees can work

together to form a learning organization.


3.2.5 The Control Phase

The last phase of the DMAIC cycle is the Control phase. Normally, in this phase, one develops

a control plan to sustain improved quality. However, our case is different. The suggestions

given in the improve phase were based on the Toyota Way model, which re-builds the company

culture by introducing a long-term philosophy. The philosophy is promoted and lived out by the

chief executives of the company. Although it may take years to lay the foundation for

transforming the organization culture, once the transformation happens, the effects are

everlasting. Because of this, if a company is successful in implementing all 14 Principles of the

Toyota Way and transforming to a lean enterprise, there is nothing left to do in the control

phase, as the success will sustain itself.

The only way to ensure success is for the company to realize the importance of sticking with the

system and not giving up when the lean transformation seems impossible. The company is

more unlikely to abandon lean efforts if the organization culture is set on lean thinking. Liker

stated some facts that one should understand about changing a culture (Liker, 2003):

1. Start from the top – this may require an executive leadership shakeup

2. Involve from the bottom up

3. Use middle managers as change agents

4. It takes time to develop people who really understand and live the philosophy

It is hoped that knowing these facts will help the company in changing the culture, so the long-

term benefits of lean can be sustained and problems can be controlled.


4. Conclusion

This thesis project has proven to be an extremely rewarding experience for our group. We have

accomplished all three goals that we outlined in our objective for this project, which were:

1. Familiarizing ourselves with the Lean Six Sigma methodology

2. Completing critical analyses of two published case studies of Lean Six Sigma

application

3. Implementing the Lean Six Sigma methodology in improving the assembly

operations at the wireless mobile manufacturing company

We dedicated the earlier phase of our project towards the research of the topic and the review

of the published case studies. From reviewing extensive literature, we had attained a deep

understanding of the Lean Six Sigma approach, and the implementation framework of the

methodology that we would later apply in the case study at the wireless mobile manufacturing

company. Learning about the use of statistical tools gave us a set of tools to apply in our case

study at the company.

For the company project, our objective was to implement the Lean Six Sigma methodology to

improve the process capability and minimize the number of defects. We executed the DMAIC

cycle to achieve our objective, going through the Define, Measure, Analyze, Improve, and

Control phases. At the completion of our case work, we defined and analyzed some root

causes for the problems identified – the most significant one being the high number of defects –

and we proposed ideas for improvements and control.

Our group was very pleased that we were able to apply this knowledge to improve the

manufacturing process at the wireless mobile manufacturing company. We have gained

tremendous knowledge on the Six Sigma statistical tools, Lean Management principles, and the

benefits of the effective and powerful hybrid Lean Six Sigma. This project has been a valuable

opportunity and experience for us to learn, develop and apply our knowledge of the

methodology, and will serve extremely useful in our career in the near future.


5. References

The following lists the journal articles we have reviewed, articles in bold are the ones for our analytical published case studies:

1. Antony. (2001). “Improving the manufacturing process quality using design of experiments: a case study”. International Journal of Operations & Production Management, Vol. 21 No. 5/6, pp. 812-822

2. Antony, Banuelas, Brace. (2005). “An Application of Six Sigma to Reduce Waste”.

Quality and Reliability Engineering International, Vol. 21, pp. 553-570

3. Antony, Kumar, Tiwari. (2005). “An Application of Six Sigma Methodology to reduce the engine-overheating problem in an automotive company”. Proc. ImechE, Vol. 219, PP. 633-646

4. Arnheiter, Maleyeff. (2005). “The integration of lean management and Six Sigma”. The

TQM Magazine, Vol. 27 No. 1, pp. 5-18

5. Behara, Fontenot, Gresham. (1995). “Customer satisfaction measurement and analysis using six sigma”. International Journal of Quality & Reliability Management, Vol. 12 No. 3, pp. 9-18

6. Buell, Turnipseed, Texaco. (2003). “Application of Lean Six Sigma in Oilfield

Operations”. Society of Petroleum Engineers Inc.

7. Dutta, Regani. (2004). “Taiichi Ohno and the Toyota Production System”. ICFAI Center for Management Research, pp. 6-7, 17.

8. Fieler, Loverro. (1991, May). “Defects Tail Off with Six-Sigma Manufacturing”. IEEE, pp.

18-20, 48

9. Furterer, Elshennawy. (2005). “Implementation of TQM and Lean Six Sigma Tools in Local Government: a Framework and a Case Study”. Total Quality Management, Vol. 16, No. 10, pp. 1179-1191

10. Koch, Yang, Gu. (2004). “Design for six sigma through robust optimization”. Journal of

Structural and Multidisciplinary Optimization, Vol. 26, pp. 235-248

11. Kumar, Antony, Singh, Tiwari, Perry. (2006). “Implementing the Lean Sigma framework in an Indian SME: a case study”. Production Planning and Control, Vol.17, no.4, pp. 407-423

12. Marcello, Gionata, Francesco. (2006). “A new Value Stream Mapping approach for

complex production systems”. International Journal of Production Research, num. 18-19, Vol. 44, pp. 3929-3952

13. Mukhopadhyay, Ray. (2006). “Reduction of Yarn Packing Defects Using Six Sigma

Methods: A Case Study”. Quality Engineering, 18:189 206


List of books reviewed:

1. Evans, Lindsay. (2005). The Management and Control of Quality, 6th Edition. Thomson South-Western.

2. George, Maxey, Rowlands (2004). The Lean Six Sigma Pocket Toolbook: A Quick

Reference Guide to 100 Tools for Improving Quality and Speed. 1st Edition. McGraw-Hill

3. Liker (1980). The Toyota Way: 14 Management Principles From The World's Greatest Manufacturer, 1st Edition. McGraw-Hill

4. Montgomery. (2005). Introduction to Statistical Quality Control and Improvement, 5th

Edition. Wiley

5. Ohno (1988). Toyota Production System. Cambridge, MA: Productivity Press

6. Thomas J. Goldsby and Robert Martichenko (2005). Lean Six Sigma Logistics: Strategic Development to Operational Success. 1st Edition. J. Ross Publishing

7. Walpole, Myers, Myers, Ye. (2002). Probability & Statistics for Engineers & Scientists. 7th

Edition. Prentice Hall

Website referenced:

3. Related Process Models – The Six Sigma Methodology. Retrieved November 15, 2006, from http://www.itil-itsm-world.com/sigma.htm

http://www.itil-itsm-world.com/sigma.htm


Appendix A: Data for Case Study 1 – Analysis Phase

Table 9: Raw Data Collected for the Analysis Phase

Depth of

Porous

Core

Sand

Leakage

(g/blow)

Blow

Pressure

(kg/cm3)

AFS

Number

Bulk

Density

(g/cm3)

Baume

of Wash

(Be)

Fin

Thickness

(mm)

Vent

ratio

1.7 85 3.8 66.78 1.76 52 0.9 0.133333

1.3 93 3.8 66.78 1.78 52 0.9 0.166667

1.6 82 3.8 66.78 1.76 53 0.7 0.150000

1.8 90 3.8 66.82 1.78 52 0.7 0.166667

1.3 85 3.8 66.82 1.78 51 0.8 0.200000

1.0 80 4.2 66.82 1.78 52 0.8 0.133333

0.7 22 4.0 69.12 1.86 53 0.8 0.333333

1.0 24 4.0 69.12 1.86 54 0.6 0.300000

1.2 18 4.0 69.12 1.86 54 0.8 0.523810

1.0 18 4.0 69.02 1.84 53 0.8 0.714286

1.3 20 4.2 69.02 1.86 53 1.0 0.761905

1.1 25 4.0 69.02 1.86 52 0.6 0.761905

1.0 28 4.0 68.77 1.82 52 0.8 0.190476

1.1 55 3.8 68.77 1.82 51 0.8 0.238095

1.3 35 4.0 68.77 1.82 52 1.0 0.190476

0.8 12 4.0 67.14 1.82 50 0.7 0.714286

1.3 12 4.0 67.14 1.82 51 0.6 0.714286

1.3 18 3.8 67.14 1.84 50 0.7 0.738095

1.0 20 4.0 68.72 1.84 52 0.6 0.571429

1.2 18 3.8 68.72 1.84 51 0.5 0.666667

1.0 22 3.8 68.72 1.84 52 1.1 0.333333

1.2 13 4.2 69.02 1.84 54 1.0 0.222222

1.2 24 3.8 69.02 1.84 54 1.1 0.222222

0.8 25 4.4 69.02 1.84 54 0.9 0.333333

1.3 18 3.8 68.77 1.80 50 0.8 0.666667

1.0 10 4.2 68.77 1.80 52 1.1 0.333333

1.5 25 4.2 68.77 1.80 52 0.9 0.388889

1.5 75 4.2 70.08 1.74 53 0.6 0.166667

1.7 82 4.0 69.02 1.74 54 1.1 0.111111

1.7 82 3.8 69.02 1.74 54 1.0 0.166667

0.8 13 4.2 68.72 1.80 53 0.9 0.400000


1.1 30 4.0 68.72 1.82 53 0.9 0.320000

0.9 15 4.2 68.72 1.82 53 0.8 0.440000

1.3 35 3.8 69.12 1.82 53 0.8 0.360000

1.2 30 3.8 69.12 1.82 54 0.9 0.320000

1.2 24 4.0 69.12 1.82 54 1.1 0.320000

Descriptive Statistics: depth porous core Variable N N* Mean SE Mean StDev Minimum Q1 Median

depth porous cor 36 0 1.2056 0.0459 0.2756 0.7000 1.0000 1.2000

Variable Q3 Maximum

depth porous cor 1.3000 1.8000

Factor 1: Sand leakage

Descriptive Statistics: sand leakage Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

sand leakage 36 0 37.86 4.70 28.18 10.00 18.00 24.50 70.00

Variable Maximum

sand leakage 93.00

Regression Analysis: depth porous core versus sand leakage The regression equation is

depth porous core = 0.962 + 0.00642 sand leakage


Constant 0.96249 0.05943 16.19 0.000

sand leakage 0.006420 0.001266 5.07 0.000

S = 0.210976 R-Sq = 43.1% R-Sq(adj) = 41.4%


Source DF SS MS F P

Regression 1 1.1455 1.1455 25.74 0.000


Total 35 2.6589


depth

sand porous

Obs leakage core Fit SE Fit Residual St Resid

6 80.0 1.0000 1.4761 0.0639 -0.4761 -2.37R



Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.61.41.21.0

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.40.20.0-0.2-0.4

8

6

4

2

0

Observation Order

Re

sid

ua

l35302520151051

0.50

0.25

0.00

-0.25

-0.50




Factor 2: Blow pressure Descriptive Statistics: blow pressure Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

blow pressure 36 0 3.9778 0.0285 0.1709 3.8000 3.8000 4.0000 4.1500

Variable Maximum

blow pressure 4.4000

Regression Analysis: depth porous core versus blow pressure The regression equation is

depth porous core = 3.83 - 0.661 blow pressure


Constant 3.834 1.005 3.82 0.001

blow pressure -0.6609 0.2523 -2.62 0.013

S = 0.255091 R-Sq = 16.8% R-Sq(adj) = 14.3%


Source DF SS MS F P

Regression 1 0.44645 0.44645 6.86 0.013

Residual Error 34 2.21243 0.06507

Total 35 2.65889



depth

blow porous

Obs pressure core Fit SE Fit Residual St Resid

24 4.40 0.8000 0.9265 0.1147 -0.1265 -0.56 X

29 4.00 1.7000 1.1909 0.0429 0.5091 2.02R


X denotes an observation whose X value gives it large influence.

Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.31.21.11.00.9

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.40.20.0-0.2-0.4

8

6

4

2

0

Observation Order

Re

sid

ua

l

35302520151051

0.50

0.25

0.00

-0.25

-0.50




Factor 3: AFS Number

Descriptive Statistics: AFS number Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

AFS number 36 0 68.448 0.156 0.933 66.780 67.535 68.770 69.020

Variable Maximum

AFS number 70.080

Regression Analysis: depth porous core versus AFS number The regression equation is

depth porous core = 6.22 - 0.0732 AFS number



Constant 6.216 3.359 1.85 0.073

AFS number -0.07320 0.04907 -1.49 0.145

S = 0.270921 R-Sq = 6.1% R-Sq(adj) = 3.4%


Source DF SS MS F P

Regression 1 0.16334 0.16334 2.23 0.145

Residual Error 34 2.49554 0.07340

Total 35 2.65889


depth

AFS porous

Obs number core Fit SE Fit Residual St Resid

29 69.0 1.7000 1.1637 0.0532 0.5363 2.02R

30 69.0 1.7000 1.1637 0.0532 0.5363 2.02R


Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.301.251.201.151.10

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.40.20.0-0.2-0.4

10.0

7.5

5.0

2.5

0.0

Observation Order

Re

sid

ua

l

35302520151051

0.50

0.25

0.00

-0.25

-0.50




Factor 4: Bulk density

Descriptive Statistics: bulk density Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

bulk density 36 0 1.8133 0.00591 0.0355 1.7400 1.7850 1.8200 1.8400

Variable Maximum


bulk density 1.8600

Regression Analysis: depth porous core versus bulk density The regression equation is

depth porous core = 10.4 - 5.06 bulk density


Constant 10.382 1.835 5.66 0.000

bulk density -5.061 1.012 -5.00 0.000

S = 0.212275 R-Sq = 42.4% R-Sq(adj) = 40.7%


Source DF SS MS F P

Regression 1 1.1268 1.1268 25.01 0.000


Total 35 2.6589


depth

bulk porous

Obs density core Fit SE Fit Residual St Resid

4 1.78 1.8000 1.3742 0.0489 0.4258 2.06R

31 1.80 0.8000 1.2730 0.0379 -0.4730 -2.26R



Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.61.41.21.0

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.40.20.0-0.2-0.4

10.0

7.5

5.0

2.5

0.0

Observation Order

Re

sid

ua

l35302520151051

0.50

0.25

0.00

-0.25

-0.50




Factor 5: Baume of wash

Descriptive Statistics: baume of wash Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

baume of wash 36 0 52.472 0.205 1.230 50.000 52.000 52.500 53.750

Variable Maximum

baume of wash 54.000

Regression Analysis: depth porous core versus baume of wash The regression equation is

depth porous core = 0.80 + 0.0077 baume of wash


Constant 0.804 2.015 0.40 0.693

baume of wash 0.00766 0.03840 0.20 0.843

S = 0.279484 R-Sq = 0.1% R-Sq(adj) = 0.0%


Source DF SS MS F P

Regression 1 0.00310 0.00310 0.04 0.843

Residual Error 34 2.65578 0.07811

Total 35 2.65889



baume depth

of porous

Obs wash core Fit SE Fit Residual St Resid

4 52.0 1.8000 1.2019 0.0500 0.5981 2.17R


Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l1.221.211.201.19

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.600.450.300.150.00-0.15-0.30-0.45

10.0

7.5

5.0

2.5

0.0

Observation Order

Re

sid

ua

l

35302520151051

0.50

0.25

0.00

-0.25

-0.50




Factor 6: Fin thickness

Descriptive Statistics: Fin thickness Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

Fin thickness 36 0 0.8361 0.0276 0.1659 0.5000 0.7000 0.8000 0.9750

Variable Maximum

Fin thickness 1.1000

Regression Analysis: depth porous core versus Fin thickness The regression equation is

depth porous core = 1.11 + 0.117 Fin thickness


Constant 1.1076 0.2422 4.57 0.000


Fin thickness 0.1171 0.2843 0.41 0.683

S = 0.278952 R-Sq = 0.5% R-Sq(adj) = 0.0%


Source DF SS MS F P

Regression 1 0.01321 0.01321 0.17 0.683

Residual Error 34 2.64568 0.07781

Total 35 2.65889


depth

porous

Obs Fin thickness core Fit SE Fit Residual St Resid

4 0.70 1.8000 1.1896 0.0605 0.6104 2.24R


Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.241.221.201.181.16

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.600.450.300.150.00-0.15-0.30-0.45

8

6

4

2

0

Observation Order

Re

sid

ua

l

35302520151051

0.50

0.25

0.00

-0.25

-0.50




Factor 7: Vent ratio

Descriptive Statistics: Vent ratio

Variable N N* Mean SE Mean StDev Minimum Q1 Median Q3

Vent ratio 36 0 0.3743 0.0357 0.2144 0.1111 0.1905 0.3267 0.5595

Variable Maximum

Vent ratio 0.7619


Regression Analysis: depth porous core versus Vent ratio The regression equation is

depth porous core = 1.37 - 0.427 Vent ratio


Constant 1.36552 0.08937 15.28 0.000

Vent ratio -0.4274 0.2079 -2.06 0.048

S = 0.263736 R-Sq = 11.1% R-Sq(adj) = 8.4%


Source DF SS MS F P

Regression 1 0.29396 0.29396 4.23 0.048

Residual Error 34 2.36493 0.06956

Total 35 2.65889


depth

Vent porous

Obs ratio core Fit SE Fit Residual St Resid

7 0.333 0.7000 1.2230 0.0448 -0.5230 -2.01R



Residual

Pe

rce

nt

0.500.250.00-0.25-0.50

99

90

50

10

1

Fitted Value

Re

sid

ua

l

1.31.21.11.0

0.50

0.25

0.00

-0.25

-0.50

Residual

Fre

qu

en

cy

0.40.20.0-0.2-0.4

4.8

3.6

2.4

1.2

0.0

Observation Order

Re

sid

ua

l35302520151051

0.50

0.25

0.00

-0.25

-0.50





Appendix B: Data for Case Study 1 – the Improve Phase

Table 10: Data for 2

3 Full Factorial Design

Sand Leakage (g/blow)

Bulk Density (g/cc)

Vent Choking Ratio

Depth of Porous Core

(mm)

Depth of Porous Core

(mm)

Replication 1 Replication 2

10 1.78 0 0.75 0.65

10 1.95 0 0.60 0.60

10 1.78 1 0.80 1.00

10 1.95 1 0.85 0.75

30 1.78 0 0.90 1.00

30 1.95 0 0.80 0.90

30 1.78 1 0.90 1.10

30 1.95 1 0.90 1.00

Results for 23 Full Factorial Design Full Factorial Design Factors: 3 Base Design: 3, 8

Runs: 16 Replicates: 2

Blocks: 1 Center pts (total): 0

All terms are free from aliasing.

Factorial Fit: Depth of porous versus Sand leakage, Bulk density, ... Estimated Effects and Coefficients for Depth of porous core observatio (coded

units)

Term Effect Coef SE Coef T P

Constant 0.84375 0.02253 37.44 0.000

Sand leakage 0.18750 0.09375 0.02253 4.16 0.003

Bulk density -0.08750 -0.04375 0.02253 -1.94 0.088

Vent choking ratio 0.13750 0.06875 0.02253 3.05 0.016

Sand leakage*Bulk density 0.01250 0.00625 0.02253 0.28 0.789

Sand leakage*Vent choking ratio -0.06250 -0.03125 0.02253 -1.39 0.203

Bulk density*Vent choking ratio 0.01250 0.00625 0.02253 0.28 0.789

Sand leakage*Bulk density* 0.01250 0.00625 0.02253 0.28 0.789

Vent choking ratio

S = 0.0901388 R-Sq = 80.27% R-Sq(adj) = 63.00%

Analysis of Variance for Depth of porous core observatio (coded units)

Source DF Seq SS Adj SS Adj MS F P

Main Effects 3 0.246875 0.246875 0.0822917 10.13 0.004

2-Way Interactions 3 0.016875 0.016875 0.0056250 0.69 0.582

3-Way Interactions 1 0.000625 0.000625 0.0006250 0.08 0.789

Residual Error 8 0.065000 0.065000 0.0081250


Pure Error 8 0.065000 0.065000 0.0081250

Total 15 0.329375

Alias Structure

I

Sand leakage

Bulk density

Vent choking ratio

Sand leakage*Bulk density

Sand leakage*Vent choking ratio

Bulk density*Vent choking ratio

Sand leakage*Bulk density*Vent choking ratio


Appendix C: Data for Variance Analysis on Supplier Capability

Top Housing Length Measurement

Insp. Lot No Valuation Inspector Start Measured

value Start date Mean

010000077843 0001 Accepted SHIRPARA 18:04:32 109.78 11/9/2005 109.83

010000077843 0002 Accepted SHIRPARA 18:04:39 109.95 11/9/2005




010000077772 0001 Accepted AHAKIM 22:49:14 109.88 11/8/2005 109.93

010000077772 0002 Accepted AHAKIM 22:49:18 109.80 11/8/2005

010000077772 0003 Accepted AHAKIM 22:49:23 109.99 11/8/2005

010000077772 0004 Accepted AHAKIM 22:49:27 109.89 11/8/2005

010000077772 0005 Accepted AHAKIM 22:49:36 110.11 11/8/2005

010000077639 0001 Accepted AHAKIM 04:12:58 110.10 11/7/2005 110.07

010000077639 0002 Accepted AHAKIM 04:13:01 110.11 11/7/2005

010000077639 0003 Accepted AHAKIM 04:13:06 110.01 11/7/2005

010000077639 0004 Accepted AHAKIM 04:13:10 110.09 11/7/2005

010000077639 0005 Accepted AHAKIM 04:13:14 110.05 11/7/2005

010000077604 0001 Accepted PCRISTESCU 03:12:04 110.07 11/6/2005 110.08

010000077604 0002 Accepted PCRISTESCU 03:12:07 110.05 11/6/2005




010000077582 0001 Accepted GORSER 16:09:50 110.04 11/5/2005 110.04

010000077582 0002 Accepted GORSER 16:09:54 110.01 11/5/2005

010000077582 0003 Accepted GORSER 16:09:58 110.05 11/5/2005

010000077582 0004 Accepted GORSER 16:10:02 110.08 11/5/2005

010000077582 0005 Accepted GORSER 16:10:04 110.04 11/5/2005






010000077428 0001 Accepted AHAKIM 13:59:05 109.80 11/4/2005 109.82

010000077428 0002 Accepted AHAKIM 13:59:36 109.80 11/4/2005

010000077428 0003 Accepted AHAKIM 13:59:40 109.81 11/4/2005

010000077428 0004 Accepted AHAKIM 13:59:48 109.81 11/4/2005

010000077428 0005 Accepted AHAKIM 13:59:51 109.88 11/4/2005











010000077342 0001 Accepted AHAKIM 17:22:36 109.94 11/3/2005 110.06

010000077342 0002 Accepted AHAKIM 17:22:51 109.99 11/3/2005

010000077342 0003 Accepted AHAKIM 17:23:00 110.13 11/3/2005

010000077342 0004 Accepted AHAKIM 17:23:03 110.11 11/3/2005

010000077342 0005 Accepted AHAKIM 17:23:12 110.15 11/3/2005

010000077322 0001 Rejected KMOORTHY 04:17:53 110.19 11/3/2005 110.16

010000077322 0002 Accepted KMOORTHY 04:18:06 110.12 11/3/2005


010000077322 0004 Rejected KMOORTHY 04:18:30 110.20 11/3/2005


010000077272 0001 Accepted GORSER 16:53:38 109.92 11/2/2005 109.94

010000077272 0002 Accepted GORSER 16:53:52 109.89 11/2/2005

010000077272 0003 Accepted GORSER 16:54:06 110.06 11/2/2005

010000077272 0004 Accepted GORSER 16:54:15 109.91 11/2/2005

010000077272 0005 Accepted GORSER 16:54:27 109.92 11/2/2005

010000077174 0001 Accepted KMOORTHY 01:42:02 110.16 11/2/2005 110.07
















010000077023 0001 Accepted SHIRPARA 04:43:53 109.99 10/30/2005 110.00





010000076874 0001 Accepted NVERMA 15:24:42 109.83 10/28/2005 109.88

010000076874 0002 Accepted NVERMA 15:24:42 109.88 10/28/2005

010000076874 0003 Accepted NVERMA 15:24:42 109.92 10/28/2005

010000076874 0004 Accepted NVERMA 15:24:42 109.94 10/28/2005

010000076874 0005 Accepted NVERMA 15:24:42 109.82 10/28/2005

010000076784 0001 Accepted AHAKIM 14:16:34 109.99 10/27/2005 110.00

010000076784 0002 Accepted AHAKIM 14:16:40 110.01 10/27/2005

010000076784 0003 Accepted AHAKIM 14:16:44 110.05 10/27/2005

010000076784 0004 Accepted AHAKIM 14:16:51 109.98 10/27/2005

010000076784 0005 Accepted AHAKIM 14:16:56 109.99 10/27/2005

010000076783 0001 Accepted AHAKIM 14:36:48 110.01 10/27/2005 110.01

010000076783 0002 Accepted AHAKIM 14:36:54 109.99 10/27/2005

010000076783 0003 Accepted AHAKIM 14:37:04 110.02 10/27/2005

010000076783 0004 Accepted AHAKIM 14:37:09 110.05 10/27/2005

010000076783 0005 Accepted AHAKIM 14:37:13 109.98 10/27/2005











010000076535 0001 Accepted AHAKIM 02:35:40 109.94 10/23/2005 109.94

010000076535 0002 Accepted AHAKIM 02:36:01 109.94 10/23/2005

010000076535 0003 Accepted AHAKIM 02:36:05 109.97 10/23/2005

010000076535 0004 Accepted AHAKIM 02:36:10 109.92 10/23/2005

010000076535 0005 Accepted AHAKIM 02:36:14 109.91 10/23/2005

010000076519 0001 Accepted YBHATTI 15:24:14 110.00 10/22/2005 109.96

010000076519 0002 Accepted YBHATTI 15:24:16 110.00 10/22/2005




















Bottom Housing Length Measurement

Insp. Lot No Valuation Inspector Start Measured

value Start date Mean

010000078509 0001 Accepted AHAKIM 02:39:22 109.10 11/18/2005 109.11

010000078509 0002 Accepted AHAKIM 02:39:24 109.12 11/18/2005

010000078509 0003 Accepted AHAKIM 02:39:29 109.08 11/18/2005

010000078509 0004 Accepted AHAKIM 02:39:32 109.10 11/18/2005

010000078509 0005 Accepted AHAKIM 02:39:35 109.15 11/18/2005

010000078322 0001 Accepted AHAKIM 14:35:58 108.18 11/15/2005 108.76

010000078322 0002 Accepted AHAKIM 14:36:02 108.19 11/15/2005

010000078322 0003 Accepted AHAKIM 14:36:23 109.12 11/15/2005

010000078322 0004 Accepted AHAKIM 14:36:30 109.20 11/15/2005

010000078322 0005 Accepted AHAKIM 14:36:36 109.10 11/15/2005











010000076144 0001 Accepted GORSER 21:59:03 110.07 10/18/2005 110.11

010000076144 0002 Accepted GORSER 21:59:16 110.11 10/18/2005

010000076144 0003 Accepted GORSER 21:59:26 110.12 10/18/2005

010000076144 0004 Accepted GORSER 21:59:34 110.09 10/18/2005

010000076144 0005 Accepted GORSER 21:59:42 110.15 10/18/2005

010000076061 0001 Rejected PCRISTESCU 16:01:30 110.20 10/17/2005 110.20

010000076061 0002 Rejected PCRISTESCU 16:16:16 110.19 10/17/2005




an application of lean six sigma to improve the assembly … · thesis: an application of lean six...

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