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MANAGEMENT SCIENCE - THEORY AND APPLICATIONS

UNDERSTANDING SIX SIGMA CONCEPTS, APPLICATIONS AND CHALLENGES

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MANAGEMENT SCIENCE - THEORY AND APPLICATIONS

UNDERSTANDING SIX SIGMA CONCEPTS, APPLICATIONS AND CHALLENGES

SEIFEDINE KADRY EDITOR


Copyright © 2018 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470 E-mail: [email protected].

NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data ISBN: H%RRN

Published by Nova Science Publishers, Inc. † New York


CONTENTS Preface Chapter 1

Chapter 2

Chapter 3

Chapter 4

vii Sustainable Development of the Environment Using Six Sigma Seifedine Kadry DMAIC Six Sigma for Improving Complex Processes A. Pugna, S. Potra, R. Negrea and M. Mocan The Lean Six Sigma Methodology: Applications in Thoracic Surgery Luca Bertolaccini, Barbara Bonfanti, Jury Brandolini, Francesca Calabrese, Sergio Nicola Forti Parri, Kenji Kawamukai, Nicola Lacava and Piergiorgio Solli The Link between Six Sigma and Business Performance Khaled Mili and Abdelmonem Snoussi


1

25

65

75

vi Chapter 5

Chapter 6

Chapter 7

Contents Integration of Lean and Six Sigma Methodology to Improve Quality Performance in Healthcare Organisations Selim Ahmed Six Sigma: A Process Improvement Methodology Swati C. Jagdale, Asawaree A. Hable and Anuruddha R. Chabukswar Integrating Six Sigma into a Business Strategy: Workshop and Leadership Jung-Lang Cheng

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123

161

About the Editor

173

Index

175


PREFACE Understanding Six Sigma: Concepts, Applications and Challenges includes seven excellent chapters that have been prepared using stateof-the-art methodologies by professional researchers in this domain from seven different countries. The chapters in the book are titled as follows: "Sustainable Development of the Environment Using Six Sigma"; "DMAIC Six Sigma for Complex Processes Improvement"; "The Lean Six Sigma Methodology: Applications in Thoracic Surgery"; "The Link between Six Sigma and Business Performance"; "Integration of the Lean and Six Sigma Methodology to Improve Quality Performance in a Healthcare Organisation"; "Six Sigma: A Process Improvement Methodology"; and "Integrating Six Sigma into a Business Strategy: Workshop and Leadership". Seifedine Kadry Beirut Arab University Beirut, Lebanon April 2018



In: Understanding Six Sigma Editor: Seifedine Kadry

ISBN: 978-1-53614-174-0 © 2018 Nova Science Publishers, Inc.

Chapter 1

SUSTAINABLE DEVELOPMENT OF THE ENVIRONMENT USING SIX SIGMA Seifedine Kadry* Mathematics and Computer Science Department Faculty of Science, Beirut Arab University, Lebanon

ABSTRACT The Six Sigma ( 6   ) methodology, as it has evolved over the last two decades, provides a proven framework for problem solving and organizational leadership and enables leaders and practitioners to employ new ways of understanding and solving their sustainability problems. While business leaders now understand the importance of environmental sustainability to both profitability and customer satisfaction, few are able to translate good intentions into concrete, measurable improvement programs. Increasingly, these leaders are looking to their corps (Six Sigma experts) of Six Sigma “Master Black belts”, “Black belts” and “Green belts” to lead and implement innovative programs that *

Email address: [email protected].


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Seifedine Kadry simultaneously reduce carbon emissions and provide large cost savings. In my experience, and that of many others, Six Sigma processes show a proven approach for businesses and organizations to improve their performance and that sustainability programs are in need of this operational approach and discipline. Six Sigma rigors will help a business leader to design a sustainable program for both short- and long-term value creations. The aim of this chapter is to show the importance of applying Six Sigma methodologies to multidisciplinary sustainabilityrelated projects and how to implement it.

1. INTRODUCTION In 2000, the carbon disclosure project [1] was launched as a centrally organized effort to get companies to be transparent about carbon emissions, and by the end of 2009, almost 2500 companies were participating. In 2010, the U.S. Securities and Exchange Commission issued guidance [2] to public companies saying that they should explain the impacts of climate change and climate regulation on their financial disclosure forms. Whether the initial triggers are intrinsic or extrinsic, there are a multitude of triggers that compel a company dialog to consider launching a formal environmental sustainability program. The aim of this chapter is to show the power of Six Sigma to solve the current global challenge of environmental sustainability. One of the most complex problems that organizations face today is achieving success through strategies that are compatible with and supportive of environmental sustainability. The goal is to show how typical Six Sigma define, measure, analyze, improve, and control (DMAIC) structures, such as program governance, transfer functions, measurement systems, risk assessment, and process design, lend themselves to environmental sustainability. In this chapter, a case study of sustainability problems, such as excess oxygen reduction, is analyzed using Six Sigma tools.


Sustainable Development of the Environment Using Six Sigma

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2. WHAT IS SIX SIGMA? The use of Total Quality Management (TQM) as an overall quality program is still prevalent in modern industry, but many companies are extending this kind of initiative to incorporate strategic and financial issues [3]. After the TQM hype of the early 1980s, Six Sigma, building on well-proven elements of TQM, can be seen as the current stage of the evolution [4]: although some conceptual differences exist between TQM activities and Six Sigma systems, the shift from the firsts to a Six Sigma program is a key to successfully implement a quality management system [5]. Six Sigma methodology was originally developed by Motorola in 1987 and it targeted a difficult goal of 3.4 parts per million (ppm) defects [6]. At that time, Motorola was facing the threat of Japanese competition in the electronics industry and needed to carry out drastic improvements in their quality levels [7]. In 1994, Six Sigma was introduced as a business initiative to ‘produce high-level results, improve work processes, and expand all employees’ skills and change the culture’ [8]. This introduction was followed by the well-revealed implementation of Six Sigma at General Electric beginning in 1995 [9]. Sigma is the Greek letter that is a statistical unit of measurement used to define the standard deviation of a population. Therefore, Six Sigma refers to six standard deviations. Likewise, Three Sigma refers to three standard deviations. In probability and statistics, the standard deviation is the most commonly used measure of statistical dispersion, i.e., it measures the degree to which values in a data set are spread. The standard deviation is defined as the square root of the variance, i.e., the root mean square (rms) deviation from the average. It is defined in this way to give us a measure of dispersion. Assuming that defects occur according to a standard normal distribution, this corresponds to approximately 2 quality failures per million parts manufactured. In


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practical application of the Six Sigma methodology, however, the rate is taken to be 3.4 per million. Initially, many believed that such high process reliability was impossible, and three sigma (67,000 defects per million opportunities, or DPMO) was considered acceptable. However, market leaders have measurably reached Six Sigma in numerous processes. According to the Six Sigma methodology a 6  process yields fewer defects than a 3, 4, or 5  processes. It is a name given to indicate how much of the data falls within the customers’ requirements. The higher the process sigma, the more of the process outputs, products and services, meet customers’ requirements – or, the fewer the defects. Table 1 and Figure 1 provide further resolution of the riddle involving the relationship between  value and process performance. The associated assumed process distributions in Table 1 are used to construct Figure 1. The challenge of the Six Sigma methodology is to utilize a set of quality and management tools, through a systematic process, to improve key operational and business processes so they achieve 6  performances for key process indicators/metrics. Table 2 provides examples of 6  performances for selected processes. Table 1. The relationship between σ, process performance and process capability Sigma value

 =1.67  =1.25  =1.00  =0.83

Process performance 3

Process capability 1.00

4

1.33

5

1.67

6

2.00

Process distribution

 =1.67) Normal ( x =10,  =1.25) Normal ( x =10,  =1.00) Normal ( x =10,  =0.83) Normal ( x =10,



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Figure 1. Three, four, five, and six sigma processes for our laboratory example.

Table 2. Examples of 6  performances Sector

Key process indicator

Manufacturing Healthcare

Outer diameter of a shaft produced on a lathe Waiting time of patients receiving primary healthcare service at a clinic Publications from funded research projects by the research administration Interruptions in mobile calls made by customers of a local service provider

Higher education Telecommunication

6  performance 3.4 defects out of 1 million shafts are produced 3.4 out of 1 million patients wait excessively 3.4 out of 1 million funded projects fail to produce publications 3.4 interruptions out of 1 million calls

According to Mikel Harry and Richard Schroeder, each sigma improvement in a business process (e.g., moving from a 5  to 6  ) translates into about “10% net income improvement, a 20% margin improvement, and a 10 to 30% capital reduction” [10]. This is supported with several success stories such as:


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Seifedine Kadry 

By 1998, AlliedSignal saved $1.5 Billion from implement-ing its 6  program in 1994.  By 1998, GE realized from initiating 6  programs in 1996 the following gains:  Revenues rose 11%,  Earnings rose 13%, and  Working capital turns rose to 9.2% from 7.2% in 1997.

2.1. DMAIC Cycle

Figure 2. DMAIC processes.

The Six Sigma methodology is basically including 5 steps. They are definition, measure, analysis, improve, and control (DMAIC). The systematic improvement methodology has been successfully approved in solution of forging defects, achieved lower costs and met customer requirements. The DMAIC problem-solving methodology and the associated tools and training to support the methodology have evolved over the past



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20 years to become a set of powerful, robust, and widely adopted practices. The methodology was specifically developed to help teams get root-cause problem solving more efficiently. The DMAIC [11] problem-solving methodology (Figure 2) was developed to help teams answer five key questions with regard to any problem:

Define The purpose of the define phase is to identify and/or validate the project opportunity, develop the process that will drive the green initiative, define critical stakeholder requirements, and prepare team members to act as an effective project team. This focused session has the effect of pulling the team together around a common understanding of the green problem that they are trying to solve and the goals and objectives that they share. Table 3. Define phase Objectives  Identify the improvement opportunity  Develop the current state process  Define critical shareholder requirement  Prepare to be an effective project team

Activities  Create team  Develop team charter  Perform stakeholder analysis  Document process map  Identify barriers within process  Perform value stream analysis

Tools  Team charter  Stakeholder analysis  Flowchart  Value analysis Deliverables  Prioritized shareholder requirements  Current state process map  Clear team charter  Quick wins

Key activities of the define phase (Table 3) include the following:  

Validate/identify the green improvement opportunity. Validate/develop the team charter.


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Seifedine Kadry    

Identify and map processes. Identify quick wins, and refine the work process. Gather expectations of various stakeholders and convert those expectations into critical project requirements. Develop team guidelines and ground rules.

This activity helps to get the team excited about the potential for the project and motivated team members to set an aggressive work plan and agree on team norms. With its defined workshop completed, the team was ready to move into the measure phase.

Measure In the measure phase, teams determine what they should measure and what techniques and tools they can use to conduct the measurement and data collection, and then they review methods for ensuring that their measurement process is valid and accurate. Once the measurement plan is in place, the measure phase continues as the measurement and data collection take place. Data collection continues until the team finds that it has a statistically valid sample size from which to conduct valid data analysis. Table 4. The measure phase Objectives  Identify key measure to evaluate the success  Establish baseline performance for the processes the team is about to analyze

Activities  Identify input process, and output indicators  Develop operational definition and measurement plan  Plot and evaluate data  Determine if special cause exist  Determine performance level  Collect benchmark data

Tools  Flowchart  Data check sheet  Benchmark data collection  Surveillance  Graph and charting Deliverables  Data collection plan  Baseline data set



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Typical activities during the measure phase (Table 4) include the following:      

Determine process performance. Identify input, process, and output indicators. Develop operational definitions and a measurement plan. Plot and analyze data. Determine if special causes exist. Collect other baseline performance data.

Analyze The purpose of the analyze phase is to provide teams with the techniques and tools they need to stratify and analyze the data collected during the measure phase in order to identify a specific problem (root cause) and create an easily understood problem statement. When teams reach a point in which they want to analyze available data, they are confronted with two potential failure modes. These failure modes are either a lack of relevant data or too much data and an inability to determine how to analyze those data in ways that will lead to relevant conclusions aligned with the problem the team is trying to solve. Teams typically follow a process of first creating a problem statement or hypothesis of what the problem is (e.g., “Lighting is the number one source of energy loss in this data center”). Then teams use data-stratification techniques, comparative analysis, and regression analysis to either prove or disprove the hypothesis. Teams will run through a number of hypothesis statements and the associated analysis until they can statistically prove that they have identified the sources of variation that are the most valid root causes of the problem.


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Seifedine Kadry Table 5. The analyze phase

Objectives  Analyze the opportunity to identify a specific problem defined by an easily understood problem statement  Determine true sources of variation and potential failure modes that lead to shareholder dissatisfaction

Activities  Analyze current state  Develop problem statement  Identify cost causes  Validate root causes  Perform statistical analysis  Identify performance gaps

Tools  Run charts  Control charts  Cause and effect diagrams  Statistical tools Deliverables  Source of variation study  Validated root causes  Problem statement  Potential solutions

The list of activities and techniques employed by teams in the analyze phase (Table 5) typically could include the following:          

Development of the problem statement Stratification of the data Comparative analysis of multiple data sets Performing sources-of-variation studies Analysis of failure modes and effects Regression analysis to determine the strongest correlations with the problem statement Identification of root causes Design of root-cause verification analysis Validation of root causes Design of experimental studies to statistically prove the root cause

Improve The purpose of the improve phase is to enable teams to identify, evaluate, and select the right improvement solutions and then to develop a change-management approach to assist the organization in adapting to the changes introduced through solution implementation


Sustainable Development of the Environment Using Six Sigma 11 Table 6. The improve phase Objectives  Identify, evaluate, and select the right improvement solutions  Assist the organization in adapting to the changes introduced through solution implementation

Activities  Brainstorm possible solutions  Perform cost/benefit analysis  Design and execute implementation plan

Tools  Brainstorming  Process simulation  Staff feedback  Implementation planning Deliverables  Ideal process design  Business case approved  Implementation plan

The typical sequence of activities during the improve phase (Table 6) is as follows:        

Generate solution ideas. Determine solution impacts and benefits. Evaluate and select solutions. Develop the process map and high-level plan. Develop financial analysis and the business case. Develop and present the solution storyboard. Develop the change-management plan. Communicate the solution to all stakeholders.

Control The purpose of the control phase is to help teams understand the importance of planning and executing against the plan and to determine the approach to be taken to ensure achievement of the targeted results. The control phase also helps teams to understand how to disseminate lessons learned, to identify replication and standardization opportunities processes, and to develop related plans. Most important, the control phase forces teams to think through strategies so that identified benefits and financial impacts actually will be realized when the solution is fully implemented and institutionalized. It also will ensure that the solution will deliver results over a long period of time.


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Seifedine Kadry Table 7. The control phase

Objectives  Understand the importance of execution against the plan  Assure targeted results  Disseminate lessons learned  Prevent reversion to current state

Activities  Determine approach to assure targeted results  Track metrics that will show if ideal process is in control  Review progress reports regularly and adjust as needed to support adoption of new process

Tools  Control charts  Statistical process control  Leadership and change management Deliverables  Process control plan  Ongoing monitor and reporting plan  Replication opportunities

Typical activities that occur during the control phase (Table 7) are as follows:        

Develop the pilot plan. Conduct and monitor the pilot. Verify reduction in root causes resulting from the solution. Identify whether additional solutions are necessary to achieve goal. Identify and develop replication and standardization opportunities. Integrate and manage solutions into the daily work processes. Integrate lessons learned. Identify the team's next steps and plans for remaining opportunities

In summary, the DMAIC problem-solving methodology, as well as the associated tools and training to support the methodology, is a powerful, robust, and widely adopted set of practices designed to improve the success rate of problem-solving teams. The methodology was developed specifically to help teams get to root-cause problem solving more efficiently and with greater consistency and repeatability


Sustainable Development of the Environment Using Six Sigma 13 across teams. This overview was developed to help the reader gain an appreciation for how the methodology can be applied in the green project team arena and encourage team members to learn the methodology and supporting tools. While the DMAIC methodology provides teams with the process and tools required, that methodology is not sufficient to ensure that the solutions developed will achieve any level of organizational acceptance and adoption. Throughout a sustainability initiative, the leadership team must implement solid change-management strategies to ensure that the team remains committed, the overall organization understands and supports the sustainability objectives, and the organization therefore is ready to support adoption of the green project team's solutions.

3. CASE STUDY I: REDUCE EXCESS OXYGEN IN PLANT X In this section the application of the DMAIC cycle to reduce the excess oxygen in plant X (Figure 3) is explained.

Figure 3. Plant X, 6 boilers.


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Define Phase In this phase the problems of excess oxygen of six boilers in plant X is examined. It is observed that there are some essential problems of the current system; the percentage of excess oxygen which leads to high cost and indirect pollution. The system structure is believed to be convenient for the Six Sigma approach and DMAIC cycle. Additionally in this phase, we must define the defect, opportunity, expected annual savings, the objective and the project plan:   



Defect: Any day for any boiler (B1, B2, B3 & B4) average Excess O2 > 4% and B5 & B6 O2 > 4.5% Opportunity: Average reading of 66% of excess O2 reading > 4.0% for the 4 boilers & 4.5% for the remaining 2 Objective: Reducing 70% of existing defect, i.e., Reduce excess O2% for (B1, B2, B3 & B4) ≦ 4.0% and B5 & B6 O2 ≦ 4.5%. Annual savings: 148.300 $/Year

See Figure 4 for Project plan.

Measure Phase For measure phase, one has to measure the right process and in the right time. It is so important for latter phases of the project. So the oxygen excess percentage in the boilers has been analyzed and relevant times are measured. The current measure of the oxygen average excess for last three years (2008-2011) is given in the following chart (Figure 5), and the current 6-sigma calculation is given in Figure 6:


Figure 4. Project plan.


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Seifedine Kadry

Figure 5. Oxygen average excess.

Figure 6. Current 6-Sigma calculation.

DPMO: In process improvement efforts, a defect per million opportunities or DPMO is a measure of process performance.

Analyze Phase After it is decided that correct and enough data is collected the analyze phase has begun. During the analysis of the data it is


Sustainable Development of the Environment Using Six Sigma 17 determined that there are five main root-causes (RC) that affect directly the oxygen excess problem: 1. Control parameter not connected to APC (Air Pollution Control) system and manual most of the time (67%) (Figure 7). 2. No close follow-up and supervision: Based on the Survey Results: 50% of surveyed operators confirmed lack of adequate follow-up.

Figure 7. APC manual most time.

3. O2 analyzer reading not matching with Lab analysis:  Operator leaves O2 in excess  Operator does not take action to reduce O2  Operator does not refer to Analyzer  Operator does not trust Analyzer reading  Lab analysis does not match Analyzer reading 4. Operators not aware of excess O2 operating limits: Based on survey results: Survey Results: 40% of surveyed operators answered correctly. 5. B2 Working below the Low air pressure alarm: Combustion air pressure Low Alarm was set at 100 mmW.G. Most of the time, operations were done while the alarm was on.


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Improve Phase In improve phase, relevant solutions are investigated. While searching for solutions, their applicability is also taken into account. Additionally, its cost should be low (Figures 8, 9, 10, 11).

Figure 8. 1st and 2nd Root-Cause solutions.

Figure 9. 3rd Root-Cause solution.


Sustainable Development of the Environment Using Six Sigma 19

Figure 10. 4th Root-Cause solution.

Figure 11. 5th Root-Cause solution.

Improvement result (Figure 12) and Six Sigma before and after (Figure 13):

Figure 12. Result of the improvement.


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Seifedine Kadry

Figure 13. Six Sigma calculation before and after.

Control Phase The control phase is applied where the changes are indeed is valid in the reducing of oxygen excess. Therefore the O2 excess percentage is being examined continually. In the phase, we should propose a control plan (Figure 14).

CONCLUSION Consumers, regulators, and shareholders are all clamoring for sustainability. With the public’s growing environmental awareness, consumers are actively seeking “greener” options. Regulators and legislators are changing the landscape for environmental reporting, compliance, and transparency. Shareholders and investors have made environmental and social performance a top consideration.


Indicators

Performance Standards

Item

Frequency

Contingency Plans

Specs, targets, control limits

What to check

When to check

Corrective actions

Excess O2: B1, B2, B3, B4, B5, B6

Not more than 4.0% and 4.5%

Item 1 Item 2

Monthly (Include Daily Average)

Reduce air flow to bring O2 reading back to less than 4.0% and 4.5%

Follow the excess O2 instruction

Combustion Air Pressure

MMWG 80-120 450-500

Item 3 Item 4

Monthly (Include Daily Average)

Re-conduct awareness to the Control Room operators


Boilers on APC mode

on Automatic mode all the time

APC

Monthly report



Monthly report

Less than 30% of the defect

Excess O2

Monthly



Figure 14. Control plan.


Procedures Standards

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At the same time, many environmentalists claim that cutting greenhouse gases, reducing waste, increasing recycling and broadly shrinking a company’s “impact footprint” will reduce costs. The sustainability imperative is growing, but along with it comes the recognition that improving sustainability is more difficult than some companies hoped – and many environmentalists would admit. However, by broadening Lean Six Sigma to include sustainability goals, companies can leverage a powerful and well-established performance improvement methodology to jump-start new sustainability programs or substantially boost existing ones. In this way, companies may well be able to marry together the critical goals of being good corporate citizens while improving their bottom line. In this chapter, we study the applicability of Six Sigma concept to the sustainable project. The studied case study shows a remarkable improvement to sustainability.

REFERENCES [1] [2]

[3]

[4]

Carbon Disclosure Project: ww.cdproject.net/enUS/WhatWeDo/ Pages/overview.aspx. Securities and Exchange Commission 2010 guidance press release: www.sec.gov/news/press/2010/2010-15.htm; final rule: www.sec.gov/rules/interp/2010/33-9106fr.pdf. Puksic, M., Goricanec, D. (2005). “Increasing Quality and Economic Efficacy of Health Institutions in Public and Private Sectors in Slovenia”, Proceedings of the 5th WSEAS Int. Conf. on Distance Learning and Web Engineering, Corfu, Greece, August 23-25, (pp59-64). Harry, M. J. (2000). “A new definition aims to connect quality with financial performance”. Quality Progress, Vol.33 No.1, pp.64-6.


Sustainable Development of the Environment Using Six Sigma 23 Wessel, G., Burcher, P. (2004). “Six sigma for small and medium-sized enterprises”. The TQM Magazine, Vol.16 No 4 pp. 264-272. [6] Barney, M., “Motorola’s second generation,” Six Sigma Forum Magazine, vol. 1, no. 3, pp. 13-16, 2002. [7] Harry, M. J., and Schroeder, R. Six Sigma: The Breakthrough Management Strategy Revolutionizing the World’s Top Corporations, Doubleday, New York, 2002. [8] ASQ, “The Honeywell edge,” Six Sigma Forum Magazine, vol. 1, no. 2, pp. 14-17, 2002. [9] Slater, R., Jack Welch and the GE Way: Management Insights and Leadership Secrets of the Legendary CEO, McGraw-Hill, New York, 1999. [10] Harry, M., and Schroeder, R. “Six Sigma – The break-through management strategy revolutionizing the world‟s top corporations.” Soundview Executive Book Summaries, vol. 22, no.11, p.2, November 2000. [11] McCarty, Tom, Jordan, Michael, Probst, Daniel. Six Sigma for Sustainability, McGraw-Hill Professional; 1 edition (July 27, 2011). [5]



In: Understanding Six Sigma Editor: Seifedine Kadry

ISBN: 978-1-53614-174-0 © 2018 Nova Science Publishers, Inc.

Chapter 2

DMAIC SIX SIGMA FOR IMPROVING COMPLEX PROCESSES A. Pugna, S. Potra, R. Negrea and M. Mocan Politehnica University of Timisoara, Romania

ABSTRACT Achieving a high degree of performance through continuous improvement is a desideratum of all companies because it ensures their success in ever-changing contemporary markets. Quality improvement theory has seen the emergence of several programs, such as Six Sigma, Total Quality Management, ISO Type Certification, Agile & Lean Manufacturing, Re-engineering, Process Excellence etc. But in the case of complex processes, the introduction and application of the Six Sigma methodology has proved most successful. The reason for this outcome can be the fact that Six Sigma incorporates the TQM philosophy and tools, offers a structured improvement model (DMAIC) with more advanced statistical tools and involves the top management through its belts system with the final purpose to tackle complex projects. Moreover, the application of the Six Sigma methodology is the one that creates a strong culture of continuous improvement. This chapter presents a


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A. Pugna, S. Potra, R. Negrea et al. solution for a complex process improvement in an automotive company in Romania by using DMAIC Six Sigma methodology.

Keywords: DMAIC Six Sigma, complex processes, statistical thinking

1. INTRODUCTION To achieve the ultimate goal of “Zero Defects” and ”Business Excellence” in all corporate activities in order to deliver perfect products and services, the management team needs to apply a sustained and continuous improvement process. Nevertheless, to fulfill these objectives, the companies must not focus only on implementing the Six Sigma methodology, but also on motivating, involving and training all the staff. McCarty et al. [1] have labeled Six Sigma as a metric, a methodology and a management system. Sigma is considered a relevant measurement which reflects the level of control over a process to meet its standard performance or a technical measure of how many unhappy customer experiences a company has per million opportunities [2]. The Six Sigma methodology takes the Sigma metric one step further, by analysing the process in order to find sources of unacceptable variation and propose alternatives to reduce them. The DMAIC (define-measure-analyze-improve-control) model represents a Six Sigma specific and widely used technique due to the fact that “it encourages creative thinking and helps people find permanent solutions to tricky business problems" [3]. The Six sigma management system builds on the metric and the methodology with a wider scope, to transform Six Sigma into a corporate culture from the top of a company to every employee. It is not easily reachable, but desirable for a sustainable organization in today’s competitive environment. Thus, Six Sigma is considered the best way to decrease constantly non-conformances and related costs, to meet


DMAIC Six Sigma for Improving Complex Processes

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customer requirements, to optimize resources consumption and waste reduction [4]. Also, Six Sigma represents the new paradigm for the 21st century [5], eliminates negative quality by reducing defects and costs and increasing the speed of the improved proceses [6]. This methodology is seen as “a strategic initiative to boost profitability, increase market share and improve customer satisfaction through statistical tools that can lead to breakthrough quantum gains in quality” [7]. Pande et al. [8] delimit six benfits of the Six Sigma implementation: sustained success, a performance goal for every employee, greater value for the customer, improvement rate acceleration, promotion of continuous learning and strategic change execution. Therefore, Six Sigma represents a valid method managers can use for improving process performance. But in what kind of projects is it advisable to opt for the Six Sigma implementation? If Lean can be used to reduce waste in simple processes, McCarty et al. [1] argue that Six Sigma is a great tool for looking at complex process interactions because it takes the statistical process control to the next level, by providing a structured and adaptable DMAIC methodology with the appropriate statistical tools for a systematic approach to process improvement. In addition, the DMAIC framework can be expanded to other problem-solving tools like: Theory of Inventive Problem Solving (TRIZ), Lean, 5 Whys and so on. When do we use the DMAIC model? Because not all projects are viable for this approach. Tellier [9] describes some possible ways to select a DMAIC project. First of all, the managers need to implement the S-C-P Model (Structure-Conduct-Performance) which uses a topdown approach as a key to an effective project. Structure in this case measures the economic value of the project, conduct is the ability to exploit the maximum value from the project and performance evaluates the potential for its success. After that, a SWOT analysis continues to determine the relevant information for the final VRIO analysis


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A. Pugna, S. Potra, R. Negrea et al.

(valuable, rare, imitable, organized), which assesses a strong project and a candidate for DMAIC. Török [10] provides another method we can use to select a possible project for Six Sigma, the 1-2-3 model which always shows that strategic improvement initiatives need to start from the top of a company and continue downwards until every employee understands his or her critical role in the project. But the 15 key selection criteria matrix of Tej Mariyapa is an allencompassing tool for project selection, based on a variety of pieces for an optimal decision to use or not the Six Sigma and specifically the DMAIC methodology. The proposed criteria are: 1) Customer impact – will the project, if successfully improved, have an impact on the customer? 2) Process stability – has the process been or reached a stable level of performance? 3) Defect definition – can we define the operational defect of the process? (according to metrics such as: cycle time, error rates, rework rates, first-time call handling percentage, straightthrough processing rates, lead times and complaint rates). 4) Data availability – can we attain data around the process metrics? 5) Solution clarity – do we know the solution? (If this is the case, the project does not need DMAIC) 6) Benefits – what are the cost-benefits and the soft benefits (related to customer satisfaction) of an improved process? 7) Impact on service quality – will the improvement contribute to enhancing service quality along the value chain? 8) Project sponsorship – is the difference between the project's success and failure important enough? (a strong sponsorship is a prerequisite for Six Sigma projects) 9) Project alignment – is the project aligned with the objectives of the company? (if not, it is not viable)



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10) Project timeline – how long do we need for the completion of the improvement? (more than 6 months is not a reasonably time period for DMAIC) 11) Probability of implementation – can the solution for the project be implemented without high resistance/high costs/corporate change? 12) Investment – will the improvement solution include large capital investments? (If so, Six Sigma may not be the methodology to use) 13) Team availability – do the team members have enough time to support the project? (if we do not have Green or Black Belts involved, the project cannot move forward) 14) Controllability of inputs – can we assess if we have sufficient measurable and controllable inputs? (it is difficult to achieve a project if we do not have control over the inputs) 15) Project redesign – can we improve the process without redesigning it? (if not, the project viability is low for DMAIC). The project viability matrix can be built by respecting the following rules:  





the relative importance of each of the criteria (the weighting scale ranges from 1 = least important to 5 = most important). after assigning a weight to each of the criteria, practitioners should give an answer to each question about the project (1 = definitely no and a 5 = definitely yes) to find the individual weighted scores we need to:  divide each weighting by 3  in each individual rating column, the X marking = 1  multiply each X marking by its weighting  find the sum of all X marks for each rating column. to find the total score we need to:


30

A. Pugna, S. Potra, R. Negrea et al.  

multiply each weighted score by its rating and sum these products divide the sum of the products by the sum of the weighted scores.

Thus, the total score will fall into one of three possible categories:   

less than 2.0 – The project is not a viable DMAIC project; it may be better to use another approach. 2.0 – 3.0 – This is a possible DMAIC project; it will require further validation. greater than 3.0 – This is a viable DMAIC project.

If for some questions the answer is a “definite no,” this will automatically disqualify the project from being a DMAIC project, regardless of the overall score. The next part of this chapter will present a case study from the automotive industry, which qualifies for a DMAIC project and we will use the Six Sigma way to improve a complex process, step by step, by using all relevant tools at hand. Preliminary results for a complex process were presented in Pugna et al. [11].

2. CASE STUDY An automotive company from Romania needed a solution for a complex process improvement. We start by determining the project's viability for DMAIC Six Sigma (Table 1). After that, we proceed with the five steps of the method, namely, Define-Measure- AnalyzeImprove-Control for a Six Sigma solution implementation.



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In this case, the total project score is 3.8, which qualifies it as a viable DMAIC project.

2.1. Define Phase The study concentrates on the Body Control Unit (BCM) which is the central element necessary to achieve a variety of functions related to lighting, car access, radio control and power. Over a period of 12 months defective proportion was high, with an average of 3240 ppm and it is desired to achieve at least half. The problems are due to microcontrollers rejected at Data I/O tests and parts rejected after SMT process due to NXT programming. The product is an extremely complex one (Figure 1), including hundreds of electronics components (capacitors, resistors, diodes, etc.). The PCB is populated with elements through the SMT process, which is based on cutting-edge technology SMT (Surface-mount technology). Process at Data I/O station occurs before populating PCBs. In this process, microcontrollers (Figure 2) are taken by a robot from supplier`s rolls, programmed according to the customer's specifications and then placed on another roll, which will then be transported to SMT line and will be placed automatically on PCBs. During the two robotic handlings, the microcontrollers may be damaged. The entire process is automated and involves elements to pass both through areas with high temperature where soldering takes place and cooling areas to ensure their fixing. To assemble these units, the following materials (which are purchased from various external suppliers) are needed: PCBs, electronic components; contact pins, plastic housings, boxes for packaging and also the following machines and equipment: SMT line (laser marking machine, tin soldering machine, electronic components assembly


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A. Pugna, S. Potra, R. Negrea et al. Table 1. The viability matrix for the automotive process improvement project

Project Viability Matrix Description Are customers (internal/external) dissatisfied or defecting? Is the process relatively stable? Is the specific defect (defined by customer) known? Is data related to the defect available or collectable? Is the solution not obvious? Are the expected benefits significant enough? Will service and/or quality be noticeably improved? Does the project have Champion and Sponsor support? Is the project aligned with department or company goals? Can the project be completed within 6 months? Considering the risk, is there a good probability of implementation? Will the solution likely involve little or no capital investment? Are the necessary team members available to support the project? Is the ability to make changes in the process largely in our control? Will the solution likely not involve the redesign of the process? Weighted Scores Total Score

W. 4 3 4

(1)

(2)

(3) X

(4)

(5)

X X

5

X

3 4 3

X X X

4

X

3

X

2 5

X X

2

X

2

X

5

X

3

X 1.3

2

4.3

5.7

4.0 3.8

machine, soldering oven, equipment for checking the presence of optical components and soldering of electronic components) and one assembly line (depaneling machine, contact pins inserting machine, ITC



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equipment, plastic housings soldering machine, radio frequency checking machine, functional final test equipment, optical pin testing machine, logistic box to check the correctness of the number on the unit and on the box). Figure 3 presents the simplified flow chart for Data I/O programming.

Figure 1. Body Control Unit.

Figure 2. Microcontroller.


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Figure 4 presents the simplified flow chart for SMT/NXT programming. Operators are involved in this process, who are directly productive staff and line technicians, maintenance technicians and line supervisors, who are classified in the category of indirectly productive staff. Employees from the departments of human resources, procurement sphere, material quality assurance, quality engineers, quality technicians and product engineers are considered staff who provide support and their contribution is quantified on the basis of percentage rates applied to directly productive costs.

Figure 3. Data I/O programming simplified flowchart.

Figure 4. SMT/NXT programming simplified flowchart.



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2.2. Measure Phase One-week production data (20000 parts) were analyzed, detecting 7,000 parts with non-conformities in the Data I/O process and 6,000 in the SMT / NXT process.

2.2.1. Pareto Analysis for Data I/O Table 2 presents Data I/O main non-conformities and Figure 5 represents the Pareto Analysis for Data I/O. One can see that the largest number of non-conformities occur due to faulty handling of microcontrollers (Class 2). Bent pins due to inappropriate handling situations cannot be fixed. If faulty programmed, microcontrollers can be reprogrammed (Class 4). According to the company`s policy, a microcontroller can be reprogrammed only once. Within this project, it has been checked if microcontrollers can be functional after a second reprogramming. When testing is performed after the Data I/O programming, only the quality of the programming microcontroller is verified and if pins are bent or if the distance between them is not within specifications, such non-conformities will be determined only in the SMT/NXT process, when dimensional parameters of microcontrollers are checked. Also, in the Data I/O process, the lack of protection foil grip (Class 2) will be analyzed. Table 2. Data I/O non-conformities categories Non-conformities category Class 1 Class 2 Class 3 Class 4 Total

Category description Errors in programming components Handling errors - bent pins Handling errors - lack of protection foil grip Mechanical errors


Quantity 740 2780 2115 740 7000

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Figure 5. Pareto Analysis for Data I/O.

2.2.2. Initial between Pins

Process

Capability

Analysis

for

Distance

Statistical checking was carried out by extracting 20 samples of 10 microcontrollers each. Due to the fact that each microcontroller has 144 pins, it was considered the largest measured distance between pins. Tests were performed to detect the random character of the sample data, as well as tests to detect and remove outliers. It was assessed whether the distance between pins can be adequately modelled by a normal distribution (Tables 3 and 4) and also indicators of process capability were assessed. Table 3. Tests for normality for distance between pins Test Chi-Square Shapiro-Wilk W Skewness Z-score Kurtosis Z-score

Statistic 138.56 0.96391 0.42535 -3.40875

P-Value 3.33067E-16 0.00163232 0.670578 0.000652708



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Table 4. Goodness-of-fit tests for distance between pins EDF Statistic Kuiper V Cramer-Von Mises W2 Watson U2 Anderson-Darling A2

Value 0.1362 0.146282 0.143826 1.06373

Modified Form 1.94087 0.146648 0.144186 1.06778

P-Value n. To find the Eigenvector X (Priority Vector), the column entries are normalized by dividing each entry by the sum of the column and then taking the overall row averages as presented in Figure 16. Figure 17 presents the weights for each criteria.

Figure 16. Priority Vector for criteria.

Figure 17. Criteria weights.


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2.3.1.2. Checking for Judgments Consistency The next step is to calculate the Consistency Ratio (CR) in order to measure how consistent the judgments have been. AHP evaluations are based on the assumption that the decision maker is rational, i.e., if A is preferred to B and B is preferred to C, then A is preferred to C. If the CR is greater than 0.1 the judgments are untrustworthy because they are too close to randomness and the judgments must be rethought. The Eigenvalue λmax is calculated from relation (1), in our case the calculations are presented in Figure 18.

Figure 18. Calculation of Eigenvalue λmax.

 max

1.49 1.09 2.19 0.28 0.43      0.26 0.21 0.39 0.05 0.08  5.38 5

Consistency Index (CI) is calculated according to relation (2):

CI 

 max  n n 1

In our case, CR 

(2)

5.38  5 CI 0.094   0.094  0.084 CI  1.12 1.12 5 1

CI=(λmax-n)/(n-1) = (5,38-5)/4 = 0,094 CR = CI/1,12 = 0,094 / 1,12 = 0,084 < 0,1 -> judgments are consistent



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The final step is to calculate the Consistency Ratio (CR) by using Table 14 [13]. The upper row is the order of the random matrix, and the lower row is the corresponding Index of Consistency for random judgments. Table 14. Corresponding indices of consistency to the order of random matrix 1 0.00

2 0.00

3 0.58

4 0.90

5 1.12

6 1.24

7 1.32

8 1.41

9 1.45

10 1.49

Table 15. AHP for selecting the most suitable roll supplier A B C D E

DT (0.26) 0.33 0.41 0.09 0.05 0.13

EXP (0.21) 0.06 0.46 0.05 0.14 0.27

QP (0.29) 0.35 0.24 0.18 0.14 0.09

TR (0.05) 0.32 0.22 0.17 0.06 0.23

CW (0.08) 0.23 0.35 0.09 0.06 0.23

Priority vector 0.27 0.34 0.12 0.12 0.15

In our case, CR  CI  0.094  0.084 < 0.1 and therefore the 1.12

1.12

judgments are consistent. Similar judgments, calculations and verification of consistency were performed for ranking the alternatives. In our case Priorities matrix, Criteria weights and Priority vector for the five potential rolls suppliers are presented in Table 15. Therefore, according to Priority vector, supplier B was chosen.

2.4. Improvement Phase For non-conformities identified and analyzed in this paper, several possible measures were identified to improve the complex process and reduce the rate of rejections. The most serious problems were identified at Data I/O station and the parts rejected a SMT line processes are also


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influenced by the Data I/O processes. For these reasons, improvement measures that will be proposed in this section will be only for Data I/O station where microcontroller programming occurs.

2.4.1. Improving Microcontroller Programming If there are programming errors, according to internal specification, components can be reprogrammed only once. After the first reprogramming, it was checked that a second reprogramming was feasible. After assessing the data obtained from the second reprogramming, it was determined that this is feasible and rejection rate dropped by 65%. Once approved, this measure will be implemented.

2.4.2. Purchasing from Supplier

Ready

Programmed

Microcontrollers

In this case, a new Automatic Optical Inspection (AOI) machine is needed to check the components from the supplier and to test the quality of programming. Such equipment costs about €100,000. There are several possibilities of return on investment: with the client's consent, costs for acquisition of equipment will be allocated totally to the new client's project; costs are allocated to the following projects until the investment is recovered; costs recovered as amortization are included in the updated hourly rates of equipment. In any case, for an average of 20,000 pieces/week, costs will be recovered within 5 weeks of production. It should be noted that this change can be implemented only with the client’s agreement, which involves a testing period in which practically a stock of components will be produced. This method is effective because it will reduce the time required for final product assembly by removing the activity of components’ programming. Once reduced the time, costs will also decrease, by reducing AOI duration and because an operator is no longer needed.



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2.4.3. Using Only New Adaptors at Data I/O Station Adapters reused after the end of a cycle seem to be one of the main reasons for which parts are not positioned correctly in programming and storage roll respectively. To verify their influence, only new adaptors will be used. If the results are positive, henceforth only new adaptors will be used, because in any case the cost of a new adaptor will be recuperated by producing more conforming parts.

2.4.4. Purchasing of New Rolls for Microcontrollers’ Storage Unprogrammed microcontrollers are taken from the initial roll, that comes from the supplier, they are programmed and then stored on another roll which will then be used on SMT line, where the programmed microcontrollers will be placed on the PCBs. It is extremely important when handling components between the two stations that these are not damaged. Thus, one of the reasons why there are scrap and non-conformities at Data I/O station (including bent pins) is determined by the roll on which the programmed microcontrollers are stored. Rolls being reused, the protective foil has no grip and as new components are placed, they fall and become scrap. The initially used rolls present sprocket holes only on one side (Figure 19 a) and it was decided to replace them with rolls presenting sprocket holes on both sides (Figure 19 b) for a better grip of protective foil. Figure 20 presents the Data I/O station with the elements that will be improved, that is new adaptors and new rolls.

2.4.5. Final between Pins

Process

Capability

Analysis

for

Distance

After the microcontrollers supplier’s process audit and improvement, a statistical checking was carried out at SMT/NXT line by extracting 20 samples of 10 microcontrollers each.


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a

b

Figure 19. Rolls with sprocket holes on one side (a) and on both sides (b).

Figure 20. Data I/O station optimization.

Tests were performed to detect the random character of the sample data, tests to detect and remove outliers, it was assessed whether the distance between pins can be adequately modeled by a normal distribution (Tables 16 and 17), and indicators of process capability were assessed as well.



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Since the smallest P-value amongst the tests performed is less than 0.05, we can reject the idea that distance between pins comes from a normal distribution with 95% confidence. The EDF statistics compare the empirical distribution function to the fitted CDF in different ways. Since the smallest P-value amongst the tests performed is less than 0.05, we can reject the idea that distance between pins comes from a normal distribution with 95% confidence. Table 18 compares the goodness-of-fit when various distributions are fit to distance between pins. According to the log likelihood statistic, the best fitting distribution is the Lognormal distribution. Table 16. Tests for normality for distance between pins Test Chi-Square Shapiro-Wilk W Skewness Z-score Kurtosis Z-score

Statistic 1379.2 0.96391 0.42535 -3.40875

P-Value 0.0 0.920085 0.612043 0.984631

Table 17. Goodness-of-fit tests for distance between pins EDF Statistic Kuiper V Cramer-Von Mises W2 Watson U2 Anderson-Darling A2

Value 0.164681 0.32 1.18358 1.18306

Modified Form 2.34999 4.58051 1.1875 1.18729

P-Value