An enhanced improvement roadmap in six sigma methodology

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An enhanced improvement roadmap in six sigma methodology

  1. 1. 元 智 大 學 工業工程與管理研究所 碩士論文 An Enhanced Process Improvement Roadmap in Six Sigma Methodology Student:Mungunshagai Enkhbold Advisor:Dr. Chi-Kuang Chen 中 華 民 國 101年 06月
  2. 2. An Enhanced Process Improvement Roadmap in Six Sigma Methodology 研 究 生:艾明佳 指導教授:陳啟光 Student: Mungunshagai Enkhbol 博士 Advisor: Dr. Chi-Kuang Chen 元 智 大 學 工業工程與管理研究所 碩士論文 A Thesis Submitted to Institute of Industrial Engineering and Management Yuan-Ze University in Partial Fulfillment of the Requirements for the Degree of Master in The Department of Industrial Engineering and Management Chung-Li, Taiwan, Republic of China June 2012 i
  3. 3. Acknowledgement There are a number of people who I would like to thank for their support during the course of this research. However, before I acknowledge them I would like to express my profound gratitude to my family. Your support, encouragement, help and love during the course of the past two years made it possible for this work to materialize. Special thanks to Dr. Chi-Kuang Chen, Industrial Engineering Department of Yuan Ze University, without whom this research could not have been completed. Many thanks for his selfless commitment to guiding me and motivating me. Thanks to Mr. Cheng-Ho Tsai for your valuable comments and corrections and for being so helpful all the time. I want to thanks also to my graduate committee member Professor Henyi Jen for the advices and suggestions. To International Cooperation and Development Fund (ICDF) and Yuan Ze University (YZU) for giving me the opportunity to study in Taiwan and for provide me a professional education, and to my professors in YZU, for sharing their knowledge and experiences. Thanks to all the people that directly or indirectly helped me to finish my thesis, thanks to all my friends around the world that gave moments of happiness and good memories. Special thanks to my friend Michael Smith for being with me all the time, for your support and help. Thanks to USIP2, where I worked before I come to Taiwan, director L. Badamkhorloo and my lovely colleagues for built up the person who I am today. All my knowledge based on your help and support and without you I cannot be reach to this point. ii
  4. 4. An Enhanced Process Improvement Roadmap in Six Sigma Methodology Student: Mungunshagai Enkhbold Advisor: Dr. Chi-Kuang Chen Department of Industrial Engineering and Management Yuan-Ze University Abstract There are numerous different approaches available nowadays to improve the performance of a process and ensure on time delivery. The Six Sigma offers a unique roadmap that is widely used in industries in order to improve the process and thereby reduce the number of defects. The most commonly used roadmap for existing process improvement in Six Sigma is the DMAIC (Define-Measure-Analyze-Improve-Control) improvement roadmap which is a five-step roadmap that utilizes different Six Sigma tools to generate ideas, collect and measure data, analyze and come up with improvement plans to improve the process under study. While analyzing the DMAIC roadmap and its application to the case studies, some deficiencies were found. Various case studies application of the DMAIC roadmap illustrated issues in reaching the Six Sigma goal (Cpk=2). In order to solve the deficiencies of the original DMAIC roadmap, the present study seeks to enhance the original improvement roadmap by some statistical tools with emphases on the process capability (Cpk) to better insure improvement. Thus, this study is proposing an enhanced improvement roadmap that seeks to achieve the required Six Sigma goal. A case study is conducted to demonstrate the feasibility and effectiveness of the proposed improvement roadmap. The result of this study will be a proposal of the enhanced improvement roadmap in Six Sigma methodology, along with the benefits deliverable from the application of the methodology. Keywords: Six-Sigma, DMAIC, Design of Experiment (DOE), Voice of the Process (VOP), Process Capability (Cp and Cpk), Enhanced Process Improvement Roadmap iii
  5. 5. Table of Contents Acknowledgement ............................................................................................. ii Abstract……………. ....................................................................................... iii Table of Contents ..............................................................................................iv List of Figures....................................................................................................vi List of Tables………. ...................................................................................... vii Chapter 1 Introduction ..................................................................................... 1 1.1 Research Background .................................................................................1 1.2 Research Motivation ...................................................................................3 1.3 Research Objective ..................................................................................... 3 1.4 Organization of the Study ...........................................................................4 Chapter 2 Improvement Roadmap of the Six Sigma Methodology ..............5 2.1 What is Six Sigma? ..................................................................................... 5 2.2 History of Six Sigma...................................................................................5 2.3 Definition of Six Sigma ..............................................................................6 2.4 Six Sigma Tools and Techniques ................................................................ 8 2.5 The DMAIC Improvement Roadmap ......................................................... 9 2.5.1 Define Phase ..............................................................................10 2.5.2 Measure Phase ........................................................................... 11 2.5.3 Analyze Phase ............................................................................12 2.5.4 Improve Phase ...........................................................................13 2.5.5 Control Phase .............................................................................13 2.6 DMADV Roadmap ................................................................................... 14 2.7 Summary ...................................................................................................15 Chapter 3 Development of an Enhanced Improvement Roadmap in Six Sigma Methodology .................................................................16 3.1 Development of the Enhanced DMAIC Improvement Roadmap .............16 3.1.1 Voices of the Customer and Process Relationship Information Measurement Enhancement ....................................................... 17 3.1.2 Define and Measure the Initial VOC, VOP and Identify CTS...19 3.1.3 Enhancement of DOE for Process Capability Analysis and Improvement ..............................................................................21 3.1.4 Development of the Process Capability via DOE...................... 22 3.2 Argumentation to the Enhancements of the Improvement Roadmap .......27 3.2.1 Enhancement Focus ...................................................................27 3.2.2 Argument Responds to the Enhancements.................................27 iv
  6. 6. 3.3 Summary ...................................................................................................29 Chapter 4 Case Study ...................................................................................... 31 4.1 Case Description ....................................................................................... 31 4.2 Application of the original Improvement Roadmap .................................32 4.2.1 Definition and Measurement of the Current Process .................32 4.2.2 Analysis and Improvement of the DMAIC Application ............33 4.3 Application of the Enhanced Improvement Roadmap .............................. 34 4.3.1 Define and measure the VOC and VOP ....................................35 4.3.2Analysis and Improvement via DOE ..........................................36 4.4 The Second Analysis and Improvement of the Payroll Process ...............39 4.5 Summary ...................................................................................................42 Chapter 5 Conclusions and Suggestion .......................................................... 44 5.1 Conclusions ............................................................................................... 44 5.2 Suggestion .................................................................................................45 Reference…….. ................................................................................................ 47 Appendix A –Full Factorial Design of Experiment for Payroll Process with Enhanced Improvement Roadmap ................................ 51 Appendix B –Full Factorial Design of Experiment for Payroll Process, the Second Analysis and Improvement ........................................53 v
  7. 7. List of Figures 1Figure 2.1: Sigma variation shown in normal curve (Itil &ITSM World, 2003) ....... 7 2Figure 2.2: A process tends to shift 1.5 sigma units (Arnheiter, 2005) ...................... 7 3Figure 2.3 SIPOC diagram ....................................................................................... 11 4Figure 2.4 Possible source of variation (Kaushik and Khanduja, 2008) .................. 12 5Figure 3.1 Comparing the VOP vs. the VOC (York, 2009) ...................................... 20 6Figure 3.2 VOP matrix template (Furterer, 2004) .................................................... 20 7Figure 3.3 Pareto Chart............................................................................................. 21 8Figure 3.4 Sigma to DPMO-conversion, assuming 1.5 sigma shift ......................... 23 9Figure 3.5 DPMO representing a Six Sigma quality level, allowing 1.5 sigma shift average............................................................................................. 23 10Figure 3.6 DOE identification of the variation factors ............................................. 24 11Figure 3.7 DOE establishment of the performance baseline .................................... 25 12Figure 3.8 DOE process capability analysis and exposed defected variations for improvement ............................................................................................ 25 13Figure 3.9 DOE optimized process variations and improved performance baseline 26 14Figure 3.10 DOE monitoring and verification procedure for optimized variations . 26 15Figure 4.1 SIPOC diagram........................................................................................ 33 16Figure 4.2 Cause and effect diagram ........................................................................ 33 17Figure 4.3 Histogram of payroll process before (left) and after (right) improvement ............................................................................................ 34 18Figure 4.4 Pareto chart for information system problems ........................................ 37 19Figure 4.5 DOE graphical analysis of the payroll process ....................................... 37 20Figure 4.6 DOE of the payroll process ..................................................................... 38 21Figure 4.7 Improved process capability with enhanced improvement roadmap ...... 39 22Figure 4.8 DOE reanalysis for the payroll process ................................................... 41 23Figure 4.9 DOE analysis of variation ....................................................................... 41 24Figure 4.10 Improved process capability of payroll process with the enhanced improvement roadmap ............................................................................. 42 vi
  8. 8. List of Tables 1Table 2: The DMAIC roadmap and the steps included in each phase ...................... 10 2Table 3: An enhanced DMAIC improvement roadmap and the tasks ...................... 18 3Table 4.1 Estimated processing time and summary of process capability of each processes .................................................................................................... 32 4Table 4.2 Raw data of the payroll process ................................................................ 32 5Table 4.3 Summary of the improved process capability and process time .............. 34 6Table 4.4 Payroll process data collection plan ......................................................... 35 8Table 4.5 VOP Matrix for payroll process................................................................ 36 9Table 4.6 Employee VOC survey results summary for reanalysis ........................... 39 10Table 4.7 Revised VOP matrix ................................................................................. 40 11Table 5: Payroll process improvements result .......................................................... 45 12Table A2: Data for the Second full factorial DOE .................................................... 54 vii
  9. 9. Chapter 1 Introduction 1.1 Research Background In the modern world of manufacturing, due to massive competition, different companies have started to look for different approaches and practices to improve the quality level of the product at a reduced cost, create a safe and rewarding workplace, and eventually achieve higher customer satisfaction. Most 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 competiveness and share in the market. Six-Sigma is a quality improvement strategy that helps companies to achieve these results. According to Harry CEO of Six Sigma Academy Phoenix, USA: Six Sigma is a well-structured, disciplined, data driven methodology for eliminating defects, waste, or quality control problems of all kinds of manufacturing, service deliver, management and other business activities; and it is the business strategy that allows companies to drastically improve their performance by designing and monitoring everyday business activities in ways that minimize waste and resources while increasing customer satisfaction. O’Neal and Duvall (2004) stated that Six Sigma is a disciplined quality improvement methodology that focuses on moving every process that touches the customers –every product service –towards near perfect quality. Hence, Six Sigma is the measure of the company’s quality. Maleyeff and Karyenvenger (2004) noted that Six Sigma implies three things: statistical measurement, management strategy and quality culture. It is a measure of how well a process is performing through statistical measuring of quality level. It is a new management strategy under the leadership of the top management that creates quality innovation and total customer satisfaction. Moreover, Six Sigma is also a quality culture. It provides the way to do things right the first time and to work smarter by using data information. It also provides an atmosphere to solve many CTS (critical-to-satisfaction) problems through team efforts. Pande, Neuman and Roland (2000) mentioned that Six-Sigma is an improvement methodology, developed by Motorola in the 1980’s, whose benefits and financial results are well documented in many areas. Six-Sigma is a way for Motorola to express its quality goal of 3.4 Defects per Million Opportunity (DPMO) where a defect opportunity is a process failure that is critical to the customer. Motorola set this goal so that process variability is +/-6 Standard Deviation (SD) from the mean. They further assumed that the process was subject to disturbances that could cause the Process Mean to shift by as much as 1.5 SD. Motorola 1
  10. 10. developed “The Six Steps to Six Sigma’’ process improvement roadmap to achieve six sigma quality (3.4 DPMO). More specifically, this improvement roadmap was focusing on the improvement of the process capability of the process. Dahlgaard (2006) noted that due to the sixth step of the Motorola’s improvement roadmap “if the process capability (Cp, Cpk) is less than two then redesign materials, product, and process as required. It is clear that the Six Sigma requires higher process capability during its application. With these stated requirements in Six Sigma, the process must be capable through successful implementation. Thus, Six-Sigma requires that the Process Capability (Cp, Cpk) have to be greater or equal to two, Cpk≥2, during the Six Sigma implementation and continuous control of the process. Later, Motorola’s six steps to six sigma roadmap replaced by GE as a 5 phases of DMAIC (Define, Measure, Analyze, Improve, and Control) improvement roadmap. The DMAIC improvement roadmap is the most commonly used roadmap in Six Sigma after all. It is a Six Sigma roadmap for improvement of an existing process. While analyzing the DMAIC improvement roadmap and its application to case studies, some deficiencies were found. Seeing from some research works and case studies, the DMAIC is not fully assured to achieve Six Sigma requirement (Cpk≥2). Antony, Kumar and Tiwari (2005)’s research work, “An application of Six Sigma methodology to reduce the engine-overheating problem in an automotive company”, that adopted DMAIC roadmap for the improvement of the processes where these adaptations did not reach the Six Sigma requirement of Cpk≥2. In order to solve the deficiencies of the original DMAIC roadmap, the present study seeks to enhance the original improvement roadmap by significant statistical tools with emphases on the process capability to better insure improvement. Thus, this study is proposing an enhanced improvement roadmap that seeks to achieve the required Six Sigma goal (Cpk≥2). The process capability enhancements achievement will bring about successful improvements for the process and product quality with the successful application of the Six Sigma project. This thesis study will provide insightful results and examinations of the methodology centering on its implementation and application to the case study. The main portion of this work will be dedicated to enhance the improvement roadmap in Six Sigma methodology. Based on the a variety of literature and case study review, an enhanced improvement roadmap in Six-Sigma methodology will be developed and implemented at the facility. The result of this study will be a proposal of the enhanced improvement roadmap in Six Sigma methodology, along with the benefits derivable from the application of the methodology. 2
  11. 11. 1.2 Research Motivation Woo and Hong (2007) noted that to satisfy a growing demand and expectation from customers while coping with increasing product complexity and limited resources, companies must improve in 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 Six Sigma methodology is one of them. Since Six Sigma’s 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 fosters some companies’ reluctance in accepting and adapting it. Furthermore, 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. Therefore, with better understanding and more insightful research of Six Sigma these misconceptions can be dismissed. The motivation for this research topic is aid in the intuitive knowledge of Six Sigma in the business environment. The major benefits of Six Sigma to the business environment are having a measureable way to track performance improvement, focusing the attention on process management at all organizational levels, improving customer relationships by addressing defects, improving efficiency and effectiveness of process by aligning them with the customer’s need. Moreover, the DMAIC improvement roadmap includes five phases of improving any existing process –Define, Measure, Analyze, Improve, and Control. These phases are virtually the same in any company that has adopted what is now known as Six Sigma. The individual steps within each phase may vary slightly from one company’s implementation to another; such variance is usually minor and almost inconsequential. It is very important that five phases be consistently followed to achieve anticipated results and keep the benefit at the appropriate level for the company. 1.3 Research Objective This study seeks to provide insightful research, and enhance the DMAIC improvement roadmap for centering on its implementation and application to better process capability requirement. Basically, this study intends to achieve the following 3
  12. 12. objectives: 1) To review and summarize the Six-Sigma’s roadmaps from the past literature and understand the original roadmap; 2) To develop an enhanced improvement roadmap in Six Sigma methodology; and 3) To conduct a case study to demonstrate the feasibility of the proposed roadmap. 1.4 Organization of the Study This report will begin with the literature review section. Some background knowledge about Six-Sigma and its roadmap will be given, along with a description of some of the tools and techniques. Attention will then be directed towards the improvement roadmap. An enhancement of the improvement roadmap would be the main focus of the thesis. Following the introduction is the implementation of proposed roadmap for the case, describing in detail the work that has been done in every stage of the DMAIC approach in the implementation of Six Sigma. Application of the proposed roadmap will be executed on the case study. A list of the research materials and some of the referenced graphs/tables will be given in Appendix at the end of the report. 4
  13. 13. Chapter 2 Improvement Roadmap of the Six Sigma Methodology 2.1 What is Six Sigma? Sigma is a letter in the Greek alphabet that has become the statistical symbol, which is used in mathematics and statistics to define standard deviation. The sigma scale of measurement is perfectly correlated to such characteristics as defects-per-unit and the probability of a failure. Six is the number of sigma measured in a process, when the variation around the target is such that only 3.4 outputs out of one million are defects. Coronado and Antony (2002) pointed that Six-Sigma methodology have recently gained wide popularity because it has proved to be successful not only at improving quality but also at producing large cost savings along with those improvements. So, an organization needs to give smarter Six Sigma solutions that linked to bottom line benefits. Kumar (2002) has stated that Six Sigma is statistical measurement, which provides that opportunity and discipline to eliminate mistakes, improve morale, and save money. Doing things rightly and keeping them consistent are the basic ideas behind Six Sigma. A fundamental objective of Six Sigma is to achieve customer satisfaction with continuous improvement in process. 2.2 History of Six Sigma The roots of Six Sigma as a measurement standard can be tracked back to Carl Frederick Gauss (1777-1855) who introduced the concept of the normal curve. Racing (2005) noted that the Six-Sigma as a measurement standard in product variation can be traced back to the 1920’s when Walter Shewhart showed that three sigma from the mean is the point where a process requires correction. Many measurement standards (Cpk, Zero Defects, etc.) later came in the scene but credit for coining the term “Six-Sigma” goes to Motorola engineer named Bill Smith. According to George (1992), in the early and mid-1980s with Chairman Bob Galvin at the helm, Motorola engineers decided that the traditional quality levels—measuring defects in thousands of opportunities—didn’t provide enough granularity. Instead, they wanted to measure the defects per million opportunities. Motorola developed this new standard and created the methodology and needed cultural change associated with it. Six-Sigma helped Motorola realize powerful bottom-line results in their organization- in fact; they documented more than $16 Billion in savings as a result of Six-Sigma efforts. In the period 1983-1989, Motorola developed “the six steps six sigma” process improvement methodology 5
  14. 14. which helped Motorola to save billions of dollars. Since then, hundreds of companies around the world have adopted Six Sigma as a way of doing business. Dahlgaard (2006) noted that this is a direct result of many of America’s leaders openly praising the benefits of Six-Sigma, such as Larry Bossidly of Allied Signal (now Honeywell), and Jack Welch General Electric Company. The Motorola “six steps top six sigma” were replaced by GE when Jack Welch, chairman and CEO of GE, declared the six sigma process to be GE’s corporate strategy for improving quality and competitiveness. As noted Park (2003), the change of roadmap directly from the extract from his speech: Motorola has defined a rigorous and proven process for improving each of the tens of millions of processes that produce the goods and services a company provides. The methodology is called the six sigma process and involves four simple but rigorous steps (MAIC): First, measuring every process and transaction; then analyzing each of them; then painstaking improving them; and finally rigorously controlling then for consistency once they have been improved. Dahlgaard (2006) mentioned that by comparing these four simple but rigorous steps with Motorola’s six steps to six sigma quality it seems on the surface as if GE (or Jack Welch) in beginning of their six sigma journey focused only on 6 Step Motorola’s Roadmap. Park (2003)stated pointed that later on that the six sigma improvement process usually followed the so-called DMAIC process, which defined as follows: Define–Identification of the process or product that needs improvement; Measure–Identify those characteristics of the product or process that are critical to the customer’s requirement for quality performance and which contribute to customer satisfaction; Analyze–Evaluate the current operation of the process to determine the potential sources of variation for critical performance parameters; Improve–Select those product or process characteristics which must be improved to achieve the goal and implement improvement; and Control–Ensure that the new process conditions are documented and monitored via statistical process control methods. Six-Sigma has evolved over time. It’s more than just a quality system like TQM or ISO. It’s a way of doing business. As Tennant (2001) describes in his book Six-Sigma: SPC and TQM in Manufacturing and Services: Six-Sigma is many things, and it would perhaps be easier to list all the things that Six-Sigma Quality is not. Six-Sigma can be seen as a vision; a philosophy; a symbol; a metric; a goal; and a methodology. 2.3 Definition of Six Sigma Statistically, the term sigma represents the standard deviation, the variation around 6
  15. 15. the process mean the objective of Six-Sigma is to achieve a quality of the at most 3.4 defect per million opportunities (DPMO) and the process capability is more than or at least 2 (Cpk≥2). Six Sigma means that there are 6 standard deviations from the process mean to the specification limits when normally distributed process is centered (See Figure 2.1). 1Figure 2.1: Sigma variation shown in normal curve (Itil &ITSM World, 2003) In the original definition of Six-Sigma, it was assumed that process could shift 1.5 sigma’s 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 units from the process mean to either side, the final products would be 99.97% detect free, having 3.4DPMO (See Figure 2.2). 2Figure 2.2: 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. Arnheiter and Maleyeff (2005) noted that 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-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. According to Antony and Banuelas (2002), 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 7
  16. 16. 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. 2.4 Six Sigma Tools and Techniques Woo and Hong (2007) illustrated that the Six Sigma 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. These tools are employed in various stages of the DMAIC roadmap. 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 the problem solving. The “Magnificent seven” is on-line processing monitoring tool while the off-line techniques are Regression Analysis, Hypothesis Testing, and Analysis of Variance (ANOVA) in DMAIC Roadmap. 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 runs charts, one pays attention to huge jumps in measurements, patterns that occur over time (e.g. whether the measurement show an increasing trend), and an increase in variance. A check sheet is similar to 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 measurement, 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 for 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. Cause and Effect diagram: A cause and effect Diagram is used to identify and 8
  17. 17. analyze a problem in 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). 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. 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 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 (X-bar, R-bar, S, I, MR) that are used for different circumstances. 2.5 The DMAIC Improvement Roadmap The DMAIC (Define-Measure-Analyze-Improve-Control) is the classic Six Sigma problem-solving process. Traditionally, the approach is to be applied to a problem with an existing, steady-state process or product and/or service offering. Variation is the enemy –variation from customer specifications in either a product or process is the primary problem. Variation can take on many forms. DMAIC resolves issues of defects or failures, deviation from target, excess cost or time, and deterioration. Six-Sigma reduces variation within and across the value-adding steps in process. DMAIC identifies key requirements, deliverables, tasks, and standard tools for a project team to utilize when tracking a problem. Banuelas, Antony, and Brace (2005) stated that Six-sigma represents the strategy combing the Six-Sigma statistical measure and TQM. The DMAIC problem-solving methodology is particularly useful when: 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. Table 2 lists the steps included in the phases of the DMAIC roadmap (Banuelas, 2005): 9
  18. 18. 1Table 2: The DMAIC roadmap and the steps included in each phase Phases Steps included   Define the scope and boundaries of the project Define team charter to identify process definition, critical-to-quality parameters, benefit impact, key milestone activities with dates, support required and core team members    Map process and identify process inputs and outputs Establish measurement system capability Establish data collection plan  Gather data  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 Discover variable relationships Establish operating tolerances 5, Control  Develop a control plan to sustain improved quality 1, Define 2, Measure 3, Analyze Arnheiter and Maleyeff (2005) pointed that with the Six-Sigma overall strategy; an organization can not only achieve near perfect quality using DMAIC methodology, but also attain superior availability, reliability, delivery performance, and after-market service. 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. 2.5.1 Define Phase Define the problem and what the customer requires. Henderson and Evans (2000) stated the define phase sets the expectation of the improvement of project and maintenance of focus of Six-Sigma strategy on customers’ requirement. The quality problem that requires break through solutions has to be defined in measurable terms. The defining of the problem is the first and the most important step of any Six-Sigma project because better understanding of the problem makes the job much easier later on during analysis. The defining of the problem forms the backbone of any Six-Sigma project. The objectives to define a problem are as numbered: 1) To identify the process or product for improvement, 2) To identify the voice of customer, 3) To identify the customer’s requirement and translate the customer needs into CTQ’s. There is many tools used in Six-Sigma methodology for defining 10
  19. 19. the problem but the “High level process map –a SIPOC diagram” as shown in figure 2.3 is one of the best tools being used in defining a problem as it fulfills all the basic objectives to define a problem. Kaushik and Khanduja (2008) stated that within SIPOC diagram, the letters stand for: Supplier–The people or organization that provides information, material and other resources to be worked on in the process; Input–The information/material provided by suppliers that are consumed or transformed by the process; Process–The series of steps that transforms the inputs; Output–The product or Service used by the customer; and Customer–the people, company or another process, that receives the output from the process (). 3Figure 2.3 SIPOC diagram According to Pyzdek (2003) plan, in this phase, had to determine which opportunities will provide the biggest payoff for the efforts. Part of task involves describing the current state of various metrics. Ask several questions to determine, such as: Are there important trends? Are the data relatively stable or are there outliers? What do the statistical distributions look like? Are the distributions what would expect from this process? Pyzdek, (2003) consider some tools and techniques during the Define phase include the following: 1) Cause-and-Effect diagrams, 4) seven management tools for quality control (7M) and 5) data mining–exploring information, contained in the enterprise data warehouse using automated. 2.5.2 Measure Phase According to Basu and Nevan (2003), Six-Sigma is based on measured data. The measure phase identifies the defects in the product, gathers valid baseline information about the process. There will be unfavorable consequence form analysis using Six-Sigma tools if there is problem with measuring system. The observed possible source of variation in a process, as shown in figure 2.4, is the actual process variation and measurement variation. To address actual process variability, firstly it is necessary to identify the variation due to measurement system and to separate it out from the process. The goal of the statistically confident otherwise if there is 11
  20. 20. problem with measuring system, the process gets worse and the experiment will end up failure. Therefore it is very important to secure a correct measuring system before the project. Raisinghani (2005) stated that in the measure phase, a measurement system analysis (MSA) is conducted which includes the Gauge R&R studies The purpose of the Gauge R&R study is to ensure that the measurement system is statistically sound. Gauge repeatability and reproducibility studies determine how much of the observe process variation is due to the measurement system variation. According to Pyzdek (2003), before trusting the information it is important to verify that it is reliable and valid. To evaluate the reliability and validity of dimensional measurement system, such as gauges, conduct a gauge repeatability and reproducibility (R&R) study. Gauge R&R studies are scientifically designed to quantify gauge error from a variety of sources. Six Sigma projects usually involve metrics that are classifications rather than determinations of physical properties such as length, width, color, etc. The classification can be binary (male/ female/ good/ bad, failed/ didn’t fail, meets requirements/ fails requirements, etc.), nominal (red-blue-green, shipped by truck/ car/ train, etc.), or ordinal (good-better-best, dissatisfied-satisfied-delighted). In this phase, summarize the results of the measurement system used to evaluate attribute data. Pyzdek (2003) consider some tools and techniques during the measure phase include the following: 1) Voice of the process (7 quality tools); 2) Evaluate measurement system gauge R&R; 3) measure the process capability (Cp); and 4) Select measures of performance (QFD), Quality Function deployment is a method of defining what the customer needs and what is critical to their business success and prioritizing performance measures to support customers need. OBSERVED PROCESS VARIATION ACTUAL PROCESS VARIATION MEASUREMENT VARIATION VARIATION DUE TO GAUGE VARIATION DUE TO OPERATOR REPEATABILITY REPRODUCIBILIT 4Figure 2.4 Possible source of variation (Kaushik and Y Khanduja, 2008) 2.5.3 Analyze Phase Kapur and Feng (2005) noted that the analyze phase examine the data collected in 12
  21. 21. order to generate a prioritized list of source of variation. Many statistical tools are used to carry out the analyses which are explained as follows: 1) Run chart; 2) Histogram; 3) Process capability analysis; 4) Fishbone Diagram; and 5) Bar chart. According to Pyzdek (2003), in this phase of the Six Sigma project cycle, must quantify the existing process to determine how best to achieve the process improvement goals. Tools and techniques useful during the analyze phase: 1) Cause & Effect; 2) Process capability analysis; 3) FMEA (Failure Mode Effective Analysis); 4) Contingence analysis; and 5) Detailed process maps. 2.5.4 Improve Phase Abbas, Li, Al-Tahat, and Fourd (2011) noted that to improve the process by removing the cause of defects. The optimal solution for reducing mean is determined and confirmed in improve phase. The gains from the improve phase are immediate and are corrective in nature. Specific problem identified during analysis are attended in improve phase. This stage involves: 1) To use of brain storming and action workouts; 2) Process optimization and confirmation experiment; 3) Extracting the vital few factors through screenings; 4) Understanding the co-relation of the few factors. Pyzdek (2003) noted that there are some improvements in every phase of the project. The work done in the Define, Measure, and Analyze phase all help better determine what the customer wants, how to measure it, and what the existing process can do to provide it. It is possible that, by the time the Improve phase has been reached, so much improvement will already been made that the project goals have been met. If so, the project may be concluded. However, if the process performance still falls short of the project’s goals, then additional activities in the improvement phase must be undertaken. Pyzdek (2003) considered some tools and techniques during the improve phase include the following: 1) Prioritize improvements –Tool commonly in uses are, Impact vs. Effort, Brainstorming, Affinity diagrams, Solution selection matrix. These tools help define the best method to meet the customer need (as defined in the QFD); 2) Tactical implementation plans –Deliver improvements to reduce variation systematically i.e. make a change, note the improvement and make the next improvement. 2.5.5 Control Phase In the last phase of the Six Sigma methodology Mukhopadahyay (2007) proposed to control the process to make sure that defects don’t recur i.e. removes the root cause of the problem. The control phase is preventive in nature. All the possible related problem of the specific identified problem from the analysis phase are tackled in control phase: 1) It mainly defines control plans specifying process monitoring and 13
  22. 22. corrective action; 2) provides systematic re-allocation of resources to ensure the process continues in a new path of organization; and 3) Ensures that new process conditions are documented and monitored. Basu and Nevan (2003) stated the real challenge of Six Sigma methodology is not in making improvements to the process but in sustaining the optimized results. This requires standardization and constant monitoring and control of the optimized process. In this phase of the Six Sigma, will develop controls to ensure that keep hard-won gains. The objective is to remove the root causes of process variation, management are only left with a few critical input variables in the process that need controlling and not all inputs as before. Basu and Nevan (2003) consider some tools and techniques during the define phase include the following: 1) Recover, control plans, escalation process; 2) Prevent by poke yoke (fool proof the process) to fundamentally remove the root causes of process variation; and 3) Monitor, control charts, checklists, documentation and standardization, to ensure that stable process is maintained and that the process does not degrade. 2.6 DMADV Roadmap DMADV stands for Define, Measure, Analyze, Design, and Verify. It is the standard Six Sigma method for designing new processes or reengineering existing processes. DMADV is a common framework that is used for Design for Six Sigma (DFSS) and these are often used synonymously, although there are other frameworks that can be used for DFSS. It is different from DMAIC, which is used for incremental improvement. A DMAIC project may be revised as DMADV if it is found that incremental improvements are insufficient or a complete redesign is otherwise the best approach. DMADV may also be used when a process has reached Entitlement, or that position where it cannot be improved further using current technology, resources and methods. The steps involved in the DMADV methodology have been outlined below: Define: The function of define step is to establish clear definition of the project. This includes product or process that will be improved or the needs that will be met, and the scope of the project, with schedule, resources, and deliverables, much like a project management plan. It also includes a management plan, identifying the known and foreseeable risks in the project. Measure: Understand, segment and prioritize customers and so determine Critical to Customer (CTC) measures. From these derive Critical to Quality (CTQ) measures, possibly using Quality Function Deployment (QFD). Also, measure is appropriate for process capability, risk and product capabilities. Analyze: The analysis focuses on identification of the different approaches that 14
  23. 23. could be used to meet customer or stakeholder requirements. Alternatives are evaluated, and the effective alternative, based on the best parts of the best concepts, is selected for Design. During the analyze stage, an estimate of the total life cycle cost of the design is made, creation of the production system or process, ongoing production, use of the product or service, disposal of the product or service and final retirement of the process or production system. Design: The design stage includes both high level and detailed design for the selected alternative. Design elements are prioritized and a high level design is developed. Following that, a more detailed model is prototyped. There is an effort to identify where errors may occur and address them through modifications. Verify: The final step involves piloting the new product or service, gathering data and evaluating performance, satisfaction, or results. A plan is developed and implemented to transition the product or service to a routine operation for the organization and ensure that the change is maintained. 2.7 Summary The strategic implementation of Six Sigma in steps (DMAIC) leads to an optimization of some selected process parameters, thus resulting in substantial saving in overall operational costs of a process industry. Through critical investigation of Six Sigma and its statistical tools, the study illustrates certain ground rules, which are required to be laid down before starting such an exercise with same kind of tools. Use of these ground rules will make Six Sigma more effective, more productive with less effort and less consumption. Many view DMAIC as the foundation of Six Sigma. DMAIC is best used as an iterative problem-solving method to combat variation in an existing, steady-state process. Some of the past researchers were developed different roadmaps and methods which may able to use in six sigma projects that satisfies Six Sigma requirement in terms of the different area. Principally, the purpose of this study is to enhance the current improvement roadmap, DMAIC is not a new improvement roadmap in Six Sigma. After creation of DMAIC, there is many roadmaps were created regarding different business field. But DMAIC is still most common used roadmap in Six Sigma methodology for improvement of the process. In this faster grooving world everything is continuously improving, so the DMAIC Improvement Roadmap itself should be continuously improved too, to step with others. An enhanced improvement roadmap will be proposed in the following chapter of this study. 15
  24. 24. Chapter 3 Development of an Enhanced Improvement Roadmap in Six Sigma Methodology Literatures from past researchers, in the previous chapter of this study, are for understanding the current status of the original DMAIC roadmap. As stated previously, the Six Sigma is a problem-solving methodology. Specifically, the process capability enhancements achievement will bring about successful improvements for the process and product quality with the successful application of the Six Sigma project. In order to satisfy the Six Sigma requirement, the process capability has to be greater than or equal to two, Cpk≥2. Antony, Kumar and Tiwari (2005)’s research work “An application of Six Sigma methodology to reduce the engine-overheating problem in an automotive company” that adopted Six Sigma DMAIC Roadmap for the improvement of processes resulted a reduction jamming problem encountered in the cylinder head and increased the process capability from Cpk=0.49 to Cpk=1.28. But the Six Sigma adaptations did not reach the Six Sigma requirement of Cpk≥2 after the application of DMAIC. Cpk=1.28 is not the Six Sigma requirement, it supposed to be at least Cpk=2. From this case, the DMAIC improvement roadmap didn’t really reach the Six Sigma requirement after its application. This study considered that there are deficiencies in the phases of the DMAIC roadmap while reviewing the research work above. In order to solve the deficiencies of the DMAIC roadmap and achieve the Six Sigma goal, this study seeks to enhance the existing roadmap with emphases on the process capability to better insure improvement. Thus, this study is proposing an enhanced improvement roadmap which can achieve process capability Cpk≥2 with DMAIC roadmap in Six Sigma methodology. 3.1 Development of the Enhanced DMAIC Improvement Roadmap The aim of this study is to enhance the existing improvement roadmap in Six Sigma methodology to achieve its specified requirements. Due to deficiencies that rose in the introduction, the DMAIC improvement roadmap is not fully guarantee to achieve six sigma requirements. Therefore, the improvement roadmap tasks have to be enhanced. Due to the Six Sigma objects to improve the process capability to specified requirement, thus the enhancement of the DMAIC roadmap emphasis on the statistical tools. DMAIC is 5 phases of roadmap for improvement of the existing process. Every phase of DMAIC have different role. By enhancing the tasks of the 16
  25. 25. phases, the application of the DMAIC can reach the six sigma requirements. Development of the enhanced improvement roadmap will bring a successful application of Six Sigma. Basically, this study enhanced the original DMAIC by different statistical and measurement tools to phases. In the first two phases enhancement focuses on the voice of the customer and process (VOC&VOP) data collection and measurement. Based on the identified and measured voice of the customer and process relationship, the current process capability will clearly described. All the voice of the customer and process data that defined and measured will help to Design of Experiment (DOE) to analyze and improve the process capability and the affected process variations. Argumentation to this statistical tools enhancement is explained in section 3.2 of this study. Basically, the enhancements to the DMAIC improvement roadmap are as follows: Phase 1–the original DMAIC helps in defining customer requirement and identifying the Voice of the Customer (VOC) whereas the enhanced DMAIC is associated with defining Voice of the Process (VOP) and its applicability; VOP and VOC relationship for Critical to Satisfaction (CTS); Phase 2–the original DMAIC is measuring the customer requirements and specifications; gathers valid baseline information about the process whereas the proposed DMAIC is enhanced to measure the VOP to identify the process capability; Phase 3–In the original DMAIC, a business process is analyzed to find the root cause of a defect or recurring problem, but in the enhanced DMAIC, except finding the root causes of a defect, develop the process capability and to identify the variations that are causing the process through DOE ; Phase 4 –In the original DMAIC, improvements are made in the business process for eliminating or reducing defects whereas in the enhanced DMAIC Perform the DOE to improve the process capability and identify optimal setting of process parameters to eliminate problem; and Phase 5 –In the enhanced DMAIC, is sustaining the optimized process, and constant monitoring and controlling of the optimized process via control charts. Table 3 is shown below is the enhanced roadmap and the tasks that highlighted by bold format represents the enhancements of the enhanced DMAIC improvement roadmap. 3.1.1 Voices of the Customer and Process Relationship Information Measurement Enhancement Define and Measure phases of the DMAIC are interrelated to each other. Main idea of these two phases is to identify and measure the current process and customer requirements. More specifically, define phase of DMAIC aims to define the scope and goals of the improvement project in terms of customer requirements and develop a process that delivers these requirements, while measure phase is 17
  26. 26. concerned with selecting one or more product characteristics, mapping the respective process, making the necessary measurements, recording the results on process control cards, and establishing a baseline of the process capability. 2Table 3: An enhanced DMAIC improvement roadmap and the tasks Define Phase Measure Phase  Define the scope and boundaries of the project  Define team charter to identify process definition  Define initial voice of the customer (VOC), voice of the process (VOP) and critical to satisfaction (CTS)  Map process and identify process inputs and outputs  Establish measurement system capability  Establish data collection plan  Measure voice of the process (VOP) and current performance  Gather data Analyze Phase Improve Phase  Perform cause and effect analysis  Select critical-to-quality parameters to improve  Develop process capability and analyze the variations that causing the process via DOE  Screen potential causes that affect process  Discover variable relationships  Establish operating tolerances  Perform the DOE to improve the process capability and identify optimal setting of process parameters to eliminate problem Control Phase  Plot control charts to check Cpk≥2 ( if not, go to the Measure Phase)  Develop a control plan to sustain improved quality In the define phase needed to identify the performance standards according to the customer requirements. Then the measure phase can translate the customer needs into measurable characteristics. Based on the specification limits, performance standards for each process parameter would establish. Having established the key process parameters and the critical to quality characteristics, it is essential to establish the accuracy of the measurement system and the quality of the data. Many researchers, such as Franza and Chakravotry (2009), Smith, Blakeslee and Koonce (2002), were mentioning about to define the Voice of the Customer (VOC) in this phase in order to understand what their needs are, but Furterer (2009) and Stauffer (2009) suggested to define not only VOC, also Voice of the Process (VOP). As noted Stauffer (2009), the match of these two voices is done via the concept of process capability. Whereas the VOC communicates customer desires, 18
  27. 27. requirements, needs, specifications, and expectations, the VOP communicates information about the performance of the process. The challenge for the process is to use VOP information to better meet the customer needs as defined by the VOC. In terms of VOP statements, during the application of DMAIC roadmap, have to clearly define what the current process capability is, and how far it can go. The DMAIC roadmap is the improvement tool for the existing process optimization. One way to describe Six Sigma is that it is measurable process which compares the VOP and VOC. Process improvement occurs to achieve the desired quality outcome and reduce variability in the VOP until it is as least as good as the VOC. Also, make charts of the process that should be improved. Therefore, this study is enhancing the first two phases by VOP to identify the current status of the process to analyze during Six Sigma application. Details of the enhancement explained in section 3.1.2. 3.1.2 Define and Measure the Initial VOC, VOP and Identify CTS In the Define and measure phases, the focus is on collecting information from the customer to understand what is important regarding the process. In the define phase, the initial VOC data collection to understand the CTS criteria, which are the elements of a process that significantly affect the output of the process. It is critical to focus on the CTS throughout the phase of the DMAIC problem-solving process. The VOC is a term used to “talk to the customer” to hear their needs and requirements or their “voice”. Many mechanism can be used collect VOCs, including interviews, focus groups, surveys, customer complaints and warranty data, market research, competitive information, and customer buying patterns. Montgomery and Woodall (2008) stated that the steps to identify the CTS are shown as follows: 1) Gather appropriate VOC data from market research, surveys, focus groups, interviews etc.; 2) Extract key verbatim from the VOC data collections, identifying why a customer would do business with your organization; 3) Sort ideas and find themes, develop an Affinity or Tree Diagram; 4) Be specific and follow up with customers where needed; 5) Extract CTS measures and specifications from customer information; and 6) Identify the missing data and fill in the gaps. Stauffer (2009) pointed out the types of the quality. Type one is fairly easy to deal with. Some specifications may be set, based on desired quality characteristics, and machines or producing processes controlled to keep output consistent with those specifications. Type two quality characteristics may also be measured as output of systems. These measurements can be tracked using process behavior charts to characterize the VOP. Type three is trickier; it requires that to tap into the VOC, articulate that voice as a set of measurable characteristics, and then translate tose CTS characteristics into process measures. Essentially, this translation should match 19
  28. 28. the two voices: VOC and VOP. The matching of these two voices is done via the concept of process capability. This process capability concept is fundamental and definitive in Six Sigma. One way to describe Six Sigma is that it is a measurable process which compares the VOP to VOC. Process improvement occurs to (a) achieve the desired quality outcome and (b) reduce variability in the VOP until it is as least as good as the VOC. York (2009) graphically illustrated and compares the VOP versus VOC in Figure 3.1. 5Figure 3.1 Comparing the VOP vs. the VOC (York, 2009) A VOP matrix, developed by Furterer (2004), can be used to achieve integration and synergy between the DMAIC phases and the critical components of the process to enhance problem solving. The VOP matrix includes the CTS, the related process factors that impact the CTS, the operational definition that describes how the CTS will be measured, the metric, and the target for the metric. A template for the VOP matrix is shown in Figure 3.2 (Furterer, 2004). CTS Process Factors Operational definition Metric Target 6Figure 3.2 VOP matrix template (Furterer, 2004) The VOP can use man quality tools, such as bar charts, Pareto charts, run charts control charts, cause and effect diagrams, and checksheets. A Pareto chart, that shown in Figure 3.3, helps to identify critical areas causing most of the problems. It provides a summary of the vital few rather than the trivial many. It helps to arrange the problems in order of importance and focus on eliminating the problems in order of highest frequency of occurrence. Following are the steps for creating for Pareto chart (Furterer, 2009): 1) define the data categories, defects, or problem types; 2) determine how to relative importance is defined; 3) Collect the data and compute the cumulative frequency of the data categories; and 4) plot a bar, showing the relative importance of each problem area in descending order. Identify the vital few to focus 20
  29. 29. on. 7Figure 3.3 Pareto Chart A check sheet is graphical tool that can be used to collect data on the process and the types of defects so that root causes can be analyzed in the analyze phase. The steps create a check sheet are: 1) choose a characteristics to track, i.e., defect types; 2) set up the data collection check sheet; and 3) collect data using the check sheet. A Pareto chart can then be created from the data collected on a check sheet. A histogram is a graphical tool that that provides a picture of the centering, shape, and variance of the distribution of data. Minitab is commonly used to create a histogram. It is important to graph the data in a histogram as the first step to understanding the data. Mainly creating a histogram to measure how capable is the current process. Statistics can be used to assess the VOP related to the metrics that measured. Once the data are collected, they can be tested to see if the data distribution follows a normal distribution using a test for normality. QFD is a method of defining what the customer needs and what is to their business success and prioritizing performance measures to support the customer need. 3.1.3 Enhancement of DOE for Process Capability Analysis and Improvement Main enhancement of the study is for the process capability improvement to require Six Sigma goal. Main role of analyze and improve phases is to improve the process capability regarding the particular analysis. Basically, analyze the root of defect and cause of deviations; find out the factors that have to be improved and by reducing the defected variations to improve the process capability and reach the Six Sigma requirements. During the analyze phase process capability must be clearly described for the improvement. Process capability is the ability of a process to produce products capable of meeting the specifications set by the customer. Process capability is based on the performance of individual products against specifications. There are several steps for to perform process capability. In order to perform process capability for the metrics that measure the CTS characteristics defined in define and 21
  30. 30. measure phase, collect the data on the process for metric and need to perform graphical analysis (histogram). Due to analyzing the graphical data determine if the process is in control and stable, using control charts. Sadraoui, Afef, and Fayza (2010) noted that if the process capability is stable the estimate the process mean and standard deviation; and calculate the capability indices, Cp and Cpk. But if the process capability is not stable, then what would be the next? For some processes several rounds of improvement may be required to achieve desired process capability. Due to Six Sigma requirement, the process capability has to be greater or equal to two. If the process capability is greater than or at least 2 (Cp≥2), then go for the Control stage, but if the process capability is less than 2 (Cp<2), which is not the Six Sigma requirement, then redesign the material, product, and process as required as Dahlgaard, (2006) was mentioned. Therefore, this research work is proposing the Design of Experiment (DOE) to analyze all process variations more clearly for improvement in order to identify defects that affecting the process. Basically, DOE will define why the process is not capable and investigate all variations which might affect the process that will identify the process that needs improvement. And then DOE will determine process input and output to measure why the defect has occurred and where the failure that makes the process incapable is. During this period the process flow and establishment of the performance baseline will be created. Since the performance baseline is created, then reanalyze the process variations that has defects. The process capability identification will be analyzed in order to identify the defected variations for improvement. Using the DOE, kill the special causes that affecting the process and process capability will be enhanced. The reduction of the variations brings the process within the specification limits. Since process variation within the specification limits the process is more capable. But not only improving the process, DOE will verify and control the variation via control charts that improvement is sustaining. After verification of the process, documentation needed to be made for next phase. Details of the enhancement explained in section 3.1.4. 3.1.4 Development of the Process Capability via DOE Six-Sigma represents a stretch goal of six standard deviations from the process mean to the specification limits when the process is centered, but also allows for a 1.5 sigma shift toward either specification limit and this represent a quality level of 3.4 defects per million. Moran and Duffy (2009) noted that to determine if a process is capable of satisfying its customer, two most commonly used indices (Cp and Cpk) are: Cp, which 22
  31. 31. measures the variation in a process or how well data fits within the upper and lower specification limits (USL, LSL). This measure is the width of the process distribution relative to a set of limits and is sometimes referred to as the process potential. The Cp should be as high as possible since the higher the Cp the lower the variability. One problem with Cp is a process may have a high Cp but is producing many defects since the actual spread does not coincide with the allowable spread of the specification limits. This why there is a need of the second index Called Cpk.The Cpk index measures the central tendency of the process. The Cpk measures how close a process is performing to its specification limits and how centered the data is between those limits. It is an indicator of the ability of a process to create product within specification. Basically, Cp is for the measurement index for the new process design while Cpk is the measurement index for the current process. The Figure 3.4 shows the 1.5 sigma shift from the mean which is Cpk measurement concept. The greater number of sigma level, the smaller the variation (the tighter the distribution) around the average. Figure 3.5 shows a Sigma-to-DPMO conversion. DPMO is calculated as (Brassard and Ritter, 2001): DPMO=Defects*100000/Units * Opportunities. 8Figure 3.4 Sigma to DPMO-conversion, assuming 1.5 sigma shift 9Figure 3.5 DPMO representing a Six Sigma quality level, allowing 1.5 sigma shift average 23
  32. 32. Process capability is the ability of a process to produce products or provide services capable of meeting the specifications set by the customer. Process capability is based on the performance of individual products or service against specifications. According to the central limit theorem, the spread or variation of the individual values will be greater than the spread of the averages of the values. The steps for performing a process capability study are: 1) define the metric or quality characteristics. Perform the process capability study for the metrics that measure the CTS characteristics defined in the define and measure phase; 2) collect data on the process for the metric; 3) perform a graphical analysis (histogram); 4) perform a test for normality; 5) determine if the process is in control and stable, using control charts. When the process is stable continue to step6; 6) estimate the process mean and standard deviation; 7) calculate the capability indices, Cp and Cpk (Summers, 2006): Cp= (Upper specification limit –Lower specification limit)/6 sigma Cpk= /Minimum of CPU, CPL/ Where: CPU= (Upper specification limit –Process mean)/3 sigma CPL= (Process mean –Lower specification limit)/3 sigma The purpose of this study is to enhance the improvement roadmap in Six Sigma methodology with focus on improving the process capability. In order to improve the process capability, this study seeks to analyze all the data variation through the DOE for the improvement. DOE help to identify which variations in the process need a reduction in order to enhance the process capability. DOE does analysis and improvement based on the data gathered from the measured VOP and VOC. The DOE defines why the process is not capable enough and investigate all variations, which might be affecting the process, which needs improvement. Basically, DOE identifies the factor’s shift from the average (A1&A2); the factors which affect variation (B1&B2); the factors which shift the average and affect variation (C1&C2); and also the factors which have no effect (D1=D2). 10Figure 3.6 DOE identification of the variation factors The variation factors needs to be identified for the characteristics of the process that 24
  33. 33. are critical to the requirements for quality performance and which contribute to customer satisfaction. The DOE will identify process input and output as shown in Figure 3.7 and to measure why the defect has occurred and where is the failure that makes the process incapable. Hence, establish the performance baseline for analysis. 11Figure 3.7 DOE establishment of the performance baseline After measurement, analyze the process variation that causing the process. DOE identifies defected variation during the analysis that shown in Figure 3.8. Process capability analysis supports to expose the defected variations. So in the improve phase, those variations that causing the process needed to be improved via DOE. 12Figure 3.8 DOE process capability analysis and exposed defected variations for improvement After analyzing the variations improve the process by killing those special causes that are affecting the process. After optimization of the process by killing the causes, 25
  34. 34. the variation should be reduced within the specification limit as shown in Figure 3.9. 13Figure 3.9 DOE optimized process variations and improved performance baseline The real challenging of Six Sigma is not in making improvements to the process but in sustaining the optimized results. This requires standardization and constant monitoring and control of the optimized process. Control the process deviations to meet customer needs. As shown in Figure 3.10, DOE will check the process status due to the required process capability (Cpk=2) achievement, and sustain the optimized result. But if the application of DMAIC is not reach the Cpk=2, then go to the measure phase to cycle again in order to reach the required Cpk. 14Figure 3.10 DOE monitoring and verification procedure for optimized variations 26
  35. 35. Proper monitoring of the process helped to detect and correct out-of-control signals before they resulted in customer dissatisfaction. Montgomery and Woodall (2008) suggested using statistical process control (SPC) charts to monitor and control process, and ensure that the process is not out of control. SPC charts are a graphical for monitoring the activity of an ongoing process. The most commonly used control charts are also referred to as Shewhart control charts. 3.2 Argumentation to the Enhancements of the Improvement Roadmap 3.2.1 Enhancement Focus The object of the enhancement of the improvement roadmap is to achieve required process capability (Cp≥2) in Six Sigma methodology. The reason why this study is necessary is because some researchers, Antony, Kumar and Tiwari (2005), who adapt the Six Sigma DMAIC improvement roadmap for the improvement of the processes did not reach the Six Sigma requirement (Cp=2). In achieving Six Sigma requirement, an enhanced improvement roadmap enhances some statistical and technical tools (DOE, VOP and more) that guarantee to reach the Cp≥2 after the application of Six Sigma. 3.2.2 Argument Responds to the Enhancements From the view of table 3, there are certain differences between the original DMAIC improvement roadmap and the enhanced DMAIC improvement roadmap. Based upon the past literatures, the present author postulates the following arguments in response to the deficiencies which were found in the existing roadmap. The DMAIC improvement roadmap is five phases of roadmap. In terms of the role of each phase, there are certain arguments raised. Since Six Sigma is statistical methodology for improvement, this study enhanced the DMAIC roadmap by statistical tools (i.e., VOP and DOE). Pyzdek (2003) noted that an argument can be made for asserting that quality begins with measurement. Only when quality is quantified can meaningful discussion about improvement begin. Conceptually, measurement is quite simple: measurement is assignment of numbers to observed phenomena according to certain rules. Define phase of the DMAIC roadmap focuses on the expectation of improvement of the project and maintenance of focus of Six Sigma strategy on customer requirements and process performance whereas measure phase of identifies the defects in the process or product, gather valid baseline information about the process. Breyfogle (1999) stated that the defining and measuring the problem is the first and most important phases of any Six Sigma project because 27
  36. 36. better understanding of the problem makes the job much easier later on during analysis. Pepper and Spedding (2010) mentioned that the Six Sigma is based on measured data. Ramamoorthy (2003) stated that measure phase is for identification of the characteristics of product or process that are critical to the customer’s requirements for quality performance and which contributed to customer satisfaction. Woo and Wong (2007) pointed that the objective of the define phase is to identify the process or product improvement, Voice of the Costumer (VOC), customer’s requirement and translate the customer needs into CTS’s. But Furterer (2009) and Stauffer (2009) were mentioned that DMAIC is the improvement tool for existing process, defining only the customer requirement VOC will not clearly describe what the status of the current process is, therefore the VOP needed to identified as well as. Furterer (2009) noted that the purpose of the measure phase is to understand and document the current state of the processes to be improved, baseline the current state (VOP), and validate measurement system. Due to the Breyfogle (1999) statement, in the define and measure phases everything must be clearly identified for analysis, so at the same time understanding VOC and VOP would be excellent choice to understand customer requirement and current process status. Larry (2009) noted that the VOP is the statistical data from or out of a process that indicates the process stability or capability that provides feedback to process performers as a tool for continual improvement. Furterer (2009) suggested to using VOP matrix to achieve integration and synergy between the DMAIC phases and the critical components of the process to enhance problem solving. Furterer (2009) and Stauffer (2009) stated that the Pareto chart, histogram, statistics, check sheet and VOP matrix are the excellent tools for the measurement of the VOP. Another argument rose due to the enhancement of the analyze phase and improve phase, and its importance of the improving the process capability. Within these two phases the process capability will be analyzed and optimized via statistical tools. According to Kitchaiya (2006) the analyze phase is for evaluating the current operation of the process to determine the potential sources of variation for critical performance parameters and improve phase is to select those process characteristics which must be improved to achieve the goal. Wang (2008) illustrated that analyze phase examine the data collected in order to generate a prioritized list of source of variation and then improve the process to remove cause of defects. Pyzdek (2003) noted that in those phases of the Six Sigma project cycle, must quantify the existing process to determine how best to achieve the process improvement goals. Furterer (2009) mentioned that first need to analyze the data related to the VOC and VOP to identify the root causes of the process problems, and the process capability (Cpk); and improve the process. Main role of those phases are to analyze the data 28
  37. 37. gathered in first two phases for improvement and improve it by reducing the variation and defects. More specifically, analyze the root of defect and cause of deviation and find out the factors that to be improved, and develop the process capability. During the analyze phase process capability must be clearly described for the improvement. Process capability is the ability of a process to produce products capable of meeting the specifications set by the customer. According to the Bewoor and Pawar (2010), in order to perform process capability for the metrics that measure the CTS characteristics defined in define and measure phase, collect the data on the process for metric and need to perform graphical analysis (histogram). Due to analyzing the graphical data determine if the process is in control and stable, using control charts. Tonini, Spinola, and Laurindo (2006) noted that if the process capability is stable the estimate the process mean and standard deviation; and calculate the capability indices, Cp and Cpk. Due to Six Sigma requirement, the process capability has to be greater or equal to two. There are many statistical tools for process capability improvement. Caleb Li, Al-Refaire and Yang (2008) suggested using the Taguchi method to improve capability of the process. But Furterer (2009), Pyzdek (2003), Henderson (2006) strongly recommended to use Design of Experiment to improve the process capability. Henderson (2006) mentioned that the DOE can optimize the process first, minimizing variation by maximizing the signal to noise ratios of the controllable factors that affect variation; second, selecting the levels of the tuning factors that affect the mean to adjust the mean in the desired direction (toward the target value). JMP (2005) stated that DOE is a very powerful analytical method that multiple process variables can be studied at the same time with these efficient design, instead of in a hit and miss approach, proving very reproducible. Furterer (2009) noted that due to the statistical balance of the designs, thousands of potential combinations of numerous variables can be evaluated for the best overall combination, in very small number of experiments. Even the Taguchi method, Caleb Li, Al-Refaire and Yang (2008) were suggested was itself using DOE to improve the capability. Due to the many reason and argument above, this study proposing to enhance the DMAIC roadmap by DOE to analyze and improve the process capability. 3.3 Summary The aim of this study is to enhance the existing improvement roadmap in Six Sigma methodology to achieve its specified requirements, as stated in beginning of this chapter. The object of the enhancement of the improvement roadmap is to achieve required process capability (Cp≥2) via statistical tools (DOE&VOP) in Six Sigma. In first two phases all the data related to the process and customer needs identified 29
  38. 38. through VOC and VOP. According to Stauffer (2009) the matches of the VOC and VOP is done via the concept of process capability. This process capability concept is fundamental and definitive in Six Sigma; the term Six Sigma came from capability concept and studies, and process metrics such as DPMO and the process sigma come directly from process capability concepts. DOE helps to analyze all the data gathered from the first two phases through VOC and VOP relationship and to improve the process capability by reducing the variation. The VOC, VOP and DOE relationships can effectively reach the Six Sigma goal. An enhanced roadmap will conduct to case study to demonstrate feasibility and effectiveness of the proposed roadmap in following chapter. 30
  39. 39. Chapter 4 Case Study The main portion of the thesis is dedicated to the development of an enhanced DMAIC improvement roadmap in Six Sigma methodology. This section of the thesis is devoted to a case study centering on the application of the enhanced DMAIC improvement roadmap. The aim of this section is to prove the proposed roadmap and reach the Six Sigma requirement (Cpk=2) by applying it to the case study through several steps. Particular steps have to be followed to reach required process capability in Six Sigma. First step is to identify what are the current status of the process and its capability, and then the application of the original DMAIC roadmap will improve that current process. If the process capability doesn’t reach to the required amount, the enhanced improvement roadmap will be applied to reach to the goal. The application of the enhanced DMAIC roadmap completes the missing tasks of the applied original DMAIC roadmap tasks. After the enhanced roadmap application, if the process capability improved to reach the required value, then implement the second round of the analysis and improvement will achieve the required goal. The following sections of the thesis present the case study in detail. The current status of the process identification of the example case will be given at first. The action taken and the insufficient findings for each of the steps will be described, and an analysis will be provided based on the study and statistical analysis. Minitab will be used for the statistical analysis. The conclusion will discuss some of the difficulties encountered during the course of the work as well as the effectiveness of the proposed Six Sigma DMAIC roadmap. The limitations of this research mainly consist of time where partnering with a company is not feasible and other minor geographical factors impose restraints that creates a lean towards a case study that is executable, appealing, and practical. 4.1 Case Description Consider a financial administration process in a business entity where the goal of the project is to streamline the payroll process and subsequently reduce its cycle time. The financial unit realizes that the current process, with respect to the process before the enhanced improvement roadmap is implemented, is insufficient, error-prone, lengthy, and have extensive number of non-value added steps. The customers are for the payroll process are employees that receive withholding payments and reports. The entire payroll reporting and withholding payment process takes between 50 to 70 employee hours depending on whether processing problems occur. Payments to 31
  40. 40. employees are frequently late. Multiple invoices for the same payment are frequently received and must be reviewed to determine if they have paid. The estimated average and range of the processing time is displayed in Table 4. 1. 3Table 4.1 Estimated processing time and summary of process capability of each processes Estimated processing Payroll Estimated average time range Process processing time 50 to 70 hours Std. Dev. Cpk 3.91937 0.84 60 hours The payroll processing time was measured from 20 samples. It was not performing capably according to the Cpk values obtained. The current process capability is Cpk=0.84 for payroll as shown in Table 4.2. 4Table 4.2 Raw data of the payroll process Labels Payroll Process Labels Payroll Process Labels Payroll Process Labels Payroll Process 1 55 6 57 11 53 16 56 2 59 7 50 12 56 17 55 3 55 8 52 13 50 18 57 4 52 9 50 14 57 19 54 5 55 10 52 15 56 20 56 4.2 Application of the original Improvement Roadmap 5 phases of the DMAIC improvement roadmap is applied for improvement of the payroll process. Successful implementation of the roadmap will be measured by the reduction of process inefficiencies, the reduction of the time it takes to process the payroll transactions, and the assignment of appropriate staffing levels to handle the workload. Each phase of the DMAIC have different roles. 4.2.1 Definition and Measurement of the Current Process Define phase defines the need of the improvement for the payroll process. In this case, the need for improving the payroll process is to reduce the process time and inefficiencies. The different inefficiencies are the following:  Additional stuff needed to complete works  Late payments to employees  Inefficient processing and depositing Another task was to develop a team charter to help team members clearly understand the scope and boundaries of the project, project objectives, project 32
  41. 41. duration, resources, roles of the team members, estimated financial gains from the project, etc. The SIPOC describes the scope of the payroll process improvement project as shown in Figure 4.1. Supplier -Payroll Input Process -Time reports -Payroll Output -Checks, Clerks Customer reports, -Employees taxes paid 15Figure 4.1 SIPOC diagram The goal of measure phase is to understand and document the current state of the process to be improved and identify the process problems that are causing inefficiencies and errors and their root causes. 4.2.2 Analysis and Improvement of the DMAIC Application The analyze phase is to analyze the problems and process inefficiencies and define improvement opportunities. Using cause and effect analysis to identify root causes related to people, methods, information technology, and hardware as presented in Figure 4.2. It’s better to compare the identified gaps of the current state process to practice a better payroll process. 16Figure 4.2 Cause and effect diagram In the analyses phase it is important to identify the improvement opportunities and develop an improvement plan. In this implementation, the study suggests that the payroll unit develop standardized process and procedure. Another improvement area is to use an excel spreadsheet to standardize batch calculations for matching, and dividing invoices amounts across different account numbers. A recommendation to the clerks who uses the payroll system is to get training for the software specifically to their streamlined payroll process. Another recommendation is to standardize the time sheets across all of the units to help reduce payroll data entry errors. Also for payroll clerks to use timesheets in excel spreadsheets to calculate the total timesheet hours by department to compare the payroll reports, instead of a calculator. 33
  42. 42. The goal of the improve phase is to implement the improvements, measure the impact of the improvements and document the procedure and train employees on the improved procedures. Validate the feasibility of the process improvement ideas in the analyze phase and implement the plan regarding the improvement suggestion for the payroll. Measure the impact of the improvements after the improvement is implemented for payroll process. It was found that the payroll processing time was reduced by 10%. The improvement is shown in table 4.3. The average time of payroll process was 60 hours and the process capability Cpk= 0.84 before improvement, after improvement the time was reduced to 54 hours, and the process capability becomes Cpk=1.21. The histogram of the observed data is shown in figure 4.3. However, this is not the Six Sigma required achievement, Six Sigma requires the Cpk=2. The next section will present the enhancement of Cpk to the required achievement with the proposed improvement roadmap. 5Table 4.3 Summary of the improved process capability and process time Process Payroll Estimated average processing time 54 hours Std. Dev. Cpk 2.5626 1.21 17Figure 4.3 Histogram of payroll process before (left) and after (right) improvement 4.3 Application of the Enhanced Improvement Roadmap With the application of the original DMAIC roadmap the current process improved from Cpk=0.84 to Cpk=1.21 but did not reach the required goal, because Six Sigma requires the Cpk=2. In order to reach the Six Sigma requirement, the study applies the proposed enhanced improvement roadmap in this section. The object of the research is to provide an improvement roadmap which reaches the Six Sigma requirement of Cpk=2. In this section, the application of the enhanced improvement roadmap is not going to repeat the application of the original DMAIC roadmap tasks but implement the proposed tasks to complete the project. During the application of the Six Sigma methodology, it is important to understand what your customer wants and what the 34
  43. 43. current capability of the process is by measuring the VOC and VOP. 4.3.1 Define and measure the VOC and VOP VOC communicates customer desires, requirements, needs, specifications, whereas the VOP communicates information about the performance of the process. Because there was no process measurement system in place to assess the CTS criteria related to cycle time, accuracy and customer satisfaction, the data collection plan is critical to help provide a way to measure the CTS. The data collection plan is shown in Table 4.4. In order to understand the VOC, customer survey was developed to assess VOC requirements for employees regarding the payroll process. There are 6 questions assessing the employee survey: 1. I receive my pay paycheck in a timely manner, 2. I receive an accurate paycheck, 3. If I call or see the payroll unit for service, I get prompt service, 4. If I call or see the payroll unit for service, I receive friendly service, 5. If I call or see the payroll unit for service, my problem gets solved completely at first time, 6. Please provide ideas for how to improve customer satisfaction with the payroll unit. The employee gives a survey by choosing numbers, such as strongly agree (1), disagree (2), neutral (3), agree (4), and strongly agree (5). 6Table 4.4 Payroll process data collection plan CTS Cycle time Metric mechanism mechanism Track for two payroll payroll cycles Type and number the process of defects Satisfaction Analysis Time to process Accuracy of Customer Data collection Employees Check sheet Survey Mean, range Pareto chart Sampling plan Time for 2 payroll months Defects for one month Statistical Survey company analysis units The VOP matrix helps to link the CTS criteria to the metrics, targets and potential process factors that affect the CTS. The VOP matrix is used to summarize the VOP (Table 4.5). The CTSs were defined as cycle time, accuracy of the process, and customer satisfaction. The cycle time was defined to be measured. The accuracy of the process would be potentially impacted by training in procedures and payroll software would be measured by assessing number and types of defects in the process. 35
  44. 44. Customer satisfaction could be impacted by whether there was repeatable process and whether the company would collect and measure VOC information. The VOC could be measured through surveys. The proposed target for each of the metrics is also included in the matrix. 7Table 4.5 VOP Matrix for payroll process CTS Process factors Operational definition Metric Target -Standard procedure exist Cycle time -Streamline processes Measure each Paid on time Paid on -training process time per schedule time -Volume of invoices and paychecks Accuracy of -Training in procedure the process and software Measure each process and defect types Measure Customer satisfaction -Repeatable process -Collect and assess VOC customer satisfaction through customer surveys Defects by process type % of positive responses for identified survey question 100% 80% of responses are rated 4 or 5 for identified questions 4.3.2Analysis and Improvement via DOE The aim of the project is to enhance the process capability by reducing variation in the process. Moreover, it is also important to understand the causes for the poor process capability. DOE helps to identify the possible sources of variation that affect the process, and by reducing the variation improve the process capability. Analyzing the gathered data, from the VOC and VOP relation, DOE will easily find out the problem and improve it. Payroll process by resolution category is shown in Figure 4.4 with help of a Pareto chart. The Pareto chart is developed to understand the process problems. Pareto analysis helps to identify employee training and knowledge gaps of the payroll information system. At this point, it is imperative to identify the parameters that are significant to the process so that they can be brought under statistical control. A simple regression analysis is performed to determine the significance of the process parameters. It is concluded form the regression analysis that the variables with P values less than 0.05 to 0.01 are statistically significant for further study. 36
  45. 45. Percentage Resolution category 18Figure 4.4 Pareto chart for information system problems DOE capabilities provide methods for simultaneously investigating the effects of multiple variables on an output variable. These experiments consist of a series of runs, or tests, in which purposeful changes are made to input variables or factors, and data are collected at each run. Quality professionals use DOE to identify the process conditions and product components that influence quality then determine the input variable settings that maximize results. Payroll process inefficiency is according the p value; it’s needed to be optimized. Full factorial DOE investigated what affects the process time. The reason a full factorial DOE is chosen for simplicity in collecting data since it is currently difficult to obtain. As well it reduces the number of possible combinations the experiment must perform. The optimization of the parameter yields an optimum response. Figure 4.5 illustrates the DOE graphical analysis of the payroll process. DOE Graphical analysis of the payroll process Individual Value Plot of Payroll process Scatterplot of Payroll process 50 55 Afternoon 58 60 Evening 65 60 Payroll 1 Payroll 2 56 54 55 Morning 65 60 52 55 50 50 A Attribute B Attribute C Attribute 55 Histogram of Payroll process Histogram of Payroll process Normal Normal 0,4 Mean 54,71 52,86 55,67 0,2 0,1 StDev 2,984 2,734 1,033 N 7 7 6 A Attribute 54 56 58 B Attribute C Attribute 3,0 1,5 0,0 48 50 52 54 56 58 60 60 3,0 1,5 Frequency Density 48 50 52 Center A Attribute B Attribute C Attribute 0,3 0,0 60 Panel variable: Center 48 50 52 54 0,0 A Attribute Mean 54,71 StDev 2,984 N 7 B Attribute Mean 52,86 StDev 2,734 N 7 C Attribute Mean 55,67 StDev 1,033 N 6 56 58 60 Panel variable: Center 19Figure 4.5 DOE graphical analysis of the payroll process DOE is conducted using the payroll process parameters. The process parameter is studied at two levels in order to keep the size of the experiment to a minimum, as well as to meet time. Full factorial design is chosen so that both main effects and 37
  46. 46. interaction effects, the trail condition were replicated twice. As the object of the experiment is to minimize the time and inefficiency of the process, the first object of the analysis is to determine the effect of the process parameters and to understand the presence of interactions, if present. The types of analysis that can be done with DOE’s include Pareto charts and normal probability plots which quickly display what combination of factor is significant. Another way to evaluate the main effects is whether p-values of the combinations is less than 0.05, if this is true it means the factor or the combination of the factors are significant. Other plots available for analysis are main effects plot which shows what effect changing one factor has the response and interaction plot which shows what impact changing one factor has on another factor that is kept unchanged. Figure 4.6 illustrate the main effect plots and interactions plot. In order to determine statistical significance of both main interaction effects, it is decided to construct normal probability plot of effect. The detailed display descriptive statistics result of the DOE for the payroll process and DOE analysis of variance (ANOVA) performance can be found in Appendix A. Main Effects Plot for Hours Interaction Plot for Hours Data Means Data Means A Current New 56 A 55,5 B 55,0 Mean Mean 55 54,5 54 54,0 53 53,5 A B Current B Normal Plot of the Standardized Effects New B Pareto Chart of the Standardized Effects (response is Hours, Alpha = ,20) (response is Hours, Alpha = ,20) 99 1,337 Effect Type Not Significant Significant 90 Factor A B B B B Term 50 Name A A B Factor Name A B Percent A A A 10 1 AB -2 -1 0 1 Standardized Effect 2 0,0 0,5 1,0 Standardized Effect 1,5 2,0 20Figure 4.6 DOE of the payroll process The following recommendations are proposed based on the analysis from the VOC and VOP with use of DOE. There are certain unnecessary steps in the payroll process, such as printing lengthy reports that were never used. The research work encourage either not printing the reports at all, or printing them to an electronic file, which took seconds, instead of hours. And also, the use of the new accounts receivable technology that automatically transferred journals entries, instead of requiring redundant data. For the payroll process, direct deposit is an important opportunity to eliminate printing of payroll cheques. A payroll process is better to 38

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