Six Sigma Unplugged
                                                        - A. Sivaram
Shewhart and Dr. Joseph M. Juran worked in the 1920s. In 1924, Shewhart first sketched out the control
chart. What has sur...
1980s to 1990s: The American Quality Movement
Loss of market share, especially dramatic in the automotive and electronic i...
implementations, the majority of Six Sigma projects are selected for measurable bottom-line or customer
impact that is com...
• Improve. The goal of the Improve phase is to implement solutions that address the problems (root
causes) identified duri...
• Validate the Measurement System
   • Collect the Data
   • Begin Developing Y=f(x) Relationship
   • Determine Process C...
Tools used
    • Brainstorming
    • Mistake Proofing
    • Design of Experiments
    • Pugh Matrix
    • House of Quality...
Fig. Six Sigma Overview

Define: Step One     Measure: Step         Analyze: Step        Improve: Step          Control: S...
only do so under the guidance of master black belts. Because of the nature of the master's duties,
communications and teac...
Six Sigma Unplugged
Upcoming SlideShare
Loading in …5

Six Sigma Unplugged


Published on

Six Sigma Unplugged

Published in: Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Six Sigma Unplugged

  1. 1. Six Sigma Unplugged - A. Sivaram Sigma The term sigma is Greek alphabet letter σ. It describes variability, where a classical measurement unit consideration of the program is defects per unit. A sigma quality level offers an indicator of how often defects are likely to occur, where higher sigma quality level indicates A process that is less likely to create defects. A six sigma quality level is said to equate to 3.4 defects per million opportunities (DPMO) Antecedents of Six Sigma While Six Sigma was invented at Motorola in the late 1980s, Six Sigma has had antecedents over the past 100 years. 1900 to 1920s: Scientific Management and Statistics Taylor and Scientific Management Frederick W. Taylor’s techniques, which became known as scientific management, made work tangible and measurable through analyzing manufacturing processes and turning them into a set of tasks that could be standardized and made repetitive. With work fragmented into a multitude of tasks, a managerial system was then required to control work. The concept of the separation of planning and execution was central to Taylor’s system. Taylor advocated planning departments staffed by engineers with the following responsibilities: • Developing scientific methods for doing work • Establishing goals for productivity • Establishing systems of rewards for meeting the goals • Training the personnel in how to use the methods and thereby meet the goals Taylor’s system dealt a blow to the concept of craftsmanship in managing work or quality as a single end-to-end process. In 1911, The Principles of Scientific Management, a collection of his writings, was published. By the 1920s, Taylor’s methods were widely adopted and Taylor’s ideas had influence across the globe. Ford Assembly Line Henry Ford adopted four principles in his goal to efficiently produce an automobile at an affordable price: interchangeable parts, continuous flow, division of labor, and a reduction of wasted effort. Influenced by Taylor’s ideas and Ford’s own observations of improved work flow in other industries, the assembly of the Model T, first produced in 1908, was broken down into 84 distinct steps, with each worker trained to do just one. Ford had Taylor do time-and-motion studies to determine the exact speed at which the work should proceed and the exact motions workers should use to accomplish their tasks. In 1913, Ford’s experiments and innovations came together in the first moving assembly line used for large-scale manufacturing. Ford’s early methods are a foundation of Just-in-Time and Lean Manufacturing. Walter A. Shewhart and Statistical Process Control Quality engineering can trace its origins to the applications of statistical methods for control of quality in manufacturing. Much of the early work was done at Bell Telephone Laboratories, where both Walter
  2. 2. Shewhart and Dr. Joseph M. Juran worked in the 1920s. In 1924, Shewhart first sketched out the control chart. What has survived of that early work is the Shewhart control chart and what has become known as Statistical Process Control. Shewhart’s work laid the foundation not only for the use of engineering methods to specify work processes, but also for the use of statistical methods that quantify the quality and variability of processes. 1950s: Deming, Juran, and Feigenbaum and the Japanese Quality Emergency Japanese upper management—presidents and general managers—assumed the leadership of the quality function in response to the quality emergency of the 1950s. Shoddy quality had made Japanese goods uncompetitive. The postwar rebuilding of Japanese industry was seen by industry leaders as a unique opportunity to radically deal with this problem. Dr. W. Edwards Deming, Dr. Armand Feigenbaum, and Dr. Joseph M. Juran are widely credited with helping the Japanese revolutionize their quality and competitiveness after World War II, and they served as consultants to the Japanese in the ensuing decades. The three became prominent in the United States after the Japanese quality revolution struck fear into American business. Although their contributions are many and complex, what we want to do here is simply point out contributions that are important to our understanding of the origins of Six Sigma. Dr. W. Edwards Deming Known for introducing statistical quality control to Japan, Deming also placed great importance on the responsibility of management, believing it to be responsible for 94 percent of quality problems. Deming is also associated with the “plan-do-check-act” (PDCA) cycle as a universal improvement cycle (also known as the Shewhart cycle, as Shewhart first advocated its use). Dr. Joseph M. Juran Juran developed the quality trilogy—quality planning, quality control, and quality improvement. Juran associated quality with customer satisfaction and dissatisfaction, emphasized ongoing quality improvement through a succession of improvement projects, and believed upper management leadership of the quality function was critical. Juran also emphasized reducing the cost of poor quality as a key to competitiveness. JURAN SAID… “All quality improvement occurs on a project-by-project basis and in no other way.” Dr. Armand Feigenbaum Known as the originator of “total quality control” or “total quality,” Feigenbaum defined total quality as an effective system to ensure production and service at the most economical levels that allow customer satisfaction. 1960s to 1980s: Japanese Quality Revolution Japanese companies chose to train almost all managers in the science of quality. Unlike in the West, quality responsibility and training were not confined to members of specialized quality functions. From the 1950s onward, Japanese companies undertook a massive training program in quality for employees and instituted annual programs of quality improvement. They also instituted a project concept of quality improvements. Improvement breakthroughs were made project by project under the guidance of managers who selected the improvement projects and mobilized and guided project teams.
  3. 3. 1980s to 1990s: The American Quality Movement Loss of market share, especially dramatic in the automotive and electronic industries, ultimately led to a reinvention of manufacturing in North America, beginning with the rediscovery of Statistical Process Control (SPC) and the introduction of quality circles, through Just-in-Time (JIT) and Total Quality Management (TQM) to business process reengineering (BPR) to Lean Manufacturing and Six Sigma. Total Quality Management (TQM) In application, TQM generally focused on organizational results rather than on business results. Although the mantra of customer focus was chanted, the tools for integrating what the customer required were not rigorous. Also, even while having a mind-set toward improving processes, entrenched Taylorism, along with the tendency of companies to ghettoize these improvement efforts as engineering and quality disciplines, have led to overall disappointment with TQM. TQM evolved during the mid-1980s into the first generation of Six Sigma at Motorola. Business Process Reengineering (BPR) Michael Hammer and James Champy’s message on business process reengineering, introduced in the early 1990s in Reengineering the Corporation, was welcome to an audience disenchanted with TQM and ready to use its new IT horsepower to automate processes and in doing so to tighten processes and eliminate unnecessary and redundant steps. Executives were looking for business results, not just organizational results. Six Sigma - Objective “Six Sigma is a methodology that provides businesses with the tools to improve the capability of their business processes. This increase in performance and decrease in process variation leads to defect reduction and vast improvement in profits, employee morale and quality of product” What is “Six Sigma”? Six Sigma is a well structured, data-driven methodology for eliminating defects, waste, or quality control problems of all kinds in manufacturing, service delivery, management, and other business activities. Six Sigma methodology is based on the combination of well established statistical quality control techniques, simple and advanced data analysis methods, and the systematic training of all personnel at every level in the organization involved in the activity or process targeted by Six Sigma. Why is Six Sigma so popular? Six Sigma methodology has recently gained wide popularity because it has proven to be successful not only at improving quality but also at producing large cost savings along with those improvements. Some spectacular Six Sigma “success stories” at large corporations have been widely publicized and they captured the imagination of many business leaders. Six Sigma Defined The Six Sigma of today speaks the language of management: bottom-line results. It institutionalizes a rigorous, disciplined, fact-based way to deliver more money to the bottom line through process improvement and process design projects—selected by the top leadership and led by high potentials trained as Black Belts or Master Black Belts in Six Sigma—that aim to create near-perfect processes, products, and services all aligned to delivering what the customer wants. In successful
  4. 4. implementations, the majority of Six Sigma projects are selected for measurable bottom-line or customer impact that is completed within two to six months. The projects deliver through the application of a well-defined set of statistical tools and process improvement techniques by well-trained people in an organization that has made it clear that Six Sigma is a career accelerator. In our practice, we see companies viewing Six Sigma in two ways: as a set of powerful tools for improving processes and products and as an approach for improving both the process- and people-related aspects of business performance. Six Sigma is used as a hands-on approach to developing leadership and change management skills. The companies that achieve the greatest benefits from Six Sigma leverage the linkages between people, processes, customer, and culture. In its 2000 annual report, GE describes the changes brought by Six Sigma this way: “Six Sigma has turned the Company’s focus from inside to outside, changed the way we think and train our future leaders and moved us toward becoming a truly customer-focused organization.” Technically Speaking... The term Six Sigma (a trademark of Motorola, where it originated over 12 years ago) reflects the statistical objective of the approach, namely striving to achieve a negligible number of defects, corresponding to the probability associated with a (quot;correctedquot; - see below) six sigma value for the normal curve: Applying the normal curve, Six Sigma attempts to relegate defects and quality problems to the very tails of the distribution, making such problems literally rare exceptions in a process that operates almost without defects. To achieve this quot;Six Sigma objective,quot; a process must not produce more than 3.4 defects per million opportunities to produce such defects (where a quot;defectquot; is defined as any kind of unacceptable outcome produced by the process under scrutiny). Note that the 3.4 defects-per- million criterion actually corresponds to a normal z value of 4.5 because the Six Sigma approach allows for 1.5 times sigma worth of so-called quot;driftquot; or process quot;slopquot; (termed by Motorola the quot;Long-Term Dynamic Mean Variationquot;). Hence, the most basic statistical tool for the Six Sigma effort is the Six Sigma calculator that will compute the number of defects given the respective one, two, .., six sigma process. In addition, a wide variety of much more complex analytic techniques are recommended by the Six Sigma approach and need to be used at the consecutive stages of the Six Sigma project, depending on the nature of the process. How does it work? The power of Six Sigma lies in its “empirical,” data-driven approach (and its focus on using quantitative measures of how the system is performing) to achieve the goal of the process improvement and variation reduction. That is done through the application of so-called “Six Sigma improvement projects” which, in turn, follow the “Six Sigma DMAIC” sequence of steps (Define, Measure, Analyze, Improve, and Control). Specifically: • Define. The Define phase is concerned with the definition of project goals and boundaries, and the identification of issues that need to be addressed to achieve the higher (better) sigma level. • Measure. The goal of the Measure phase of the Six Sigma strategy is to gather information about the current situation, to obtain baseline data on current process performance, and to identify problem areas. • Analyze. The goal of the Analyze phase of the Six Sigma quality effort is to identify the root cause(s) of quality problems, and to confirm those causes using the appropriate data analysis tools.
  5. 5. • Improve. The goal of the Improve phase is to implement solutions that address the problems (root causes) identified during the previous (Analyze) phase. • Control. The goal of the Control phase is to evaluate and monitor the results of the previous phase (Improve). There is also a variation of the fundamental Six Sigma DMAIC sequence, called DMADV, applicable to the design of new processes. In the DMADV sequence, the Define stage is identical to the one in DMAIC (see above); the Measure stage focuses on the measurement of the customer and/or market/application needs, the Analyze stage deals with the analysis of the process options and, finally, the Improve and Control stages are replaced by the Design (design the process to meet the customer and/or market/application needs) and Verify (verify the design performance and ability to meet the criteria as set at the Design level) stages. Each of these steps involve using specific analytic (quantitative) methods from a wide selection of methods recommended by the Six Sigma approach (depending on the nature of the process). DMAIC Phase Steps D - Define Phase: Define the project goals and customer (internal and external) deliverables. Steps • Define Customers and Requirements (CTQs) • Develop Problem Statement, Goals and Benefits • Identify Champion, Process Owner and Team • Define Resources • Evaluate Key Organizational Support • Develop Project Plan and Milestones • Develop High Level Process Map Tools Used • Project Charter • Process Flowchart • SIPOC Diagram • Stakeholder Analysis • DMAIC Work Breakdown Structure • CTQ Definitions • Voice of the Customer Gathering Deliverables of Define Phase: • Fully trained team is formed, supported, and committed to work on improvement project • Customers identified and high impact characteristics (CTQs) defined, team charter developed, business process mapped. M - Measure Phase: Measure the process to determine current performance; quantify the problem. • Define Defect, Opportunity, Unit and Metrics • Detailed Process Map of Appropriate Areas • Develop Data Collection Plan
  6. 6. • Validate the Measurement System • Collect the Data • Begin Developing Y=f(x) Relationship • Determine Process Capability and Sigma Baseline Tools used • Process Flowchart • Data Collection Plan/Example • Benchmarking • Measurement System Analysis/Gage R&R • Voice of the Customer Gathering • Process Sigma Calculation Deliverables of Measure Phase: • Key measures identified, data collection planned and executed, process variation displayed and communicated, performance baselined, sigma level calculated A - Analyze Phase: Analyze and determine the root cause(s) of the defects. • Define Performance Objectives • Identify Value/Non-Value Added Process Steps • Identify Sources of Variation • Determine Root Cause(s) • Determine Vital Few x's, Y=f(x) Relationship Tools used • Histogram • Pareto Chart • Time Series/Run Chart • Scatter Plot • Regression Analysis • Cause and Effect/Fishbone Diagram • 5 Whys • Process Map Review and Analysis • Statistical Analysis • Hypothesis Testing (Continuous and Discrete) • Non-Normal Data Analysis Deliverables of Analyze Phase: • Data and process analysis, root cause analysis, quantifying the gap/opportunity I - Improve Phase: Improve the process by eliminating defects. • Perform Design of Experiments • Develop Potential Solutions • Define Operating Tolerances of Potential System • Assess Failure Modes of Potential Solutions • Validate Potential Improvement by Pilot Studies • Correct/Re-Evaluate Potential Solution
  7. 7. Tools used • Brainstorming • Mistake Proofing • Design of Experiments • Pugh Matrix • House of Quality • Failure Modes and Effects Analysis (FMEA) • Simulation Software Deliverables of Improve Phase: • Generate (and test) possible solutions, select the best solutions, design implementation plan. C - Control Phase: Control future process performance. • Define and Validate Monitoring and Control System • Develop Standards and Procedures • Implement Statistical Process Control • Determine Process Capability • Develop Transfer Plan, Handoff to Process Owner • Verify Benefits, Cost Savings/Avoidance, Profit Growth • Close Project, Finalize Documentation • Communicate to Business, Celebrate Tools used • Process Sigma Calculation • Control Charts (Variable and Attribute) • Cost Savings Calculations • Control Plan Deliverables of Control Phase: • Documented and implemented monitoring plan, standardized process, documented procedures, response plan established and deployed, transfer of ownership (project closure).
  8. 8. Fig. Six Sigma Overview Define: Step One Measure: Step Analyze: Step Improve: Step Control: Step of DMAIC Two of DMAIC Three of DMAIC Four of DMAIC Five of DMAIC •Project charter, • Measurement •Logical cause •Unleashing your •History of problem process and steps analysis creativity Statistical statement, stretch •Process Mapping •Root cause •Think like a Process Control goal, etc. •Functional- analysis genius (SPC) •Voice of the Activity Flow •Narrowing down •Brainstorming •Cautions in SPC customer (VOC) Chart root causes and other •Using code •Converting VOC •Fishbone, Cause •Frequency plots creativity tools values to ease data into Critical to & Effect, •Histograms •Sixteen process collection Quality (CTQ) Ishikawa •Run charts redesign creativity •Control charts: variables to Diagrams •Scatter plots and techniques an overview measure •Checksheets correlation •Thirty-five •Choosing the •SIPOC diagram •Pareto diagrams diagrams “design right type of •Customer and •Cycle time •Cycle time principles” for control chart supplier measurement causal analysis solving process •Process evaluations system •Verifying root problems capability analysis •Quality •Continuous and causes with data •Lean enterprise •Control plan Function Discrete variables approach Deployment •Stratified •Failure Mode (QFD) sampling and Effects •Gauge R&R Analysis (FMEA) •DPMO •Turning wild •Rolled ideas into winners throughput yield •Selecting your •Project optimal process storyboard •Developing an implementation plan •De-risk your process proposal •Implementation options Master Black Belt - This is the highest level of technical and organizational proficiency. This is the highest level of technical and organizational proficiency. Because master black belts train black belts, they must know everything the black belts know, as well as understand the mathematical theory on which the statistical methods are based. Masters must be able to assist black belts in applying the methods correctly in unusual situations. Whenever possible, statistical training should be conducted only by master black belts. If it's necessary for black belts and green belts to provide training, they should
  9. 9. only do so under the guidance of master black belts. Because of the nature of the master's duties, communications and teaching skills should be judged as important as technical competence in selecting candidates. Black Belt Candidates for technical leader (black belt) status are technically oriented individuals held in high regard by their peers. They should be actively involved in the organizational change and development process. Candidates may come from a wide range of disciplines and need not be formally trained statisticians or engineers. However, because they are expected to master a wide variety of technical tools in a relatively short period of time, technical leader candidates will probably possess a background in college-level mathematics, the basic tool of quantitative analysis. Six sigma technical leaders work to extract actionable knowledge from an organization's information warehouse. Green Belts Green belts are six sigma team leaders capable of forming and facilitating six sigma teams and managing six sigma projects from concept to completion. Typically, green-belt training consists of five days of classroom training and is conducted in conjunction with six sigma team projects. Training covers facilitation techniques and meeting management, project management, quality management tools, quality control tools, problem solving, and exploratory data analysis. Usually, six sigma black belts help green belts choose their projects prior to the training, attend training with their green belts and assist them with their projects after the training. Six Sigma – Key points Objective: Customer focused approach & Minimizing variations in business projections Metrics: Anything that falls outside specifications is considered defect. To achieve Six Sigma quality levels processes should not produce more than 3.4 defects per million opportunities. DMAIC: Define, Measure, Analyze, Improve, Control –For improving performance of existing projects DMADV: Design of new processes. Define, Measure, Analyze, Design, Verify DFSS: Design for Six Sigma. DFSS is used to design or re-design a product or service from the ground up. One popular Design for Six Sigma methodology is called DMADV, and retains the same number of letters, number of phases, and general feel as the DMAIC acronym. Master Black Belt – senior process improvement specialist, with deep skills in the process improvement sciences Black Belt – team leader of large process improvement projects Green Belt – team leader of small to medium size process improvement projects Champion –A Six Sigma Champion manages, supports, defends, protects, fights for, maintains and deploys an organization’s Six Sigma effort. He is the management owner of process improvement projects. A Master Black Belt is a mentor, trainer and coach of black belts and others in the Six Sigma organization. A Black Belt is a leader of teams implementing the Six Sigma methodology on projects. A Green Belt delivers successful, focused projects using the Six Sigma methodology and tools.