six sigma
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In statistics , the 68-95-99.7 rule, or three-sigma rule, or empirical rule, states that for a normal distribution , nearly all values lie within 3 standard deviations of the mean . About 68% of the values lie within 1 standard deviation of the mean. In statistical notation, this is represented as: . About 95% of the values lie within 2 standard deviations of the mean. The statistical notation for this is: . Nearly all (99.7%) of the values lie within 3 standard deviations of the mean. Statisticians use the following notation to represent this: . This rule is often used to quickly get a rough probability estimate of something, given its standard deviation, if the population is assumed normal, thus also as a simple test for outliers (if the population is assumed normal), and as a normality test (if the population is potentially not normal). Recall that to pass from a sample to a number of standard deviations, one computes the deviation , either the error or residual (accordingly if one knows the population mean or only estimates it), and then either uses standardizing (dividing by the population standard deviation), if the population parameters are known, or studentizing (dividing by an estimate of the standard deviation), if the parameters are unknown and only estimated. To use as a test for outliers or a normality test, one computes the size of deviations in terms of standard deviations, and compares this to expected frequency. Given a sample set, compute the studentized residuals and compare these to the expected frequency: points that fall more than 3 standard deviations from the norm are likely outliers (unless the sample size is significantly large, by which point one expects a sample this extreme), and if there are many points more than 3 standard deviations from the norm, one likely has reason to question the assumed normality of the distribution. This holds ever more strongly for moves of 4 or more standard deviations.
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Transcript of six sigma
In statistics, the 68-95-99.7 rule, or three-sigma rule, or empirical rule, states that for a normal distribution, nearly all values lie within 3 standard deviations of the mean.
About 68% of the values lie within 1 standard deviation of the mean. In statistical notation, this is represented as: .
About 95% of the values lie within 2 standard deviations of the mean. The statistical notation for this is: .
Nearly all (99.7%) of the values lie within 3 standard deviations of the mean. Statisticians use the following notation to represent this: .
This rule is often used to quickly get a rough probability estimate of something, given its standard deviation, if the population is assumed normal, thus also as a simple test for outliers (if the population is assumed normal), and as a normality test (if the population is potentially not normal).
Recall that to pass from a sample to a number of standard deviations, one computes the deviation, either the error or residual (accordingly if one knows the population mean or only estimates it), and then either uses standardizing (dividing by the population standard deviation), if the population parameters are known, or studentizing (dividing by an estimate of the standard deviation), if the parameters are unknown and only estimated.
To use as a test for outliers or a normality test, one computes the size of deviations in terms of standard deviations, and compares this to expected frequency. Given a sample set, compute the studentized residuals and compare these to the expected frequency: points that fall more than 3 standard deviations from the norm are likely outliers (unless the sample size is significantly large, by which point one expects a sample this extreme), and if there are many points more than 3 standard deviations from the norm, one likely has reason to question the assumed normality of the distribution. This holds ever more strongly for moves of 4 or more standard deviations.
One can compute more precisely, approximating the number of extreme moves of a given magnitude or greater by a Poisson distribution, but simply, if one has multiple 4 standard deviation moves in a sample of size 1,000, one has strong reason to consider these outliers or question the assumed normality of the distribution.
Six Sigma is a business management strategy originally developed by Motorola, USA in 1986.[1]
[2] As of 2010, it is widely used in many sectors of industry, although its use is not without controversy.
Six Sigma seeks to improve the quality of process outputs by identifying and removing the causes of defects (errors) and minimizing variability in manufacturing and business processes.[3] It uses a set of quality management methods, including statistical methods, and creates a special infrastructure of people within the organization ("Black Belts", "Green Belts", etc.) who are experts in these methods.[3] Each Six Sigma project carried out within an organization follows a
defined sequence of steps and has quantified financial targets (cost reduction or profit increase).[3]
The term six sigma originated from terminology associated with manufacturing, specifically terms associated with statistical modelling of manufacturing processes. The maturity of a manufacturing process can be described by a sigma rating indicating its yield, or the percentage of defect-free products it creates. A six-sigma process is one in which 99.99966% of the products manufactured are statistically expected to be free of defects (3.4 defects per million). Motorola set a goal of "six sigmas" for all of its manufacturing operations, and this goal became a byword for the management and engineering practices used to achieve it.
METHODS
Six Sigma projects follow two project methodologies inspired by Deming's Plan-Do-Check-Act Cycle. These methodologies, composed of five phases each, bear the acronyms DMAIC and DMADV.[12]
DMAIC is used for projects aimed at improving an existing business process.[12] DMAIC is pronounced as "duh-may-ick".
DMADV is used for projects aimed at creating new product or process designs.[12] DMADV is pronounced as "duh-mad-vee".
[edit] DMAIC
The DMAIC project methodology has five phases:
Define the problem, the voice of the customer, and the project goals, specifically. Measure key aspects of the current process and collect relevant data. Analyze the data to investigate and verify cause-and-effect relationships. Determine what the
relationships are, and attempt to ensure that all factors have been considered. Seek out root cause of the defect under investigation.
Improve or optimize the current process based upon data analysis using techniques such as design of experiments, poka yoke or mistake proofing, and standard work to create a new, future state process. Set up pilot runs to establish process capability.
Control the future state process to ensure that any deviations from target are corrected before they result in defects. Implement control systems such as statistical process control, production boards, and visual workplaces, and continuously monitor the process.
[edit] DMADV or DFSS
The DMADV project methodology, also known as DFSS ("Design For Six Sigma"),[12] features five phases:
Define design goals that are consistent with customer demands and the enterprise strategy. Measure and identify CTQs (characteristics that are Critical To Quality), product capabilities,
production process capability, and risks.
Analyze to develop and design alternatives, create a high-level design and evaluate design capability to select the best design.
Design details, optimize the design, and plan for design verification. This phase may require simulations.
Verify the design, set up pilot runs, implement the production process and hand it over to the process owner(s).
[edit] Quality management tools and methods used in Six Sigma
Within the individual phases of a DMAIC or DMADV project, Six Sigma utilizes many established quality-management tools that are also used outside of Six Sigma. The following table shows an overview of the main methods used.
5 Whys Analysis of variance ANOVA Gauge R&R Axiomatic design Business Process Mapping Cause & effects diagram (also known as
fishbone or Ishikawa diagram) Chi-square test of independence and fits Control chart Correlation Cost-benefit analysis CTQ tree Design of experiments Failure mode and effects analysis (FMEA) General linear model
Histograms Quality Function Deployment (QFD) Pareto chart Pick chart Process capability Quantitative marketing research through
use of Enterprise Feedback Management (EFM) systems
Regression analysis Root cause analysis Run charts SIPOC analysis (Suppliers, Inputs, Process,
Outputs, Customers) Taguchi methods Taguchi Loss Function TRIZ
DMAIC ProcessDefine theProblemMeasure &AnalysisImproveControl- Provide full information about the problem such as the
rejection %, Suspected sources of variations for the problem,type of responseApply DOE techniques and arrive at the “Root cause”(Controllable cause) for the problemValidate the Root cause using B vs CIdentify and Implement solution for eliminating the root causesIdentify and Implement control measures for the root cause to
make sure that the problem is prevented from occurring
D:
Define the Strategic Direction of the organization M:
Set Measures for the strategic objectives of the organization A:
On a continual basis collect data on the measures set and Analyze using Six Sigma tools and techniques
I:Identify the opportunities for Improvement and convert them to Lean Six Sigma projects for improvement
C:Set up a management Control action of continuous reviews on the improvements made on the Lean Six Sigma Projects
Six Sigma is a proven disciplined approach for improving measurable results for any organization. Six Sigma project success stories exist from organizations including manufacturing, service, nonprofit, government, research and healthcare. The key to Six Sigma is the completion of leadership sponsored projects. Six Sigma Certification requires completing an actual Six Sigma project. SixSigma.us offers both live and online programs
Six Sigma – what does it mean?
Six Sigma at many organizations simply means a measure of quality that strives for near perfection. Six Sigma is a disciplined, data-driven approach and methodology for eliminating defects (driving toward six standard deviations between the mean and the nearest specification limit) in any process -- from manufacturing to transactional and from product to service.
The statistical representation of Six Sigma describes quantitatively how a process is performing. To achieve Six Sigma, a process must not produce more than 3.4 defects per million opportunities. A Six Sigma defect is defined as anything outside of customer specifications. A Six Sigma opportunity is then the total quantity of chances for a defect. Process sigma can easily be calculated using a Six Sigma calculator.
The fundamental objective of the Six Sigma methodology is the implementation of a measurement-based strategy that focuses on process improvement and variation reduction through the application of Six Sigma improvement projects. This is accomplished through the use of two Six Sigma sub-methodologies: DMAIC and DMADV. The Six Sigma DMAIC process (define, measure, analyze, improve, control) is an improvement system for existing processes falling below specification and looking for incremental improvement. The Six Sigma DMADV process (define, measure, analyze, design, verify) is an improvement system used to develop new processes or products at Six Sigma quality levels. It can also be employed if a current process requires more than just incremental improvement. Both Six Sigma processes are executed by Six Sigma Green Belts and Six Sigma Black Belts, and are overseen by Six Sigma Master Black Belts.
According to the Six Sigma Academy, Black Belts save companies approximately $230,000 per project and can complete four to 6 projects per year. General Electric, one of the most successful companies implementing Six Sigma, has estimated benefits on the order of $10 billion during the first five years of implementation. GE first began Six Sigma in 1995 after Motorola and Allied Signal blazed the Six Sigma trail. Since then, thousands of companies around the world have discovered the far reaching benefits of Six Sigma.
Many frameworks exist for implementing the Six Sigma methodology. Six Sigma Consultants all over the world have developed proprietary methodologies for implementing Six Sigma quality, based on the similar change management philosophies and applications of tools.
Six Sigma Green Belt Certificate Program
Six Sigma is a disciplined, data-driven approach and methodology for eliminating
defects in any process—from manufacturing to transactional and from product to service.
It is a measurement-based methodology that focuses on process improvement and variation reduction. It is based on the organized application of a set of statistical/analytical and problem solving tools/techniques.
Overview
Six Sigma is all about mindset and culture shift that eliminates the marginal methods that have been used in the past and replacing them with a set of simple yet sophisticated statistical/analytical tools that continually produce exceptional results in how processes operate. It helps to create a high performance organization.
This is a 4days certificate program consisting of 6 modules that is designed
Topics Covered in the Program
Introduction to six sigma
Define
Measure
Analyze
Improve
Control
Six Sigma Green Belt Certificate Program
to teach the six sigma way of process improvement. Each module contains theory followed by case studies These sessions include group time and teaching/applying the Six Sigma body of knowledge including:
The identification of core processes Defining customer requirements Measuring current performance Defining opportunities for improvement Measuring the relevant processes requiring improvement Gathering and analyzing the data required to investigate causes Improving, controlling, and redesigning the processes.
Benefits to the individual
Acquire the necessary abilities and skills to implement six sigma effectively and economically.
Acquire certification in a set of management disciplines that are fast-becoming indispensable to meeting world-class standards of performance.
In firms undertaking the six sigma transformation, this program equips participants to play a major role in its initiation and implementation.
Acquire the prerequisites for accepting a leadership role in six sigma transformations.
Acquire the understanding and skills required be a high impact player in the six sigma transformations.
Become highly visible to top management as a result of participation in successful six sigma transformations.
Benefits to the company
Develop a core group of managers and professional personnel who can work as a team in the implementation of six sigma.
Develop a comprehensive and integrated approach to six sigma that takes in to account the necessary training required in all functions and at all levels within the organization.
Develop an approach to six sigma that is appropriate for the management team, resources, technology, and work force of the organization.
Develop a more accountable and responsible management group capable of becoming industry leaders.
Become more effective operationally in the following ways: o Shorter cycle times for all key processes. o Striving for and/or meeting six sigma quality standards o Continuous cost reduction based on continuous process
improvements o Increased productivity as both management, staff, and workforce
become more disciplined in the six sigma methodology o Synchronous work flow based on more effective process linkage o Elimination of waiting time within and between processes o Waste reduction resulting from meeting more demanding
performance standards o Improved customer service based on more robust and
dependable process designs o More efficient use of resources resulting from both improved
Course Code
PI 09
Term: 2006
Qualification
DEGEREE/DIPLOMA/STUDENTS IN FINAL YEAR
Location BANGALORE
Dates29 SEPTEMBER-2nd
OCTOBER (4days)
Timing 8.30AM-5.30 PM
Required Fees /CANDIDATE Rs 15000*
Online Registration Limited seats register early.
TOOLS
Brain storming
Pareto chart
Project Charter
SIPOC
Stake holder analysis
Process mapping
Fish bone analysis
Measurement system analysis and
gage R&R
SPC
Control charts
Process capability
Hypothesis testing
ANOVA
DOE
process designs and better management practices o Elimination of “silo management” in the organization structure
Topics
Introduction to six sigma
Six Sigma and achieving competitive advantage Understanding roles of leadership and key players Overview of DMAIC methodology
Define
Identifying customers and customer requirements Developing team objectives, goals and targets Process Mapping, SIPOC
Measure
Process Analysis and documentation Determining measurements Probability and statistics Analyzing the measurement system Analyzing process capability Introduction to
Analyze
Exploratory data analysis Sources of variation Hypothesis testing Introduction to ANOVA Regression Analysis
Improve
Developing solutions Design of experiments Process Optimization Response Surface Methodology Evolutionary Operations
Control
Statistical process control Advanced statistical process control Implementing process controls Attribute and variable control charts Monitoring progress
Project Work (Apply DMAIC)
Participants define a problem, perform , identify critical-to-quality (CTQ) characteristics, establish a process map, analyze for root cause, improve the process and implement process controls. At the end of the class, candidates present their project work
Who Should Attend
GENERAL MANAGERS FACTORY MANAGERS BUSINESS MANAGERS FINANCE /COSTING
MANAGERS PRODUCTION MANAGERS PROJECT MANAGERS QUALITY MANAGERS GROUP LEADS SERVICE MANAGERS
MANUFACTURING BPO SOFTWARE MEDICAL HOTELS AIR LINES BANKING CONSTRUCTION COLLEGES
What is Six Sigma?
Six Sigma is a management methodology meant to drive process improvements in the organization through quantitative approach. Six Sigma has kept its edge while some other management approaches are in perishable stage or already perished due to following reasons:
1. The quantitative analytics – that we cannot remove from the business environment2. There is no other tangible benefits apart from the actual improvement which the
organization realizes during the Six Sigma journey.
The Six Sigma methodology is well rooted in statistics and statistical mathematics. Learn why six standard deviations is worthwhile for your organization to measure
What does it mean to be "Six Sigma"? Six Sigma at many organizations simply means a measure of quality that strives for near perfection. But the statistical implications of a Six Sigma program go well beyond the qualitative eradication of customer-perceptible defects. It's a methodology that is well rooted in mathematics and statistics.
The objective of Six Sigma Quality is to reduce process output variation so that on a long term basis, which is the customer's aggregate experience with our process over time, this will result in no more than 3.4 defect Parts Per Million (PPM) opportunities (or 3.4 Defects Per Million Opportunities – DPMO). For a process with only one specification limit (Upper or Lower), this results in six process standard deviations between the mean of the process and the customer's specification limit (hence, 6 Sigma). For a process with two specification limits (Upper and Lower), this translates to slightly more than six process standard deviations between the mean and each specification limit such that the total defect rate corresponds to equivalent of six process standard deviations.
How Is Six Sigma Different? Articles
Debunking Dubious Statistics from a Six Sigma Critic The principal and founder of a performance improvement consulting firm, professes that businesses using Six Sigma are underperforming their peers in terms of stock price.…
Is Six Sigma for Real? Question Deserves Solid Answers Many quality improvement methods talk about measuring and improving, but what specifically makes Six Sigma more valuable than the others and then, in this case,…
Is This A Six Sigma, Lean, Or Kaizen Project? Executives who develop a working knowledge of Six Sigma, Lean, and Kaizen are much better prepared to build the right infrastructure and lead their organizations…
Linking Quality to Business Finances If it didn't make sense financially, you wouldn't do it. Learn why linking Quality to financial results benefits everyone in the business.
Linking Quality to Finances Process improvements should be tied to financial results in order to sustain a business.
Six Sigma Is Just A Fad Most likely you've heard that Six Sigma is just a fad. But should we add Six Sigma to the list of growing management fads? Various…
Six Sigma Versus Lean In this excerpt from the iSixSigma Discussion Forum, posters debate the differences between Lean and Six Sigma, and how the two can be used together.
Six Sigma Versus TQM In this excerpt from the iSixSigma Discussion Forum, posters compare and contrast Six Sigma and its predecessor, TQM.
What Makes Six Sigma Work? Consultants are earning seven figure salaries, CEOs are claiming billions of dollars saved, conferences are popping up everywhere, and yet one can argue that Six…
Why Choose Six Sigma Over Kaizen? In this excerpt from the iSixSigma Discussion Forum, posters discuss the benefits of Six Sigma and Kazien, and how to integrate the two.
Sigma Level Articles
A Method for Aligning the Six Sigma Performance Metric Some Six Sigma practitioners are concerned about the current method used to calculate Z-scores and express process capability. A proposed modification, based on Berryman's scorecard,…
Customer CTQs - Defining Defect, Unit and Opportunity In order for any process capability to accurately be calculated, one must properly define and quantify the process defect, unit and opportunity of a customer…
Should You Calculate Your Process Sigma? Practitioners must learn when and how to calculate the sigma level of a process.
Sigma Performance Levels - One to Six Sigma When learning about Six Sigma, it may help to consider these charts, which detail how sigma level relates to defects per million opportunities (DPMO), and…
In this case study, pharmaceutical developers use the DFSS methodolgy Identify, Design, Optimize, Verify to find the best formula for masking the bitter taste of…
DFSS Meets Agile Development - Friend or Foe? Can DFSS work in a software organization with an Agile development orientation? At first glance, conflict seems likely. But with some flexibility in viewpoints, the…
DFSS Study: Develop Software to Track Drug Side Effects A pharmaceutical company used DFSS tools to develop a new pharmacovigilance system (captures and analyzes observed drug side effects) in association with the launch of…
DFSS Technology Tips New technology implementations can be tricky business for Quality professionals. This article summarizes lessons-learned involving champions, modifying culture and behaviors, performing a gap analysis, involving…
DMAIC and DFSS Roadmaps: How to Connect and Integrate? While Six Sigma has grown from being primarily aimed at process improvement to also target design and innovation, making a single DMAIC-DFSS roadmap elicits numerous…
Design for Six Sigma (DFSS) Sections
DFSS Roadmaps
DFSS Project Examples
Design for Six Sigma (DFSS) Articles
DMAIC Versus DMADV DMAIC and DMADV are two Six Sigma methodologies that eliminate defects from a process or product. Learn about DMAIC and DMADV and when it is…
Doing Some Software Six Sigma and Agile ‘Mythbusting’ In serious software discussions, a couple of notions about Six Sigma that are being accepted as truths are a bit out of step with the…
Implementing Design for Six Sigma (DFSS) in Europe Sets of questions offer help for implementing Design for Six Sigma (DFSS) to those working on the development of new products, new services or design…
Importance of the Validate Phase in a Design Project As important as the initial phases of the IDOV process are, the last step -- the Validate phase -- is critical to the long-term success…
Process Model Improves Wound Care Turnaround Time When routine analysis of turnaround times at LDS Hospital revealed that the ED treatment of patients with simple lacerations took an average of more than…
Product Development: Orchestrating a Better Vision Many similarities exist between an orchestra and the product development process. The analogy is a one good for understanding product development, DFSS and coaching project…
Promises of Brand Strategy and Design for Six Sigma When a company is able to consistently deliver the products, services and experiences it promises - and customers know about it - both the customers…
Six Sigma Drives Design Efficiencies at Black & Veatch Beginning in 2003, Black & Veatch established Six Sigma to unify and control change in its processes. Now in its fifth year, Six Sigma is…
Taking Advantage of Computer-Based Analysis for DFSS DFSS integrates marketing, engineering and production information into the design world. Product and process complexity and fast-paced marketing schedules demand that DFSS takes advantage of…
Tapping Voice of the Lead User for Design for Six Sigma Not all average consumers are average, some are “lead users” -- those who have strong needs before average consumers. If lead users can be identified…
To Use DMEDI or to Use DMAIC? That Is the Question As Six Sigma organizations mature they begin to question when a process will benefit from improvement (DMAIC) versus one that offers a new design (DMEDI).…
Using DFLSS to Evaluate a New Loan Referral Process A business can save time and effort by being able to evaluate the feasibility of an idea early on. And that is exactly the capability…
Design for Six Sigma (DFSS) Articles
Using DFSS to Improve Offshore Outsourcing Efficiency Offshore outsourcing of software development work has been an emerging trend for about a dozen years. Using Design for Six Sigma (DFSS) is one way…
What Is DFSS? Design for Six Sigma (DFSS), or the Six Sigma DMADV process (Define, Measure, Analyze, Design, Verify), is an improvement system used to develop new processes…
Define, Measure, Analyze, Improve, Control. Incremental process improvement using Six Sigma methodology. See DMAIC Methodology
Pronounced (Duh-May-Ick).
DMAIC refers to a data-driven quality strategy for improving processes, and is an integral part of the company's Six Sigma Quality Initiative. DMAIC is an acronym for five interconnected phases: Define, Measure, Analyze, Improve, and Control.
Each step in the cyclical DMAIC Process is required to ensure the best possible results. The process steps:
Define the Customer, their Critical to Quality (CTQ) issues, and the Core Business Process involved.
Define who customers are, what their requirements are for products and services, and what their expectations are
Define project boundaries the stop and start of the process
Define the process to be improved by mapping the process flow
Measure the performance of the Core Business Process involved.
Develop a data collection plan for the process
Collect data from many sources to determine types of defects and metrics
Compare to customer survey results to determine shortfall
Analyze the data collected and process map to determine root causes of defects and opportunities for improvement.
Identify gaps between current performance and goal performance
Prioritize opportunities to improve
Identify sources of variation
Improve the target process by designing creative solutions to fix and prevent problems.
Create innovate solutions using technology and discipline
Develop and deploy implementation plan
Control the improvements to keep the process on the new course.
Prevent reverting back to the "old way"
Require the development, documentation and implementation of an ongoing monitoring plan
Institutionalize the improvements through the modification of systems and structures (staffing, training, incentives)
The Six Sigma methodology follows the Define, Measure, Analyze, Improve, Control (DMAIC) roadmap for process improvement.
The Six Sigma DMAIC (Define, Measure, Analyze, Improve, Control) methodology can be thought of as a roadmap for problem solving and product/process improvement. Most companies
begin implementing Six Sigma using the DMAIC methodology, and later add the DFSS (Design for Six Sigma, also known as DMADV or IDDOV) methodologies when the organizational culture and experience level permits. You can read the main differences between DMAIC and DMADV, but we'll focus on the DMAIC in this article.
While the DMAIC methodology presented below may appear linear and explicitly defined, it should be noted that an iterative approach may be necessary -- especially for Black Belts and Green Belts that are new to the tools and techniques that make up DMAIC. For instance, you may find that upon analyzing your data (Analyze phase) you did not gather enough data to isolate the root cause of the problem. At this point, you may iterate back to the Measure phase. In addition, prior knowledge of the tools and techniques is necessary in determining which tools are useful in each phase. Remember, the appropriate application of tools becomes more critical for effectiveness than correctness, and you don't need to use all the tools all the time.
DMAIC Phase Steps Tools Used
D - Define Phase: Define the project goals and customer (internal and external) deliverables.
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
Project Charter Process Flowchart SIPOC Diagram Stakeholder Analysis DMAIC Work Breakdown Structure CTQ Definitions Voice of the Customer Gathering
Define Tollgate Review
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 Validate the Measurement System Collect the Data Begin Developing Y=f(x) Relationship Determine Process Capability and Sigma
Baseline
Process Flowchart Data Collection Plan/Example Benchmarking Measurement System Analysis/Gage
R&R Voice of the Customer Gathering Process Sigma Calculation
Measure Tollgate Review
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
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
Analyze Tollgate Review
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
Brainstorming Mistake Proofing Design of Experiments Pugh Matrix House of Quality Failure Modes and Effects Analysis
(FMEA) Simulation Software
Improve Tollgate Review
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
Process Sigma Calculation Control Charts (Variable and Attribute) Cost Savings Calculations Control Plan
Control Tollgate Review
The DMAIC methodology should be used when a product or process is in existence at your company but is not meeting customer specification or is not performing adequately. For DMAIC milestone reviews, there are certain deliverables, checkpoints, questions and concerns that the Black Belt and improvement team should be aware of prior to a tollgate/milestone review.
In lieu of or in addition to your Master Black Belt tollgate/milestone preparation review, the following Six Sigma DMAIC quick reference sheets can help prepare for your milestone review.
Jump to: Define, Measure, Analyze, Improve, Control
Define Phase
Deliverables Of 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.
Checkpoints For Completion:
Team Readiness
Team is sponsored by a champion or business leader. Team formed and team leaders (MBBs/Coaches and BBs/Project Leads) assigned. Improvement team members fully trained on Six Sigma and DMAIC. Full participation by members in regularly held team meetings. Team members perform project work when assigned and in a timely fashion. Team members regularly document their project work. Team is equipped with available and reliable resources.
Customers (and CTQs)
Customer(s) identified and segmented according to their different needs and requirements. Data collected and displayed to better understand customer(s) critical needs and requirements.
Team Charter
Project management charter, including business case, problem and goal statements, project scope, milestones, roles and responsibilities, communication plan.
Business Process Mapping
Completed, verified, and validated high-level 'as is' (not 'should be' or 'could be') business process map.
Completed SIPOC representation, describing the Suppliers, Inputs, Process, Outputs, and Customers.
Questions To Determine Appropriate Application:
Team Readiness
Who are the improvement project team members, including BBs/Project Leads and MBBs/Coaches?
Has everyone on the team, including the team leaders, been properly trained (on DMAIC)? Does the team have regular meetings? How often are the team meetings? Is there regularly 100% attendance at the team meetings? If not, have appointed substitutes
attended to preserve cross-functionality and full representation? If substitutes have been appointed, have they been briefed on the project charter and goals and
received regular communications as to the project's progress to date? Has the project work been fairly and/or equitably divided and delegated among team members
who are qualified and capable to perform the work? Has everyone contributed? Are there any constraints known that bear on the ability to perform project work? How is the
team addressing them? How is the team tracking and documenting its work? Is the team adequately staffed with the desired cross-functionality? If not, what additional
resources are available to the team?
Customers (and CTQs)
Has the customer(s) been identified? Are there different segments of customers? Has the improvement team collected the 'voice of the customer' (obtained feedback -
qualitative and quantitative)? What customer feedback methods were used to solicit their input? Have the customer needs been translated into specific, measurable requirements? How?
Team Charter
Has a team charter been developed and communicated? Has the charter changed at all during the course of the project? If so, when did it change and
why? Does the charter include the following?
- Business Case: What are the compelling business reasons for embarking on this project? Is the project linked to key business goals and objectives? What key business process output measure(s) will the project leverage and how? What are the rough order estimates on cost savings/opportunities on this project?
- Problem Statement: What specifically is the problem? Where does it occur? When does it occur? What is its extent?
- Goal Statement: What is the goal or target for the improvement team's project? Do the problem and goal statements meet the SMART criteria (specific, measurable, attainable, relevant, and
time-bound)? Has anyone else (internal or external to the organization) attempted to solve this problem or a similar one before? If so, what knowledge can be leveraged from these previous efforts? How will the project team and the organization measure complete success for this project?
- Roles and Responsibilities: What are they for each team member and its leadership? Where is this documented?
- Project Scope: What are the boundaries of the scope? What is in bounds and what is not? What is the start point? What is the stop point? How does the project manager ensure against scope creep? Is the project scope manageable? What constraints exist that might impact the team?
- Milestones: When was the project start date? When is the estimated completion date? Is the project currently on schedule according to the plan? Has a project plan, Gantt chart, or similar been developed/completed? How did the project manager receive input to the development of the plan and the estimated completion dates/times of each activity? Is there a critical path to complete the project? How will variation in the actual durations of each activity be dealt with to ensure that the expected project completion date is met?
- Communication Plan: What are the dynamics of the communication plan? What critical content must be communicated - who, what, when, where, and how? When are meeting minutes sent out? Who is on the distribution list? How do you keep key subject matter experts in the loop? Business Process Mapping
Has a high-level 'as is' process map been completed, verified and validated? Has a SIPOC diagram been produced describing the Suppliers, Inputs, Process, Outputs, and
Customers? Is the improvement team aware of the different versions of a process: what they think it is vs.
what it actually is vs. what it should be vs. what it could be? Is the current 'as is' process being followed? If not, what are the discrepancies? Are different versions of process maps needed to account for the different types of inputs? How was the 'as is' process map developed, reviewed, verified and validated? What tools and roadmaps did you use for getting through the Define phase?
Measure Phase
Deliverables Of Phase:
Key measures identified, data collection planned and executed, process variation displayed and communicated, performance baselined, sigma level calculated.
Checkpoints For Completion:
Key Measures Identified
Key measures identified and agreed upon. High impact defects defined and identified in the business process.
Data Collection Planned and Executed
Solid data collection plan established that includes measurement systems analysis. Data collected on key measures that were identified.
Process Variation Displayed/Communicated
Process variation components displayed/communicated using suitable charts, graphs, plots. Long term and short term variability accounted for.
Performance Baseline/Sigma Calculation
Measure baseline process performance (capability, yield, sigma level).
Questions To Determine Appropriate Application:
Key Measures Identified
What are the key input variables? What the key process variables? What are the key output variables?
What key measures identified indicate the performance of the business process? What are the agreed upon definitions of the high impact characteristics (CTQs), defect(s),
unit(s), and opportunities that will figure into the sigma calculations and process capability metrics?
Data Collection Planning and Execution
Was a data collection plan established? What data was collected (past, present, future/ongoing)? Who participated in the data collection? How did the team select a sample? What has the team done to assure the stability and accuracy of the measurement process? Was a gauge R&R conducted? Was stratification needed in the data collection and analysis?
Process Variation Displayed/Communicated
What charts has the team used to display the components of variation in the process? What does the chart tell us in terms of variation?
Performance Baseline/Sigma Calculation
What is the current process performance in terms of it capability indices? What is the current process performance in terms of its yield or sigma level(s)?
How large is the gap between current performance and the customer-specified (goal) performance?
Have you found any 'ground fruit' or 'low-hanging fruit' for immediate remedies to the gap in performance?
What particular quality tools did the team find helpful in getting through the measure phase?
Analyze Phase
Deliverables Of Phase:
Data and process analysis, root cause analysis, quantifying the gap/opportunity. Checkpoints For Completion:
Data and Process Analysis
Identify gaps between current performance and the goal performance.
Root Cause Analysis
Generate list of possible causes (sources of variation). Segment and stratify possible causes (sources of variation). Prioritize list of 'vital few' causes (key sources of variation). Verify and quantify the root causes of variation.
Quantifying the Gap/Opportunity
Determine the performance gap. Display and communicate the gap/opportunity in financial terms.
Questions To Determine Appropriate Application:
Data and Process Analysis
What does the data say about the performance of the business process? Did any value-added analysis or 'lean thinking' take place to identify some of the gaps shown on
the 'as is' process map? Was a detailed process map created to amplify critical steps of the 'as is' business process? How was the map generated, verified, and validated? What did the team gain from developing a sub-process map? What were the crucial 'moments of truth' on the map? Were there any cycle time improvement opportunities identified from the process analysis? Were any designed experiments used to generate additional insight into the data analysis? Did any additional data need to be collected? What model would best explain the behavior of output variables in relation to input variables?
Root Cause Analysis
What tools were used to generate the list of possible causes? Was a cause-and-effect diagram used to explore the different types of causes (or sources of
variation)? What tools were used to narrow the list of possible causes? Were Pareto charts (or similar) used to portray the 'heavy hitters' (or key sources of variation)? What conclusions were drawn from the team's data collection and analysis? How did the team reach these conclusions?
Quantifying the Gap/Opportunity
What is the cost of poor quality as supported by the team's analysis? Is the process severely broken such that a re-design is necessary? Would this project lend itself to a DFSS project? What are the revised rough order estimates of the financial savings/opportunity for the
improvement project? Have the problem and goal statements been updated to reflect the additional knowledge gained
from the analyze phase? Have any additional benefits been identified that will result from closing all or most of the gaps? What were the financial benefits resulting from any 'ground fruit or low-hanging fruit' (quick
fixes)? What quality tools were used to get through the analyze phase?
Improve Phase
Deliverables Of Phase:
Generate (and test) possible solutions, select the best solutions, design implementation plan.
Checkpoints For Completion:
Generating (and Testing) Possible Solutions
Possible solutions generated and tested.
Selecting The Best Solution(s)
Optimal solution selected based on testing and analysis. New and improved process ('should be') maps developed. Cost/benefit analysis of optimal solution(s). Small-scale pilot for proposed improvement(s). Pilot data collected and analyzed. Improved process ('should be') maps modified based on pilot data and analysis. Project impact on utilizing the best solution(s).
Designing Implementation Plan
Solution implementation plan established, including schedule/work breakdown structure, resources, risk management plan, cost/budget, and control plan.
Contingency plan established.
Questions To Determine Appropriate Application:
Generating (And Testing) Possible Solutions
How did the team generate the list of possible solutions? What tools were used to tap into the creativity and encourage 'outside the box' thinking?
Selecting The Best Solution(s)
What tools were used to evaluate the potential solutions? Were any criteria developed to assist the team in testing and evaluating potential solutions? What were the underlying assumptions on the cost-benefit analysis? Are there any constraints (technical, political, cultural, or otherwise) that would inhibit certain
solutions? Was a pilot designed for the proposed solution(s)? Describe the design of the pilot and what tests were conducted, if any? What conclusions were drawn from the outcomes of the pilot? What lessons, if any, from the pilot were incorporated into the design of the full-scale solution?
Designing The Implementation Plan
Is the improvement plan best served by using the DFSS approach? What is the implementation plan? What poka-yoke or error proofing will be done to address some of the discrepancies observed in
the 'as is' process? What does the 'should be' process map/design look like? How does the solution remove the key sources of variation discovered in the analyze phase? What attendant changes will need to be made to ensure that the solution is successful? What communications are necessary to support the implementation of the solution? How will the team or the process owner(s) monitor the implementation plan to see that it is
working as intended? What is the team's contingency plan for potential problems occurring in implementation? How will the organization know that the solution worked? What tools were most useful during the improve phase?
Control Phase
Deliverables Of Phase:
Documented and implemented monitoring plan, standardized process, documented procedures, response plan established and deployed, transfer of ownership (project closure).
Checkpoints For Completion:
Monitoring Plan
Control plan in place for sustaining improvements (short and long-term).
Process Standardization
New process steps, standards, and documentation are ingrained into normal operations.
Documented Procedures
Operating procedures are consistent. Knowledge gained on process is shared and institutionalized.
Response Plan
Response plans established, understood, and deployed.
Transfer of Ownership (Project Closure)
Transfer ownership and knowledge to process owner and process team tasked with the responsibilities.
Questions To Determine Appropriate Application:
Monitoring Plan
What is the control/monitoring plan? How will the process owner and team be able to hold the gains? What key inputs and outputs are being measured on an ongoing basis? How will input, process, and output variables be checked to detect for sub-optimal conditions? How will new or emerging customer needs/requirements be checked/communicated to orient
the process toward meeting the new specifications and continually reducing variation? Are control charts being used or needed? How will control chart readings and control chart limits be checked to effectively monitor
performance? Will any special training be provided for control chart interpretation? Is this knowledge imbedded in the response plan? What is the most recent process yield (or sigma calculation)? Does the process performance meet the customer's requirements?
Process Standardization
Has the improved process and its steps been standardized?
Documented Procedures
Is there documentation that will support the successful operation of the improvement?
Does job training on the documented procedures need to be part of the process team's education and training?
Have new or revised work instructions resulted? Are they clear and easy to follow for the operators?
Response Plan
Is a response plan in place for when the input, process, or output measures indicate an 'out-of-control' condition?
What are the critical parameters to watch? Does the response plan contain a definite closed loop continual improvement scheme (e.g.,
plan-do-check-act)? Are suggested corrective/restorative actions indicated on the response plan for known causes to
problems that might surface? Does a troubleshooting guide exist or is it needed?
Transfer Of Ownership (Project Closure)
Who is the process owner? How will the day-to-day responsibilities for monitoring and continual improvement be
transferred from the improvement team to the process owner? How will the process owner verify improvement in present and future sigma levels, process
capabilities? Is there a recommended audit plan for routine surveillance inspections of the DMAIC project's
gains? What is the recommended frequency of auditing? What should the next improvement project be that is related to the process? What quality tools were useful in the control phase?
Integrating and Institutionalizing Improvements, Knowledge and Learnings
What other areas of the organization might benefit from the project team's improvements, knowledge, and learning?
How might the organization capture best practices and lessons learned so as to leverage improvements across the business?
What other systems, operations, processes, and infrastructures (hiring practices, staffing, training, incentives/rewards, metrics/dashboards/scorecards, etc.) need updates, additions, changes, or deletions in order to facilitate knowledge transfer and improvements?
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Six Sigma Quality Tools and Templates
5S Affinity Diagram/KJ Analysis Analysis of Variance (ANOVA) Analytic Hierarchy Process (AHP) Brainstorming Calculators Capability Indices/Process Capability Cause & Effect Control Charts Design of Experiments (DOE) FMEA Graphical Analysis Charts Hypothesis Testing Kano Analysis
Measurement Systems Analysis (MSA)/Gage R&R Normality Pareto Poka Yoke Process Mapping Project Charter QFD/House of Quality RACI Diagram Regression Risk Management Sampling/Data Simulation SIPOC/COPIS Software Statistical Analysis Templates Value Stream Mapping Variation Wizards
Six Sigma Costs And Savings
In the world of Six Sigma quality, the saying holds true: it takes money to save money using the Six Sigma quality methodology. You can't expect to significantly reduce costs and increase sales using Six Sigma without investing in training, organizational infrastructure and culture evolution.
Many people say that it takes money to make money. In the world of Six Sigma quality, the saying also holds true: it takes money to save money using the Six Sigma quality methodology. You can't expect to significantly reduce costs and increase sales using Six Sigma without investing in training, organizational infrastructure and culture evolution.
Sure you can reduce costs and increase sales in a localized area of a business using the Six Sigma quality methodology -- and you can probably do it inexpensively by hiring an ex-Motorola or GE Black Belt. I like to think of that scenario as a "get rich quick" application of Six Sigma. But is it going to last when a manager is promoted to a different area or leaves the company? Probably not. If you want to produce a culture shift within your organization, a shift that causes every employee to think about how their actions impact the customer and to communicate within the business using a consistent language, it's going to require a resource commitment. It takes money to save money.
How much financial commitment does Six Sigma require and what magnitude of financial benefit can you expect to receive? We all have people that we must answer to -- and rhetoric doesn't pay the bills or keep the stockholders happy (anymore). I was tired of reading web pages or hearing people say:
"Companies of all types and sizes are in the midst of a quality revolution. GE saved $12 billion over five years and added $1 to its earnings per share. Honeywell (AlliedSignal) recorded more than $800 million in savings."
"GE produces annual benefits of over $2.5 billion across the organization from Six Sigma."
"Motorola reduced manufacturing costs by $1.4 billion from 1987-1994."
"Six Sigma reportedly saved Motorola $15 billion over the last 11 years."
The above quotations may in fact be true, but pulling the numbers out of the context of the organization's revenues does nothing to help a company figure out if Six Sigma is right for them. For example, how much can a $10 million or $100 million company expect to save?
I investigated what the companies themselves had to say about their Six Sigma costs and savings -- I didn't believe anything that was written on third party websites, was estimated by "experts," or was written in books on the topic. I reviewed literature and only captured facts found in annual reports, website pages and presentations found on company websites.
While recent corporate events like the Enron and WorldCom scandals might lead us to believe that not everything we read in a company's annual report is valid, I am going to provide the following information based on the assumption that these Six Sigma companies operate with integrity until proven otherwise.
I investigated Motorola, Allied Signal, GE and Honeywell. I choose these four companies because they are the companies that invented and refined Six Sigma -- they are the most mature in their deployments and culture changes. As the Motorola website says, they invented it in 1986. Allied Signal deployed Six Sigma in 1994, GE in 1995. Honeywell was included because Allied Signal merged with Honeywell in 1999 (they launched their own initiative in 1998). Many companies have deployed Six Sigma between the years of GE and Honeywell -- we'll leave those companies for another article.
Table 1: Companies And The Year They Implemented Six Sigma
Company Name Year Began Six Sigma
Motorola (NYSE:MOT) 1986
Allied Signal (Merged With Honeywell in 1999) 1994
GE (NYSE:GE) 1995
Honeywell (NYSE:HON) 1998
Ford (NYSE:F) 2000
Table 2 identifies by company, the yearly revenues, the Six Sigma costs (investment) per year, where available, and the financial benefits (savings). There are many blanks, especially where the investment is concerned. I've presented as much information as the companies have publicly disclosed.
Table 2: Six Sigma Cost And Savings By Company
Year Revenue ($B) Invested ($B)% Revenue
InvestedSavings ($B)
% Revenue Savings
Motorola
1986-2001 356.9(e) ND - 16 1 4.5
Allied Signal
1998 15.1 ND - 0.5 2 3.3
GE
1996 79.2 0.2 0.3 0.2 0.2
1997 90.8 0.4 0.4 1 1.1
1998 100.5 0.5 0.4 1.3 1.2
1999 111.6 0.6 0.5 2 1.8
1996-1999 382.1 1.6 0.4 4.4 3 1.2
Honeywell
1998 23.6 ND - 0.5 2.2
1999 23.7 ND - 0.6 2.5
2000 25.0 ND - 0.7 2.6
1998-2000 72.3 ND - 1.8 4 2.4
Ford
2000-2002 43.9 ND - 1 6 2.3
Key:$B = $ Billions, United States(e) = Estimated, Yearly Revenue 1986-1992 Could Not Be FoundND = Not DisclosedNote: Numbers Are Rounded To The Nearest Tenth
Although the complete picture of investment and savings by year is not present, Six Sigma savings can clearly be significant to a company. The savings as a percentage of revenue vary from 1.2% to 4.5%. And what we can see from the GE deployment is that a company shouldn't expect more than a breakeven the first year of implementation. Six Sigma is not a "get rich quick" methodology. I like to think of it like my retirement savings plan -- Six Sigma is a get rich slow methodology -- the take-away point being that you will get rich if you plan properly and execute consistently.
As GE's 1996 annual report states, "It has been estimated that less than Six Sigma quality, i.e., the three-to-four Sigma levels that are average for most U.S. companies, can cost a company as much as 10-15% of its revenues. For GE, that would mean $8-12 billion." With GE's 2001 revenue of $111.6 billion, this would translate into $11.2-16.7 billion of savings. Although $2 billion worth of savings in 1999 is impressive, it appears that even GE hasn't been able to yet capture the losses due to poor quality -- or maybe they're above the three-to-four Sigma levels that are the average for most U.S. companies?
In either case, 1.2-4.5% of revenue is significant and should catch the eye of any CEO or CFO. For a $30 million a year company, that can translate into between $360,000 and $1,350,000 in bottom-line-impacting savings per year. It takes money to make money. Is investing in Six Sigma quality, your employees and your organization's culture worth the money? Only you and your executive leadership team can decide the answer to that question.
References:1. Motorola Six Sigma Services. Motorola University. 22 July 2002 <http://mu.motorola.com/sigmasplash.htm>.2. AlliedSignal Inc. 1998 Annual Report. Honeywell Inc. 22 July 2002 <http://www.honeywell.com/investor/otherpdfs/ALD98.pdf>.3. GE Investor Relations Annual Reports. General Electric Company. 22 July 2002 <http://www.ge.com/company/investor/annreports.htm>.4. Honeywell Annual Reports. Honeywell Inc. 22 July 2002 <http://investor.honeywell.com/ireye/ir_site.zhtml?ticker=HON&script=700>.5. Better Understand Six Sigma Plus With Honeywell's Special PowerPoint Presentation. Honeywell Inc. 22 July 2002 <http://www.honeywell.com/sixsigma/page4_1.html>.6. Quality Digest, "Six Sigma at Ford Revisited", June 2003, p. 30. <http://www.qualitydigest.com/june03/articles/02_article.shtml>.
Where did the name "Six Sigma" come from?
Wednesday, 25 February 2004 16:15
In my recollection, two recurring questions have dominated the field of six sigma. The first inquiry can be described by the global question: “Why 6s and not some other level of capability?” The second inquiry is more molecular. It can be summarized by the question: “Where does the 1.5s shift factor come from – and why 1.5 versus some other magnitude?” For details on this subject, reference: “Harry, M. J. “Resolving the Mysteries of Six Sigma: Statistical Constructs and Engineering Rationale.” First Edition 2003. Palladyne Publishing. Phoenix, Arizona. (Note: this particular publication will be available by October 2003). But until then, we will consider the following thumbnail sketch.
At the onset of six sigma in 1985, this writer was working as an engineer for the Government Electronics Group of Motorola. By chance connection, I linked up with another engineer by the name of Bill Smith (originator of the six sigma concept in 1984). At that time, he suggested Motorola should require 50 percent design margins for all of its key product performance specifications. Statistically speaking, such a "safety margin" is equivalent to a 6 sigma level of capability.
When considering the performance tolerance of a critical design feature, he believed a 25 percent “cushion” was not sufficient for absorbing a sudden shift in process centering. Bill believed the typical shift was on the order of 1.5s (relative to the target value). In other words, a four sigma level of capability would normally be considered sufficient, if centered. However, if the process center was somehow knocked off its central location (on the order of 1.5s), the initial capability of 4s would be degraded to 4.0s – 1.5s = 2.5s. Of course, this would have a consequential impact on defects. In turn, a sudden increase in defects would have an adverse effect on reliability. As should be apparent, such a domino effect would continue straight up the value chain.
Regardless of the shift magnitude, those of us working this issue fully recognized that the initial estimate of capability will often erode over time in a “very natural way” – thereby increasing the expected rate of product defects (when considering a protracted period of production).
Extending beyond this, we concluded that the product defect rate was highly correlated to the long-term process capability, not the short-term capability. Of course, such conclusions were predicated on the statistical analysis of empirical data gathered on a wide array of electronic devices.
Thus, we come to understand three things. First, we recognized that the instantaneous reproducibility of a critical-to-quality characteristic is fully dependent on the “goodness of fit” between the operating bandwidth of the process and the corresponding bandwidth of the performance specification. Second, the quality of that interface can be substantively and consequentially disturbed by process centering error. Of course, both of these factors profoundly impact long-term capability. Third, we must seek to qualify our critical processes at a 6s level of short-term capability if we are to enjoy a long-term capbility of 4s.
By further developing these insights through applied research, we were able to greatly extend our understanding of the many statistical connections between such things as design margin, process capability, defects, field reliability, customer satisfaction, and economic success.
The History of Six Sigma
Six Sigma has evolved over time. The concepts behind Six Sigma can be traced through the centuries as the method took shape into what it is today.
The roots of Six Sigma as a measurement standard can be traced back to Carl Frederick Gauss (1777-1855) who introduced the concept of the normal curve. 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 on the scene but credit for coining the term "Six Sigma" goes to a Motorola engineer named Bill Smith. (Incidentally, "Six Sigma" is a federally registered trademark of Motorola).
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 our Six Sigma efforts.
Since then, hundreds of companies around the world have adopted Six Sigma as a way of doing business. This is a direct result of many of America's leaders openly praising the benefits of Six Sigma. Leaders such as Larry Bossidy of Allied Signal (now Honeywell), and Jack Welch of General Electric Company. Rumor has it that Larry and Jack were playing golf one day and Jack bet Larry that he could implement Six Sigma faster and with greater results at GE than Larry did at Allied Signal. The results speak for themselves.
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 Geoff Tennant 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; a methodology." We couldn't agree more.
