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Introduction to Six Sigma

Topics (Session 1) ¨ Understanding Six Sigma ¨ History of Six Sigma ¨ Six Sigma Methodologies & Tools ¨ Roles & Responsibilities ¨ How YOU can use Six Sigma

Six Sigma is. . . ¨ A performance goal, representing 3. 4 defects for every million opportunities to make one. ¨ A series of tools and methods used to improve or design products, processes, and/or services. ¨ A statistical measure indicating the number of standard deviations within customer expectations. ¨ A disciplined, fact-based approach to managing a business and its processes.

What’s in a name? ¨ Sigma is the Greek letter representing the standard deviation of a population of data. ¨ Sigma is a measure of variation (the data spread) σ μ

What does variation mean? ¨ Variation means that a process does not produce the same result (the “Y”) every time. ¨ Some variation will exist in all processes. ¨ Variation directly affects customer experiences. Customers do not feel averages!

Measuring Process Performance The pizza delivery example. . . ¨ Customers want their pizza delivered fast! ¨ Guarantee = “ 30 minutes or less” ¨ What if we measured performance and found an average delivery time of 23. 5 minutes? – On-time performance is great, right? – Our customers must be happy with us, right?

How often are we delivering on time? Answer: Look at the variation! 30 min. or less s 0 10 20 x 30 40 50 ¨ Managing by the average doesn’t tell the whole story. The average and the variation together show what’s happening.

Reduce Variation to Improve Performance How many standard deviations can you “fit” within customer expectations? 0 10 20 30 min. or less s x 30 40 50 ¨ Sigma level measures how often we meet (or fail to meet) the requirement(s) of our customer(s).

Managing Up the Sigma Scale Sigma 1 2 3 4 5 6 % Good % Bad DPMO 30. 9% 69. 1% 691, 462 69. 1% 30. 9% 308, 538 93. 3% 6. 7% 66, 807 99. 38% 0. 62% 6, 210 99. 977% 0. 023% 233 99. 9997% 0. 00034% 3. 4

Examples of the Sigma Scale In a world at 3 sigma. . . In a world at 6 sigma. . . ¨ There are 964 U. S. flight ¨ 1 U. S. flight is cancelled every ¨ The police make 7 false arrests ¨ There are fewer than 4 false ¨ In MA, 5, 390 newborns are ¨ 1 newborn is dropped every 4 ¨ In one hour, 47, 283 ¨ It would take more than cancellations per day. every 4 minutes. dropped each year. international long distance calls are accidentally disconnected. 3 weeks. arrests per month. years in MA. 2 years to see the same number of dropped international calls.

Topics ¨ Understanding Six Sigma ¨ History of Six Sigma ¨ Six Sigma Methodologies & Tools ¨ Roles & Responsibilities ¨ How YOU can use Six Sigma

The Six Sigma Evolutionary Timeline 1818: Gauss uses the normal curve to explore the mathematics of error analysis for measurement, probability analysis, and hypothesis testing. 1736: French mathematician Abraham de Moivre publishes an article introducing the normal curve. 1924: Walter A. Shewhart introduces the control chart and the distinction of special vs. common cause variation as contributors to process problems. 1896: Italian sociologist Vilfredo Alfredo Pareto introduces the 80/20 rule and the Pareto distribution in Cours d’Economie Politique. 1949: U. S. DOD issues Military Procedure MIL-P-1629, Procedures for Performing a Failure Mode Effects and Criticality Analysis. 1960: Kaoru Ishikawa introduces his now famous cause-and-effect diagram. 1941: Alex Osborn, head of BBDO Advertising, fathers a widely-adopted set of rules for “brainstorming”. 1970 s: Dr. Noriaki Kano introduces his two-dimensional quality model and the three types of quality. 1986: Bill Smith, a senior engineer and scientist introduces the concept of Six Sigma at Motorola 1995: Jack Welch launches Six Sigma at GE. 1994: Larry Bossidy launches Six Sigma at Allied Signal.

Six Sigma Companies

Six Sigma and Financial Services

Topics ¨ Understanding Six Sigma ¨ History of Six Sigma ¨ Six Sigma Methodologies & Tools ¨ Roles & Responsibilities ¨ How YOU can use Six Sigma

DMAIC – The Improvement Methodology Define Measure Analyze Improve Control Objective: DEFINE the opportunity Objective: MEASURE current ANALYZE the performance root causes of problems Objective: IMPROVE the process to eliminate root causes Key Define Tools: • Cost of Poor Quality (COPQ) • Voice of the Stakeholder (VOS) • Project Charter • As-Is Process Map(s) • Primary Metric (Y) Key Measure Tools: • Critical to Quality Requirements (CTQs) • Sample Plan • Capability Analysis • Failure Modes and Effect Analysis (FMEA) Key Improve Key Control Tools: • Solution Selection • Control Charts Matrix • Contingency • To-Be Process and/or Action Map(s) Plan(s) Key Analyze Tools: • Histograms, Boxplots, Multi. Vari Charts, etc. • Hypothesis Tests • Regression Analysis Objective: CONTROL the process to sustain the gains.

Define – DMAIC Project What is the project? \$ Project Charter Voice of the Stakeholde r Cost of Poor Quality Six Sigma ¨ What is the problem? The “problem” is the Output (a “Y” in a math equation Y=f(x 1, x 2, x 3) etc). ¨ What is the cost of this problem ¨ Who are the stake holders / decision makers ¨ Align resources and expectations

Define – As-Is Process How does our existing process work? Does EVERYONE agree how the current process works? Define the Non Value Add steps

Define – Customer Requirements Listening Posts Industry Intel What are the CTQs? What motivates the customer? SECONDARY RESEARCH Market Data Customer Service Industry Benchmarking Customer Correspondence PRIMARY RESEARCH Survey s OTM Focus Groups Observations Voice of the Customer Key Customer Issue Critical to Quality

Measure – Baselines and Capability What is our current level of performance? ¨ Sample some data / not all data ¨ Current Process actuals measured against the Customer expectation ¨ What is the chance that we will succeed at this level every time?

Measure – Failures and Risks Where does our process fail and why? Subjective opinion mapped into an “objective” risk profile number X 1 X 2 X 3 X 4 etc

Analyze – Potential Root Causes What affects our process? Ishikawa Diagram (Fishbone) Six Sigma y = f (x 1, x 2, x 3. . . xn)

Analyze – Validated Root Causes What are the key root causes? Data Stratification Regression Analysis Process Simulatio n Six Sigma y = f (x 1, x 2, x 3. . . xn) Critical Xs

Improve – Potential Solutions How can we address the root causes we identified? ¨ Address the causes, not the symptoms. Evaluate Clarify Generate y = f (x 1, x 2, x 3. . . xn) Critical Xs Divergent | Convergent Decision

Improve – Solution Selection How do we choose the best solution? Solution Selection Matrix Qualit y Time Solution Sigma Cost Six Sigma Implementation Bad Good Solution Right Wrong ☺ Nice Try Nice Idea X Solution Implementation Plan Time CBA Other Score

Control – Sustainable Benefits How do we ”hold the gains” of our new process? ¨ Some variation is normal and OK ¨ How High and Low can an “X” go yet not materially impact the “Y” ¨ Pre-plan approach for control exceptions

DFSS – The Design Methodology Design for Six Sigma Define Measure Analyze Develop Verify ¨ Uses – Design new processes, products, and/or services from scratch – Replace old processes where improvement will not suffice ¨ Differences between DFSS and DMAIC – Projects typically longer than 4 -6 months – Extensive definition of Customer Requirements (CTQs) – Heavy emphasis on benchmarking and simulation; less emphasis on baselining ¨ Key Tools – Multi-Generational Planning (MGP) – Quality Function Deployment (QFD)

Topics ¨ Understanding Six Sigma ¨ History of Six Sigma ¨ Six Sigma Methodologies & Tools ¨ Roles & Responsibilities ¨ How YOU can use Six Sigma

Champions ¨ Promote awareness and execution of Six Sigma within lines of business and/or functions ¨ Identify potential Six Sigma projects to be executed by Black Belts and Green Belts ¨ Identify, select, and support Black Belt and Green Belt candidates ¨ Participate in 2 -3 days of workshop training

Black Belts ¨ Use Six Sigma methodologies and advanced tools (to execute business improvement projects ¨ Are dedicated full-time (100%) to Six Sigma ¨ Serve as Six Sigma knowledge leaders within Business Unit(s) ¨ Undergo 5 weeks of training over 5 -10 months

Green Belts ¨ Use Six Sigma DMAIC methodology and basic tools to execute improvements within their existing job function(s) ¨ May lead smaller improvement projects within Business Unit(s) ¨ Bring knowledge of Six Sigma concepts & tools to their respective job function(s) ¨ Undergo 8 -11 days of training over 3 -6 months

Other Roles ¨ Subject Matter Experts – Provide specific process knowledge to Six Sigma teams – Ad hoc members of Six Sigma project teams ¨ Financial Controllers – Ensure validity and reliability of financial figures used by Six Sigma project teams – Assist in development of financial components of initial business case and final cost-benefit analysis

Topics ¨ Understanding Six Sigma ¨ History of Six Sigma ¨ Six Sigma Methodologies & Tools ¨ Roles & Responsibilities ¨ How YOU can use Six Sigma

Questions?

Topics for Detailed Discussion ¨ Problem Identification ¨ Cost of Poor Quality ¨ Problem Refinement ¨ Process Understanding ¨ Potential X to Critical X ¨ Improvement

Problem Identification “If it ain’t broke, why fix it “This is the way we’ve always done it…”

Problem Identification • First Pass Yield • Roll Throughput Yield • Histogram • Pareto

Problem Identification First Pass Yield (FPY): The probability that any given unit can go through a system defect-free without rework. 100 Units Step 1 Outputs / Inputs 100 / 100 = 1 100 Scrap 10 Units Step 2 90 / 100 =. 90 90 Scrap 3 Units Step 3 87 / 90 =. 96 87 Scrap 2 Units At first glance, the yield would seem to be 85% (85/100 but…. ) Step 4 85 85 / 87 =. 97 When in fact the FPY is (1 x. 90 x. 96 x. 97 =. 838)

Problem Identification Rolled Throughput Yield (RTY): The yield of individual process steps multiplied together. Reflects the hidden factory rework issues associated with a process. 100 Units Step 1 Re-Work 10 Units Outputs / Inputs 90 / 100 =. 90 100 Units 97 / 100 =. 97 Step 2 Re-Work 3 Units 100 Units 98 / 100 =. 98 Step 3 Re-Work 2 Units 100 Units Step 4 100 Units . 90 x. 97 x. 98 =. 855

Problem Identification RTY Examples - Widgets 50 Roll Throughput Yield 50/50 = 1 Function 1 (50 -5)/50 =. 90 50 Function 2 (50 -10)/50 =. 80 5 (50 -5)/50 =. 90 50 Function 3 10 1 x. 90 x. 80 x. 90 =. 65 50 Function 4 5 50 Put another way, this process is operating a 65% efficiency

Problem Identification RTY Example - Loan Underwriting Roll Throughput Yield 50/50 = 1 (50 -7 -2)/50 =. 82 (43 -6)/43 =. 86 (43 -1 -2)/43 =. 93 1 x. 82 x. 86 x. 93 =. 66 Put another way, this process is operating a 66% efficiency

Problem Identification Histogram – A histogram is a basic graphing tool that displays the relative frequency or occurrence of continuous data values showing which values occur most and least frequently. A histogram illustrates the shape, centering, and spread of data distribution and indicates whethere any outliers.

Problem Identification Histogram – Can also help us graphically understand the data

Problem Identification Pareto – The Pareto principle states that 80% of the impact of the problem will show up in 20% of the causes. A bar chart that displays by frequency, in descending order, the most important defects.

Topics (Session 2) ¨ Problem Identification ¨ Cost of Poor Quality ¨ Problem Refinement ¨ Process Understanding ¨ Potential X to Critical X ¨ Improvement

Cost of Poor Quality COPQ - The cost involved in fulfilling the gap between the desired and actual product/service quality. It also includes the cost of lost opportunity due to the loss of resources used in rectifying the defect. Hard Savings - Six Sigma project benefits that allow you to do the same amount of business with less employees (cost savings) or handle more business without adding people (cost avoidance). Soft Savings - Six Sigma project benefits such as reduced time to market, cost avoidance, lost profit avoidance, improved employee morale, enhanced image for the organization and other intangibles may result in additional savings to your organization, but are harder to quantify. Examples / Buckets– Roll Throughput Yield Inefficiencies (GAP between desired result and current result multiplied by direct costs AND indirect costs in the process). Cycle Time GAP (stated as a percentage between current results and desired results) multiplied by direct and indirect costs in the process. Square Footage opportunity cost, advertising costs, overhead costs, etc…

Topics (Session 2) ¨ Problem Identification ¨ Cost of Poor Quality ¨ Problem Refinement ¨ Process Understanding ¨ Potential X to Critical X ¨ Improvement

Problem Refinement Multi Level Pareto – Logically Break down initial Pareto data into subsets (to help refine area of focus)

Problem Refinement Problem Statement – A crisp description of what we are trying to solve. Primary Metric – An objective measurement of what we are attempting to solve (the “y” in the y = f(x 1, x 2, x 3…. ) calculation). Secondary Metric – An objective measurement that ensures that a Six Sigma Project does not create a new problem as it fixes the primary problem. For example, a quality metric would be a good secondary metric for an improve cycle time primary metric.

Problem Refinement Fish Bone Diagram - A tool used to solve quality problems by brainstorming causes and logically organizing them by branches. Also called the Cause & Effect diagram and Ishikawa diagram Provides tool for exploring cause / effect and 5 whys

Topics (Session 2) ¨ Problem Identification ¨ Cost of Poor Quality ¨ Problem Refinement ¨ Process Understanding ¨ Potential X to Critical X ¨ Improvement

Process Understanding SIPOC – Suppliers, Inputs, Process, Outputs, Customers You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements.

Process Understanding Process Map – should allow people unfamiliar with the process to understand the interaction of causes during the work-flow. Should outline Value Added (VA) steps and non-value add (NVA) steps.

Process Understanding

Topics (Session 2) ¨ Problem Identification ¨ Cost of Poor Quality ¨ Problem Refinement ¨ Process Understanding ¨ Potential X to Critical X ¨ Improvement

Potential X to Critical X “Y” is the dependent output of a variable process. In other words, output is a function of input variables (Y=f(x 1, x 2, x 3…). Through hypothesis testing, Six Sigma allows one to determine which attributes (basic descriptor (generally limited or binary in nature) for data we gather – ie. day of the week, shift, supervisor, site location, machine type, work type, affect the output. For example, statistically, does one shift make more errors or have a longer cycle time than another? Do we make more errors on Fridays than on Mondays? Is one site faster than another? Once we determine which attributes affect our output, we determine the degree of impact using Design of Experiment (DOE).

Potential X to Critical X A Design of Experiment (DOE) is a structured, organized method for determining the relationship between factors (Xs) affecting a process and the output of that process (Y). Not only is the direct affect of an X 1 gauged against Y but also the affect of X 1 on X 2 against Y is also gauged. In other words, DOE allows us to determine - does one input (x 1) affect another input (x 2) as well as Output (Y).

Potential X to Critical X DOE Example Main Effects Plot – Direct impact to Y Interaction Plot – Impacts of X’s on each other

Potential X to Critical X DOE Optimizer – Allows us to statistically predict the Output (Y) based on optimizing the inputs (X) from the Design of experiment data.

Topics (Session 2) ¨ Problem Identification ¨ Cost of Poor Quality ¨ Problem Refinement ¨ Process Understanding ¨ Potential X to Critical X ¨ Improvement

Improvement Once we know the degree to which inputs (X) affect our output (Y), we can explore improvement ideas, focusing on the cost benefit of a given improvement as it relates to the degree it will affect the output. In other words, we generally will not attempt to fix every X, only those that give us the greatest impact and are financially or customer justified.

Control Once improvements are made, the question becomes, are the improvement consistent with predicted Design of Experiment results (ie – are they what we expected) and, are they statistically different than pre-improvement results.

Control Chart - A graphical tool for monitoring changes that occur within a process, by distinguishing variation that is inherent in the process(common cause) from variation that yields a change to the process(special cause). This change may be a single point or a series of points in time - each is a signal that something is different from what was previously observed and measured.

Wrap Up