Measure Phase Lean Six Sigma Measure Phase Tollgate Review.

Measure Phase Lean Six Sigma Measure Phase Tollgate Review

Lean Six Sigma DMAIC Tools and Activities
Review Project Charter Validate High-Level Value Stream Map and Scope Validate Voice of the Customer & Voice of the Business Validate Problem Statement and Goals Validate Financial Benefits Create Communication Plan Select and Launch Team Develop Project Schedule Complete Define Tollgate Value Stream Map Flow Identify Key Input, Process and Output Metrics Develop Operational Definitions Develop Data Collection Plan Validate Measurement System Collect Baseline Data Determine Process Capability Complete Measure Tollgate Identify Root Causes Reduce List of Potential Root Causes Confirm Root Cause to Output Relationship Estimate Impact of Root Causes on Key Outputs Prioritize Root Causes Value-Add Analysis Takt Rate Analysis Quick Wins Statistical Analysis Complete Analyze Tollgate Develop Potential Solutions Evaluate, Select, and Optimize Best Solutions Develop ‘To-Be’ Value Stream Map(s) Develop and Implement Pilot Solution Implement 5s Program Develop Full Scale Implementation Plan Cost/Benefit Analysis Benchmarking Complete Improve Tollgate Develop SOP’s, Training Plan & Process Controls Implement Solution and Ongoing Process Measurements Confirm Attainment of Project Goals Identify Project Replication Opportunities Training Complete Control Tollgate Transition Project to Process Owner Define Measure Analyze Improve Control Project Charter Voice of the Customer and Kano Analysis SIPOC Map Project Valuation/ROIC Analysis Tools RACI and Quad Charts Stakeholder Analysis Communication Plan Effective Meeting Tools Inquiry and Advocacy Skills Time Lines, Milestones, and Gantt Charting Pareto Analysis Value Stream Mapping Process Cycle Efficiency/Little’s Law Operational Definitions Data Collection Plan Statistical Sampling Measurement System Analysis (MSA) Gage R&R Kappa Studies Control Charts Spaghetti Diagrams Histograms Normality Test Process Capability Analysis Process Constraint ID and Takt Time Analysis Cause & Effect Analysis FMEA Hypothesis Tests/Conf. Intervals Simple & Multiple Regression ANOVA Components of Variation Conquering Product and Process Complexity Queuing Theory Replenishment Pull/Kanban Stocking Strategy Process Flow Improvement Process Balancing Analytical Batch Sizing Total Productive Maintenance Design of Experiments (DOE) Solution Selection Matrix Piloting and Simulation Work Control System Setup reduction Pugh Matrix Pull System Mistake-Proofing/ Zero Defects Standard Operating Procedures (SOP’s) Process Control Plans Visual Process Control Tools MGPP Statistical Process Controls (SPC) Solution Replication Visual Workplace Metrics Project Transition Model Team Feedback Session Kaizen Events Targeted in Measure to Accelerate Results International Standards for Lean Six Sigma

Project Charter Problem/Goal Statement Financial Impact Team
State financial impact of project Expenses Investments (inventory, capital, A/R) Revenues Separate “hard” from “soft” dollars State financial impact of leverage opportunities (future projects) Problem: Describe problem in non-technical terms Statement should explain why project is important; why working on it is a priority Goal: Goals communicate “before” and “after” conditions Shift mean, variance, or both? Should impact cost, time, quality dimensions Express goals using SMART criteria Specific, Measurable, Attainable, Resource Requirements, Time Boundaries Explain leverage and strategic implications (if any) Team Tollgate Review Schedule PES Name Project Executive Sponsor (if different from PS) PS Name Project Sponsor/Process Owner DC Name Deployment Champion GB/BB Name Green Belt/Black Belt MBB Name Master Black Belt Core Team Role % Contrib. LSS Training Team Member 1 SME XX YB Team Member 2 TM XX GB Team Member 3 SME XX PS Extended Team Team Member 1 BFM XX Not Trained Team Member 2 IT XX Not Trained Tollgate Scheduled Revised Complete Define: XX/XX/XX - XX/XX/XX Measure: XX/XX/XX XX/XX/XX XX/XX/XX Analyze: XX/XX/XX XX/XX/XX XX/XX/XX Improve: XX/XX/XX XX/XX/XX XX/XX/XX Control: XX/XX/XX XX/XX/XX XX/XX/XX Readiness Checklist – Measure Project Charter – Confirm that the project will make a significant impact on the Business. How have you confirmed the key customers, the CCR's and KPOVs? What are the revised Business Benefits? Does Business strategy still drive the project? Review high-level schedule milestones here: Phase Completions Tollgate Reviews Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Data Collection Plan Performance Measure Operational Definition Data Source and Location How Will Data Be Collected Who Will Collect Data When Will Data Be Collected Sample Size Stratification Factors How will data be used? VOC MSA Process VSM Financials Others For each performance measure (Y), update a data collection plan Include MSA measure plan (Gantt chart, MS project plan is Optional) Add Financial measure plan if separate from performance Y Add any Time Study or other data collection plans for Value Stream Map Sample Size Calculation Use additional slides if needed Input, Process and Output Metrics and the Data Measurement Plan – Confirm that the right process metrics have been chosen and logical trade-offs have been made in determining what to measure. Is this project driven by Customer specifications? If so, how do you know that the specifications satisfy customer CCR's? What was the process for determining the metrics in the Data Measurement Plan? What trade-off’s were made is determining the final set of metrics for which to gather data? Major Activities in each DMAIC phase are shown above. The extent to which these have been addressed or executed form the basis of evaluating whether or not to allow the project to proceed to the next phase. Key Question: Does the data currently exist? Existing Data – Taking advantage of archived data or current measures to learn about the Output, Process or Input This is preferred when the data is in a form we can use and the Measurement System is valid (a big assumption and concern) New Data – Capturing and recording observations we have not or don’t normally capture May involve looking at the same “stuff,” but with new Operational Definitions This is preferred when the data it is readily and quickly collectable (it has less concerns with measurement problems) Existing vs. New Considerations - Is existing or “historical” data adequate? Meet the Operational Definition? Truly representative of the process, group? Contain enough data to be analyzed? Gathered with a capable Measurement System? - Cost of gathering new data - Time required to gather new data The trade-offs made here, I.e. should the time and effort be taken to gather new data, or only work with what we have, are significant and can have a dramatic impact on the project success Check Sheets The workhorse of data collection Enhance ease of collection Faster capture Consistent data from different people Quicker to compile data Capture essential descriptors of data “Stratification factors” Need to be designed for each job How will Data Be Collected 1. Select specific data & factors to be included 2. Determine time period to be covered by the form - Day, Week, Shift, Quarter, etc. 3. Construct form. - Be sure to include: Clear labels Enough room Space for notes Test the form! Tips Include name of collector(s) (first & last) Reason/comment columns should be clear and concise Use full dates (month, date, year) Use explanatory title Consider lowest common denominator on metric Minutes vs. Hours Inches vs. Feet Test and validate your design (try it out) Don’t change form once you’ve started, or you’ll be “starting over”! As you set up Check Sheets... Prepare a spreadsheet to compile the data. Think about how you’ll DO the compiling (and who’ll do it). Consider what sorting, graphing or other reports you’ll want to create. Continuous or Discrete Data? Adequate level of Discrimination and Accuracy? Adjust check sheet as needed to ensure usable data later. But don’t make data harder to collect. Who Will Collect the Data? Considerations: - Familiarity with the process - Availability/impact on job Rule of Thumb – If it takes someone more than 15 minutes per day it isn’t likely to be done - Potential Bias - Will finding “defects” be considered risky or a “negative”? - Benefits of Data Collection Will data collection benefit the collector? Be Sure They ... Give input on the check sheet design Understand operational definitions (!) Understand how data will be tabulated Helps them see the consequences of changing Have been trained and allowed to practice Have knowledge and are unbiased Narrow Potential Key Process Input Variables (KPIVs) Have the potential root causes been narrowed? Was a Cause and Effect (C&E) Matrix used? If so, what were the results? How were the KPOVs rated? Did people who operate the process, technical experts, and supervisors collaborate to produce the C&E Analysis? Have you characterized the variables (controllable, uncontrolled [noise], etc)? Was a Pareto Chart used to select potential Key Process Input Variables (KPIVs) from the C&E Matrix? How many KPIVs do you have at the beginning and end of C&E Matrix? Are there any potential KPIVs which need immediate Baseline capability and MSA? Are these potential KPIVs monitored in the workplace? Which process steps stand out as especially significant in the C&E Matrix? Is there any process step that the team feel can be eliminated or combined? Data Stratification - Capturing and use of characteristics to sort data into different categories (also known as “slicing the data”) Focuses on the Process Outputs, the Y’s Used to: Provide clues to root causes (Analyze) Verify suspected root causes (Analyze) Uncover times, places where problems are severe (“vital few”) Surface suspicious patterns to investigate If you don’t collect stratification factors “up front,” you may have to start all over later. On the other hand, seeking too many factors makes the data more difficult and/or more costly to collect. Key Steps Fill in the Output measure Y. Fill in the key stratification questions you have about the process in relationship to the Y. List out all the levels and ways you can look at the data in order to determine specific areas of concern. Create specific measurements for each subgroup or stratification factor. Review each of the measurements (include the Y measure) and determine whether or not current data exists. Discuss with the team whether or not these measurements will help to predict the output Y, if not, think of where to apply the measures so that they will help you to predict Y. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Operational Definitions
Y – Continuous data (Process start/stop and cycle time boundaries (such as the unit of measure (ex minutes), the unit (the thing you are measuring), will you include weekends, holidays, non-business hours?) Y – Discrete data (Define Success/Defect or other attribute values you will measure X – The subgroups values or X-factor groupings you will use on your project data collection Other unique terms that apply to your project that require clear operational definitions Use additional slides as needed to complete your operational definitions Operational Definitions apply to MANY things we encounter every day. For example, all the measurement systems we use (feet/inches, weight, temperature) are based on common definitions that we all know and accept. Sometimes these are called “standards.” Other times, our operational definitions are more vague. For example, when someone says a loan is “closed” they may mean papers have been sent, but not signed; another person may mean signed but not funded; a third person might mean funded but not recorded. While here we are focused on operational definitions in the context of measurement, the concept applies equally well to “operationally defining” a customer requirement, a procedure, a regulation – or anything else that benefits from clear, unambiguous understanding. Learning to pay attention to and clarify operational definitions can be a major side benefit of the Six Sigma process. What it is... A clear, precise description of the factor being measured Why it’s critical... So each individual “counts” things the same way So we can plan how to measure effectively To ensure common, consistent interpretation of results So we can operate with a clear understanding and with fewer surprises From General to Specific: Step 1 – Translate what you want to know into something you can count Step 2 – Create an “air-tight” description of the item or characteristic to be counted Step 3 – Test your Operational Definition to make sure it’s truly “air-tight” Note: Sometimes you’ll need to do some “digging” up-front to arrive at good operational definitions. It’s usually worth the effort!! A quantified evaluation of characteristics and/or level of performance based on observable data Examples include: Length of time (speed, age) Size (length, height, weight) Dollars (costs, sales revenue, profits) Counts of characteristics or “attributes” (types of customer, property size, gender) Counts of defects (number of errors, late checkouts, complaints) Types of Data - Continuous – Any variable measured on a continuum or scale that can be infinitely divided. Primary types include time, dollars, size, weight, temperature, speed. Always preferred over Discrete/ Cycle time Cost or price Length of call Temperature of rooms Attribute Data: Discrete or Attribute – A count, proportion or percentage of a characteristic or category. Service process data is often discrete. Late delivery Gender Region/location Room type Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Enter Key Slide Take Away (Key Point) Here
MSA Conclusions The measurement systems are acceptable. The data is considered to have no potential for significant error. Need to be careful to appropriately use the data during the Analyze Phase. Type of Measurement Error Description Considerations to this Project Discrimination (resolution) The ability of the measurement system to divide into “data categories” Work hrs can be measured to <.25 hrs. Tool usage measure to +- 2 min. Bias The difference between an observed average measurement result and a reference value No bias - Work hours and radar start-stop times consistent through population. Stability The change in bias over time No bias of work hrs & radar usage data. Repeatability The extent variability is consistent Not an issue. Labor and radar usage is historical and felt to be accurate enough for insight and analysis. Reproducibility Different appraisers produce consistent results Remarks in usage data deemed not reproducible, not used in determining which radars were used in each op Variation The difference between parts n/a to this process. Measurement System Analysis (MSA) Is the Measurement process good enough to guide process improvement efforts and to meet customer needs? Is there a formal process for measuring the variable? How have you determined you do not have sampling problems (when, how, sample stability, sampling the sample jar, etc)? What is the design of the MSA experiment? What measurement or sampling issues were resolved? Were all problems communicated to all appropriate people (local and globally)? Is there a control plan in place which includes ownership, calibration, procedures, troubleshooting guide, SPC, etc)? How Would You Assess Your Measurement System Today? Talk to the individuals conducting the measurements? Have a few measurements taken and compare them? Have other individuals or “experts” verify our measurements? Hope your customers get the same measurements? Assume computers are always right? Conduct a Gage R&R study? Measurement Variation is broken down into two components: (The two R’s of Gage R&R) Reproducibility (Operator Variability) Different individuals get different measurements for the same thing. Repeatability (Equipment/Gage Variability) A given individual gets different measurements for the same thing when measured multiple times. The tool we use to determine the magnitude of these two sources of measurement system variation is called Gage R&R Reproducibility is the variation in the average of the measurements made by different operators using the same measuring instrument when measuring the identical characteristic on the same part. Repeatability is the variation between successive measurements of the same part, same characteristic, by the same person using the same equipment (gage). Also known as test /re-test error, used as an estimate of short-term variation Stability = If measurements do not change or drift over time, the instrument is considered to be stable. Bias is the difference between the observed average value of measurements and the master value. The master value is determined by precise measurement typically by calibration tools linked to an accepted, traceable reference standard. Average of measurements are different by a fixed amount. Bias effects include: Operator Bias – Different operators get detectable different averages for the same value, Instrument Bias – Different instruments get detectable different averages for the same measurement, and Other Bias – Day-to-day (environment), fixtures, customer and supplier (sites). Discrimination is the capability of detecting small changes in the characteristic being measured. The instrument may not be appropriate to identify process variation or quantify individual part characteristic values if the discrimination is unacceptable. If an instrument does not allow differentiation between common variation in the process and special cause variation, it is unsatisfactory. Acceptable Measurement Systems Properties that all acceptable measurement systems must have: The measurement system must be in control (only common cause variation; i.e., in statistical control). Variability of the measurement system must be small in relation to the process variation. Variability of the measurement system must be small compared with the specification limits. The increments of the measurement must be small relative to the smaller of: a) the process variability or b) the specification limits (Rule of thumb: increments are to be no greater than 1/10th of the smaller of: a) process variability or b) specification limits). AIAG Gage R&R Standards The Automotive Industry Action Group (AIAG) has two recognized standards for Gage R&R : Short Form – Five samples measured two times by two different individuals. Long Form – Ten samples measured three time each by three different individuals. For good insight into Gage R&R, go to [ Remember that the Measurement System is acceptable if the Gage R&R variability is small compared to the process variability or specification limits. Preparation for a Measurement System Study Plan the approach. Select number of appraisers, number of samples, and number of repeat measures. Use at least 2 appraisers and 5 samples, where each appraiser measures each sample at least twice (all using same device). Select appraisers who normally do the measurement. Select samples from the process that represent its entire operating range. Label each sample discretely so the label is not visible to the operator. Check that the instrument has a discrimination that is equal to or less than 1/10 of the expected process variability or specification limits. Setting Up the Measurement Study Assure that the gage/instrument has been maintained and calibrated to traceable standards. Parts are selected specifically to represent the full process variation Parts should come from both outside the specs (high side and low side) and from within the specification range Running the Measurement Study Each sample should be measured 2-3 times by each operator (2 times is the Short Test). Make sure the parts are marked for ease of data collection but remain “blind”(unidentifiable) to the operators. Be there for the study. Watch for unplanned influences. Randomize the parts continuously during the study to preclude operators influencing the test. The first time evaluating a given measurement process, let the process run as it would normally run. Because in many cases we are unsure of how noise can affect our measurement system, we recommend the following procedure: Have the first operator measure all the samples once in random order. Have the second operator measure all the samples once in random order. Continue until all operators have measured the samples once (this is Trial 1). Repeat steps for the required number of trials. Use a form to collect information. Analyze results. Determine follow-up action, if any. If Process Tolerance and Historical Sigma values are not used in Minitab, a critical assumption is then made that the sample parts chosen for the study, truthfully exhibit the true process variation. In this case, the acceptability of the measurement system is based upon comparison only to the part variation seen in the study. This can be a valid assumption if care is taken in selecting the study sample parts. AIAG states that “One element of criteria whether a measurement system is acceptable to analyze a process is the percentage of the part tolerance or the operational process variation that is consumed by measurement system variation” Remember that the guidelines are: Under 10 % – Acceptable. From 10 to 30 % – Marginal. May be acceptable based upon the risk of the application, cost of measurement device, cost of repair, etc. Over 30 % – Not Acceptable. Every effort should be made to improve the measurement system. Repeatability is checked by using a special Range Chart where the differences in the measurements by each operator on each part is charted. If the difference between the largest value of a measured part and the smallest value of the same part does not exceed the UCL, then that gage and operator are considered to be Repeatable Reproducibility is best determined analytically using the tabulation analysis in the Minitab Session (discussed in following slides) . Graphically it may be seen if there are significant differences in the operator patterns generated by each operator measuring the same samples. This tabulation from Minitab builds the % of Study Variation that each source contributes to a calculated potential Total Variation seen in the study. The 6.0 * SD is how statistically 99.73% of the Total Variation is calculated and this is assumed to equal 99.73% of the true process variation unless the Historical Sigma is input into Minitab. The %’s are used to grade the validity of the measurement system to perform measurement analysis using %’s already taught. If the process is performing well, the % Tolerance is then important. The sum of the %’s may add to more than 100% due to the math. The Number of Distinct Categories represents the number of non-overlapping measurement groups that this measurement system can reliably distinguish in the Study Variation. We would like that number to be 5 or higher. Four is marginal. Fewer than 4 implies that the measurement system can only work with attribute data - Most physical measurement systems use measurement devices that provide continuous data. For continuous data Measurement System Analysis we can use control charts or Gage R&R methods. - Attribute/ordinal measurement systems utilize accept/reject criteria or ratings (such as 1 - 5) to determine if an acceptable level of quality has been attained. Kappa techniques can be used to evaluate these Attribute and Ordinal Measurement Systems. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Baseline Basic Statistics
The current process has a non-normal distribution with the P-Value < but does have a normal bell-shape. Since the mean and median are the same in days (29) +/- 0.5 days, we will not transform data. The range is 35 and the standard deviation is 2.7 days What Is a Confidence Interval? We know that when we take the average of a sample, it is probably not exactly the same as the average of the population. Confidence intervals help us determine the likely range of the population parameter. For example, if my 95% confidence interval is 5 +/- 2, then I have 95% confidence that the mean of the population is between 3 and 7. Why Do We Need Confidence Intervals? Sample statistics, such as Mean and Standard Deviation, are only estimates of the population’s parameters. Because there is variability in these estimates from sample to sample, we can quantify our uncertainty using statistically-based confidence intervals. Confidence intervals provide a range of plausible values for the population parameters ( and ). Any sample statistic will vary from one sample to another and, therefore, from the true population or process parameter value. Confidence Interval for the Mean (Mu) with Population Standard Deviation (Sigma) Unknown A very important point to remember is that for this example we assumed that we knew the population standard deviation, and many times that is not the case. Often, we have to estimate both the mean and the standard deviation from the sample. When Sigma is not known, we use the t-distribution rather than the normal (z) distribution. The t-distribution will be explained next. In many cases, the true population Sigma is not known, so we must use our sample standard deviation (s) as an estimate for the population standard deviation (s). Since there is less certainty (not knowing Mu or Sigma ), the t-distribution essentially “relaxes” or “expands” our confidence intervals to allow for this additional uncertainty. In other words, for a 95% confidence interval, you would multiply the standard error by a number greater than 1.96, depending on the sample size. 1.96 comes from the normal distribution, but the number we will use in this case will come from the t-distribution. What Is This t-Distribution? The t-distribution is actually a family of distributions. They are similar in shape to the normal distribution (symmetric and bell- shaped), although wider, and flatter in the tails. How wide and flat the specific t-distribution is depends on the sample size. The smaller the sample size, the wider and flatter the distribution tails. As sample size increases, the t-distribution approaches the exact shape of the normal distribution. Sample Size Concerns If we sample only one item, how close do we expect to get to the true population mean? How well do you think this one item represents the true mean? How much ability do we have to draw conclusions about the mean? What if we sample 900 items? Now, how close would we expect to get to the true population mean? Three concepts affect the conclusions drawn from a single sample data set of (n) items: Variation in the underlying population (sigma) Risk of drawing the wrong conclusions (alpha, beta) How small a Difference is significant (delta) These 3 factors work together. Each affects the others. Variation: When there’s greater variation, a larger sample is needed to have the same level of confidence that the test will be valid. More variation diminishes our confidence level. Risk: If we want to be more confident that we are not going to make a decision error or miss a significant event, we must increase the sample size. Difference: If we want to be confident that we can identify a smaller difference between two test samples, the sample size must increase. Larger samples improve our confidence level. Lower confidence levels allow smaller samples. All of these translate into a specific confidence interval for a given parameter, set of data, confidence level and sample size. They also translate into what types of conclusions result from hypothesis tests. Testing for larger differences between the samples, reduces the size of the sample. This is known as delta (D). Type I Error Alpha Risk Producer Risk The risk of rejecting the null, and taking action, when none was necessary Type II Error Beta Risk Consumer Risk The risk of accepting the null when you should have rejected it. No action is taken when there should have been action. The Type I Error is determined up front. It is the alpha value you choose. The confidence level is one minus the alpha level. The Type II Error is determined from the circumstances of the situation. If alpha is made very small, then beta increases (all else being equal). Requiring overwhelming evidence to reject the null increases the chances of a type II error. To minimize beta, while holding alpha constant, requires increased sample sizes. One minus beta is the probability of rejecting the null hypothesis when it is false. This is referred to as the Power of the test. The p-Value If we reject the null hypothesis, the p-value is the probability of being wrong. In other words, if we reject the null hypothesis, the p-value is the probability of making a Type I error. It is the critical alpha value at which the null hypothesis is rejected. If we don’t want alpha to be more than 0.05, then we simply reject the null hypothesis when the p-value is 0.05 or less. As we will learn later, it isn’t always this simple. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Baseline Process Capability
266 data points collected between 11/1/04 thru 11/30/04 Mean 29 days, St. Dev days, CP is indicating process needs centering to the LSL of 10 and USL of 30 days. Cpk is .1 indicating that the process is exceeding the USL. With an overall PPM of 371,895 defects per million opportunity, the current process has a Sigma Quality Level of 1.8 or a 62% yield Baseline Capability Analysis At what level is the process operating now? What is the baseline capability of the potential KPIVs? What is the baseline capability of the KPOVs? How reliable are your capability estimates? (Is the process stable, unimodal & normal?) If no, do you have ideas of possible special causes? Process Capability – Cp Ratio of total variation allowed by the specification to the total variation actually measured from the process Use Cp when the mean can easily be adjusted (i.e., transactional processes where resources can easily be added with no or minor impact on quality) AND the mean is monitored (so process owner will know when adjustment is necessary – doing control charting is one way of monitoring). Typical goals for Cp are greater than 1.33 (or 1.67 for safety items) If Cp < 1, then the variability of the process is greater than the specification limits. Process Capability – Cpk This index accounts for the dynamic mean shift in the process – the amount that the process is off target. Calculate both values and report the smaller number. Most common calculation of Process Capability - Ratio of 1/2 total variation allowed by spec. to 1/2 the actual variation, with only the portion closest to a spec. limit being counted. - Use when the mean cannot be easily adjusted (i.e., Cycle times, customer satisfaction indices, etc.). - Typical goals for Cpk are greater than 1.33 (or 1.67 if safety related). - For Std. Deviation estimates use: Minitab calculated Std. Deviation (Display Descriptive Statistics) R/d2 [short term] (calculated from Xbar-R chart) s = [long term] (calculated from all data points) Long term: When the data has been collected over a time period and over enough different sources of variation that over 80% of the variation is likely to be included. Uses of Capability Analysis Performed on existing processes as a means of establishing a baseline of current operations (so it’s possible to tell when improvement has occurred). When done periodically, is a means of monitoring deterioration of a process for whatever reason (system, personnel, environment, etc.). Can be done on any process that has a target spec. established (target spec. is needed for the values in numerator), and has a capable measuring system (needed for valid values in denominator). Process Capability – CPU CPU indicates capability against an Upper Specification Limit. Process Capability – CPL CPL indicates capability against a Lower Specification Limit. Cpk is the index used when a process has a “two-sided” specification. Cpk is the lower of the two! Capability Action Plan - Give highest priority to parameters with Cpk’s less than 1.0 (center the dimension, reduce the variation or both). If possible, get tolerance relief. (If product/process is mature, and there have been no customer problems, what is the need for this formal spec when another “de facto” spec has been used historically?) 100% inspect, measure and sort. Chart using the data from the measurements. - Use SPC Charting on parameters with Cpk’s between 1.0 and 1.33 (or 1.67 if safety related). Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sigma Calculator: Continuous Data
Assumptions No analysis would be complete without properly noting the assumptions made. In the above Sigma conversion table, we have assumed that the standard sigma shift of 1.5 is appropriate, the data is normally distributed, and the process is stable. In addition, the calculations are made with using one-tail values of the normal distribution. Understanding The Formula Defects Per Million Opportunities (DPMO) = ((Total Defects) / (Total Opportunities)) * 1,000,000 Defects (%) = ((Total Defects) / (Total Opportunities)) * 100 Yield (%) = (Defects Percentage) process Sigma = NORMSINV(1-((Total Defects) / (Total Opportunities))) + 1.5 Alternatively, process Sigma = SQRT( * (ln(DPMO))). Examples Here are a couple of examples to help illustrate the calculations. - A long-term 93% yield (e.g. 100 opportunities, 7 defects) equates to a process Sigma long-term value of 1.48 (with no Sigma shift) or a process Sigma short-term value of 2.98 (with a 1.5 Sigma shift). - A long-term 99.7% yield (e.g. 1,000 opportunities, 3 defects) equates to a process Sigma long-term value of 2.75 (with no Sigma shift) or a process Sigma short-term value of 4.25 (with the 1.5 sigma shift). When we talk about a Lean Six Sigma process, we are referring to the process short-term (now). When we talk about DPMO of the process, we are referring to long-term (the future). We refer to 3.4 defects per million opportunities as our goal. This means that we will have a 6 sigma process now in order to have in the future (with the correction of 1.5) a 4.5 sigma process -- which produces 3.4 defects per million opportunities. Notice: Sigma with a capital "S" is used above to denote the process Sigma, which is different than the typical statistical reference to sigma with a small "s" which denotes the standard deviation. How to Calculate Process Sigma Consider the power company example from the previous page: A power company measures their performance in uptime of available power to their grid. Here is the 5 step process to calculate your process sigma. Step 1: Define Your Opportunities An opportunity is the lowest defect noticeable by a customer. Many Six Sigma professionals support the counter point. I always like to think back to the pioneer of Six Sigma, Motorola. They built pagers that did not require testing prior to shipment to the customer. Their process sigma was around six, meaning that only approximately 3.4 pagers out of a million shipped did not function properly when the customer received it. The customer doesn't care if the diode is backwards or is missing, just that the pager works. Returning to our power company example, an opportunity was defined as a minute of uptime. That was the lowest (shortest) time period that was noticeable by a customer. Step 2: Define Your Defects Defining what a defect is to your customer is not easy either. You need to first communicate with your customer through focus groups, surveys, or other voice of the customer tools. To Motorola pager customers, a defect was defined as a pager that did not function properly. Returning to our power company example, a defect is defined by the customer as one minute of no power. An additional defect would be noticed for every minute that elapsed where the customer didn't have power available. Step 3: Measure Your Opportunities and Defects Now that you have clear definitions of what an opportunity and defect are, you can measure them. The power company example is relatively straight forward, but sometimes you may need to set up a formal data collection plan and organize the process of data collection. Be sure to read 'Building a Sound Data Collection Plan' to ensure that you gather reliable and statistically valid data. Returning to our power company example, here is the data we collected: Opportunities (last year): 525,600 minutes; Defects (last year): 500 minutes Step 4: Calculate Your Yield The process yield is calculated by subtracting the total number of defects from the total number of opportunities, dividing by the total number of opportunities, and finally multiplying the result by 100. Returning to our power company example, the yield would be calculated as:((525, ) / 525,600) * 100 = 99.90% Alternatively, the yield can be calculated for you by using the iSixSigma Process Sigma Calculator - just input your process opportunities and defects. Step 5: Look Up Process Sigma The final step (if not using the iSixSigma Process Sigma Calculator) is to look up your sigma on a sigma conversion table, using your process yield calculated in Step 4 Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sigma Calculator: Discrete Data
Unit A unit is any item that is produced or processed which is liable for measurement or evaluation against predetermined criteria or standards. Opportunity Any area within a product, process, service, or other system where a defect could be produced or where you fail to achieve the ideal product in the eyes of the customer. In a product, the areas where defects could be produced are the parts or connection of parts within the product. In a process, the areas are the value added process steps. If the process step is not value added, such as an inspection step, then it is not considered an opportunity. Opportunities are the things which must go right to satisfy the customer. It is not the number of things we can imagine that can go wrong . Defect Any type of undesired result is a defect. A failure to meet one of the acceptance criteria of your customers. A defective unit may have one or more defects. A defect is a failure to conform to requirements', whether or not those requirements have been articulated or specified. The non-conformance to intended usage requirement. Defective The word defective describes an entire unit that fails to meet acceptance criteria, regardless of the number of defects within the unit. A unit may be defective because of one or more defects. Defects Per Unit - DPU DPU or Defects Per Unit is the average number of defects observed when sampling a population. DPU = Total # of Defects / Total population Consider 100 electronic assemblies going through a functional test. If 10 of these fail the first time around, we have a first pass yield of 90%. Let's say the 10 fails get reworked and re-tested and 5 pass the second time around; the 5 remaining fails pass on the third attempt. Feel free to work out how this would look as a rolling yield. (100 'passes'/115 tests). DPU takes a fundamentally different approach to the traditional measurement of yield. It is simply a ratio of the number of defects over the number of units tested (don't worry about how many tests or how many opportunities for defects). In the above example, the DPU is 15/100 or There are 100 units which were found to have a cumulative total of 15 defects when tested. One interesting feature of DPU is that if you have sequential test nodes, i.e. if the above 100 units had to go through 'Final Test' and threw up a DPU figure of 0.1 there, you simply add the DPU figures from both nodes to get the overall DPU of 0.25 (this is telling you that there were 25 defects in your 100 units). There are a few assumptions which must be realised for this statement to be wholly accurate, but there isn't really time to go there in a 'definition' space. If out of the 100 loans applications there are 30 defects, the FTT yield is .70 or 70 percent. Further investigation finds that 10 of the 70 had to be reworked to achieve that yield so our Rolled Throughput Yield is 100-(30+10)/100 = .6 or 60 percent yield. To consider the defects per unit in this process we divide the number of defects by the result of multiplying the sample by the number of opportunities in each item. No.of defects/(no. of units)*(no. of opportunities for a defect)= 30/100*3 = 30/300 = .1 or we would say that there is a 10 percent chance for a defect to occur in this process. Defects Per Million Opportunities - DPMO Defects per million opportunities (DPMO) is the average number of defects per unit observed during an average production run divided by the number of opportunities to make a defect on the product under study during that run normalized to one million. Defects Per Million Opportunities. Synonymous with PPM. To convert DPU to DPMO, the calculation step is actually DPU/(opportunities/unit) * 1,000,000. Yield Yield is the percentage of a process that is free of defects. OR Yield is defined as a percentage of met commitments (total of defect free events) over the total number of opportunities. First Time Yield - FTY Rolled Throughput Yield - RTY Z Score A measure of the distance in standard deviations of a sample from the mean. Calculated as (X - X bar) / sigma Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Quick Win Documentation Template
Process Name: __________________ Process Lead: ___________________ Process Owner: ______________________ Start Date: ______________ Process Area: ________________________ Stop Date: ______________ Root Cause: _________________________________________________ Obvious Solution: __________________________________________ Low or No Cost: __________________________________________ Low Risk: ________________________________________________ Implementation Plan: ______________________________________________ Stakeholder (s) Approval: ___________________________________________ 5s 4-Step Setup Reduction Inventory Reduction MSA Improvements Price reductions Reduced DOWNTIME (NVA steps or work) Pull System Kaizen events Other The primary difference is in the work required to implement the idea. - A ‘Quick Win’ is already a developed solution idea, i.e., it is in the Improve Phase already. The only determination left is ‘how to implement.’ There is still a requirement to complete Define and Measure, to clarify scope and to be able to measure a change, but there is no need to go through Analyze Phase. - A Kaizen Event is essentially an accelerated DMAIC. Focuses on specific improvement objective; Setup Reduction, 5S, Process Improvement, Line Balancing, etc. Although the Vision of the ‘Future State’ may be in place, there is still a requirement to go through the Analyze Phase to determine HOW to make it happen (as opposed to just ‘how to implement’ a developed idea, as in the case of the ‘Quick Win’). Benefits of Quick Improvement Provides momentum for the project Drives value ($) early, thus improving ROI Provides confidence to the broader organization that Lean Six Sigma is a viable approach to process improvement Reduces stress on project team to ‘Get Something Done!’ When we find these opportunities there is no need to wait months for implementation We should implement change as soon as possible to begin reaping the benefits. We by-pass the Analyze phase and move straight to Improve ‘Quick Win’ Improvement Criteria Minimal or no Capital Expenditure Low Risk Narrow scope Buy-in to solutions by all Stakeholders Certainty the change will generate a positive impact Improvements May be Implemented Quickly (within 1-2 weeks) The project team has the authority to implement the desired changes ‘Quick Win’ Examples Process Step Elimination Procedure Change Safety Stock Elimination (Just in Case Inventory) Communication Improvement Supplier Price Reduction Part Substitution Training on Best Practices Error Proof a Process Step Process Balancing / Layout Quick Wins Cautions Risk assessment must be an essential part of the ‘quick win’ decision process. What are the potential ‘quick win’ impacts on: Customers/Suppliers Other functional areas Cost/Benefit analysis Other teams efforts Quick Improvement Control Plans Quick Improvements, whether ‘Quick Wins’ or Kaizen improvements, must have implemented Control Plans in place before being considered complete. It is desirable to implement improvements as soon as possible but implementation without control can be worse than no implementation at all. See the Control Plan Module in Week 4 for Control Plan implementation and details. Benefits: __________________________________________________________ Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sources of Waste Sources of Waste NVA
Defect Overproduction Transportation Sources of Waste ? Area 1 Area 1 Area 1 Sub area 1 Sub area 1 Sub area 1 NVA Area 1 Area 1 Area 1 Sub area 1 Sub area 1 Sub area 1 Processing Motion Inventory Waiting What Represents the relationship between an effect (problem) and its potential causes. Categorizes causes. Why To help ensure that a balanced list of ideas have been generated during brainstorming Sort and relate the factors affecting a process while little quantifiable data is available Assist discussion when determining root causes To determine the real cause of the problem (as opposed to a symptom of the problem) To refine brainstormed ideas into more detailed causes To identify a team's level of understanding How Name the problem or effect of interest Decide the major categories for causes. Major causes may include the 6 M’s: manpower (or personnel), machines, materials, methods, measurements, and mother nature (or environment) Brainstorm for more detailed causes. Ask "why" each major cause happens at least 5 times Eliminate causes that do not apply Discuss the causes and decide which are most important Work on most important root causes Brainstorm for more ideas in those categories that contain fewer items. This helps counter the “theme” or “group think” effect common in brainstorming Perform another iteration to determine root causes if necessary More on the “Theme” Effect Very often, brainstorming sessions tend to go off in a particular direction based on a common “theme” or a thread of thinking. One or two good ideas get the rest of the group thinking along those lines. The rest of the brainstorming session continues along this “theme.” The cause & effect diagram helps overcome the “theme” effect by allowing the group to visualize the categories into which their ideas fall. The group can then be redirected to focus on generating more ideas in those categories that contain fewer ideas Cause & Effect Diagram Conclusions Represents the relationship between an effect (problem) and its potential causes. Categorizes causes Helps ensure that a balanced list of ideas have been generated during brainstorming Helps us overcome the “theme” or “group think” effect Sorts and relates the factors affecting a process while little quantifiable data is available Serves as a discussion guide to assist in determining root causes Helps determine the real cause of the problem as opposed to just highlighting a symptom of the problem Helps refine brainstormed ideas into more detailed causes Helps identify a team's level of understanding Affinity Diagram When breakthrough is needed to help organize. To help develop central theme. Facts on a problem are not well organized. Breakthrough in traditional thinking is needed. A tool for organizing facts, opinions and issues into natural grouping as an aid to diagnose a complex problem. The inputs are listed on cards which are then rearranged until useful groups are identified. Assemble the right team. Clearly state the problem to be addressed. Brainstorm ideas on cards. Clearly display cards on wall as ideas are generated. Sort cards into related groups. Create header cards. Draw the completed diagram. < Insert your waste percentage as shown in pie chart > Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Swim Lane Process Map Enter Key Slide Take Away (Key Point) Here
Oval shapes : Start/Stop of process Diamonds: Decision points Rectangles: process steps Half-Moon: Delay/Queue Time Client Mgr Notify HR of employee exit date Note: Steps in blue shapes are non-value added steps Form require approval? Places information into HR database Sends exit date to IT, telecom & facilities Avg. Delay 2 days No Re-verifies with mgr on employee’s exit status Client HR Yes Avg. Delay 2 days Avg. Delay 2 days Sends to Admin Secure approval(s) Sends to Admin Client Contact Create ticket if request coming directly from client Admin closes ticket and manager notified Utilize vendor’s web tool to submit delete request to vendor NT Admin What Is a Process Map? A graphical representation of a process flow identifying the steps of the process, the X’s (inputs) and Y’s (outputs) of the process, and of each individual step Process maps need to be modified to fit the particular needs of any specific process. Process Mapping Hints Don’t try to map EVERY service/process – select a typical service to follow Map with the team – Real place, real people, real work Identify CVA, BVA, NVA Activities (more on this later!) Add project critical metrics as applicable One person should ‘coordinate’ the mapping of the total flow Do not ask individual area managers to map “their” process and paste these together Don’t map your organization but rather the flow of products through the organization (“be the paper, product, etc.”) Problem Definition and Process Mapping The first step in creating a process map is to have a well-defined problem statement (see the Project Charter) Process mapping will illustrate a process or business function with respect to the problem statement You can help refine the problem statement using the 5 why’s, but management must ultimately define the issue Process Map Goals What does your process look like? Where do the key inputs occur that affect the outputs? How do you categorize the variables? Are there any hidden rework loops? High-Level View Depicts the major elements and their interactions. Should show the role of feedback and information flow Low-Level View Each process has sub-processes which have micro-processes (see the Top-Down model). Go to the level necessary to address the root cause of the problem and to assure ownership is clear. Do not describe or review the entire process (or system) at this level Current State Hints Clarify process mapping scope with the sponsor Start with the SIPOC Use broad Value Stream Map or Process Outline if available Team should walk the process, gather data Staple yourself to a request/order Use paper to draw first drafts, not computer Top-Down Chart - A High-Level Chart which is selectively expanded from the highest level down to the level where the root cause is located. Determine the Start and Finish Points of the broad Process. Define 5 to 9 high level activities between Start and Finish. Expand the high level activity(ies) most likely to contain the root cause into 5 to 9 medium level activities. Choose the medium level activity(ies) most likely to contain the root cause into 5 to 9 lower level activities. Expand again (and again!) until the level at which the cause(s) of the problem is reached. At this level complete detailed process map(s). A tool to aid in focusing flow-charting effort Don’t flow chart a large process to a significant detail, FOCUS! “Swim Lane” Map Use for large, complex processes when: Multiple departments/functions are involved, including outside the firm. Sequence and time of processes is important (as in cycle time reduction). Can show information and service flows if needed. Top lane is typically process customer Mapping Helpful Hints Always create Process Maps and Value Stream Maps with a team. Rarely does one person have all process knowledge. Interrogate the process by watching in many different conditions. You must watch the process as it happens to see the detail you need. Don’t let space be an issue. Consider using post-its, as the process steps and post on a wall to get your initial ideas across. If your map does not have enough space to list all the information, use numbered reference sheets as attachments. Maintain your Process & Value-Stream Maps with dates and update them as necessary. Use them as a reference. Always maintain a baseline and version control. Avg.Delay 1 day Avg. Delay 1 day Generates ticket & forwards to Admin Admin Delete account Avg.Delay 4 days Mark request as completed on admin web site Vendor Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Value Stream Map Current State
SUPPLIERS Customer call time = 24 min Service lead time = 384 min Order Mgmt Supervisor CUSTOMER Weekly Update Phone Call Phone Call Screen for Acct Mgr Order Mgmt Manual Update Automate Monitoring P/T = 3 min Lost calls=10% 2-5 days Volume=1200 Large Business Simplify/ Combine Improve Visibility Forecast Improvement 6 Customers Small Business Verify the Current State VSM Peer Review Use non-team members who know the process. Review process both internally and “at its edges” (the interfaces). Simulation Use Excel and a static format. Use simulation software (i.e., ProModel) and a dynamic approach. Piloting Pick a part of the organization that is a good representation of the rest. Committed upper management, enthusiastic operations personnel. Implement and observe the improvement. Be prepared to evaluate. The Future State - When to complete the ‘Future State’ map? Often done in Improve Phase for 3-5 DMAIC project Done much sooner in a Kaizen event (more on this later) - Map the Future State to Prioritize Improvement Opportunities: Utilize Takt calculations. Have a goal of continuous flow. Establish queue programs (utilizing Kanbans) where continuous flow is not possible. Find opportunities to shorten/reduce defects, downtime, setup time, value-add time. Don’t try to change service designs, technology, or facility locations on 1st iterations of your future state design. Questions About Future State What is the Takt Rate? Will I have a queue system or ship direct? Where can I utilize continuous flow? Where will I need replenishment pull systems? At what single point will I schedule services? How will I level the service mix at the Bottleneck/Constraint? What will be my project priority? Use of Future State Map Prioritize Opportunities “See” and Manage the Overall Material Flow Communication of “the Future” Create a Plan with Timeframes Effective Process and Value-Stream Maps - Initially, serve to clarify the problem and possible causes. - Gain agreement on current operations: Who are the current customers of the process, and by customer set? What is currently being delivered – what is value added, what is not? - Show relationships/interfaces between disparate elements – where are the disconnects in service or information flow? - Determine where process is most likely to give the most pertinent information: What do we need to know? Where are we going to get it? - Then, utilize map as a template for gathering data and showing data relationships. - Use as a skeleton to display relevant data. - Finally, maps assist the improvement discussions and implementation planning as well as the actual implementation. - Show results of “what if” exercises. Additionally, can use maps on long term basis to communicate the process’ performance to the organization Process/Value Stream Maps are valuable for describing the current situation and establishing requirements. Process/Value Stream Maps are the single MOST important deliverable for the Measure Phase. It is important to match the tool used (the type of Map) to the needs – there may even be multiple Maps used in one Project. Important to show both the flow of services as well as the flow of the data and information necessary for success. Order Mgmt Order Mgmt Order Mgmt Order Mgmt DIST Customer Info Product Need Shipping Info Pick Pack & Ship Pricing Simplify/ Mistake Proof 5 Customers 4 4 4 4 10 P/T = 2 min P/T = 6 Min P/T = 2 Min 20 Orders P/T = 6 Min P/T = 120 Min Home Error Rate=2% Error Rate=0% Error Rate=2% Error Rate=1% Error Rate=1% Volume=800 Volume=800 Volume=800 Volume=800 Volume=1200 3 Customers 5 min 240 min 3 min 2 min 6 min 6 min 2 min 120 min Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Business Impact State financial impact of future project leverage opportunities Separate “hard or Type 1” from “soft Type 2 or 3” dollars Annual Estimate Replicated Estimate Revenue Enhancement Type 1: ? Type 2: ? Type 3: ? Expenses Reduction Loss Reduction Cost Avoidance Total Savings Lean 6 Sigma Project Documentation General Guidance The project charter templates provided will document both the functional and financial goals to be realized. When the Project Charter portion of the project is documented sufficiently for a Black Belt to begin work, a copy of the documentation can be placed in the Project Hopper. When a Black Belt begins work on a particular project, that project’s Project Charter should be removed from the Project Hopper as it is no longer available to be worked. The financial portion of the Project Charter should initially be completed by the Project Sponsor and the Financial Representative to show projected savings. This portion should also be updated as the project progresses to clearly show true savings achieved. When the project is completed, the Financial Representative should validate that the project savings were achieved as recorded. The financial portion (now validated savings) should then be forwarded to Deployment Champion. ***************************************************************************************************** State financial impact of project Expenses Investments (inventory, capital, A/R) Budget Separate “hard” from “soft” dollars State financial impact of future project leverage opportunities Benefits – Will the project make a significant impact on the Business? Is the project driven by the Business strategy? What are the expected Business Benefits of the project? What are the critical assumptions in the benefit analysis? Does the Financial Manager endorse the Benefit calculation? Definitions Growth = Percentage Increase in Revenue Return on Invested Capital: If a shareholder invests a dollar, how much will be earned each year? Return = Profit After Tax (Net Operating Profit After Tax, NOPAT) Invested Capital = Working Capital + Plant, Property & Equip. (PP&E) Return on Invested Capital = Profit After Tax/Invested Capital Cost of Capital = How Much a Company Pays for Its Capital ~ 12% Compare the Cost of Capital to the Internal Rate of Return (IRR) of a project’s cash flows (more on this later) Finding the Financial Impact We must be able to satisfactorily demonstrate the impact of our improvement project activities Typically, there are at least four categories of impact: “Green” dollars positive dollars that exit or enter the organization, that appear on the profit and loss (P&L) statement (Measurable) Redeployment of resources – people or equipment no P&L impact, versus a baseline, but reduces cost needed to support business growth, realized immediately upon redeployment (Can be difficult to measure) Cost avoidance, reduces the need to add cost to support business growth, ex. capacity improvement. Realized when the business grows (Can be difficult to measure with certainty) Strategic. May be no “hard” financial impact, but the right thing to do in terms of the future of the business. (Very difficult to measure the true impact) Examples of Potential Revenue Enhancement: New sales of existing or new products Increased retention of profitable customers Examples of Potential Cost Reduction: Price increases Labor Cost – Reduced number of hand-offs Servicing Cost – Decreased number of quality related service calls Rework – Less defective work due to improving quality at the source of the defect Examples of Potential Capital Reduction Supplies – Decreased volume of inventory due to process improvements Cost of Capital – Decreased time of service to customer payment Occupancy – Decreased storage area due to quicker shipping times Direct Costs – These are costs that can be traced directly to providing a service. For example direct labor charges to include wage and benefit costs. Also, any cost associated with materials or service maintenance . One Time Costs – These are costs which are incurred only once. Examples include new equipment or facilities, initial training, initial software installation and configuration, etc. On-Going Costs – These are costs which will continuously be incurred. Examples included labor, on-going training, supplies and other operating costs. If you must prove the bottom line impact of a project, reduce the dollars that exit the organization or increase the Revenue entering the organization! Make sure that all stakeholders agree on the project’s impact and how it will be measured in financial terms! Regularly seek the advice and input of members of the financial community, especially on the assumptions to make and means of calculating the stream of financial benefits from an improvement project. Each project should have a financial rep (a person from outside the team) to assist and review financial benefits calculations. Economic Profit can be impacted in three ways: Revenue Growth Volume increases due to improved customer satisfaction, faster delivery, differentiated features, etc. Cost Reduction Purchase items and services Waste elimination Make vs. Buy Labor reduction Productivity improvements Scrap/Rework reductions Capital Reduction Inventory Accounts Receivables Assets While overall project benefits are frequently a combination of all three levels, it is important to understand the distinction. Lean Six Sigma Financial Principles Every step is taken to ensure the integrity and accuracy of the data. Processes developed to calculate and assess benefits must be simple and not overly complex. Benefits are measured using established financial, cost and investment methodologies/principles/practices/guidelines of the company as appropriate. Impacts on internal controls are proactively identified and communicated. The primary reporting metric for Lean Six Sigma projects is the impact on Economic Profit. All benefits and costs should be reported in $US dollars using the current year plan exchange rate. All organizations consistently define, quantify, measure and report Lean Six Sigma projects in the tracking system. Lean Six Sigma metrics complements, but does not replace, other critical business measures and analyses. Black Belt, Project Sponsor and Financial Representative need to form a partnership with the Financial Representative acting as a key consultant to the project team Roles and Responsibilities Black Belt/Project Sponsor Define current operational process and establish baseline with metrics and data Determine process improvement relative to baseline and metrics Define logic for benefits Determine type of benefits Calculate financial benefit outlook Support validation of benefits during realization Ensure benefits info is included in DMS by Measure tollgate Ensure Type 1 savings are tracked to BLI and customers are in agreement by Control Certifies savings Financial Representative Review and consult on expected benefit projections at Define phase Validate financial benefits before Control tollgate Validate actual project benefits during realization Expertise solicited only at critical points Available to advise Project Sponsor if needed during tollgate reviews Charter Benefits Review (before Launch): Financial Representative should be consulted with on the expected Benefits identified in the Project Charter Benefits Realization Schedule Review (before Implementation) Financial Representative validates the Tracking Methodology and Benefit Realization Schedule (the monthly Benefit projection) prior to implementation. Financial representatives review and consult on expected benefit projections at Define phase. BBs, ensure benefits info is included in Measure. This will be added to the measure tollgate checklist. FR Validate financial benefits before Control tollgate. Black/Green Belt Certification Review (after 2 months of realization) Financial Representative reviews the Benefits calculations made to-date by the Process Owner. PS ensure actual project benefits are identified during realization. FR Validate actual project benefits during realization. Project Validation Review (after 6 months of realization) Financial Rep reviews the Benefits calculations made to-date by the Process Owner. Realization Review (after 12 months of realization) Financial Rep reviews the final Benefits calculations made by the Process Owner. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Business Impact Details
Type 1: Describe the chain of causality that shows how you determined the Type 1 savings. (tell the story with cause–effect relationships, on how the proposed change should create the desired financial result (savings) in your project ) Show the financial calculation savings and assumptions used. Assumption #1 (i.e. source of data, clear Operational Definitions?) Assumption #2 (i.e. hourly rate + incremental benefit cost + travel) Type 2: Describe the chain of causality that shows how you determined the Type 2 savings. (tell the story with cause–effect relationships, on how the proposed change should create the desired financial result (savings) in your project ) Assumption #1 (i.e. Labor rate used, period of time, etc…) Assumption #2 (i.e. contractor hrs or FTE, source of data, etc…) Describe the Type 3 Business Impact(s) areas and how these were measured Assumption #1 (i.e. project is driven by the Business strategy?) Assumption #2 (i.e. Customer service rating, employee moral, etc…) Other Questions Stakeholders agree on the project’s impact and how it will be measured in financial terms? What steps were taken to ensure the integrity & accuracy of the data? Has the project tracking worksheet been updated? Describe the chain of causality that shows how you determined the Type 1 savings. (tell the story on how you will capture these savings in your project) Show the Type 1 financial calculation savings and assumptions used. Assumption #1 (i.e. source of data, clear Operational Definitions?) Assumption #2 (i.e. hourly rate + incremental benefit cost + travel) Describe the chain of causality that shows how you determined the Type 2 savings. (tell the story on how you will capture these savings in your project) Show the Type 2 financial calculation savings and assumptions used. Assumption #1 (i.e. Labor rate used, period of time, etc…) Assumption #2 (i.e. contractor hrs or FTE, source of data, etc…) Describe the Type 3 Business Impact(s) areas and how these were measured Assumption #1 (i.e project is driven by the Business strategy?) Assumption #2 (i.e. Customer service rating, employee moral, etc…) Other Questions All stakeholders agree on the project’s impact and how it will be measured in financial terms? What steps were taken to ensure the integrity & accuracy of the data? Has the project tracking worksheet been updated? ************************************************************************** Validating financial benefit projections is one of the most important, but challenging, tasks to perform in validating the Project Charter Often the financial benefit projections written in the “Business Impact” section of the Project Charter are notional “rough order of magnitude (ROM)” or “back of the envelope” estimates without much analytical rigor applied in the calculations The “Future Reality Tree” is a tool we can use to analyze and document the “chain of causality” from the operationally-oriented “Goal Statement” to the financial dollar savings/benefits stated in the “Business Impact” section of the Project Charter The Future Reality Tree is a graphical approach to map out the cause–effect relationships which form the basis of the change you intend to make. It shows in clear, cause–effect relationships, how the proposed change should create the desired result, surfacing potential side effects along the way. In Lean Six Sigma, we use it to: Define the thinking behind our proposed project Confirm the logic of the project Assess the valuation of the project Surface risk issues *************************************************************************************************** Identify the basic improvements desired from the proposed project (intended to produce the desired result). Avoid identifying specific solutions; rather identify the nature of the improvement desired. Make sure that the cause–effect relationship is clear and self-evident. Avoid making “trans-Atlantic leaps” but rather take “baby steps”. If you find yourself thinking of effects that stretch far from the causes, insert intermediate steps Review with one or more knowledgeable associates to ensure clarity, correctness and completeness. Strive for clarity, correctness and completeness Use non-offensive wording Build shared understanding Make sure you are satisfied that the solutions really will produce the desired results (and the financial impact!) Welcome negative branches, then deal with them When objections are raised, try to determine the specific logic that is being challenged. ************************************************************************************ Valid Objections Clarity – I don’t understand what you mean Entity Existence – I don’t believe that “A” happens Cause Existence – I don’t believe that “A” causes “B” Additional Effect – But “A” also causes “D” Cause Insufficiency – But “A” won’t cause “B” unless “C” happens too Additional Cause – But “B” is also caused by “E” Circular Reasoning – How can “A” cause “B” if “B” causes “A” Comptrollers Role Provide input regarding project viability from a financial perspective What type of savings are expected? What effort will be required to capture them? Will investment be required? Review actual project savings claims as project approaches completion Are the savings numbers accurate? Can we capture them in a meaningful way? Audit actual results in the ‘Sustain’ stage Have the actual savings been realized? If no, what is the recovery plan? Types of Benefits Type I: Hard savings that can be readily identified in budget terms and can be used for recapitalization Type II: Resources that are freed up to move to value-added work TYPE III: Intangible benefits Type I benefits have a clear impact on the NAVSUP budget. The relationship between the project and the benefits are direct. Type I benefits result in an adjustment to the business plan and are permanent. Examples of Type I Benefits: Reduction in labor costs and billets Non-labor reductions (I.e., NMCI seats, supplies) Space reductions that result in termination of a lease Scrap or material reductions Contract cost reductions Type I reductions must be a primary goal in order for NAVSUP to deal with solvency concerns and accomplish required efficiencies Booking Manpower Savings Manpower savings will be booked in the fiscal year in which the savings are actually realized The booking of manpower savings must begin no later than the fiscal year after project completion Projects must focus on current savings; deferring manpower savings into the future defeats the purpose of Lean 6 Sigma Type II benefits do not have an immediate impact on available funds. Type II benefits are assets or resources that are freed up and may be re-utilized on value-added work. Type II benefits may result in future savings Examples of Type II Benefits: Floor space is reduced, but building is not vacated Allows reallocation of the space Future resource requirements are reduced in existing budget documentation Type III benefits are mainly intangible and cannot be captured in the budget or other documentation. Examples of Type III Benefits: Customer Satisfaction increases An expenditure that was not budgeted is avoided Employees are more satisfied Response time is improved ********************************************************************* Projects requiring investments No project requiring investment will be approved unless: Type I and II project savings will exceed investment cost within 1 year Projects that produce savings without requiring investment are preferred Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Current Status Key actions completed Issues Lessons learned Communication, team building, organizational activities Status Plan Weekly planning tool used to coordinate a work team’s activities. Limited look-ahead (usually 2 weeks), Black Belt/team can decide window Plan work with Black Belt and team – leverage and focus Shows activities, issues, and need-helps correlated to deliverables/timeline Separated into work planning, status, issues, need-helps Usually created at the end of each week. Typical scenario: Stream lead coordinates with client counter-part Thursday prior to departing account, after coordination with their team Stream lead provides status and requests to team in Friday Stat/plan meeting Standardize approach for identifying deliverables, tasks, etc. – need same “look and feel” across teams Proactively assign accountabilities and to-do’s as team reviews Teams have a propensity to use this tool as simply a reporting mechanism rather than a tool for leverage and planning In team meetings, select items that are meaningful to that team for review and planning Ensure tasks’ relevance to deliverables is clear Must be overlaid on environment of accountability Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Next Steps Key actions Planned Lean Six Sigma Tool use Questions to answer Barrier/risk mitigation activities Successful project management requires an understanding of: Individual Team Roles Basic Skills and Tools Project Budget Negotiation Skills Communications Forming and Leading High-Performing Teams Team Meetings Managing Conflict Time Management Skills Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sign Off I concur that the Measure phase was successfully completed on MM/DD/YYYY I concur the project is ready to proceed to next phase: Analyze Enter Name Here Green Belt/Black Belt Enter Name Here Deployment Champion Enter Name Here Sponsor / Process Owner Enter Name Here Financial Representative Enter Name Here Master Black Belt Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Tollgate Reviews Backup Slides
Halt - Hold D M A I C Go Forward Wait - Go Back

Measure Tollgate Checklist
Has a more detailed Value Stream Map been completed to better understand the process and problem, and where in the process the root causes might reside? Has the team conducted a value-added and cycle time analysis, identifying areas where time and resources are devoted to tasks not critical to the customer? Has the team identified the specific input (x), process (x), and output (y) measures needing to be collected for both effectiveness and efficiency categories (i.e. Quality, Speed, and Cost measures)? Has the team developed clear, unambiguous operational definitions for each measurement and tested them with others to ensure clarity/consistent interpretation? Has a clear, reasonable choice been made between gathering new data or taking advantage of existing data already collected by the organization? Has an appropriate sample size and sampling frequency been established to ensure valid representation of the process we’re measuring? Has the measurement system been checked for repeatability and reproducibility, potentially including training of data collectors? Has the team developed and tested data collection forms or check sheets which are easy to use and provide consistent, complete data? Has baseline performance and process capability been established? How large is the gap between current performance and the customer (or project) requirements? Has the team been able to identify any complete ‘Quick Wins’? Have any Kaizen opportunities been identified to accelerate momentum and results? Have key learning(s) to-date required any modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders? Have any new risks to project success been identified, added to the Risk Mitigation Plan, and a mitigation strategy put in place? Key Deliverables: Detailed Value Stream Map(s) Data Collection Plan Measurement Collection Results Process Capability Results Current Baseline Process Performance Quick Wins, if applicable Identification of Kaizen Opportunities, if applicable Refined Charter, as necessary Updated Risk Mitigation Plan Deliverables Uploaded to Central Storage Location or Deployment Management System. Tollgate Review Stop Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Analyze Tollgate Checklist
Has the team examined the process and identified potential bottlenecks, disconnects and redundancies that could contribute to the problem statement? Has the team analyzed data about the process and its performance to help stratify the problem, understand reasons for variation in the process, and generate hypothesis as to the root causes of the current process performance? Has an evaluation been done to determine whether the problem can be solved without a fundamental recreation of the process? Has the decision been confirmed with the Project Sponsor? Has the team investigated and validated (or devalidated) the root cause hypotheses generated earlier, to gain confidence that the “vital few” root causes have been uncovered? Does the team understand why the problem (the Quality, Cycle Time or Cost Efficiency issue identified in the Problem Statement) is being seen? Has the team been able to identify any additional ‘Quick Wins’? Have learnings to-date required modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders? Have any new risks to project success been identified, added to the Risk Mitigation Plan, and a mitigation strategy put in place? Deliverables: List of Potential Root causes Prioritized List of Validated Root Causes Additional “Quick Wins”, if applicable Refined Charter, as necessary Updated Risk Mitigation Plan Deliverables Uploaded to Central Storage Location or Deployment Management System Tollgate Review Stop Has the team identified the key factors (critical X’s) that have the biggest impact on process performance? Have they validated the root causes? International Standards for Lean Six Sigma 22

Bonacorsi Consulting

Lean Six Sigma DMAIC Improvement Process
Define Define the opportunity from both the customer and business perspective Tollgate Review Understand the baseline process performance Stop Measure Tollgate Review Identify the critical X factors and root causes impacting process performance Stop Analyze Tollgate Review Stop Develop solutions linked to critical x’s Improve Tollgate Review Stop Implement solutions & control plan Control Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Attitude Charting & Key Constituency Map (Optional)
“Critical mass must be won-over” Key Constituents Map: Seeks to identify and label key clusters of constituents who will be impacted by the change initiative. Using a simple pie chart model, constituent groups are identified and analyzed in terms of their relative interest/ involvement in the change effort. As the chart is being developed, questions about who will and won’t be on board with the change effort will naturally surface. Uses Before launching into a more detailed analysis of specific pockets of support and resistance, teams have often found it useful to identify the broad base of KEY CONSTITUENTS impacted by the change initiative. As a prelude to the more detailed analysis of individual constituents, this process can help a team broaden its perspective regarding who the constituents are, where they reside in the organization, and their relative size as pieces of the overall constituent base, and decide how to spend limited resources (such as time). For team unfamiliar with how to mobilize commitment, this tool is relatively low-risk activity which can set the stage for more pointed discussions regarding the critical process of building a constituency for change. How To Steps: 1. List all of the groups impacted by the change initiative. 2. Cluster like groups where appropriate to maintain a common elevation of analysis (sourcing, purchasing and expediting might be grouped as purchasing for simplicity sake if the rest of the groups are at similar level. 3. Create the pie chart with attention to the magnitude of impact expected on each group (example: if engineering has 150 people whose lives will be impacted by the change and purchasing has only 15, the engineering group would be proportionally larger). 4. Debate and discuss the chart until all team members agree that it represents an accurate pictorial representation of the constituent groups that must be won over for the change initiative to be successful. 5. OPTION: In some cases, it may be useful to additionally create more detailed maps at a lower elevation (example: If purchasing is determined to be a key block of constituents, it may be useful to break purchasing down into a more detailed pie chart representing sourcing, purchasing, expediting, etc.) Timing: Though not necessarily the first thing a team does to identify and analyze sources of support and resistance, this tool is most useful is used early, before the team becomes mired in detailed, specific discussions of who's with us and who's against us. As soon as the issue of support and resistance surfaces in the team, the stage is set to develop the first map of key constituents. Tips: Try to identify the broadest collection of constituents first, before applying the tool to more discrete groups. (For example, a quick response change initiative team might construct its first map at a 30,000 level (to use an elevation metaphor). Such a map would include broad groups of constituents like engineering, manufacturing, sourcing, distribution etc... Staying at this level of analysis is useful in that ensures that no broad group is overlooked. More detailed maps may follow, but, at a minimum, the team can now see the challenge ahead in terms of building commitment among broad groups of constituents impacted by the change initiative. *********************************************************************************************************************** Attitude Charting: Takes up one or more of the populations from the original pie chart map of constituent groups and develops a graphic representation of the attitudes toward the change initiative within the various sub-populations, using a bell-curve type chart. Each population of constituents from the pie chart is described as some mix of four groups: Innovators, Early Adopters, Late Adopters, and Resistors. Emphasis is placed on determining how normal the populations is. Foe example, is there a normal population mix of 15% Innovators, 35% Early Adopters, 35% Late Adopters, and 15% Resistors? Or is the population skewed in some fashion (Example: a larger block of Resistors and lasted Adopters due to failures in the past)? Uses: With this tool, The team becomes more focused in its analysis and more pointed in its discussion of the nature of the support and resistance to the change initiative. The emphasis shifts to an analysis of the range of support or resistance within each group of key constituents. 1. Select one of the groups impacted by the change initiative from the pie chart analysis (See The Key Constituency Map Template) 2. Have each member of the team draw a Population Chart indicating how he/she perceives the group members' attitude toward the change effort. If necessary, clarify what each piece of the piece of the population means (example: Innovators = Those who will readily endorse this change initiative and work on behalf of the team; Early Adopters = while not first in line, this population will quickly follow the lead of others and actively support the change initiative; Late Adopters = while not necessarily hostile or overtly resistant, this population will lag behind in terms of actively supporting the change initiative; resistors = will actively and openly resist the change initiative. 3. Share individual charts and work to reach consensus on how the population actually looks. If significant differences of opinions exist within the team, it may be useful to seek another perspective, perhaps even from some members of the population under construction. 4. At a minimum, the team should check their perceptions and assumptions about the population with others outside the team before accepting this chart as the correct view of the population. 5. OPTION: Use this population charting process to uncover the attitudes of specific individuals within a population. If the team chooses to use the tool in this fashion, the discussion should include a debate about where each individual status is for the change effort to be successful (example: a late adopter may only need to be helped to not become a resistor). Should occur relatively early on in the process of mobilizing commitment or in response to a clear signal of consensus within the group that there are one or more groups of significant stakeholders whose support must be gained. Using this tool can help a team warm up to the value of a debate about the nature of resistance. Once begun, this type of analysis can quickly lead to more pointed discussions of who are the supporters and who are the resistors. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sample Size: Continuous Data (Optional)
? ? Consider the following example: We want to estimate average call length in handling customer inquiries, and we want our estimate to be accurate to within + 1 minute. Based on a small random sample of 30 inquiries, we know that the variation in call length, as measured by a statistic called the standard deviation, is 5 minutes. We want to have 95% confidence that the estimate will be in the range of specified accuracy–i.e., ± 1 minute. Therefore, from the statistical theory, we can answer according to the formula: where n = sample size, ó = standard deviation, and = degree of precision required. In our example, the required sample size is: n = [(1.96x5)/1] 2 = or 96 samples Sample Size For Continuous Data Sample size (n) depends on three things: Level of confidence required for the result, “How confident I am that the result represents the true population” Level of confidence increases as sample size increases. Precision or accuracy (.) required in the result, “The error bars or uncertainty in my result” Precision increases as sample size increases Standard deviation of the population (ó), “How much variation is in the total data population” An estimate of standard deviation is needed to start. As standard deviation increases, a larger sample size is needed to obtain reliable results. In this equation, “1.96” represents a 95% confidence level. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sample Size: Discrete Data (Optional)
? ? Collecting the correct type of sample is often more important than collecting the right number of samples. Statistical procedures are based on the assumption that the units tested are a random sample from the population of interest. This is seldom true in practice. We often test pilot samples that are made with different tools and under a different environment than production samples. Samples should be collected from as wide of a range of conditions as possible. Some examples: If you will be pulling 20 samples from current production, do not pull 20 consecutive units made during a short time period. You may be sampling from a time when the line is running abnormally well, and producing exceptionally good product. It would be better to select 4 samples per week, for 5 weeks. If a new product is being introduced, some samples should be tested from the model shop build, the pre-pilot run, the pilot run, and early production. If testing parts from a supplier, request that they supply parts from a variety of batches or runs. It is often a good idea to determine which dimensions and processing conditions are most likely to affect the performance. Parts can be made at the extremes of these dimensions and conditions, in order to determine if the design is "robust," and will perform well despite these variations. These methods of selecting samples do not guarantee that they will be representative of production, but there is less likelihood of a systematic bias in the results if the units are selected over a wide range. In summary, the goal is to establish that the product or service is ALWAYS good. Thus, it is beneficial to test over as wide of a range as possible. ******************************************** State the objective of the study. Ensure that the test is appropriate. Estimate the variation (standard deviation). This may be available from past tests. Determine the required length of the confidence interval. Determined the required level of confidence. Determine if one of the cases given here is appropriate for your situation. (See Backup Slides) Determine the required sample size for you problem. If the required sample size is too large, then graph precision versus sample size. Make a trade-off between precision and cost, and choose a sample size from the graph. Collect the samples from a wide range. Get help when you need it. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Measurement Systems Analysis (MSA) (Optional)
Gage R&R %Contribution Source VarComp (of VarComp) Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation Study Var %Study Var Source StdDev (SD) (6 * SD) (%SV) Total Gage R&R Repeatability Reproducibility Operator Operator*Part Part-To-Part Total Variation Number of Distinct Categories = 7 Measurement System Analysis (MSA) Is the Measurement process good enough to guide process improvement efforts and to meet customer needs? Is there a formal process for measuring the variable? How have you determined you do not have sampling problems (when, how, sample stability, sampling the sample jar, etc)? What is the design of the MSA experiment? What measurement or sampling issues were resolved? Were all problems communicated to all appropriate people (local and globally)? Is there a control plan in place which includes ownership, calibration, procedures, troubleshooting guide, SPC, etc)? How Would You Assess Your Measurement System Today? Talk to the individuals conducting the measurements? Have a few measurements taken and compare them? Have other individuals or “experts” verify our measurements? Hope your customers get the same measurements? Assume computers are always right? Conduct a Gage R&R study? Measurement Variation is broken down into two components: (The two R’s of Gage R&R) Reproducibility (Operator Variability) Different individuals get different measurements for the same thing. Repeatability (Equipment/Gage Variability) A given individual gets different measurements for the same thing when measured multiple times. The tool we use to determine the magnitude of these two sources of measurement system variation is called Gage R&R Reproducibility is the variation in the average of the measurements made by different operators using the same measuring instrument when measuring the identical characteristic on the same part. Repeatability is the variation between successive measurements of the same part, same characteristic, by the same person using the same equipment (gage). Also known as test /re-test error, used as an estimate of short-term variation Stability = If measurements do not change or drift over time, the instrument is considered to be stable. Bias is the difference between the observed average value of measurements and the master value. The master value is determined by precise measurement typically by calibration tools linked to an accepted, traceable reference standard. Average of measurements are different by a fixed amount. Bias effects include: Operator Bias – Different operators get detectable different averages for the same value, Instrument Bias – Different instruments get detectable different averages for the same measurement, and Other Bias – Day-to-day (environment), fixtures, customer and supplier (sites). Discrimination is the capability of detecting small changes in the characteristic being measured. The instrument may not be appropriate to identify process variation or quantify individual part characteristic values if the discrimination is unacceptable. If an instrument does not allow differentiation between common variation in the process and special cause variation, it is unsatisfactory. Acceptable Measurement Systems Properties that all acceptable measurement systems must have: The measurement system must be in control (only common cause variation; i.e., in statistical control). Variability of the measurement system must be small in relation to the process variation. Variability of the measurement system must be small compared with the specification limits. The increments of the measurement must be small relative to the smaller of: a) the process variability or b) the specification limits (Rule of thumb: increments are to be no greater than 1/10th of the smaller of: a) process variability or b) specification limits). AIAG Gage R&R Standards The Automotive Industry Action Group (AIAG) has two recognized standards for Gage R&R : Short Form – Five samples measured two times by two different individuals. Long Form – Ten samples measured three time each by three different individuals. For good insight into Gage R&R, go to [ Remember that the Measurement System is acceptable if the Gage R&R variability is small compared to the process variability or specification limits. Preparation for a Measurement System Study Plan the approach. Select number of appraisers, number of samples, and number of repeat measures. Use at least 2 appraisers and 5 samples, where each appraiser measures each sample at least twice (all using same device). Select appraisers who normally do the measurement. Select samples from the process that represent its entire operating range. Label each sample discretely so the label is not visible to the operator. Check that the instrument has a discrimination that is equal to or less than 1/10 of the expected process variability or specification limits. Setting Up the Measurement Study Assure that the gage/instrument has been maintained and calibrated to traceable standards. Parts are selected specifically to represent the full process variation Parts should come from both outside the specs (high side and low side) and from within the specification range Running the Measurement Study Each sample should be measured 2-3 times by each operator (2 times is the Short Test). Make sure the parts are marked for ease of data collection but remain “blind”(unidentifiable) to the operators. Be there for the study. Watch for unplanned influences. Randomize the parts continuously during the study to preclude operators influencing the test. The first time evaluating a given measurement process, let the process run as it would normally run. Because in many cases we are unsure of how noise can affect our measurement system, we recommend the following procedure: Have the first operator measure all the samples once in random order. Have the second operator measure all the samples once in random order. Continue until all operators have measured the samples once (this is Trial 1). Repeat steps for the required number of trials. Use a form to collect information. Analyze results. Determine follow-up action, if any. If Process Tolerance and Historical Sigma values are not used in Minitab, a critical assumption is then made that the sample parts chosen for the study, truthfully exhibit the true process variation. In this case, the acceptability of the measurement system is based upon comparison only to the part variation seen in the study. This can be a valid assumption if care is taken in selecting the study sample parts. AIAG states that “One element of criteria whether a measurement system is acceptable to analyze a process is the percentage of the part tolerance or the operational process variation that is consumed by measurement system variation” Remember that the guidelines are: Under 10 % – Acceptable. From 10 to 30 % – Marginal. May be acceptable based upon the risk of the application, cost of measurement device, cost of repair, etc. Over 30 % – Not Acceptable. Every effort should be made to improve the measurement system. Repeatability is checked by using a special Range Chart where the differences in the measurements by each operator on each part is charted. If the difference between the largest value of a measured part and the smallest value of the same part does not exceed the UCL, then that gage and operator are considered to be Repeatable Reproducibility is best determined analytically using the tabulation analysis in the Minitab Session (discussed in following slides) . Graphically it may be seen if there are significant differences in the operator patterns generated by each operator measuring the same samples. This tabulation from Minitab builds the % of Study Variation that each source contributes to a calculated potential Total Variation seen in the study. The 6.0 * SD is how statistically 99.73% of the Total Variation is calculated and this is assumed to equal 99.73% of the true process variation unless the Historical Sigma is input into Minitab. The %’s are used to grade the validity of the measurement system to perform measurement analysis using %’s already taught. If the process is performing well, the % Tolerance is then important. The sum of the %’s may add to more than 100% due to the math. The Number of Distinct Categories represents the number of non-overlapping measurement groups that this measurement system can reliably distinguish in the Study Variation. We would like that number to be 5 or higher. Four is marginal. Fewer than 4 implies that the measurement system can only work with attribute data - Most physical measurement systems use measurement devices that provide continuous data. For continuous data Measurement System Analysis we can use control charts or Gage R&R methods. - Attribute/ordinal measurement systems utilize accept/reject criteria or ratings (such as 1 - 5) to determine if an acceptable level of quality has been attained. Kappa techniques can be used to evaluate these Attribute and Ordinal Measurement Systems. Measurement system is acceptable with the Total % Contribution <10% Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Probability Plot (Optional)
Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Control Chart (Optional)
The current baseline delivery time is stable over time with both the Moving Range (MR) (3.22 days) and Individual Average (29.13 days) experiencing common cause variation 255 data points collected with zero subgroups, thus the I&MR control chart selected The Shewhart model decomposes this variation into either: Common Cause Variation This is the consistent, stable, random variability within the process We will have to make a fundamental improvement to reduce common cause variation Is usually hard to reduce Special Cause Variation This is due to a specific cause that we can isolate Special cause variation can be detected by spotting outliers or patterns in the data Usually easy to eliminate When a process is “in control” This implies a stable, predictable amount of variation (common cause variation). This does not mean a "good" or desirable amount of variation. When a process is “out-of-control” This implies an unstable, unpredictable amount of variation. It is subject to both common AND special causes of variation. A process can be in statistical control and not capable of consistently producing good output within specification limits. Continuous Data Control Charts The Theory of all Control Charts can be learned by studying the Xbar (Average) and R (Range) chart, for continuous data. Thus, it is introduced first. We will then explore the I-MR (Individuals Moving Range) Chart. Xbar-R Charts allow us to study: Variation “within each subgroup” or “process precision” on the R chart. Variation “between each subgroup” or “process accuracy” on the Xbar chart Note: we always look at the R chart first, if it is in control, then we look at the Xbar chart. Examples of continuous data: width, diameter, temperature, weight, cycle times, etc. Utilize probabilities and knowledge of the Normal Distribution The I-MR chart (sometimes called XmR in some books) is used: When you are learning about a process with few data points, When sampling is very expensive, When the sampling is by destructive testing, and When you are building data to begin another chart type The Xbar-R Chart is used with a sampling plan to monitor repetitive processes. The sub-group sizes are from 3 to 9 items. Frequently practitioners will choose subgroups of 5. All of the theory of Control Charts can be applied with these charts The Xbar-S Chart is used with larger sample groups of 10 or more items. Statisticians sometimes state that the standard deviation is only robust when the subgroup size is greater than 9. These charts are similar to the Xbar-R Chart. Xbar-R Charts are a way of displaying variables data. Examples of variables data: width, diameter, temperature, weight, time, etc. R Chart: a look at “Precision” Displays changes in the ‘within’ subgroup dispersion of the process. Often called “Short-Term Variation”. Asks "Is the variation in the measurements ‘within’ subgroups consistent?” Must be “in control” before we can build or use the Xbar chart. Xbar Chart: a look at “Accuracy” Shows changes in the average value of the process and is a visualization of the “Longer-Term Variation” Asks "Is the variation ‘between’ the averages of the subgroups more than that predicted by the variation within the subgroups?“ Shewhart Control Chart Assumptions Normally Distributed Data Recall that the control limits approximate +/- 3 sigma from the mean. These control limits are based upon a normal distribution. If the distribution the data is taken from is non-normal, the control limits cannot be used to detect out-of-control conditions such as outliers. Independent Data Points “Independence” means the value of any given data point is not influenced by the value of any other data point (i.e., it is random). Violation of this assumption means the probability of any given data value occurring is not determined by its distance from the mean, but by its place in the sequence in a data series or pattern. Control Chart Data Requirements Data requirements for control chart applications: Minimum of 25 consecutive (no time gaps) subgroups, or Minimum of 100 consecutive observations Must be in time series order Detecting “Out of Control” Situations The Rules We Will Commonly Use: Rule#1: One point outside the UCL or LCL (3-sigma limit) Rule#2: Nine points in a row on the same side of X (avg.) Rule#3: Six points in a row all ascending or descending. Rule#4: Fourteen points in a row all alternating Rule #5: An obvious non-random pattern. Pattern Rule: A pattern that repeats itself Process for Identifying Special Causes Check all the W.E. rules each time you plot a point. Look across the entire chart. Circle all special causes. Investigate immediately – this especially important. Don’t lose the opportunity to learn as much as possible about the conditions that caused this special cause variability. Take notes on the investigation YOU must investigate and eliminate the special cause ! And, remember, time is your enemy. The longer it takes, the less likely you’ll find the root cause. Out of bounds is always a special cause on the range chart. Other special causes could be false alarms. Record your notes on the investigation in the Notes section of the control chart so that they will be available to everyone who sees the chart. The average chart shows variation over time; therefore, special causes on the average chart mean the process location is changing. The range chart shows variation at a particular point in time; therefore, special causes on the range chart mean a change in process variation. Attribute Data Charts Two categories of attribute data: Count data (outcomes: 0, 1, 2, 3, 4, 5, etc.) Good/bad product data (only 2 possible outcomes) Four common attribute charts: C and U charts: used for count data of Errors in the process, a step or the overall process, or Defects in the process’ or steps’ deliverables NP and P charts: used for good/bad process, service or product data (items or process steps that are defective or flawed) Attribute data is counted, unlike variable data which is measured on a continuous scale. Good/bad product data: often used to describe a unit of product as defective or not (e.g. firsts vs. seconds). Count data: often used to count the number of defects per unit of product (e.g., number of major defects/1,000 yards). Attribute charts: p chart: monitors the proportion or percentage of bad products in a subgroup. np chart: monitors the actual number of bad products in a subgroup. c chart: monitors the number of nonconformities in a subgroup. u chart: monitors the average number of nonconformities per unit. Many transactional processes and manufacturing processes only record data as to the service or the products being either bad or good, defective or not defective. There are two sub-families in the Attribute control charts: If we count defects, with any item having more than one opportunity for a defect, we use the C- or U-charts. If the sample size is always the same, use a C-chart. If the sample size varies, use a U-chart. If we count defectives, with any item having one opportunity to be defective, we use the NP- or P-charts. If the sample size is always the same, use a NP-chart. If the sample size varies, use a P-chart. Steps in Control Charting Select process characteristic to control, the key x or Y. Collect data and calculate appropriate statistics. Construct preliminary control charts. Establish control (find and eliminate special causes). Assess data distribution normality. Construct final control charts. Establish capability (find and reduce common causes). Use for ongoing control purposes. Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

PDCA (Optional) Plan: ? Do: Check: Act: ? Plan ? Do ? Check The plan–do–check–act cycle is a four-step model for carrying out change. Just as a circle has no end, the PDCA cycle should be repeated again and again for continuous improvement. When to Use: As a model for continuous improvement When starting a new improvement project When developing a new or improved design of a process, product or service When defining a repetitive work process When planning data collection and analysis in order to verify and prioritize problems or root causes When implementing any change Procedure: Plan: Recognize an opportunity and plan a change Do: Test the change Carry out a small-scale study Check: Review the test Analyze the results and identify what you’ve learned Act: Take action based on what you learned in the check step: If the change did not work, go through the cycle again with a different plan. If you were successful, incorporate what you learned from the test into wider changes. Use what you learned to plan new improvements, beginning the cycle again. ? Act Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

5s (Optional) Sort ? Set Order Shine Standardize Sustain 5S is a process and method for creating and maintaining an organized, clean, and high performance workplace 5S enables anyone to distinguish between normal and abnormal conditions at a glance 5S is the foundation for continuous improvement, zero defects, cost reduction, and a more productive work space 5S is a systematic way to improve the workplace, our processes and our products through employee involvement Sort Clearly distinguish needed items from unneeded items and eliminate the latter Set In Order (also known as Simplify) Keep needed items in the correct place to allow for easy and immediate retrieval Shine Keep the work space orderly and clean Standardize Standardized cleanup. This is the condition we support when we maintain the first three pillars Sustain (also known as Self-Discipline) Make a habit of maintaining established procedures Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Benchmark Analysis (Optional)
Based on the information above, what is the performance objective*? Reduce defects by % Reduce long-term DPMO from to . Improve short-term Z from to . *If you do not benchmark, performance standards are based on: For a process with  3 sigma level, decrease % defects by 10x. For a process with > 3 sigma level, decrease % defects by 2x. Other….please explain (corporate mandate, compliance/legal, VOC data, etc) Benchmarking is simple as a concept but much more involved as a process. The ultimate payoff is that you can become the best of what you do, and continuously improve upon that superiority. Benchmarking is a means of identifying best practices and using this knowledge to continuously improve our products, services, and systems so that we increase our capability to provide total customer satisfaction. What does it mean to Define Performance Objectives? A performance objective is a statement of your project Y’s performance level that will satisfy the project CTQ(s). It is the projected reduction in defects you plan to achieve for your process or product. Typically, this is stated in terms of defects per million opportunities (DPMO) reduction and a corresponding target Z-value. In Step 4 you determined the current process performance. In Step 5 you will state what the end results of the Six Sigma project will be by statistically defining the goal of the project. In addition, an estimate of financial benefits is due in Analyze. Why is it important to Define Performance Objectives? It is important to identify your improvement goals in measurable terms in order to define the level of improvement you wish to achieve and provide a focused target toward which you can direct your efforts. Key for best results: Be creative and think out of the box Consider all organizations, not just corporations Review all sectors (Private, Public and Non-Profit Study domestic and International organizations Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Key Buying Factor Analysis (Optional)
Explanation: Yellow bars show relative importance of key buying factors to customers; Red line rates company performance against key buying factors; Other lines rate competitors’ performance against key buying factors Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Sample Value Stream Mapping Symbols (Optional)
Machining Quotes C/T = 36 Sec I Flow (Physical) Set Up Time 7 Min Uptime 86% Queue/ Inventory Electronic Information Data Box Flow (Information) Process Box 1 Physical Pull Electronic Data System Personnel “Go See” Monitoring Truck Shipment F I F O Sign Off Point Graphical display of time factor of value stream - Times on top = Queue time Base on historical data if possible, observation otherwise May be calculated from inventory amounts (Inventory/takt time) - Times on bottom = Processing time May include wait times within process step - Summary box on right Total queue time in top Total processing time in bottom FIFO Lane Physical Transport Project Burst Supplier/ Customer Supermarket Replenishment Kanban Station Paper Kanban Push Systems Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Spaghetti Diagram (Optional)
To Office Parking Lot EAST Vault (finance) Supply Room (paper and office supplies) Records (Order Management) OM Supr Office Reception Engineering Offices Cafeteria Restrooms Foyer Order Taker 1 Order Taker 2 Order Taker 3 Order Entry 1 Order Entry 2 Order Entry 3 CC & Val 2 CC & Val 3 CC & Val 1 OM Lead Printer, Fax Copier Lines indicate paper/information travel: - No set path - Lots of rework Indicates an in-box or outbox where work (forms/ information) waits to be worked on or transferred Planning & Scheduling Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

AS-IS Process Mapping Symbols (Optional)
Enter Key Slide Take Away (Key Point) Here International Standards for Lean Six Sigma

Measure Phase Lean Six Sigma Measure Phase Tollgate Review.

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Measure Phase Lean Six Sigma Measure Phase Tollgate Review.

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Presentation on theme: "Measure Phase Lean Six Sigma Measure Phase Tollgate Review."— Presentation transcript:

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