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Six Sigma Templates for helping you step by step on the project

Six Sigma Templates for helping you step by step on the project

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Six Sigma Tools Template Six Sigma Tools Template Document Transcript

  • Action Items Who? Assign only to a Team Member Date What? present When Done? Where?
  • 1. Action items. Action items are to-do's assigned to attendees at the meeting How? Decision or Open? 2. Decisions. All decisions that may affect future choices of the group should 3. Open issues. New issues raised at the meeting but not resolved there shou
  • d to attendees at the meeting. Record the task, the person responsible and the date agreed upon to complete the task. choices of the group should be recorded. g but not resolved there should be recorded so they can be carried over to a future meeting. View slide
  • complete the task. View slide
  • DEFINE OPPORTUNITIES What is Important? revised 8/28Charter Worksheet Team 6 Sigma Terms Team Charter Worksheet Define Phase Cost Savings Site Team Charter Title: Problem Statement Major Milestones for this project Result/Date due Business Case for Action (Current Situation) Vision (Opportunities) Why have you selected this project over others? Opportunities - Processes Affected: What pain are you currently experiencing Value-added Proposition - AS-IS Process - Stretch Goals and Key Benefits - TO-BE Process - High Level Business Requirements - How Vision supports Business, Customers, Actors - Consequences of No Change - Competitive Analysis - How Vision supports SM Stategy and Scorecard - How much is this project worth (estimate)? Scope - Goal Statement Project Scope 11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • Smart Goals Lower acceptable limit Specific (What?) What authority does team have? Measurable (Where are targets?) Attainable (How?) Upper acceptable limit Relevant (Why?) Time Bound (When?) ProjectTime-Line - Month 1 Month 1 Month 1 Month 1 Month 1 Month 1 Month 1 Actors (Alignment of Key People) Mechanisms Metrics Team Selection 11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • Stakeholders- Trigger Points Link to SM Scorecard- Sponsor - Affect on Cash Flow and Margin- Champion- Key Business Partners - System Resources- ROI - Benefit - Investment Team Leader (Owner) - What can we measure, how often, and by whom - Training needed- Team Members (cross functional Deliverables- experts)- Quick Wins- Finance Expert- Key Process Indicators affected - IT Resources - Other Resources Need Measureable Results - Best Communication Methods - Best Training Methods - DMAIC deals mostly with root causes DMADV what design features (opportunities) we have under our control that we can work on that will impact the ccrs DMADDD Design Digitize Drawdown 11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • 11:42:1111/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • 11:42:1211/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • 1) Why are you working on this project rather than others? Explain quantitatively. BUSINESS CASE & OPPORTUNITY 2) Show from schematic how vital (result)? to big Y; Analyze numbers. BUSINESS CASE 3) What are you trying to achieve X relates GOAL STATEMENT 4) What are the critical Customer Requirements and how were they determined? 5) What are the risks of this project and is there a plan to minimize them? 6) What is our communication plan going forward (who is setting up meetings and communicating)? 11:42:1211/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • DMADV 11:42:1311/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • DMADD 11:42:1311/27/2008SIXSigmaToolstemplate-122781483509-phpapp03.xls
  • DEFINE OPPORTUNITIES What is Important? Voice of the Customer VOB CBR's CTQ's Voice of the Business Critical to Quality Critical Business Price/Unit Requirements Delivery time What do comustomers Spec Reqs want? Quality? Cost? Service level Delivery? Service & Safety? Business Issues Who Receives an Output to the Process? External Customers - Who pays money for your product? Vendors End Customers
  • Internal - Depts who receive outputs from your process Buyers Commodity Mgrs
  • Factory/Reqers Design Receiving
  • AP EQE
  • NPI Supply Chain Mgmt
  • E OPPORTUNITIES What complaints do you get from your at is Important? customers about the outputs of your process? ce of the Customer Write them in the Voice of the Business Identify them as CBR's CTQ's or CTP's From these identify CCR's Identify how to measure the current status of the C If possible, Dollarize the Cost Currently Incurred CTP's CCR's VOC Critical to Process Critical Customer Voice of the Customer Key measures Cost/Unit Requirements How does this effect the Productivity Must have - Must be ultimate customer? What and How Regulation Compliance Key issues Would the Customer Pay Rework for it? Safety Training hours
  • ints do you get from your out the outputs of your process? the Voice of the Business s CBR's CTQ's or CTP's entify CCR's measure the current status of the CCR's llarize the Cost Currently Incurred Opportunity Dollarized Sort 1 1 1 1 1 1 1 1 1 1 1 1 1
  • 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  • 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  • 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
  • 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 $-
  • DEFINE OPPORTUNITIES What is Important? Kano Analysis
  • Help
  • DEFINE OPPORTUNITIES What's Important? Supplier Input Using a Survey 12 Steps to creating a Survey Step 1 - Objective : Describe the Objective of your survey in a few bullet points. Avoid accumulating objectives. 2 or 3 main points are a limit to a good survey. Although it may sound restrictive, it is very unlikely that your interviewees will be able to stomach more than this (with the exception of highly specialized business to business surveys focussed on professionals) Failing to describe your objective will make you lose sight of the overall purpose of the survey and will have serious consequences on the length, the accuracy and the sequencing of your questions. Step 2 - Population : Define the total population concerned with your survey Match this definition with the objective stated in Step 1, i.e. neither too wide or too narrow Step 3 - Pre-study : A Pre-study (caution, a pre-study is NOT a pre-test) should never be overlooked Carry out the study on a few individuals concerned with your study Avoid prejudices (yours or more likely, your customer's !) which could make you jump to conclusions even before the beginning of the survey Step 4 - Assumptions : State clearly the points (in accordance with Step 1), that you wish to clarify or verify, or even contradict Do not filter these assumptions according to your own prejudices, i.e. avoid attitudes such as quot;I don't need to check that point because I feel that ... quot; unless you already have evidence of the phenomenon in the first place. Step 5 - Writing the Questions : Check the wording of your questions so that they are ... 1. Understandable 2. Unbiased 3. Non suggestive 4. Not built in a way that interviewees react defensively
  • 5. Not long-winded and repetitive so as to avoid weariness factor Step 6 - Contact Methods : Pre test your questionnaire on a representative sample of the given population If cards have to be shown to the interviewees, then add to pre test Choose Contact mode 1. Mail Questionnaire 2. Personal interviewing (arranged or quot;interceptquot;) 3. Phone interviewing (New methods may include fax, Email, or even Html forms, but one may wish to stress the difficulty in using these new media. In a sense, they bear a strong resemblance with the phone in so far as the interviewee is invisible and difficult to identify. These media can only be used for short questions aimed at checking precise assumptions. As for the population involved, it goes without saying that Web usage is not pervasive enough - even in the USA - in order to use this medium to survey wide populations of interviewees. At the moment, the web is best suited for Internet survey, but things might change in the near future) Modify questions in accordance with Pre test results Modify sequencing of questions in accordance with Pre test results Suppress redundant questions (this is often a sore point) Insert missing questions If cards have to be shown to the interviewees, ensure that contents match questionnaire. Step 7 - Sampling : Define the representative sample Give a sufficient size to the sample (methods of appraisal of error margins can be used) Step 8 - Administration : Administer Questionnaire in a neutral fashion (i.e. preventing interviewers from introducing personal biases while asking the questions. This may imply that these interviewers must be either trained or skilled professionals, and that a clear, thorough and comprehensive debriefing session takes place). Step 9 - Non responses : Response rate is an important factor. Do not just overlook them. They are a good indicator the quality of your questionnaire (amongst other things, namely if a gift is sent to all respondents) Step 10 - Interpretation : Interpretation should be distinct from the opinion of the collector of the data Step 11 - Analysis : Analyse ALL the responses (avoid partial analysis)
  • Step 11 - Analysis : Do not extend results that are valid for the given sample to the entire population without taking the necessary precautions Note: A famous example of a bad survey analysis that is given by the focus groups that led Coca Cola to change their flagship product at the end of the 1980's. The analysts of the focus groups were adamant that traditional Coke had to be changed, but the assumption was wrong, the methodology too, and eventually, the decision that arose from these groups almost led the Atlanta giant to a disaster. Step 12 - Report : Write a report that will describe the results comprehensively Avoid biased conclusions Minimise prejudices Avoid quot;politically correctquot; conclusions Mention numbers and percentages (both are necessary)
  • Use Compass Polls example Survey
  • DEFINE OPPORTUNITIES What's Important? revised 4/8 Supplier Input Process Output to Customers (SI Starting Date Ending Date Suppliers Suppliers Inputs to Process External Inputs Requirement supplied to SM Internal Inputs Requirement supplied to SM
  • DEFINE OPPORTUNITIES What's Important? revised uploaded upplier Input Process Output to Customers (SIPOC) SIPOC Diagram AS-IS PROCESS FLOW Outputs from Process Customers (see Functional Deployment Process Map) Results of Process External to SM Results of Process Internal to SM
  • SIPOC stands for suppliers, inputs, process, output, and customers. You obtain inputs from suppliers, add value through your process, and provide an output that meets or exceeds your customer's requirements. Supplier-Input-Process-Output-Customer: Method that helps you not to forget something when mapping help processes.
  • DEFINE OPPORTUNITIES What's Important? Supplier Input Process Output to Customers Financially Valuating Key Meas (Total Investment + Cost of Capital) / Benefit per month= Paybac Key measures Hard Dollar Soft Dollar Operating Non Capital Financing Benefits Benefits Activities Investing Activities Costs (qual) Activities Costs Example $1,000,000.00 $50,000.00 $1,000.00 $-
  • Payback Pareto $120,000.00 $100,000.00 $80,000.00 $60,000.00 $40,000.00 $20,000.00 $- Example
  • E OPPORTUNITIES hat's Important? ocess Output to Customers (SIPOC) ancially Valuating Key Measures Cost Benefit Analysis pital) / Benefit per month= Payback Months Team Costs Capital Costs Cost to Train Payback $ Payback 5 years NPV of $1000n & Implement Months Benefit? Future $1000n Funds $50,000.00 $100,000.00 $10,000.00 $949,000.00 2.54 $4,745,000.00 4% $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- $- #DIV/0! $- 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0
  • 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 0 #DIV/0! 0 Payback Pareto Column Q
  • Total 5 year How long Savings this Where can Estimated Benefit will it take to year Benefit be Parallel achieve applied Benefit Benefit? elsewhere? $4,934,800.00 6mo ### $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- $- 0 0 0 0 0 0 0 0 0 0
  • 0 0 0 0 0 0 0 0 0 Column Q
  • DEFINE OPPORTUNITIES What's Important? Process Mapping PURCHASE ORDER PROCESS CONTROLS Rules, Regulations, Standards, Polices, Procedures, Work Instructions, Goals, Metrics, & People (Process Owner) QPL/QSL: Vendor Master; Material Master; SOP 8-4; SIC 3.2 314W checklist GVM Profile ↓ SUPPLIER INPUT (Requisitioner, PROCUREMENT PROCESS Customer, Planner, Commodity Team) Materials or Information consumed Work done (by the Buyer) to the Inputs to or transformed by the Process to Transform Inputs into Output (PO) produce output Master Data Vendor, Info Record, Source List, QPL Requisition/Demand Create and Transmit PO from Inputs. (Schedule Sharing) Review & Approval MECHANISM People, Equipment, Tools, Software that perform the Process SAP Buyer Commodity Team WWCM PO Type TIGERS
  • ROCESS OUTPUT TO CUSTOMER (Receiving, AP, Requisitioner or Planner and the Supplier) Information or Product created by the Process Good PO that can be performed to by the Supplier, received, and paid.
  • DEFINE OPPORTUNITIES What is Important? revised 4/8 Functional Deployment Process Map AS_IS Process Flow Process Mapping help Department Sub-Process Start boundary
  • ss Map What costs are associatied with each step Qualitative Review (Value Added Analysis) Would anyone pay for this? Low Hanging Customer? Operations? Other fruit
  • DEFINE OPPORTUNITIES What's Important? Supplier Input Process Output to Customers (SIPOC) Using Science to Influence to Reduce Uncertain Compliance force, have to, coersion, rules and regulations, Conformity do it cuz you want to, ought to, peer pressure, ethics Internalization Willing total and deliberate, do it cuz you want to, Contrast Phenomenon Contrast something or some other process with yours as a competing activity Illustration how it will be used-illustrate aspects of use Context established before making a requrest Selling something as something it is not - Humbie - Military/status symbol Pet Rock, Chia Pet Concensus Jim wants it Amplifier - number of people behaving a certain way Stock goes up Bandwagon effect = everyone is doing it Management supports Product endorsements IBM and Intels secret too Similar groups of people do it 6 sigma is above the ben Like people do like things Uncertainty Consistancy Phone service ads that mention once you can get someone to take a stand on something…the bigger a commitment they will b Moto phones need to say more Value system appeal - Get commitment to become active - even a one word commitment Have people write down your own ideas…then share with the team Make their commitments public - issue minutes after meeting, have instrumental member issu Make goals public Want voluntary commitments - easist done when you know their values then to talk someone i Ask your friends for a commitment - people play golf together - build relationships
  • Reciprocity - doing someone a favor and getting a return- Scarcity People perceive things to be more valuable if they think it is scarce or in limited accessability Phrasing scarcity in terms of a loss is 2 - 4 times more effective than possitive Make it Unique Competition for something scarce Exclusive Information is more persuasive - Limited number - only supplier Limited run - Collectables market Limited edition cell phone - combines several different features Have a one of a kind cell phone - with unique personalized addons Have a cell phone contest that cell phones have a unique phone number in them to call and may win vaca What is the market segment? Who should we target? Why should we target them? What's the benefit to the customer? What are the features
  • duce Uncertainty Jim wants it Stock goes up Management supports IBM and Intels secret tool revealed 6 sigma is above the bench r a commitment they will begin to make nstrumental member issue minutes, presentation sharing es then to talk someone into something they don’t believe in relationships
  • r in limited accessability to call and may win vacation to ?
  • OPPORTUNITIES What is Important? Kansei Emotional/Subjective Aspects of the Customer Requirements Measuring Perceptions Touch and Feel Stylish Functional Simple Complex Powerful Weak Easy to Use Hard to Use Usability Stylish Functional Simple Complex Powerful Weak Visual Stylish Functional Simple Complex Powerful Weak Stylish Functional Simple Complex Powerful Weak Stylish Functional Simple Complex Powerful Weak
  • Kansei Definition Kansei Engineering means psychological feeling or image of a product. Kansei engineering refers to the translation of consumers' psychological feeling about a product into perceptual design elements. Kansei engineering is also sometimes referred to as quot;sensory engineeringquot; or quot;emotional usability.quot; Random Sample of Market Segment Know audience so words have meaning Use a Factor analysis to compute 2-5 major customer drivers Hard to Use source more...
  • DEFINE OPPORTUN What is Importan Quic Goals: Elilminate Change Rearrange Combine Simplify Imagine Potential Quick Win Easy to Implement Fast to Opportunity (ü) Implement (ü) Kill a Defect WEFEC defect 1 Within sphere of influence Easy to implement Fast to implement Easily reversible Cheap to implement
  • DEFINE OPPORTUNITIES What is Important? Quick Win Diagram Benefits will be gained Within the Implement Implemented by: Cheap to Team’s Implement (ü) Control (ü) (Yes/No) (ü) Kill a Defect WEFEC defect2 defect3 defect4
  • Implemented date
  • DEFINE OPPORTUNITIES What is Important? QFD = House of Quality QFD QFD is a good tool to employ in all phases 1. Derive top-level product requirements or technical characteristics from customer needs (Product Planning Matrix 2. Develop product concepts to satisfy these requirements. 3. Evaluate product concepts to select most optimum (Concept Selection Matrix). 4. Partition system concept or architecture into subsystems or assemblies and flow-down higher- level requirement 5. Derive lower-level product requirements (assembly or part characteristics) and specifications from subsystem/as 6. For critical assemblies or parts, flow-down lower-level product requirements (assembly or part characteristics) to 7. Determine manufacturing process steps to meet these assembly or part characteristics. 8. Based in these process steps, determine set-up requirements, process controls and quality controls to assure ac
  • PPORTUNITIES is Important? ouse of Quality http://www.qualisoft.com/ tool to employ in all phases om customer needs (Product Planning Matrix). lies and flow-down higher- level requirements or technical characteristics to these subsystems or assemblies. ristics) and specifications from subsystem/assembly requirements (Assembly/Part Deployment Matrix). irements (assembly or part characteristics) to process planning. part characteristics. ess controls and quality controls to assure achievement of these critical assembly or part characteristics.
  • DEFINE OPPORTUNITIES What is Important? revised 4/8 Documentation Related to this Process Doc. Number Title Creation date Last Revised Related Specifications Policies Procedures Work Instructions Guidelines Forms Websites Training Materials Key Terms Term Definition
  • RTUNITIES ortant? d to this Process Req to Pay Reference Guide Approvers URL Audit Results Changes needed Definition
  • y=f(x1..x2..x3…) What knobs when turned will improve y CTQ Flowdown -variance allocation Capability Flowup Transfer functions - predict the Y and X influence y= y= x= x knobs (inputs) x knobs what drives the Y? what drives the Y? y= y= x= x knobs x knobs what drives the Y? what drives the Y?
  • nd X influence x= what drives the Y? x= what drives the Y?
  • Average high - Average low = effect Plot Y data Capability analysis IMR chart one way anova
  • revised 4/8 What can we meas Represen MiniTab MEASUREMENT WO 6 Sigma Terms Search Minitab Website X Factors Quantitative Verifiable Predictive Process Predictive Input Indicators Measurement Indicators Scrap Rework Cycle Time Random Selection will eliminate bias Surveys are often biased, must sample the Non-Resp
  • Is population finite or infinate? Risks in Decisions Defendant's True Status Inocent Not Guilty Correct Decision Guilty Alph Error Patientt's True Status Inocent Healthy Correct Decision Ill Alph Error Test Statistics Drive the P Value Observed value of Population Parameter Subtract the standard value Divided by measure of dispersion Assuming that Null Hypothesis is true Risk and Circumstance always go in the oposite direction construct a reference distribution for the test statistic how reliable is the sample in representing the populat
  • Determine what to MEASURE How are we doing? What can we measure that shows our current status? Representative Samples are a must Top Level quot;Yquot; is observable at the Business Unit level and often verified with the Strong MEASUREMENT WORKSHEET End Customer Relationship Y Factors Output Performance Quantiative Verifiable Indicators Quantitative Verifiable Measurement CTQ's CTP's Measurement Priority ten biased, must sample the Non-Responders
  • nite or infinate? Defendant's True Status Guilty Beta Error Correct Decsion 1 compared to a sample 1 compared to 1 x Multiple Patientt's True Status Guilty Beta Error Correct Decsion Don’t have to worry about normality Drive the P Value e of Population Parameter andard value sure of dispersion expected variability in the y Null Hypothesis is true oposite direction rence distribution for the test statistic the sample in representing the population?
  • Medium Week No Relationship Relationship Relationship Sampling Plan function of the risk of being wrong; variability of the population; difference to be Factor Factor Factor detected
  • 1 compared to a sample 1 sample 1 1 compared to 1 F test Bartlett or Lavine Don’t have to worry about normality if you are working with averages - Central Limits Theorum
  • Determine what to MEASURE How are we doing? revised 4/8 What can we measure that shows our current status? Search Minitab Website From SIPOC Gather Input and Output Indicators X FACTORS Y FACTOR Input Indicators Process Indicators Steps Output Indicators What measures evaluate the degree to What measures evaluate dimen which the inputs to a process provided by What are the steps and the output - may focus on the suppliers are consistent with what the What measure evaluates the activities used to convert performance of the business as process needs to effectively and efficiently effectiveness, efficiency and quality of the the inputs into customer that associated with the deliver convert into customer satisfying outputs transforation processes satisfying outputs? services and products to custom Ex. Number of Receipts Ex. Number of defective Receipts Ex. Backup Receipt Ex. Payment issues
  • ent status? MiniTab Y FACTOR Most Output Indicators important What measures evaluate dimensions of the output - may focus on the performance of the business as well as that associated with the delivery of the services and products to customers Ex. Payment issues
  • Determine what to MEASUR How are we doing? revised 4/8 Data Collection Search Minitab Website What can we measure that shows our curre Performance Data Source that Strongly affects Measurement Operational Definition Output Display Describe Defect Continuous Discrete Analysis Tool Or Metric Ordinal Nominal How will data be used? ID Largest Contributors Is Data Normally Distributed? Sigma Level and Variation Root Cause Analysis? Correlation Analysis? Is Comparision of 1 sample or two or many What is NULL Hypothesis= 0= no relationship= Start with this assumption is TRUE Want to accept the Alternative Hypothesis Sometimes we do not want to look for an alternative
  • ne what to MEASURE w are we doing? ta Collection MiniTab sure that shows our current status? Charts Sample Size When will How will Other Data Who will collect Data be Data be collected at Cost? collected collected same time Practical? Frequency? Random? How will Data be Displayed? Pareto Chart Histogram Control Chart Scatter Diagram
  • Determine wha How are w What can we measu Measurement Sy MSA help Search Minitab Website Where are sources of Variation in Process Procedure Transaction Source of Data Report Used Measurement system analysis (MSA) is an experimental and mathematical method of determining There are five parameters to investigate in an MSA: bias, linearity, stability, repeatability and repro According to AIAG (2002), a general rule of thumb for measurement system acceptability is: Under 10 percent error is acceptable.
  • Determine what to MEASURE How are we doing? What can we measure that shows our current status? Measurement Systems Analysis Definitions re sources of Variation in Measurement System? Physical Verification Bias to Standard Repeatable? Accurate? Precise? al method of determining how much the variation within the measurement process contributes to overall process variability. ity, repeatability and reproducibility. tem acceptability is:
  • How can Variation be eliminated overall process variability.
  • Determine what to MEASURE How are we doing? revised 4/8 What can we measure that shows our current status? Search Minitab Website Display and Evaluate Understanding Variation, Stability, and Capablity Use MiniTab Evaluate Measurement System How much error could be in the tool being used to gather da
  • ASURE g? ur current status? MiniTab and Capablity Normality Test Look at Pvalue if lower than .05 Null must go Compare line with dots - same shows Normal If not normal use Box-Cox Control Chart IM chart with reference data Gage R&R Nested if did not use the same thing to test R&R by Operator - Precision Total Gage R&R must be less than 10 - 30% Capability Cpk potential capability g used to gather data?
  • Determine what to MEASURE How are we doing? revised 4/14 What can we measure that shows our current status? Determine Process Performance What is the Cost of Poor Quality COPQ help AS-IS Process Items from A Item Item Prevention MTD YTD MTD Quality Management Preventative Maintenance Factory Training Supply Chain Process Improvement Appraisal Inspection Material Inspection Engineering Maintenance Internal Failure Scrap Excess Inventory Obsolete Inventory Rework External Failure Warranty Costs Call Centers Totals 0 0 0 TO-BE Process Items from Cause Item Item Prevention MTD YTD MTD Quality Management Preventative Maintenance Factory Training Supply Chain Process Improvement
  • Appraisal Factory Inspection Material Inspection Engineering Maintenance Internal Failure Scrap Excess Inventory Obsolete Inventory Rework External Failure Warranty Costs Call Centers Totals 0 0 0
  • t to MEASURE e doing? hows our current status? ss Performance Help of Poor Quality Characterize Costs in AS-IS Process Estimate costs fitting the 4 Categories In TO-BE Process Try to eliminate costs of Appraisal, Internal Failure and External Failure. Items from AS-IS Process Item Item Item Item YTD MTD YTD MTD YTD MTD YTD COPQ – Suppliers Cost of Poor Quality from Supplier Suppliers can generally affect our c a) Producing defective material. b) Damaging material during delive Our COPQ will generally cover the 1) Cost of labor to fix the problem. 2) Cost of extra material used. 3) Cost of extra utilities . 4) Cost of lost opportunity a) Loss of sales/revenue (profit ma b) Potential loss of market share c) Lower service level to customers 0 0 0 0 0 0 0 Items from Cause and Effect Diagram Item Item Item Item YTD MTD YTD MTD YTD MTD YTD
  • 0 0 0 0 0 0 0
  • rnal Failure and External Failure. COPQ – Suppliers Cost of Poor Quality from Suppliers Suppliers can generally affect our cost due to: a) Producing defective material. b) Damaging material during delivery. Our COPQ will generally cover the followings: 1) Cost of labor to fix the problem. 2) Cost of extra material used. 3) Cost of extra utilities . 4) Cost of lost opportunity a) Loss of sales/revenue (profit margin) b) Potential loss of market share c) Lower service level to customers/consumers
  • ANALYZE THE OPPORTUNITY What is Wrong? revised 4/8 IDENTIFY ROOT CAUSE Help PROBLEM STATEMENT What is the REAL Problem that needs to be solved? What is the root cause that is resulting in the effect? 5 WHY'S Ask WHY 5 TIMES to arrive at root cause Problem: Why? Why? Why? Why? Why? REAL PROBLEM:
  • PPORTUNITY rong? OT CAUSE SOURCES OF VARIATION OR DEFECTS: INPUT DEFECTS: what inputs were defective? t cause PROCESS DEFECTS: look at fdpm and find process defects DOCUMENTATION/MATERIALS what documents need to be changed DMADV Identify, prioritize, and quantify impact of key factors affecting product process performance Derive transfer functions which capture key relationships Select high level desing alternatives paradigm busting most often occurs here Track CTQ Flow Down, assess attainment of goals document assumptions Revise business case if necessary KISS or KILL
  • ANALYZE THE OPPORTUNITY What is Wrong? Transfer Functions - Finding Relationships in Data Transfer Function = Regression Model Y= the function (x1,x2,x3…) indicator functions 0= no affect; n for affect Pancakes Taste = Assembly - bottlenecks make this difficult Productivity = F(Layout, WIP, capacities, changeover times Use Process simulation instead Relationship based on scientific principles physics etchrate thermodynamics Empirical data ANOVA, Multiple Regressions Designed experimentation or existing data Financial relationships Income COGS Direct Costs Indirect Costs Business Models Simulation as transfer functions Simulation software Physical simulations (experiment) What if some of the Xs are things outside of my control Measure Interaction between these and other variables Calculate the if the affect on the Big Y
  • Transfer function Fitted Line Plot Is there a relationship between X and Y? acities, changeover times) tion or existing data Red Band is 95% confidence of existing residuals Blue Band is 95% predictive of future residuals Process Model Alph Risk reject the null lhypothesis when it is true Beta Risk accept the null hypothesis when it is false
  • ANALYZE THE OPPORTUNITY What is Wrong? IDENTIFY ROOT CAUSE How does the variablity in X drive the Y Hierarchical Transfer Fuctions and how Variability in the X's At Risk Program is being looked at by one team Quantifying how capability of Y drives limits on X's Monte Carlo Simutation Low cost test Insurance policy for system performance Random number generation Transfer Functions use Random numbers as Inputs Select Input Values Transfer Function Calculated Output values Research time + Review Time = Total Cycle Time Indicator variable split worksheet
  • Definitions umbers as Inputs Output values
  • ANALYZE THE OPPORTUNITY What is Wrong? revised 4/8 FISHBONE
  • Help PROCESS TITLE The 4 M's Methods, Machines, Materials, Manpower The 4 P’s Place, Procedure, People, Policies The 4 S’s Surroundings, Suppliers, Systems, Skills
  • S TITLE ople, Policies iers, Systems, Skills
  • ANALYZE THE OPPORTUNITY What is Wrong? revised 4/8 Pareto Stratification USE MINITAB Data 16 14 12 10 Data 8 6 4 2 0 Column E Column F Column G Column H Column I Data 15 10 9 5 3
  • Help Data
  • ANALYZ W revised 4/8 Process steps 1 List process step and input Process/Product 2 List potential failure modes Failure 3 List potential effects 4 Assign severity rating 5 List potential causes 6 Assign occurrence Rating 7 List current controls 8 Assign detection rating 9 Calculate Risk Priority number 1-10 10 Use RPNs to help decide on high priority failure modes 11 Plan to reduce or eliminate the risk associated with high priority failure modes 12 Re-computing RPN to reflect impact of action taken on failure mode Process Potential Item Potential Potential Cause(s) Process Process Failure Effects of of the Current Risk Steps inputs Mode the Failure Severity Failure Occurance Controls Detection Priority Failure Modes & Effects Analysis
  • Process/Product: FMEA Team: Black Belt: Process Item Process Steps Potential Effects of Failure Occurrence inputs Severity Potential Failure Mode Potential Cause(s) of Failure Current Controls Text Text Text Number Text Number Text 5 Correct 7 Stock checked twice Stock Stock in Unable to locate location is a year inventory wrong stock full location Damage Insufficient d product
  • ANALYZE THE OPPORTUNITY What is Wrong? Help FMEA Meeting schedule Failure Mode Effects Analysis 1 Review/create process flow diagr RISK ANALYSIS 2 Review information/begin FMEA 3 - 4 Finish FMEA/assign actions ailure modes How can the process go wrong? Actions Results Target Completion Recommended Action Responsibility Date Action Taken Severity Occurance
  • 10 Detection Number Risk Priority Number Calculation 0 0 0 0 350 Recommended Action Text Actions Responsibility and Target Completion Date Text Page: (Revised) FMEA Date: (original)
  • Review/create process flow diagram and review FMEA procedures Review information/begin FMEA Finish FMEA/assign actions s Risk Detection Priority
  • Responsibility and Target Completion Date Text Action Taken Text Severity Number of Results Occurrence Number Detection n Risk Priority Number Number Calculatio 0 0 0 0 0
  • ANALYZE THE OPPORTUNITY What is Wrong? revised 4/8 Sources of Variation Show Correlation between Defect and Problem When preparing to numerically analyze sources of variation in Minitab, ALWAYS do a tree diagram and multivari chart to assess the data Process Variation Source 1 Where in the process? Measurement Variation Source 2 Where in the Measurement Tool? Total Variation σ 2 Process Variation = σ2 Factor is an input to your process Time Crossed or Nested Highest level of sampling T1 T2 Crossed are controlable factors Temporature, flow rate Sub level of sampling P1,P2,P3 P4,P5,P6 Nested factors are parts, locations
  • Sub-Sub level of sampling L1,L2,L3 L1,L2,L3 component veriation = day/dow/
  • SOV Behavior of the process is observed in production mode. Data is collected of a long enough period of time to capture a high percentage of the historical variation Graphs are used to illustrate and identify major causes of total variation multivari chart Statistical methods are used Minitab can be used to model data between Multiple X factors and Y with General Linear Model MultiVari Study- graphical tool to assess factors Construct Tree Diagram Box Plot Understand the data: Nested - Random Crossed - Fixed ANOVA + Measurement system variation σ2 T3 P7,P8,P9
  • L1,L2,L3
  • ANALYZE THE OPPORTUNITY What is Wrong? Analysis of Variance Use Averages Analysis of Variation One Way ANOVA - one factor Multi Way ANOVA - Two way ANOVA - two fixed variables MiniTab Analysis of Variance Table Degress of Freedom Sum of the Squares Mean of Sigma P-Value
  • ANOVA Is Sample size, sample rate and sample structure good? Tree Diagram Input a MODEL into MiniTab Model = representation of a relationship of items MiniTab Response= factor1+factor2+factor3 Response= Mean+factor1+factor2+factor3 Expect factors to be 0 variation Balanced ANOVA Nested ANOVA General Linear ANOVA (mix fixed and random) Measurement System %R to R, % P to T Excellent if MSA is 10% Random effect puts a variance on the X - tells you how big the proble is Fixed descrete effects estimates the differences or contrast between the categories Fixed continuous effects give you an equation describing the effect If you treat a variable (X) as random, estimating the effect on Y If you treat a variable (X) as continuous, you will attach an equation on it mean square error is the estimate of the unexplained residual variability contrasts are generally measured from the mean
  • ANALYZE THE OPPORTUNITY What is Wrong? Regression - Curve Fitting Find what is important by estimating the relationship of X and Y Matrix Plot - identifies where there is no data in your test - wrong decisions may be made introducing bias
  • Fit is the relationship between X and Y Minimize the Residual Variance Sigma is the estimate of residual values Red and Blue lines are bands estimated events be made introducing bias Appliying regression techniques to passive data extablish Correlation not Causation Use simple linear regression when you only have one continuous Y against one continuous X Graphical tool for evaluationg correlation is a scatter plot or a matrix plot One of the dangers in using Multiple Regression Analysis is confounded data To compare coefficients in the model, code the data on a -1 and +1 then all are on an equal scaled and can be comp On a fitted line plot a prediction interval tells us the interval around the fitted line where the next point will occur
  • scaled and can be compared ext point will occur
  • ANALYZE THE OPPORTUNITY What is Wrong? Residuals - What is Left Over Residual = Observed Valu - Predicted Value Assumptions Tested Residuals Equal Independe Plot Variance Normality nce Random Lack of Fit Vs Run Order Vs. X Vs. Predicted Y Normal Prob. OK not enough data straight line normal distribution OFAT= one factor at a time
  • you can see a lot by looking yogi Collect Data from the Process Passively Confounded Data - Lack of interdependance of data Are we seeing a variable change due to another variable affecting it Care should be taken to draw conclusions - ie. Infomercial claims Outliers Find FIT and Residuals time series Assumptions: residuals are independent look for trends nomality plot residuals are normally distriubed control chart From a stable population Equal population variance Is there some systematic reaction going on? Are there Outliers - can seriously affect the results R sq is the amount of variation that is explained R sq over 100% indicates that data is not dependent Slop is the residual value on the Regression Plot for every unit of response, slop up or down value of unit Control Chart slop = 13.1387 In Control Prediction (Fit) against Residuals scatter
  • ANALYZE THE OPPORTUNITY What is Wrong? Regression - Curve Fitting Find what is important by estimating the relationship of X and Y How can we evaluate a response due to multiple factors Have I caught the important causes?
  • R sqd adjusted begins to be significant want R sq and R sq adjusted to close to one another Slopes are in different units No simple graph in 2 dimensions Correlation between X factors can dramactically affect results Graph Time Series on Matrix Plot Dotplot Correlation coefficient measure of the strenghth of correlation P value shows if hypothese is good SE Standard Error Coefficient Remaining error variation T = Signal to Noise ratio Model Lack of Fit Approximation Replication error - If you run the same test several times, you wont get the same measurement error Input error error of observation Bucketize Error Tot Error=Model error, lack of fit error (missing variations), observation error, Measurement (X's) error (p v Care most about: Lack of Fit error (all the right X's with measurements) Measurement error depends on how big it is Observation error is important to know how much is affecting LOF
  • measurement error asurement (X's) error (p value)
  • ANALYZE THE OPPORTUNITY What is Wrong? Theory of Solving Problems Inventively Classify solutions that come out of invention Most problems have been solved in other fields State problem as contradiction There are 40 Inovative Principles which can be viewed here Segmentation Divide an object or system into independent parts Make an object easy to disassemble Separation Separate the only necessary part (or property) or an interfering part of property from an object or system Symmetry Change The Collaborative Innovation (CI) Process Additional Resources: Inventive Problem Solving (IPS) -- A powerful method for eliminating technological roadblocks related to the developm Anticipatory Failure Determination (AFD) -- an efficient and effective method for analyzing, predicting and eliminating Directed Evolution (DE) -- A method for developing a comprehensive set of scenarios describing future generations o
  • http://www.ideationtriz.com/ http://inp.nsk.su/~dolgash/triz http://ideaconnections.com http://triz-journal.com I-TRIZ began evolving in the mid-1980s when TRIZ, in its classical form, ceased development. The research and development of I-TRIZ has continued unabated since its inception. As a result, I- TRIZ not only contains enhanced versions of classical TRIZ tools, but includes an expanded knowledge base, new tools for applying this knowledge and for analyzing problems more effectively, and the following comprehensive applications: rom an object or system ks related to the development and use of products and processes. Applicable to all engineering disciplines, IPS can be used to solve problem redicting and eliminating failures in systems, products, and processes. ibing future generations of a system. DE is based on an extensive set of patterns that reveal the evolutionary tendencies of technological sys
  • IMPROVE PERFORMANCE What needs to be done? revised 4/7 GENERATE SOLUTIONS PROBLEM STATEMENT: USE BRAINSTORM TOOLS TO IDENTIFY POSSIBLE SOLUTIONS FROM ROOT CAUSE ANALYSIS FROM PARETO CHARTS FROM FMEA FROM 2B PROCESS FLOW FROM AFFINITY DIAGRAM FROM 6 HATS FROM SOLUTION MAP
  • LE SOLUTIONS Then Remove showstoppers Remove items that do not fit the organization Narrow list to best 4 - 7 using CDAM: combine, delete, add or modify Solution Matrix based on Sigma impact, Timeing, Cost and Benefits Weight ideas by performance factor Prioritize Solutions based on data
  • IMPROVE PERFORMANCE What needs to be done? revised 4/8 AFFINITY DIAGRAM PROBLEM STATEMENT: GROUP LIKE (AFFINITY) SOLUTIONS TOGETHER BRAINSTORM SOLUTIONS AFFINITY SETS
  • HELP 1. Rapidly group ideas that seem to belong together. 2. It isn't important to define why they belong together. 3. Clarify any ideas in question. IONS TOGETHER 4. Copy an idea into in more than one affinity set if appropriate. 5. Look for small sets. Should they belong in a larger group? 6. Do large sets need to be broken down more precisely? the ideas have been sorted, 7. When most of you can start to enter titles for each affinity set
  • IMPROVE PERFORMANCE What needs to be done? revised 4/8 revised uploaded Nominal Group Technique List Team Members List Causes 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
  • To find the priority of a list of causes according to the team: 1 List Team and Causes 2 Count the number of Causes (n) 3 Team Members individually assign a value to each cause. The highest priority should be noted as n. The least important cause should be noted as 1 4 Each team member continues to choose the next most important and the next least important until all cau 5 Record each members scoring 3 Total all scores for each cause. 4 Sort total scores decending 5 Pareto the results. Priority 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
  • ast important until all causes are assigned a value.
  • IMPROVE PERFORMANCE What needs to be done? revised 4/8 Solution Map CORE PREMISE:
  • Start with core premise (goal you want to achieve), root cause, or problem statement. Brainstorm related clusters of potential solutions in the boxes. Brainstorm solutions from around the core premise When one person offers and idea, have other team mebers try to expand it or turn it into another idea.
  • revised 4/8 IMPROVE PERFORMANCE What needs to be done? FORCE FIELD ANALYSIS SOLUTION: DRIVING FORCES RESTRAINING FORCES
  • IMPROVE PERFORMANCE What needs to be done? revised 4/8 Functional Deployment Process Map TO - BE Process Flow Process Steps Department Sub-Process Start boundary
  • DMADDD Design - If this is a new ss Map process the 2B will be the new Design which will be Verified by the Pilot Qualitative Review (Value Added Analysis) Would anyone pay for this? Low Hanging Customer? Operations? Other fruit
  • IMPROVE PERFORMANCE What needs to be done? revised 4/8 Evaluate your new Process Process Steps Department What is Sub-Process What is the Cost Fits Impact on the Time Benefit Other Show Stopper? Organization? Sigma? Impact? Impact? Impacts? Start boundary
  • cess Can this step be Combined Don't need Need to This Step with this step - Add this has been another? Delete Step Modified Qualitative Review (Value Added Analysis) Would anyone pay for this? Low Hanging Customer? Operations? Other fruit
  • revised 4/8 IMPROVE PERFORMANCE What needs to be done? PILOT THE PROJECT Implementation checklist Pilot Team Detailed Implementation Training Plan Communication Plan Event Select Small Group Design DOE RSM Validated/Refined Model Identified sigma x's Variance Allocation Optimized design parameters Reduced variation sensitivity Analysis Monte Carlo Simulation Product Requirements Design Features 1 How well are Customer wants Expectations satisfied? Product Requirements 3 Measurements Measurements
  • Capability Flow-UP
  • Take part of a process (where you have the RMANCE least confidence, and pilot or simulate the e done? process Controls Measures Verify Results Verify Capability Flow-up Set Xs according to design parameters Flow-up to CCRs thruough transfer function Determine whether Ys hit targets Mfg Processes Process Operation Design features 3 Mfg Processes 4
  • Measureme nts Measurem ents
  • IMPROVE PERFORMANCE What needs to be done? revised 4/8 Verify Results End Product: Customer: Process Tested: Desired Results: Process Steps tested:
  • ERFORMANCE s to be done? Pilot Observations: GAP Analysis/Root Cause: Follow-up Actions: Questions:
  • Control Performance revised 4/8 How do we guarantee performance? Implementation Plan Potential Problems (RISK) of Recommended Internal implementing Solution (FMEA) Controls quot;Hardwired Controlsquot; What Data is to be Process Control System collected Who will collect this Data Documented Procedures Documented Guidelines Training Plan Changed Changed Method of Communication Plan Leadership support Communication Business Case from Budget and Benefits Case quot;Definequot; Solution Benefits Detailed Implementation Event Objectives Milestones
  • trol Performance e guarantee performance? Implementation Plan Check Lists How will this Data be When will it be What is the Out of Shown Regularly Reviewed? Control Plan? Documented Documented Forms Training Materials Changed Changed Audience When? Who is responsible Benefits to Other Non-Financial Cost to Implement Customers Benefits Roles and Dates/Time/Room/M Respnsibilities Resource Planning aterials
  • Control Performance revised 4/8 How do we guarantee performance? Documentation Related to this New Process Review Documentation and Change to reflect New Process Doc. Number Title Creation date Last Revised Related Specifications Policies Procedures Work Instructions Forms Websites Training Materials Key Terms Term Definition
  • rmance performance? ted to this New s Approvers URL Audit Results Changes needed Definition
  • Control Performance revised 4/14 How do we guarantee performance? Poka-Yoke Mistake Proofing Example: You leave your Gas Cap with your keys on top of your hood to avoid forgetting to put it back on. Review 2Be Process. Where can possible Errors be made Where does Error Occur that if undectected, will cause a defect that your custom that will cause a Defect? What can be done to order the process so that the Error is Self Evident before it What can be done to quot;hardcodequot; the Process so that the Error will not be passe What can be done using a Checklist to identify the Error before it is pass on? What Does Digitization Add to Six Sigma ?…. E-Training - Ability to Train & Test Thousands of people in Mont E-Processes – Digitized Improvement Actions Stay in Control E-Tools – e-Surveys, Simulators, and Modeling Tools Improve phases of Six Sigma Projects E-Tracking - Ability to Monitor All Vital Xs and the improvement E-Visibility - Ability to Monitor All Business Ys and The Sectors the Results (good and bad)
  • DMADDD rformance Digitize the Process ntee performance? by using Hard Coding to take the cost out of the process by automating certain tasks ur Gas Cap with your od to avoid forgetting cause a defect that your customer will notice. e Error is Self Evident before it is passed on? that the Error will not be passed on? e Error before it is pass on? gma ?…. ousands of people in Months not Years nt Actions Stay in Control nd Modeling Tools Improve the Analyze and Improve al Xs and the improvement projects attached to them siness Ys and The Sectors / Functions Driving
  • Control Performance How do we guarantee performance? Control Tollgate Review Questions Now is a good time to reflect on your experiences during the Control phase. Here are some questions to consider. Methodology 1. Describe the implementation plan. How will the plan be monitored to ensure its success? Who is accountable? 2. What are the potential problems with the plan? What are the contingency plans? 3. What controls are in place to ensure that the problem does not reoccur? 4. How has the scorecard been integrated into the management review process? 5. Who is the process owner? How will the responsibility for continued review be transferred from the improvement team to the process owner? How frequent are the reviews? 6. What is being measured? What evidence do you have that would indicate the process is in control? How well and consisten the process performing? Is a response plan in place for when the process experiences out-of-control occurrences? 7. How has the process been standardized? How have you documented the process changes? 8. What is the new process sigma? Building Organizational Support 9. How has the training plan been revised from the Improve phase? How has training been conducted to ensure understanding the process changes? How effective was this training? What continuing issues does your team need to address in the area of training? 10. What is the communication plan for implementation? How will your team use communication to manage this change, minim resistance, and mobilize stakeholders? 11. Based on your implementation and communication with key stakeholders, what are the barriers to successful change? How you plan to address them? How are your actions to overcome barriers reflected in your implementation plan?
  • Control Performance revised 4/8 How do we guarantee performance? Process Control System List Output Indicators associated with What UPSTREAM events best predict this What Check will be put in place to i CCR's Output before it is passed on to the Custom
  • k will be put in place to identify this Error Contingency Plan or passed on to the Customer? OCAP
  • Control Performance revised 4/8 How do we guarantee performance? IMPLEMENT Statistical Process Control to Monitor the Process Design a Detection System How will you know if your new process has gone out of control? List Key Measures from VOC, Calculate Out of Control Points SIPOC Data Operational Definition (MiniTab)
  • Design for Manufacturability (DFM) Design for Service (DFS) Design for Assembly (DFA) Verify Functionality What must be done when New Process is Out of Control OCAP
  • Control Performance revised 4/14 How do we guarantee performance? IMPLEMENT Statistical Process Control to Monitor the Process List Lessons Learned using the DMAIC What Other Similar Processes Could Also Process Be Improved Pilot Site Level World Wide Level
  • rformance DMADDD ntee performance? Draw Down - Find where this projects results can be al Process Control to directly applied to e Process other processes rocesses Could Also Collaboration with other Process Owners with Similar Processes
  • What needs to be d Experiment = Understand the nature of a system by manipulating inputs Screening What are the variables or factors that affect the response and their interaction? Separate the vital few from the trivial many Optimization Optimize the performance of some process or product Comparison Compare two or more things: vendors, processes, tools, designs etc Robust design Is the performance or product or process insensitive to the environment in which they are used? System improves as the factors get more fundamental - Vendor - On time delivery PURCHASE ORDER PROCES SUPPLIER INPUT (Requisitioner, Customer, Planner, Commodity Team)
  • Materials or Information consumed or transformed by the Process to produce output Master Data Vendor, Info Record, Source List, QPL Requisition/Demand (Schedule Sharing) Review & Approval
  • Design How do we guarantee performance? What needs to be done? Design of Experiments ating inputs nse and their interaction? ls, designs etc o the environment in which they are used? On time delivery PURCHASE ORDER PROCESS What factors are uncontrolled? Categoriacal CONTROLS Rules, Regulations, Standards, Polices, Procedures, Work Instructions, Goals, Metrics, & People (Process Owner) QPL/QSL: Vendor Master; Material Master; SOP 8-4; SIC 3.2 314W checklist GVM Profile ↓ PROCUREMENT PROCESS OUTPUT TO CUSTOMER (Receiving, AP, Requisitioner or Planner
  • Work done (by the Buyer) to the and the Supplier) Inputs to Transform Inputs into Output (PO) Information or Product created by the Process Create and Transmit PO from Good PO that can be performed Inputs. to by the Supplier, received, and paid. MECHANISM People, Behaviors, Equipment, Tools, Software that perform the Process SAP Buyer Commodity Team WWCM PO Type TIGERS What Factors are Uncontrollable? Continuous
  • Design How do we guarantee performance? What needs to be done? ANOVA = one factor at a time Understand how the data is structured Draw Tree diagram Cycle Time = Y Day DOW Location No. of Employee Lead Rep nexted (different people) Loan/Lease Amount Application used Approval Time Wait for Info OBSR_NO. Cycle TimeDAY Lead Rep Location No. Empl DOW Application UsedLoan/Lease model = Discrete X's General Linear Model overview how to examples data see also Stat > ANOVA > General Linear Model Use General Linear Model (GLM) to perform univariate analysis of variance with balanced and unbalanced design Calculations are done using a regression approach. A quot;full rankquot; design matrix is formed from the factors and cova Factors may be crossed or nested, fixed or random. Covariates may be crossed with each other or with factors, or Dialog box items Responses: Select the column(s) containing the response variable(s). Model: Specify the terms to be included in the model. See Specifying a Model for more information. Random factors: Specify any columns containing random factors. Do not include model terms that involve other fa <Covariates> <Options> <Comparisons>
  • <Graphs> <Results> <Storage> <Factor Plots> © All Rights Reserved. 2000 Minitab, Inc. use Lowess
  • Analysis of Variance Table Degress of Freedom Sum of the Squares Total Variation = sum of(y-averagey)squared Mean of Sigma P-Value residual error = not explained by the model - unexplained variation R squared value = explained variation divided by the total variation times 100 Covarient is continuous data Use Discrete data for interation plot Factor Type Levels Values DAY fixed 20 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Location fixed 3 New York Paris Sydney Analysis of Variance for Cycle Ti, using Adjusted SS for Tests Source DF Seq SS Adj SS Adj MS F P DAY 19 5333.6 966.8 50.9 6.24 0.000 Wait for Info Location 2 23916.6 23916.6 11958.3 1466.91 0.000 Wait - Discrete Error 1998 16287.8 16287.8 8.2 Total 2019 45538.1 Approved/Dis Amount Approval Time for Info Wait Wait - Discrete Time Discrete Cycle d and unbalanced designs, analysis of covariance, and regression, for each response variable. from the factors and covariates and each response variable is regressed on the columns of the design matrix. h other or with factors, or nested within factors. You can analyze up to 50 response variables with up to 31 factors and 50 covariates at one t terms that involve other factors.
  • and 50 covariates at one time.
  • 9 STEP ANALYSIS PROCESS - FACTORIAL DESIGNS STEP MINITAB COMMANDS 1 Graph…Dotplot view data Graph…Plot Graph…Matrix Plot 2 and 3 Stat…DOE…Factorial…Analyze create/fit model 4 Stat…Regression…Residual Plots evaluate residuals 5 Calculator - create natural log or square root consider transform 6 go back to steps 2/3 take out terms 7 Stat…DOE…Factorial…Analyze store equation 8 Stat…DOE…Factorial…Factorial Plots optimize Stat…DOE…Factorial…Contour Plots Stat…DOE…Factorial…Response Optimizer 9 Stat…Regression…Regression prediction interval
  • ORIAL DESIGNS OPTIONS COMMENTS Y symbol - group - CenterPt Y versus Run Order connect - graph Y versus all X factors Terms - 2nd order, deselect center points in model Graphs - Normal, Pareto, Residuals Storage - Fits, Residuals store in order to plot residuals under Stat/Regr/Residual Plots Fits versus Residuals else use automated plots only if needed-residual problems Storage - coefficients Main Effects - Setup Interaction - Setup Contour Plot - Setup Setup - choose optimization criteria (I.e Max) Options-input optimum conditions and check enter optimum conditions in same prediction and confidence limits order as predictors box; make sure there is a column of data for each effect in final model.
  • 9 STEP ANALYSIS PROCESS - RSM DESIGNS STEP MINITAB COMMANDS 1 Graph…Dotplot view data Graph…Plot Graph…Matrix Plot 2 and 3 Stat…DOE…Response Surface…Analyze create/fit model 4 Stat…Regression…Residual Plots evaluate residuals 5 Calculator - create natural log or square root consider transform 6 go back to steps 2/3 take out terms 7 Stat…DOE…Response Surface…Analyze store equation 8 Stat…DOE…Response Surface…Contour Plots optimize Stat…DOE…Response Surface…Response Optimizer 9 Stat…Regression…Regression prediction interval
  • DESIGNS OPTIONS COMMENTS Y symbol - group - CenterPt Y versus Run Order connect - graph Y versus all X factors Select Coded Units Graphs - appropriate residual plots Storage - Fits, Residuals store in order to plot residuals under Stat/Regr/Residual Plots Fits versus Residuals else use automated plots only if needed-residual problems Storage - coefficients Contour Plot - Setup choose uncoded; make sure data file is in uncoded units to match Setup - choose optimization criteria (I.e Max) Options-input optimum conditions and check enter optimum conditions in same prediction and confidence limits order as predictors box; make sure there is a column of data for each effect in final model.
  • Design How do we guarantee performance? Design of Experiments A Crossed Effect Method Use data found in Analyze Phase to develop a theory to test Building a statistical model between experimental responses Y and factor inputs X Experimental Sequence Two Factor Two Level M Fraction Factorial ( chosen X factors) B lo Subset of the Factors identify leading indicators B hi Screening Experiments 5-7 X's To calculate the Main E State the problem and objectives of experiment Planning - Design the experiment and plan for data collection Write Operational Definitions Operating Range Experimental Range as a subset of Operating Range (nominal value +- Select a Nominal value of the factors Fixed Effect Model - Conduct the experiment Analyze the results - continuous - Miltivari regression - Discrete - ANOVA Interpret the results View the data Create the model Fit the Model Perform Residual Diagnostices Check for possible transformations Remove non-significant terms/refit reduced model Choose improved model Interpret Chosen Model Continue Experimentation/Make confirmation runs Full Factorial (all known X's) 2k experiment All factors of significance used in a 2k Frational 2k = 2 times the number of factors Look for Interactions x1 and x2 Randomization to reduce bias Run Order must be randomized Randomize to minimize Uncontrolled Factors Independent trials Blocking- use with uncontrolled variation Limit the nuisance (noise) factor Block what you can, and randomize what you cannot block Replication - repeat the experiment over again Repetition - remeasure over again Run all or some of the experiments twice Ensures independent replicates Look for variablity in the controlled factors Center points are replicate runs where all continuous factors are at their center lev Estimate the true experimental error Determine if a drift or innstability occurred during the experiment
  • Evaluation of the curvature in the response Experimental Error- unexplained variation pure error - where you repeat yourself in error LOF - lack of fit - model error Error that occurs when the estimated model does not correctly predict the respons Error that is the noise caused by uncontrolled and unknown nuisance factors Experimental Unit Material to which a particular experiment run or factor-level combination is applied Orthogonal Coding - reduce controllable input factors as -1 , 0 or +1 Remove any interactions effect of one factor is dependent on the other Determine Region of Optimum Path of Assent Response Surface Methodology Model Optimal Region Plackett- Burman Confirm Model Predictions Confirmation Runs - Pilot testing
  • center points replicates = how many times you are running the experiment blocks = analysis cube plot 3d plot of 3 variables
  • 4 Objectives for doing a DOE Screening Don’t look at just CORRELATION, but look at CAUSATION Optimization Comparison Robustness ses Y and factor inputs X Two Factor Two Level Matrix A lo A hi To calculate the Main Effect of a Factor from a DOE….average high minus the average low Range (nominal value +- 10 to 20% of Nominal value Two reasons for not doing an OFAT type design cannot identify interactions cannot define the true optimal Uncontrolled Categorical Factors Process inputs Process Process outputs Uncontrollable Continuous Factors Designs should be: Balanced - 4 hi levels and 4 lo levels Orthogonal - right angles to each others - allows you to independently assess the amount of each of the X's explain +1's and -1's reduce X's to common value independent value of the effects Center points can be used for curvature - curvature checks your assumption p value of curvature tells you th 2 to the 4 plus centerpoints = number of runs Orthogonal check in Minitab tors are at their center level midway between low and high values ing the experiment
  • rectly predict the response wn nuisance factors el combination is applied
  • switch between Orthogonal and uncoded correlation checks for confoundness in the X's
  • t CAUSATION p value of curvature tells you the percentage you will be wrong
  • change to full factorial
  • confoundness in the X's all zeros indicate that there is no correlation between the X's represents a balanced design
  • Robust Design resliency to internal design internal customers variability in training, resiliency to external factors external customers environment usage environment factors CTQ flowdown transfer functions can capture the depenence of both design and environmental factors design of experiments helps quantify relationships and derive the transfer functions Monte Carlo helps validate results and help make choices among different alternatives Design product to perfom well without variables that are not in our control effecting it Design variables are under our control Environmental variables are not under our control How does your design perform under different conditions external and internal?
  • Objective Experimental Unit Treatment Screening which X's are important FractionalFactorial 5 - 7 X variable Plackett-Burman Comparision which X is better Full Factorial AB AA BA Optimization want to get a picture of the process Response Surface (RSM) 2 - 4 X variables Robust Design Not sensative to certain variables Response Surface (RSM) Determine Important Variables Resolve any interactions Determine Region of Opt Cause and Effect diagrams Full Factorials Path of Ascent All combinations A with three settings (levels) B with 2 settings (levels) Number of factoral combinations = A levels * B levels Simplest Full Factorial is a 2 factor 2 level Hi 2k Full Factoral Designes k= number of factors Y=B B 2= number of levels A=X Lo A Look for interactions Average HI - Average Lo is the Main Effect Effect divided by 2 = Slope Slope = change is y over the change in x Do an ANOVA>>Interactions Plot Planning Step Task 1. State the Problem and Objective Outline the specific continuous improvement objective Collect available background information Summarize data applicable to this problem area 2. Design the experiment and plan for data collection Hold a meeting of all concerned individuals Develop a preliminary experimenatal design program Review the design with all concerned parties
  • 3. Conduct the experiment Develop methods and acquire necessary materails and equipment Conduct the experiment. Be sure to follow the protocol exactly Record any unusual events. 4. Analyze the results Record results as the analysis progresses Interpret the experimental results Consider all observed data Confine conclusions to strict deductions from the experimental evidence Test any questions that are suggested by the results by independent experiments Make conclusions regarding the technical meaing of results as well as their statistic State results clearly in understandable terms 5. Interpret the results Describe work clearly. Provide the background, pertinence of the problems, and m Use graphs and tables. Present the data in a usable form Supply sufficient information to enable the reader to verify results and draw conclu Limit conclusions to an objective summary of evidence so that the work recommen
  • WHICH HOW Surface (RSM) PICTURE Surface (RSM) SPECIAL TYPE OF SCREENING Determine Region of Optimum Model Optimal Region Path of Ascent Response of Surface Methodology Process Hi
  • ental evidence ndependent experiments lts as well as their statistical significance e of the problems, and meaning of the results y results and draw conclusions that the work recommends itself for prompt considerations and decisive actions
  • Y=Temperature X Factors Low High User Input Argon 30 70 45 -0.25 Oxygen 1 9 5 0 Nitrogen 200 500 350 0 Ion Rate 5 10 7.5 0 Supplier 1 2 1 -1 ReactionTime 10 20 15 0 1 Run Equation (coded units) = 500 - 132*IonRate + 45*Supplier*Oxygen + 25*ReactionTime Need Specs For now assume 600 to 700? sd(noise)= Stdev=20? Verify if any factors should affect sd?
  • 1 Number: Normal Random: 1 Temperature(Y) = 512.19 Noise: 8.77 Temperature (Exact) 500 Temperature + Noise: 508.77 25*ReactionTime 50 4 350 7.5 1 15
  • Enable Manual Entry
  • Path of Accent Where is the most promising region to lead us to the optimal? Contour plot will show where to do experiments
  • to the optimal?
  • Fractional Factorials Determine Important Variables Resolve any interactions Determine Region of Optimum Cause and Effect diagrams Full Factorials Path of Ascent All combinations Confirm Model Predictions Confirmation Runs Sparcity of Effects - not everything is important I=abcd center on the top 4 factors a(i)=a(abcd sacrafice the highest order interactions a=bcd use the 1st 8 of the 16 1/2 fraction select all + or all - of the abcd bd=ac
  • Region of Optimum Model Optimal Region Response of Surface Methodology a(i)=a(abcd Resolution Finger rule Resolution III is not recommended Resolution IV is ok..but need to watch out for confounding. If there are interactions present we will see them, but we w Resolution V is good
  • we will see them, but we will not be able to tell what they are without further experimentation
  • Blocking is used to protect against known nuisance factors Use randomization to protect agains unknown nuisance factors Average high - Average low = effect
  • Significant Block effect Take blocking out when significant
  • Capability Flowup CTQ Flowdown CTQ Flowdown – A very Variance Allocation You want to do your final Defining and Partitioning Tolerances How much tolerance is tolerable sigma squared of the total = sig sq'd1 + sig sq'd2….. Sensitivity Analysis degree of variability in the output of a transfer function (Y) which results in variability in the inputs (X's) Input variability can cause unacceptable changes in output performance Monte Carlo- good for Capability Flowup but not CTQ Flowdown estimate how variability in the input variables will cause poor output performance Simulates - but only by trial and error Calculating Variances (estimate) quadradic transfer function k y k=slope x sigma of y = k sigma x y= square the variances and add them together if sigma x's are know plug them into the calculation (capability analysis) yield = f(time, temp) time= 325 temp= 480 dYield CTQ Flowdown - what is the tolarable combined variance in the x's
  • CTQ Flowdown – A very rigorous methodology for allocating requirements and assessing capabilities of the most critical segments of a prod You want to do your final CTQ flowdown into CTPs (critical to processes) at the beginning of the design phase. You need to know what is cri ility in the inputs (X's) http://www.umoncton.ca/cie/Conferences/29thconf/29thICCIE/papers/paper004.PD
  • ritical segments of a product prior to M1. need to know what is critical to the process before you can design your new detailed processes CIE/papers/paper004.PDF
  • Verify
  • Use Predictive Tools to Demonstrate Design Fulfillment of CCRs Design DOE RSM Validated/Refined Model Identified sigma x's Variance Allocation Optimized design parameters Reduced variation Product Requirements Design Features 1 Customer How well are Expectations wants satisfied? Product Requirements 3 Measurements Measurements Capability Flow-UP
  • nt of CCRs Verify Capability Flow-up Set Xs according to design parameters Flow-up to CCRs thruough transfer function Determine whether Ys hit targets Mfg Processes Process Operation Design 3 features Mfg Processes 4 Measurements Measurements