Mod5

,Module 5- Identify Spec for CTQs Module 6- Validate Measurement System and Baseline Process Performance,Module 7- Identify Sources of VariationModule 8- Determine Relationships between Variables,Module 9- Optimize Performance and Implement Improvement,Module 10- Verify the Improvement and Implement Process Controls,Measure,Analyze,Improve,Control,Module 3- Construct Problem Statement Module 4- Identify CTQs (Critical to Quality),Define,Module 1- Administrative and Def. Basic ConceptsModule 2- Provide Six Sigma and Lean Overview,Module 5- Id. Spec for CTQs and Est. Target,Learning Objectives,Overview,This module begins the Measurement phase of DMAIC. This module introduces techniques used to baseline current performance and generate an improvement target.,Specifically this module enables you to: Identify your current specification Review descriptive statistics and graphical techniques Define corporate-wide metrics and apply them to your project Set a target specification,Descriptive Statistics allow us to summarize dataThere are two general types of data, we call: DiscreteContinuous/VariableThe statistics used to summarize data and the analytical tools we use vary according to the data type.,Review- Descriptive Statistics,Based on countsCan not be broken down into smaller increments there are a limited number of possible outcomes.Examples : # of defects, number of days, pass/fail,Payment Terms,# Days,Discrete Data,A special form of discrete data, using names to represent categories or possibilities,Transportation,Air vs. Ground,Shipped Pallet Quality,Day of the Week,Night ShiftDay Shift,53 Pass,Final Test,9 Fail,Attribute Data,Data that indicates how much or how many preciselyAt least 10 times better than the value (e.g. minutes for something that takes hours),Time to load a trailer (hrs)2.18, 2.30, 2.21. . . . . . . . . 2.15, 2.27, 2.43Upper Limit 2 hrs 40 mins(2.7 hrs),Usually represented by a histogram and/or normal curve, which lets us predict the process from a few measurements,Defects,Variable Data,We describe data with 2 measuresLocationDispersion For variable data you may well know these as Average or MeanVariation or Standard Deviation,Analyzing Data,LocationThe relation of the mean to the targetA well located process effectively matches the mean of the result to the targetIn other wordsCentered Dispersion The amount of variation in the process,Location and Dispersion,MeanThe average of a set of valuesStrongly affected by extreme valuesUsed for Normal data (variable)MedianThe midpoint when data is ranked in ascending orderNot affected by extreme valuesUsed for non-Normal data (variable)ModeMost frequently occurring valueUsed for discrete data,Metrics of Location,RangeLargest value - smallest valueVarianceA measure of the average distance each point is away from the meanIn statistical speak, the average squared deviation of each individual data point from the meanStandard DeviationThe square root of the varianceThe average distance of the data from the mean,Metrics of Dispersion,nnumber of measurementsxan individual measurementx1, xnthe first and last measurementsx or x barmean of the measurementsRrangestandard deviation2variance,Notation,Calculations,Population vs Sample,There are several reasons why we study samplesinstead of populations:It is to slow and expensive to study entire populationPredictions based on samples are almost as accurate as predictions based on the entire population,We match a statistical distribution to our sample data to represent the population and be able to predict performance,Why Sample?,Quantifying customer requirements (CTQ) and internal requirements defines the Performance Standard (Specification).Performance is measured against our Standards. Failure to meet the Performance Standard is defined as a defect. The presence of defects results in a defective.,Defining Performance Standards,A defective is any characteristic(s) which causes an item to fail to meet the specification (CTQ).Defectives result in rework, scrap or warranty returns.First Time Pass (FTP) and Parts per Million (PPM) are traditional measures of defectives.,Defectives are items with one or more defects.,Defective,FTP =Quantity Passed * 100Total Quantity,PPM =Quantity Defective * 100Total Quantity,100 motors were inspected at the lead welding machine. Each motor has 16 wires that must be welded to terminals on the commutator. Inspection reveals that 8 motors had one or more missing welds. A total of 24 improperly welded terminals were identified. First Time Pass (FTP) = (92/100) x 100 = 92% PPM = (8/100) x 1,000,000 = 80,000.,Example,A defect is any occurrence of a failure to meet a performance standard or produce the expected outcome of a process. A defective unit may contain more than one defect.Accurately counting defects requires that we not stop looking for defects after the first one is identified.,Anything that is not done correctlythe first time is a defect,Defects,Defects per Unit (DPU) and Defects per Million Opportunities (DPMO) are used to quantify defects. Defects (Responsible for Scrap or Rework)DPU = - Units (Originally Submitted to Process),DPU is the average number of defectswithin each unit produced,Defect Metrics,DPMO gives a more complete picture of part/process defects than measures of defectives (PPM).We must first define Opportunity:An opportunity is a chance for creating a defect.,Defect Metrics,DPMO reveals the capability of an entire process,100 motors were inspected at the lead welding machine. Each motor has 16 wires that must be welded to terminals on the commutator. Inspection reveals that 8 motors had one or more missing welds. A total of 24 improperly welded terminals were identified.Defect count = 24Opportunities = 100 x 16 = 1600DPU = 24/100 = 0.24DPMO = 24/1600 x 1,000,000 = 15,000,Example- Manufacturing,A Black Belt was working on a project to track defective product returns by product date code at one of our major distributors. In 2001, total returns for our top 10 defective products equaled 43,344. The total number of products with date codes over 1 year old were 5,914.Defect count = 5,914Opportunities = 43,344DPMO = 5,914 / 43,344* 1,000,000 = 136443PPM = 5,914 / 43,344* 1,000,000 = 136443,Why does DPMO = PPM in this example?,Example- Commercial,Where opportunities equal one, DPMO will be the same as PPM.Defectives drive customer satisfaction. Defects drive process improvement.DPMO provides a stable measure to gauge process performance. PPM is sensitive to the distribution of defects, not total quantity. Whereas DPMO is driven solely by defect quantity.Using the motors example, the 24 defects could have resulted in anywhere from 1 to 24 defectives. DPMO is always constant at 15,000.,DPMO vs. PPM,Calculate the DPMO for your project!Ask trainer if you need help.,Exercise,First Time Pass (FTP) is a measure of defectives, typically taken at the last step of the process.FTP does not give us insight into what defects are created within our process. Rolled Throughput Yield (RTY) is a measure that quantifies the performance of all of our process steps.RTY is calculated by multiplying the yields of each individual process step together.,RTY gives a more complete picture of process performance,Measures of Yield,RTY = .962 x .936 x .976 = 0.879 = 87.9%,Measuring RTY,488 Good,Step 1,Step 2,Step 3,500,96.2 %Yield,93.6 %Yield,97.6 %Yield,FTP = 488 / 500 X 100 = 97.6%,Step 1,Step 2,Suppose we want to find out the average yield per opportunity. Normalized yield gives us a baseline measure for yield that one could expect at any given step in process. Normalized yield is calculated from RTY.,Assuming n opportunities per unit,Normalized Yield = RTY (1/n),Normalized Yield,RTY exposes and pinpoints the “Hidden Factory.” RTY keeps us focused on the entire process by measuring total impact of improvement projects.RTY gives us the probability of one unit completing each measured step in the process with zero defects.,RTY drives overall process improvement,Why is RTY important?,We need 8 volunteers to work an assembly line in a factory.3 line workers2 Material Handlers3 Quality TechniciansSet up the process as follows:,RTY Exercise,The Process: There are 3 process steps. At each step, the line worker drops a card from the vertical position at shoulder height onto a piece of paper on the floor. Each card landing within target gets passed onto the next step by the material handler. The QC technician will keep count of how many drops it takes to get 20 cards in target. After completing one cycle, repeat the experiment again, this time holding the card horizontally before dropping it onto target.,Calculate RTY for this example.,RTY Exercise Contd.,Key B&D Metrics- Sourcing,Productivity Year to year savings in SAP final unit cost Goal: 7% year to yearQuality DPMO (Total # bad parts received) / Total amount shipped) * 1,000,000 Goal: 98% Quality Finished Stock Audit- (# units defective/ # audited) * 1,000,000 Goal: 30 turns Efficiency (Credit Time * Total Units Built) / Total Labor Goal: 100%,Key B&D Metrics- Engineering,Schedule Attainment Meeting Key Milestone and PDR Dates Goal: 100% Quality Each development team scored Cost Measured at each step in Milestone process Goal: CAR target Warranty % (Returns + Repairs + Exchanges) / Moving Annual Total,Key B&D Metrics- Supply Chain,Service Level ( Customer Satisfaction ) Fill Rate : Qty of units shipped / Qty of units Ordered Goal: = 95% / 98% On Time : Order shipped with-in stated CTQ/ Total Orders Shipped Goal : = 90% Inventory ( Asset Management ) Finished Goods Turns : STD Cost Sales/ FG Inventory on hand ( 4pt or PIT) Goal: 5.0 turns Cost ( Efficiency & Productivity ) Total Expenses / Total Invoice Sales Value Goal: 5.0%,Establish the Target,Is the process operating at o