【正文】 measurements: ? Number of Observations within each unit (Family 1): Two, front and back. ? Number of Units: Three consecutive panels sampled at every bination of days and time of day (Family 2). ? Family 3: Longerterm variation (morning and afternoon)。Process Door Versus Data Door 2 ANALYZE Goal ?Identify root causes and confirm them with data Output ?A theory that has been tested and confirmed IMPROVE 3 You Are Here Develop a focused problem statement Process Door Versus Data Door Organize potential causes Hypothesis Testing and Regression Analysis Design of Experiments and Response Surface ? Green Belt Materials—Process Analysis ?Activity Flow Charts ?Deployment Flow ?Charts ?Value Stream Maps ? Data Door: ?Stratified Time and Frequency Plots ?Scatter Plots Bridge Material ? MultiVari Analysis 4 Goals ?Know how to interpret a multivari analysis ?Be able to use multivari to gather information on potential causes 5 The Focus of Analyze Y = f (X1, X2, X3, ..., Xn) X1, X3, X5 Identification Verification Quantification What vital few process and input variables (Xs) affect critical to quality process performance or output measures (Ys)? 6 Process or Data Door? Process Door Data Door ?To understand the drivers of variation in the process ?To tackle quality problems and waste ?To understand the root cause of differences between outputs ?To improve the understanding of process flow ?To tackle cycle time problems ?To identify opportunities to reduce process costs Stratification Scatter Diagrams MultiVari Analysis Detailed Process Map Value Added Analysis Cycle Time Analysis It is remended to go through both doors to make sure that potential causes are not overlooked. MultiVari Analysis 8 What is the Tool? MultiVari analysis helps you see patterns of variation in response variable(s) that you can correlate with potential causal variables. The specific tool used is called the MultiVari chart. Measurement Time or Condition Change Overtime or Condition Within Piece Schematic of a Multivari Chart Between Pieces 9 Why Is It Useful? When Is it used ?Multivari analysis can help you narrow a list of potential causes to a much smaller list suitable for detailed investigation, through designed experiments, for example. It helps you focus your attention as you funnel down to find potential variables that you may need to study. ?In the Analyze step to plement process analysis and stratification. It usually follows stratification analysis. 10 When Should It Be Used? ?In the Analyze step to plement process analysis and stratification. It usually follows stratification analysis. 11 Planning a Multivari Study: Overview A. Preliminary work B. Main work Choose product characteristics relevant to the problem (from Define or Measure steps) Develop list of potential factors (causes) of variation in the characteristic of interest 7. Analyze the results 1. Define scope or domain of the study 2. Define time profile of the sample selection 3. Make a detailed plan for collection of the product samples 4. Collect the samples 5. Measure the samples 6. Plot the sample data 12 Families (Components) of Variation Typical families of variation in a manufacturing environment can be clustered together: Unit families: ?Within Unit variation: different measurements or results at different points within the unit or product ?UnittoUnit: one unit to the next (within subgroup variation) ?Subgroup to subgroup ?Streamtostream: suppliertosupplier, linetoline, machinetomachine, tooltotool, operatortooperator, etc. 13 Families (Components) of Variation, cont. Time families ?Shortterm to Longterm Measurement family ? Gage Ramp。 two levels, AM and PM. ? Family 4: Longterm variation (day to day)。s. 52 Illustration (1) Suppose the team in the paint thickness example had been able to record a pair of continuous X39。s to guide your sampling approach ?Seek to observe the process when the X39。 here we select the two time families as factors 2 and 3 38 Minitab FollowAlong, Part 2, cont. Unless you check the first box, Minitab will only display the means of the first three factors. 39 Minitab FollowAlong, Part 2, cont. 4 / 1 9 / 9 94 / 1 8 / 9 9PMAMPMAM0 . 0 80 . 0 70 . 0 60 . 0 50 . 0 4D a t eFilmThicknesB a c k F r o n tM u l t i V a ri C h a rt f o r F i l mT h i ckn e s By Po si t i o n D a t eA M / P MP o si t i o n40 Minitab FollowAlong, Part 2, cont. 4 / 1 9 / 9 94 / 1 8 / 9 9PMAMPMAM0 . 0 80 . 0 70 . 0 60 . 0 50 . 0 4D a t eFilmThicknes123M u l t i V a ri C h a rt f o r F i l mT h i ckn e s By U n i t N a me D a t eA M / P MU n i t N a m e41 Alternative Display: Main Effects Plot We want the data means to be displayed。 F a ct o r 2 : A M / PM 。 F a ct o r 2 : A M / PM 。s): ?Gather information on relevant continuous X3