SQA Capability.ppt

1、Measurement System Analysis,Normality - Data Modeling,Stability,Capability,Control,Step 4 - Establish Process Capability,Objective: Baseline current ability to meet customer CTQs,Tools in this Step: Variable Product Capability Attribute Process Capability Non-Normal Process Capability Weibull Life P

2、lotting Central Limit Theorem Confidence Intervals Sample Size Selection,Elements of this Step: Determine if the product/service currently meets customer performance standards. Establish short term DPMO and Sigma. If process is 6 sigma capable, then maintain it.,Goal: Establish how well the current

3、product/service meets CTQs today.,Variable Data - Product Capability .,Why do we need to Assess Product Capability?,Allows us to quantify the nature of the problem we will attack as one of the following: Are the specifications correct for the parameter (Y) of interest (process or performance output

4、variable)? Is the location of the central tendency of the parameter (Y) centered within the appropriate specifications? Is the process variation in the parameter greater than allowed by the specifications? Allows the organization to predict defect levels escaping the process Justifies fixing the pro

5、cess if the product is not meeting customer specifications.,Long Term VS. Short Term Capability,-,Short Term:,Lower Limit,Upper Limit,Capability Indices (long-term vs short-term),(USL - LSL),=,Long -Term Capability Indices,(USL - LSL),C,p (best),=,Short -Term Capability Indices,Cp is process capabil

6、ity potential Cpk is process capability,Pp is process performance potential Ppk is process performance,We are concerned about Long-Term results in Step 4,Think Zmin here,Process Study Method,Step 1: Obtain 50 to 100 parts manufactured over time. Step 2: Measure product feature based on QC dimensions

7、. Step 3: Enter variable data into Minitab Step 4: Calculate Cpk and Ppk StatQuality ToolsCapability Analysis (Normal) Data arranged in single column. Subgroup size: For only short-term data - set equal to total parts For only long-term data - set equal to 1 For short-long combination - set to subgr

8、oup size Enter upper and lower specifications Step 5: Read Cpk, Ppk, and estimated PPM off chart produced.,Class Example,Create random data with a mean of 10 and sigma of 1 CalcRandom DataNormal Generate 100 rows Mean = 10 Standard Deviation = 1,Run normal capability analysis on data Stat Quality To

9、ols Capability analysis (normal),Ppk,PPM (DPMO),Are these results long Term or Short Term?,What do these Results Mean?,DPMOLTSigmaSTCpkST,66,8073.01.00 22,7503.51.17 6,2104.01.33 1,3504.51.50 2335.01.67 325.51.83 3.46.02.0,Cpk = Ppk + .5 (based on 1.5 sigma shift),Poor,Good,Excellent,At 6 Sigma!,As

10、%R&R decreases the Cp increases,Aside: R&R Effect on Capability,Non-normal Process Capability .,Box-Cox Transformation Example,Calcrandom dataChi square, 100 rows, df = 2,Lets first generate non-normal data:,Perform a normal capability study on the data:,Stat Quality Tools capability analysis (norma

11、l) USL=10, LSL = 0,PPM=116,000 Why so high?,Transform the data using Box cox power transformation. Store transformed data for normality test (sometines Box-Cox doesnt fix it).,Box-Cox transformation,Stat Control Charts Box Cox Transformations.,Lambda Est= .337,Normal Capability Analysis (w/ Box-Cox),Analyze normalized data after transformation.,Stat Quality Tools capability analysis (normal) USL=10, LSL = 0,PPM=8,600,Next adjust curve: