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More Variable Control Charts

More Variable Control Charts. A. A. Elimam. What about the Short Run?. X-bar and R charts track process with long production runs or repeated services No. of sample measurements : Insufficient to create either chart Would SPC ideas apply to new processes or short runs?

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More Variable Control Charts

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  1. More Variable Control Charts A. A. Elimam

  2. What about the Short Run? • X-bar and R charts track process with long production runs or repeated services • No. of sample measurements : Insufficient to create either chart • Would SPC ideas apply to new processes or short runs? • What happens when only one sample is taken from a process? • Situations when the traditional X-bar, R and S charts cannot be used.

  3. Individual & Moving Range Charts When ? • data is collected once per period • single value measurement • few units of each product • individual Values Chart • Plot Individual measurements, Xi • X-bar is the average of all Xi

  4. Individual & Moving Range Charts • Moving Range Chart • Value-to-value difference of individual data, Ri • R-bar is the average of all Ri • (m-1) ranges • Plot Individual measurements, Ri starting on the second observation

  5. Individual & Moving Range Charts Control Limits • Individual Charts UCL x = X + 2.66 R LCL x = X - 2.66 R • Moving Range Charts UCL R = 3.27 R LCL R = 0 • At least 80 samples • Interpret similar to traditional charts

  6. Moving-Average & Moving-Range Charts • Combine n individual values to form a group • Create average & range per group • Moving: new value in- oldest one out • Find UCL, LCL & Process Capability using the same methods for traditional control charts (TCC)

  7. Moving-Average Charts • Moving Average smoothes out short term variation • User Concentrate on trends • Mostly used for seasonal products • Always lag behind changes in process • Best when process changes slowly

  8. A Chart Plotting Individual Values • Explains concept of variation compared to the average • Picture worth 1000 words • Useful in training staff on interpreting R or S charts

  9. Median and Range Charts • Study process variation • Steps: • record subgroup measurements • rank in decreasing order • find median & range in each subgroup • Median Chart Center = all medians AVG • Range Chart Center = all ranges AVG • Determine UCL & LCL for the Median & Range Charts

  10. Median and Range Charts • Median Charts: UCL Md = XMd + A6 RMd LCL Md = XMd - A6 RMd • Range Charts UCL R = D4 RMd LCL R = D3 RMd • Record Median & Range on chart • Interpret Charts similar to TCC

  11. Run Charts • Monitor changes in a particular characteristic over time • Can be used for Variable or Attribute • Data: measurements, counts, subgroup averages • Easily spot trends, runs and other patterns

  12. Run Charts: Steps • Identify time increments to study process • Scale the Y axis to reflect values • Collect data • Record data on chart • Interpret the chart (limited to looking for data patterns) • No out of control points

  13. Variable Subgroup Size Charts • Subgroup size, n, Varies • Re-compute Control Limits (CL) for each n • As n increases - CLs closer to center • Too many calculations • Limit the useful of this chart

  14. Precontrol Charts • Compare product made against tolerance limits • Assumes process is capable of meeting specifications • Uses specifications for limits • More false alarms or missed signals • Simple to setup

  15. Precontrol Charts • Useful for setup operations or short production runs • Less powerful than TCC • Provide little about actual process performance • Cannot be used in problem solving or calculating process capability

  16. Precontrol Charts • Use Portion of Tolerance Spread (PTS) to account for difference in spread for individuals and averages • Desired Process Capability (PC) dictates this portion: PC index PTS 1.2 (100/1.2) = 83 % 1.1 (100/1.1) = 90 %

  17. Precontrol Charts: StepsCreate the zones for the used PTS • Place USL, LSL and center (SC) on chart • Divide (USL-SC) in 2 equal zones: green- yellow • Divide (SC-LSL) in 2 equal zones: green- yellow • Green zones (GO SECTION) are next to center • Yellow zones (CAUTION) are next to the limits • Zones above or below yellow area are colored in RED (UNDESIRABLE)

  18. Precontrol Charts: StepsTake measurements & apply setup rules • Record and plot measurement • If measured piece is • in green zone-continue running • inside limits but outside green zone-check next piece • second piece is also outside green zone-reset process • in red zone, stop, make corrections & reset process • If 2 successive pieces fall outside green zone, one high and the other low, reduce variability • Whenever process is reset, need 5 successive pieces inside the green zone before sampling

  19. Precontrol Charts: StepsApply the precontrol sampling plan • If 5 pieces in a row fall in the green zone-begin running the job • Use the run rules, randomly sampling 2 pieces at intervals to monitor process • For example: Sampling Two PARTS every 15 minutes. • Suggest Sampling >= 25 pairs after setups • Repeat whenever the process is reset

  20. Short-Run Charts • TCC : effective in long continuous operations • Real life: need to switch products (FMS) • Use Short-Run charts • Different Methods: • First and last pieces • 100 % inspection (costly, maybe inaccurate) • TCC for each part # & each different run of each part # (many charts- little information)

  21. Nominal X-bar and R Charts • Uses coded measurements based on nominal dimension. For example “Print Dimension” of 3.75 (+ or -) 0.005 = 3.75 • Shows process centering and spread • Assumes similar variations for each of the part numbers • If variation of a part > 1.3 R-bar, then it must be plotted on a separate graph

  22. Nominal X-bar and R ChartsSteps • Identify parts monitored using same chart (same operator, machine , material, ...) • Find nominal spec. for each part • Collect data using same subgroup size for all parts • Coded Xi= measured value-nominal value • Calculate X-bar for each subgroup

  23. Nominal X-bar and R ChartsSteps • Plot all X-bars on the chart • Continue the above for the entire run of this particular part number • Repeat the above for another part number • If the number of subgroups (from any combination of parts) >= 20, calculate the control limits

  24. Nominal X-bar and R ChartsSteps • Centerline = Average of all coded X-bars • Control limits: Nominal X-bar Chart UCLx = Centerline + A2 R LCLx = Centerline - A2 R • Control limits: Nominal range Charts UCLR = D4 R LCLR = D3 R • Draw center and CL on the chart • Interpret the chart

  25. Nominal X-bar and R Charts • Most useful when FOR ALL PARTS • Subgroup size, n, is the same • Nominal is the most appropriate target value • Control charts should be selected based on: • What aspect of process need to be monitored. • Identifying the chart that best meet such need.

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