OPEMAN

• Statistical Process Control (SPC)


  Monitoring production process to detect and prevent poor quality
• Sample


  Subset of items produced to use for inspection
• Control Charts


  Process is within statistical control limits

  
• Process Variability


  • Random - inherent in a process, depends on equipment and machinery, engineering, operator, and system of measurement, natural occurrences
  • Non-Random - special causes, identifiable and correctable, include equipment out of adjustment, defective materials, changes in parts or materials, broken machinery or equipment, operator fatigue or poor work methods, or errors due to lack of training
• SPC in Quality Management


  • Is the process in control?
  • Identify problems in order to make improvements
  • Contribute to the TQM goal of continuous improvement
• Attribute


  A characteristic which is evaluated with a discrete response (good/bad; yes/no; correct/incorrect)
• Variable measure


  A characteristic that is continuous and can be measured (weight, length, voltage, volume)
• SPC Applied to Services

  Nature of defects is different in services, Service defect is a failure to meet customer requirements, Monitor time and customer satisfaction
• SPC Applied to Services

  • Hospitals - timeliness & quickness of care, staff responses to requests, accuracy of lab tests, cleanliness, courtesy, accuracy of paperwork, speed of admittance & checkouts
  • Grocery stores - waiting time to check out, frequency of out-of-stock items, quality of food items, cleanliness, customer complaints, checkout register errors
  • Airlines - flight delays, lost luggage & luggage handling, waiting time at ticket counters & check-in, agent & flight attendant courtesy, accurate flight information, cabin cleanliness & maintenance
  • Fast-food restaurants - waiting time for service, customer complaints, cleanliness, food quality, order accuracy, employee courtesy
  • Catalogue-order companies - order accuracy, operator knowledge & courtesy, packaging, delivery time, phone order waiting time
  • Insurance companies - billing accuracy, timeliness of claims processing, agent availability & response time
• Where to Use Control Charts

  Process has a tendency to go out of control, Is particularly harmful and costly if it goes out of control, At beginning of process because of waste to begin production process with bad supplies, Before a costly or irreversible point, after which product is difficult to rework or correct, Before and after assembly or painting operations that might cover defects, Before the outgoing final product or service is delivered
• Control Charts


  A graph that monitors process quality, Control limits - upper and lower bands of a control chart

  
• Attributes chart


  1. chart, c-chart

  
• Variables chart


  mean (x bar – chart), range (R-chart)

  
• A Process Is in Control If ... no sample points outside limits, most points near process average, about equal number of points above and below centerline, points appear randomly distributed

  
• 1. chart


  Uses portion defective in a sample

  
• Construction of p-Chart


  Calculate UCL = p + z(p(1-p)/n), LCL = p - z(p(1-p)/n), where p = sample proportion defective, p = standard deviation of sample proportion

  
• 1. Chart in Excel


  • Insert chart, use formulas I4 + 3*SQRT(I4*(1-I4)/100) and I4 - 3*SQRT(I4*(1-I4)/100) to calculate UCL and LCL

  
• 1. chart


  Uses number of defects (non-conformities) in a sample

  
• Construction of c-Chart


  Calculate UCL = c + zc, LCL = c - zc, where c = number of defects per sample, c = square root of c

  
• 1. bar Chart


  Plot sample averages
• 1. Chart


  Plot sample range (variability)

  
• 1. bar Chart: Known


  UCL = x + z(x/sqrt(n)), LCL = x - z(x/sqrt(n)), where x = process standard deviation, x = standard deviation of sample means

  
• 1. bar Chart: Unknown


  UCL = x + A2R, LCL = x - A2R, where x = average of the sample means, R = average range value

  
• Control Chart Factors table provided for determining A2, D3, D4 based on sample size

  
• Observations (slip-ring diameter, cm)


  • 5.02
  • 5.01
  • 4.94
  • 4.99
  • 4.96
  • 5.01
  • 5.03
  • 5.07
  • 4.95
  • 4.96
  • 4.99
  • 5.00
  • 4.93
  • 4.92
  • 4.99
  • 5.03
  • 4.91
  • 5.01
  • 4.98
  • 4.89
  • 4.95
  • 4.92
  • 5.03
  • 5.05
  • 5.01
  • 4.97
  • 5.06
  • 5.06
  • 4.96
  • 5.03
  • 5.05
  • 5.01
  • 5.10
  • 4.96
  • 4.99
  • 5.09
  • 5.10
  • 5.00
  • 4.99
  • 5.08
  • 5.14
  • 5.10
  • 4.99
  • 5.08
  • 5.09
  • 5.01
  • 4.98
  • 5.08
  • 5.07
  • 4.99
• Totals: 50.09, 1.15
• 1. bar


  Sample average
• R


  Range of each sample
• UCL = D4R


  Upper control limit for R-chart
• LCL = D3R


  Lower control limit for R-chart
• D4 = 2.11, D3 = 0
• Process average and process variability must be in control
• Samples can have very narrow ranges, but sample averages might be beyond control limits
• Sample averages may be in control, but ranges might be out of control
• An R-chart might show a distinct downward trend, suggesting some nonrandom cause is reducing variation
• Run


  Sequence of sample values that display same characteristic
• Pattern test


  Determines if observations within limits of a control chart display a nonrandom pattern

  
• Patterns to look for

  • 8 consecutive points on one side of the center line
  • 8 consecutive points up or down
  • 14 points alternating up or down
  • 2 out of 3 consecutive points in zone A (on one side of center line)
  • 4 out of 5 consecutive points in zone A or B (on one side of center line)
• Attribute charts require larger sample sizes (50 to 100 parts)
• Variable charts require smaller sample sizes (2 to 10 parts)