Quality and Operations Management Process Control and Capability Analysis Process Control • Recognizes that variance exists in all processes • Sources of variation – systematic – assignable • Purpose – to detect and eliminate ‘out-of-control’ conditions – to return a process to an ‘in-control’ state • Basic tool -- the SPC chart(s) Measuring A Process • Types of measurements – variables data • length, weight, speed, output, etc • discrete values – attributes data • good vs bad, pass vs fail, etc • binary values • Types of charts – variables -- X-R chart – attributes -- p, np, c and u • Basic assumption -- sample means are normally distributed Getting Started with SPC X-R Charts • • • • • • • • • Determine sample size and frequency of data collection Collect sufficient historical data Ensure normality of distribution Calculate factors for control charts X UCLx LCLx R LCLr UCLr Construct control chart Plot data points Determine outliers and eliminate assignable causes Recalculate control limits with reduced data set Implement new process control chart Basic Properties • sx = std dev of sample mean = s/n (where s = process standard deviation) • conventional approach uses 3 s/n • limitations of control charts – Type I Error: probability that an in-control value would appear as out-of-control – Type II Error: probability that a shift causing an out-ofcontrol situation would be mis-reported as in-control – delays due to sampling interval – charting without taking action on assignable causes – over control actions Type 1 and Type 2 Error In Control Out of Control Alarm No Alarm Type 1 error No error No error Type 2 error Suppose m1 > m, then Type 2 Error = Z [(m + 3 sx - m1) / sx ] Type 1 Error = 0.0027 for 3s charts Type 2 Error Example Suppose: m = 10 m1= 10.2 s = 4/3 n=9 thus, sx = 4/9 Then, Type 2 Error = Z [(m + 3 sx - m1) / sx ] = Z [(10 + 12/9 - 10.2) / (4/9)] = Z [2.55] = 0.9946 if m1= 11.0, then Type 2 Error = Z[0.75] = .7734 if m1= 12.0, then Type 2 Error = Z[-1.50] = .0668 Prob.{shift will be detected in 3rd sample after shift occurs} = 0.0668*0.0668*(1-0.0668) = 0.0042 Average number of samples taken before shift is detected = 1/(1-0.0668) = 1.0716 Prob.{no false alarms first 32 runs, but false alarm on 33rd} = (0.9973)32*(0.0027) = .0025 Average number of samples taken before a false alarm = 1/0.0027 = 370 Tests for Unnatural Patterns • Probability that “odd” patterns observed are not “natural” variability are calculated by using the probabilities associated with each zone of the control chart • Use the assumption that the population is normally distributed • Probabilities for X-chart are shown on next slide Normal Distribution Applied to X-R Control Charts Probability = .00135 UCLx Outer 3rd A Probability = .02135 +3s Middle 3rd B Probability = .1360 +2s Inner 3rd C Probability = .3413 +1s X Inner 3rd C Probability = .3413 -1s Middle 3rd B Probability = .1360 -2s Outer 3rd A Probability = .02135 -3s LCLx Probability = .00135 A Few Standard Tests • 1 point outside Zone A • 2 out of 3 in Zone A or above (below) • 4 out of 5 in Zone B or above (below) • 8 in a row in Zone C or above (below) • 10 out of 11 on one side of center Tests for Unnatural Patterns • 2 out of 3 in A or beyond – .0227 x .0227 x (1-.0227) x 3 = .0015 • 4 out of 5 in B or beyond – .15874 x (1-.1587) x 5 = .0027 • 8 in a row on one side of center – .508 = .0039 Other Charts • P-chart – based on fraction (percentage) of defective units in a varying sample size • np-chart – based on number of defective units in a fixed sample size • u-chart – based on the counts of defects in a varying sample size • c-chart – based on the count of defects found in a fixed sample size SPC Quick Reference Card • P-chart – based on fraction (percentage) of defective units in a varying sample size – UCL/LCLp = p 3(p)(1-p)/n • np-chart – based on number of defective units in a fixed sample size – UCL/LCLnp = np 3(np)(1-p) • u-chart – based on the counts of defects in a varying sample size – UCL/LCLu = u 3u/n • c-chart – based on the count of defects found in a fixed sample size – UCL/LCLc = c 3c • X-R chart – variables data – UCL/LCLX = X 3 sx = X 3 s/ n = X A2R where s = R/d2 – UCLR = D4R and A2 = 3/d2 n – LCLR = D3R • for p, np, u, c and R chart the LCL can not be less than zero. Process Capability • Cp: process capability ratio – a measure of how the distribution compares to the width of the specification – not a measure of conformance – a measure of capability, if distribution center were to match center of specification range • Cpk: process capability index – a measure of conformance (capability) to specification – biased towards “worst case” – compares sample mean to nearest spec. against distribution width How Good is Good Enough? • Cp = 1.0 => 3s => 99.73% (in acceptance) => .9973 => 2700 ppm out of tolerance – PG&E operates non-stop • 23.65 hours per year without electricity – average car driven 15,000 miles per year • 41 breakdowns or problems per year • Cp = 2.0 => 6s => 99.99983% (in acceptance) => .9999983 => 3.4 ppm out of tolerance – PG&E operates non-stop • 1.78 minutes per year without electricity – average car driven 15,000 miles per year • 0.051 breakdowns or problems per year or one every 20 years