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© 2006 Prentice Hall, Inc.S6 – 1 Operations Management Supplement 6 – Statistical Process Control © 2006 Prentice Hall, Inc. PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 6e Operations Management, 8e
© 2006 Prentice Hall, Inc.S6 – 2 Variability is inherent in every process Natural or common causes Special or assignable causes Provides a statistical signal when assignable causes are present Detect and eliminate assignable causes of variation Statistical Process Control (SPC)
© 2006 Prentice Hall, Inc.S6 – 3 Natural Variations Natural variations in the production process These are to be expected Output measures follow a probability distribution For any distribution there is a measure of central tendency and dispersion
© 2006 Prentice Hall, Inc.S6 – 4 Assignable Variations Variations that can be traced to a specific reason (machine wear, misadjusted equipment, fatigued or untrained workers) The objective is to discover when assignable causes are present and eliminate them
© 2006 Prentice Hall, Inc.S6 – 5 Samples To measure the process, we take samples and analyze the sample statistics following these steps (a)Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Frequency Weight # ## # ## ## # ### #### ######### # Each of these represents one sample of five boxes of cereal Figure S6.1
© 2006 Prentice Hall, Inc.S6 – 6 Samples (b)After enough samples are taken from a stable process, they form a pattern called a distribution The solid line represents the distribution Frequency Weight Figure S6.1
© 2006 Prentice Hall, Inc.S6 – 7 Samples (c)There are many types of distributions, including the normal (bell-shaped) distribution, but distributions do differ in terms of central tendency (mean), standard deviation or variance, and shape Weight Central tendency Weight Variation Weight Shape Frequency Figure S6.1
© 2006 Prentice Hall, Inc.S6 – 8 Samples (d)If only natural causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable Weight Time Frequency Prediction Figure S6.1
© 2006 Prentice Hall, Inc.S6 – 9 Samples (e)If assignable causes are present, the process output is not stable over time and is not predicable Weight Time Frequency Prediction?? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? Figure S6.1
© 2006 Prentice Hall, Inc.S6 – 10 Control Charts Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes
© 2006 Prentice Hall, Inc.S6 – 11 Types of Data Characteristics that can take any real value May be in whole or in fractional numbers Continuous random variables VariablesAttributes Defect-related characteristics Classify products as either good or bad or count defects Categorical or discrete random variables
© 2006 Prentice Hall, Inc.S6 – 12 Control Charts for Variables For variables that have continuous dimensions Weight, speed, length, strength, etc. x-charts are to control the central tendency of the process R-charts are to control the dispersion of the process
© 2006 Prentice Hall, Inc.S6 – 13 Setting Chart Limits For x-Charts when we know Upper control limit (UCL) = x + z x Lower control limit (LCL) = x - z x wherex=mean of the sample means or a target value set for the process z=number of normal standard deviations x =standard deviation of the sample means = / n =population standard deviation n=sample size
© 2006 Prentice Hall, Inc.S6 – 14 Setting Control Limits Hour 1 SampleWeight of NumberOat Flakes Mean16.1 =1 HourMeanHourMean n = 9 LCL x = x - z x = (1/3) = 15 ozs For 99.73% control limits, z = 3 UCL x = x + z x = (1/3) = 17 ozs
© 2006 Prentice Hall, Inc.S6 – = UCL 15 = LCL 16 = Mean Setting Control Limits Control Chart for sample of 9 boxes Sample number |||||||||||| Variation due to assignable causes Variation due to natural causes Out of control
© 2006 Prentice Hall, Inc.S6 – 16 Setting Chart Limits For x-Charts when we don’t know Lower control limit (LCL) = x - A 2 R Upper control limit (UCL) = x + A 2 R whereR=average range of the samples A 2 =control chart factor found in Table S6.1 x=mean of the sample means
© 2006 Prentice Hall, Inc.S6 – 17 Control Chart Factors Table S6.1 Sample Size Mean Factor Upper Range Lower Range n A 2 D 4 D
© 2006 Prentice Hall, Inc.S6 – 18 Setting Control Limits Process average x = ounces Average range R =.25 Sample size n = 5
© 2006 Prentice Hall, Inc.S6 – 19 Setting Control Limits UCL x = x + A 2 R = (.577)(.25) = = ounces Process average x = ounces Average range R =.25 Sample size n = 5 From Table S6.1
© 2006 Prentice Hall, Inc.S6 – 20 Setting Control Limits UCL x = x + A 2 R = (.577)(.25) = = ounces LCL x = x - A 2 R = = ounces Process average x = ounces Average range R =.25 Sample size n = 5 UCL = Mean = LCL =
© 2006 Prentice Hall, Inc.S6 – 21 R – Chart Type of variables control chart Shows sample ranges over time Difference between smallest and largest values in sample Monitors process variability Independent from process mean
© 2006 Prentice Hall, Inc.S6 – 22 Setting Chart Limits For R-Charts Lower control limit (LCL R ) = D 3 R Upper control limit (UCL R ) = D 4 R where R=average range of the samples D 3 and D 4 =control chart factors from Table S6.1
© 2006 Prentice Hall, Inc.S6 – 23 Setting Control Limits UCL R = D 4 R = (2.115)(5.3) = 11.2 pounds LCL R = D 3 R = (0)(5.3) = 0 pounds Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D 4 = 2.115, D 3 = 0 UCL = 11.2 Mean = 5.3 LCL = 0
© 2006 Prentice Hall, Inc.S6 – 24 Mean and Range Charts (a) These sampling distributions result in the charts below (Sampling mean is shifting upward but range is consistent) R-chart (R-chart does not detect change in mean) UCLLCL Figure S6.5 x-chart (x-chart detects shift in central tendency) UCLLCL
© 2006 Prentice Hall, Inc.S6 – 25 Mean and Range Charts R-chart (R-chart detects increase in dispersion) UCLLCL Figure S6.5 (b) These sampling distributions result in the charts below (Sampling mean is constant but dispersion is increasing) x-chart (x-chart does not detect the increase in dispersion) UCLLCL
© 2006 Prentice Hall, Inc.S6 – 26 Automated Control Charts
© 2006 Prentice Hall, Inc.S6 – 27 Control Charts for Attributes For variables that are categorical Good/bad, yes/no, acceptable/unacceptable Measurement is typically counting defectives Charts may measure Percent defective (p-chart) Number of defects (c-chart)
© 2006 Prentice Hall, Inc.S6 – 28 Control Limits for p-Charts Population will be a binomial distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics UCL p = p + z p ^ LCL p = p - z p ^ wherep=mean fraction defective in the sample z=number of standard deviations p =standard deviation of the sampling distribution n=sample size ^ p(1 - p) n p =p =p =p = ^
© 2006 Prentice Hall, Inc.S6 – 29 p-Chart for Data Entry SampleNumberFractionSampleNumberFraction Numberof ErrorsDefectiveNumberof ErrorsDefective Total = 80 (.04)(1 -.04) 100 p = =.02 ^ p = =.04 80(100)(20)
© 2006 Prentice Hall, Inc.S6 – – – – – – – – – – – – – Sample number Fraction defective |||||||||| p-Chart for Data Entry UCL p = p + z p = (.02) =.10 ^ LCL p = p - z p = (.02) = 0 ^ UCL p = 0.10 LCL p = 0.00 p = 0.04
© 2006 Prentice Hall, Inc.S6 – – – – – – – – – – – – – Sample number Fraction defective |||||||||| UCL p = p + z p = (.02) =.10 ^ LCL p = p - z p = (.02) = 0 ^ UCL p = 0.10 LCL p = 0.00 p = 0.04 p-Chart for Data Entry Possible assignable causes present
© 2006 Prentice Hall, Inc.S6 – 32 Control Limits for c-Charts Population will be a Poisson distribution, but applying the Central Limit Theorem allows us to assume a normal distribution for the sample statistics wherec=mean number defective in the sample UCL c = c + 3 c LCL c = c - 3 c
© 2006 Prentice Hall, Inc.S6 – 33 c-Chart for Cab Company c = 54 complaints/9 days = 6 complaints/day |1|1 |2|2 |3|3 |4|4 |5|5 |6|6 |7|7 |8|8 |9|9 Day Number defective – – – 8 8 – 6 6 – 4 – 2 – 0 0 – UCL c = c + 3 c = = LCL c = c - 3 c = = 0 UCL c = LCL c = 0 c = 6
© 2006 Prentice Hall, Inc.S6 – 34 Patterns in Control Charts Normal behavior. Process is “in control.” Upper control limit Target Lower control limit Figure S6.7
© 2006 Prentice Hall, Inc.S6 – 35 Upper control limit Target Lower control limit Patterns in Control Charts One plot out above (or below). Investigate for cause. Process is “out of control.” Figure S6.7
© 2006 Prentice Hall, Inc.S6 – 36 Upper control limit Target Lower control limit Patterns in Control Charts Trends in either direction, 5 plots. Investigate for cause of progressive change. Figure S6.7
© 2006 Prentice Hall, Inc.S6 – 37 Upper control limit Target Lower control limit Patterns in Control Charts Two plots very near lower (or upper) control. Investigate for cause. Figure S6.7
© 2006 Prentice Hall, Inc.S6 – 38 Upper control limit Target Lower control limit Patterns in Control Charts Run of 5 above (or below) central line. Investigate for cause. Figure S6.7
© 2006 Prentice Hall, Inc.S6 – 39 Upper control limit Target Lower control limit Patterns in Control Charts Erratic behavior. Investigate. Figure S6.7
© 2006 Prentice Hall, Inc.S6 – 40 Which Control Chart to Use Using an x-chart and R-chart: Observations are variables Collect samples of n = 4, or n = 5, or more, each from a stable process and compute the mean for the x-chart and range for the R-chart Track samples of n observations each Variables Data
© 2006 Prentice Hall, Inc.S6 – 41 Which Control Chart to Use Using the p-chart: Observations are attributes that can be categorized in two states We deal with fraction, proportion, or percent defectives Have several samples, each with many observations Attribute Data
© 2006 Prentice Hall, Inc.S6 – 42 Which Control Chart to Use Using a c-Chart: Observations are attributes whose defects per unit of output can be counted The number counted is often a small part of the possible occurrences Defects such as number of blemishes on a desk, number of typos in a page of text, flaws in a bolt of cloth Attribute Data
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