“It costs a lot to produce a bad product.” Norman Augustine Quality Management “It costs a lot to produce a bad product.” Norman Augustine

Cost of quality Prevention costs Appraisal costs Internal failure costs External failure costs Opportunity costs

What is quality management all about? Try to manage all aspects of the organization in order to excel in all dimensions that are important to “customers” Two aspects of quality: features: more features that meet customer needs = higher quality freedom from trouble: fewer defects = higher quality

The Quality Gurus – Edward Deming Quality is “uniformity and dependability” Focus on SPC and statistical tools “14 Points” for management PDCA method 1900-1993 1986

The Quality Gurus – Joseph Juran Quality is “fitness for use” Pareto Principle Cost of Quality General management approach as well as statistics 1904 - 2008 1951

Defining Quality The totality of features and characteristics of a product or service that bears on its ability to satisfy stated or implied needs American Society for Quality

MNBQA Leadership: How upper management leads the organization, and how the organization leads within the community. Strategic planning: How the organization establishes and plans to implement strategic directions. Customer and market focus: How the organization builds and maintains strong, lasting relationships with customers. Measurement, analysis, and knowledge management: How the organization uses data to support key processes and manage performance. Human resource focus: How the organization empowers and involves its workforce. Process management: How the organization designs, manages and improves key processes. Business/organizational performance results: How the organization performs in terms of customer satisfaction, finances, human resources, supplier and partner performance, operations, governance and social responsibility, and how the organization compares to its competitors.

(Process Analysis, SPC, QFD) What does Total Quality Management encompass? TQM is a management philosophy: continuous improvement leadership development partnership development Cultural Alignment Technical Tools (Process Analysis, SPC, QFD) Customer

Developing quality specifications Design Design quality Input Process Output Dimensions of quality Conformance quality

Quality Improvement Continuous Improvement Quality Traditional Time

Continuous improvement philosophy Kaizen: Japanese term for continuous improvement. A step-by-step improvement of business processes. PDCA: Plan-do-check-act as defined by Deming. Plan Do Act Check Benchmarking : what do top performers do?

Tools used for continuous improvement 1. Process flowchart

Tools used for continuous improvement 2. Run Chart Performance Time

Tools used for continuous improvement 3. Control Charts Performance Metric Time

Tools used for continuous improvement 4. Cause and effect diagram (fishbone) Environment Machine Man Method Material

Tools used for continuous improvement 5. Check sheet Item A B C D E F G ------- √ √ √ √ √ √

Tools used for continuous improvement 6. Histogram Frequency

Tools used for continuous improvement 7. Pareto Analysis 100% 60 75% 50 40 Frequency 50% 30 Percentage 20 25% 10 0% A B C D E F

Six Sigma Quality A philosophy and set of methods companies use to eliminate defects in their products and processes Seeks to reduce variation in the processes that lead to product defects The name “six sigma” refers to the variation that exists within plus or minus six standard deviations of the process outputs

Six Sigma Quality

Absent receiving party Working system of operators Fishbone diagram analysis Makes customer wait Absent receiving party Working system of operators Customer Operator Absent Out of office Not at desk Lunchtime Too many phone calls Absent Not giving receiving party’s coordinates Complaining Leaving a message Lengthy talk Does not know organization well Takes too much time to explain Does not understand customer

Reasons why customers have to wait (12-day analysis with check sheet) Daily average Total number A One operator (partner out of office) 14.3 172 B Receiving party not present 6.1 73 C No one present in the section receiving call 5.1 61 D Section and name of the party not given 1.6 19 E Inquiry about branch office locations 1.3 16 F Other reasons 0.8 10 29.2 351

Pareto Analysis: reasons why customers have to wait B C D E F Frequency Percentage 0% 49% 71.2% 100 200 300 87.1% 150 250

In general, how can we monitor quality…? By observing variation in output measures! Assignable variation: we can assess the cause Common variation: variation that may not be possible to correct (random variation, random noise)

SPC – suppl ch. 6 Statistical Process Control (SPC) Control Charts for Variables The Central Limit Theorem Setting Mean Chart Limits (x-Charts) Setting Range Chart Limits (R-Charts) Using Mean and Range Charts Control Charts for Attributes Managerial Issues and Control Charts

Statistical Process Control (SPC) 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 Points which might be emphasized include: - Statistical process control measures the performance of a process, it does not help to identify a particular specimen produced as being “good” or “bad,” in or out of tolerance. - Statistical process control requires the collection and analysis of data - therefore it is not helpful when total production consists of a small number of units - While statistical process control can not help identify a “good” or “bad” unit, it can enable one to decide whether or not to accept an entire production lot. If a sample of a production lot contains more than a specified number of defective items, statistical process control can give us a basis for rejecting the entire lot. The issue of rejecting a lot which was actually good can be raised here, but is probably better left to later.

Each of these represents one sample of five boxes of cereal Samples To measure the process, we take samples and analyze the sample statistics following these steps Each of these represents one sample of five boxes of cereal (a) Samples of the product, say five boxes of cereal taken off the filling machine line, vary from each other in weight Frequency Weight # Figure S6.1

The solid line represents the distribution Samples To measure the process, we take samples and analyze the sample statistics following these steps The solid line represents the distribution (b) After enough samples are taken from a stable process, they form a pattern called a distribution Frequency Weight Figure S6.1

Samples To measure the process, we take samples and analyze the sample statistics following these steps (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 Figure S6.1 Weight Central tendency Variation Shape Frequency

Samples To measure the process, we take samples and analyze the sample statistics following these steps (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 Prediction Weight Time Frequency Figure S6.1

Samples To measure the process, we take samples and analyze the sample statistics following these steps Prediction ? (e) If assignable causes are present, the process output is not stable over time and is not predicable Weight Time Frequency Figure S6.1

Control Charts Constructed from historical data, the purpose of control charts is to help distinguish between natural variations and variations due to assignable causes Students should understand both the concepts of natural and assignable variation, and the nature of the efforts required to deal with them.

Types of Data Variables Attributes Characteristics that can take any real value May be in whole or in fractional numbers Continuous random variables Defect-related characteristics Classify products as either good or bad or count defects Categorical or discrete random variables Once the categories are outlined, students may be asked to provide examples of items for which variable or attribute inspection might be appropriate. They might also be asked to provide examples of products for which both characteristics might be important at different stages of the production process.

Process Control (a) In statistical control and capable of producing within control limits Frequency Lower control limit Upper control limit (b) In statistical control but not capable of producing within control limits This slide helps introduce different process outputs. It can also be used to illustrate natural and assignable variation. (c) Out of control (weight, length, speed, etc.) Size Figure S6.2

Population and Sampling Distributions Three population distributions Beta Normal Uniform Distribution of sample means Standard deviation of the sample means = sx = s n Mean of sample means = x | | | | | | | -3sx -2sx -1sx x +1sx +2sx +3sx 99.73% of all x fall within ± 3sx 95.45% fall within ± 2sx Figure S6.3

Sampling Distribution Sampling distribution of means Process distribution of means x = m (mean) It may be useful to spend some time explicitly discussing the difference between the sampling distribution of the means and the mean of the process population. Figure S6.4

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 These two charts must be used together

Setting Chart Limits For x-Charts when we know s Upper control limit (UCL) = x + zsx Lower control limit (LCL) = x - zsx where x = mean of the sample means or a target value set for the process z = number of normal standard deviations sx = standard deviation of the sample means = s/ n s = population standard deviation n = sample size

Setting Control Limits Hour 1 Sample Weight of Number Oat Flakes 1 17 2 13 3 16 4 18 5 17 6 16 7 15 8 17 9 16 Mean 16.1 s = 1 Hour Mean Hour Mean 1 16.1 7 15.2 2 16.8 8 16.4 3 15.5 9 16.3 4 16.5 10 14.8 5 16.5 11 14.2 6 16.4 12 17.3 n = 9 For 99.73% control limits, z = 3 UCLx = x + zsx = 16 + 3(1/3) = 17 ozs LCLx = x - zsx = 16 - 3(1/3) = 15 ozs

Setting Control Limits Control Chart for sample of 9 boxes Variation due to assignable causes Out of control Sample number | | | | | | | | | | | | 1 2 3 4 5 6 7 8 9 10 11 12 17 = UCL 15 = LCL 16 = Mean Variation due to natural causes Out of control

Setting Chart Limits For x-Charts when we don’t know s Upper control limit (UCL) = x + A2R Lower control limit (LCL) = x - A2R where R = average range of the samples A2 = control chart factor found in Table S6.1 x = mean of the sample means

Control Chart Factors Sample Size Mean Factor Upper Range Lower Range n A2 D4 D3 2 1.880 3.268 0 3 1.023 2.574 0 4 .729 2.282 0 5 .577 2.115 0 6 .483 2.004 0 7 .419 1.924 0.076 8 .373 1.864 0.136 9 .337 1.816 0.184 10 .308 1.777 0.223 12 .266 1.716 0.284 Table S6.1

Setting Control Limits Process average x = 16.01 ounces Average range R = .25 Sample size n = 5

Setting Control Limits Process average x = 16.01 ounces Average range R = .25 Sample size n = 5 UCLx = x + A2R = 16.01 + (.577)(.25) = 16.01 + .144 = 16.154 ounces From Table S6.1

Setting Control Limits Process average x = 16.01 ounces Average range R = .25 Sample size n = 5 UCL = 16.154 Mean = 16.01 LCL = 15.866 UCLx = x + A2R = 16.01 + (.577)(.25) = 16.01 + .144 = 16.154 ounces LCLx = x - A2R = 16.01 - .144 = 15.866 ounces

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

Setting Chart Limits For R-Charts Upper control limit (UCLR) = D4R Lower control limit (LCLR) = D3R where R = average range of the samples D3 and D4 = control chart factors from Table S6.1

Setting Control Limits Average range R = 5.3 pounds Sample size n = 5 From Table S6.1 D4 = 2.115, D3 = 0 UCL = 11.2 Mean = 5.3 LCL = 0 UCLR = D4R = (2.115)(5.3) = 11.2 pounds LCLR = D3R = (0)(5.3) = 0 pounds

Mean and Range Charts (a) These sampling distributions result in the charts below (Sampling mean is shifting upward but range is consistent) x-chart (x-chart detects shift in central tendency) UCL LCL R-chart (R-chart does not detect change in mean) UCL LCL Figure S6.5

Mean and Range Charts (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) UCL LCL R-chart (R-chart detects increase in dispersion) UCL LCL Figure S6.5

Automated Control Charts

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)

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 UCLp = p + zsp ^ p(1 - p) n sp = ^ Instructors may wish to point out the calculation of the standard deviation reflects the binomial distribution of the population LCLp = p - zsp ^ where p = mean fraction defective in the sample z = number of standard deviations sp = standard deviation of the sampling distribution n = sample size ^

p-Chart for Data Entry p = = .04 sp = = .02 1 6 .06 11 6 .06 Sample Number Fraction Sample Number Fraction Number of Errors Defective Number of Errors Defective 1 6 .06 11 6 .06 2 5 .05 12 1 .01 3 0 .00 13 8 .08 4 1 .01 14 7 .07 5 4 .04 15 5 .05 6 2 .02 16 4 .04 7 5 .05 17 11 .11 8 3 .03 18 3 .03 9 3 .03 19 0 .00 10 2 .02 20 4 .04 Total = 80 p = = .04 80 (100)(20) (.04)(1 - .04) 100 sp = = .02 ^

p-Chart for Data Entry UCLp = p + zsp = .04 + 3(.02) = .10 ^ LCLp = p - zsp = .04 - 3(.02) = 0 ^ .11 – .10 – .09 – .08 – .07 – .06 – .05 – .04 – .03 – .02 – .01 – .00 – Sample number Fraction defective | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 UCLp = 0.10 LCLp = 0.00 p = 0.04

Possible assignable causes present p-Chart for Data Entry .11 – .10 – .09 – .08 – .07 – .06 – .05 – .04 – .03 – .02 – .01 – .00 – Sample number Fraction defective | | | | | | | | | | 2 4 6 8 10 12 14 16 18 20 UCLp = p + zsp = .04 + 3(.02) = .10 ^ LCLp = p - zsp = .04 - 3(.02) = 0 UCLp = 0.10 LCLp = 0.00 p = 0.04 Possible assignable causes present There is always a focus on finding and eliminating problems. But control charts find any process changed, good or bad. The clever company will be looking at Operator 3 and 19 as they reported no errors during this period. The company should find out why (find the assignable cause) and see if there are skills or processes that can be applied to the other operators.

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 UCLc = c + 3 c LCLc = c - 3 c Instructors may wish to point out the calculation of the standard deviation reflects the Poisson distribution of the population where the standard deviation equals the square root of the mean where c = mean number defective in the sample

c-Chart for Cab Company c = 54 complaints/9 days = 6 complaints/day UCLc = c + 3 c = 6 + 3 6 = 13.35 | 1 2 3 4 5 6 7 8 9 Day Number defective 14 – 12 – 10 – 8 – 6 – 4 – 2 – 0 – UCLc = 13.35 LCLc = 0 c = 6 LCLc = c - 3 c = 3 - 3 6 = 0

Patterns in Control Charts Upper control limit Target Lower control limit Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated. Normal behavior. Process is “in control.” Figure S6.7

Patterns in Control Charts Upper control limit Target Lower control limit Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated. One plot out above (or below). Investigate for cause. Process is “out of control.” Figure S6.7

Patterns in Control Charts Upper control limit Target Lower control limit Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated. Trends in either direction, 5 plots. Investigate for cause of progressive change. Figure S6.7

Patterns in Control Charts Upper control limit Target Lower control limit Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated. Two plots very near lower (or upper) control. Investigate for cause. Figure S6.7

Patterns in Control Charts Upper control limit Target Lower control limit Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated. Run of 5 above (or below) central line. Investigate for cause. Figure S6.7

Patterns in Control Charts Upper control limit Target Lower control limit Ask the students to imagine a product, and consider what problem might cause each of the graph configurations illustrated. Erratic behavior. Investigate. Figure S6.7

Which Control Chart to Use Variables Data Using an x-chart and R-chart: Observations are variables Collect 20 - 25 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

Which Control Chart to Use Attribute Data 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

Which Control Chart to Use Attribute Data 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