Managing for Quality and Performance Excellence 9th Edition

1 CHAPTER 1 Introduction to Quality Teaching Notes In the first class session, we typically provide a few introductory remarks about the importance Chapter 1 Overview and Key Objective The first chapter provides an overview

Introduction to Quality 16 7. This approach by PCWorld Magazine particularly impacts, and seems to down-play, the value-based definition of quality. Price is a quality characteristic which most consumers consider. If price is held constant, while other characteristics

Case: Is Quality Good Marketing or Is Good Marketing Quality 1 Vignettes show the different styles (country, traditional, contemporary, and transitional) that are available. The vignettes are intended to provide the customer with an "impression of quality" upon entering the store. Much care is taken in designing the vignettes, right down to

have the authority to do whatever it takes to satisfy the customer. When a complaint is raised, the employee should act immediately to solve to the problem, listen attentively to the customer, and apologize. No matter what, the customer should always be thanked for bringing the complaint to the staff’s

ANS: F PTS: 1 DIF: Difficulty: Moderate NAT: BUSPROG: Analytic TOP: A-head: Defining Quality KEY: Bloom's: Comprehension ANS: T PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP:

management meetings and quarterly planning sessions. • Make teams the central focus of all jobs and allow all staff to process customer requests; for example, a busser can order an additional beverage without first getting a server to process the transaction. • Compensate performance based on teamwork. • Give employees

1 CHAPTER 10 The Baldrige Framework for Performance Excellence Teaching Notes This chapter focuses on the Malcolm Baldrige National Quality Improvement Act which was signed into law (Public Law 100-107) on August 20, 1987, and on the quality

The Baldrige Framework for Performance Excellence

ANS: F DIF: Difficulty: Moderate NAT: BUSPROG: Analytic TOP: A-head: The Criteria for Performance Excellence KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: The Criteria for Performance

TecSmart Electronics 1 • Senior leaders participate in quarterly communication meetings with all employees to discuss company issues and answer questions. All employees receive full financial information from their managers each month. • Senior leaders teach courses in TecSmart University on change management, customer service, quality, and leadership; meet with

Case – The Graduation Party Part 1 “Let’s plan a graduation party for our seniors,” suggested Jim Teacher, president of the Delta Mu Zeta fraternity at State U. Everyone on the fraternity council thought that it was a good idea, so they agreed to brainstorm ideas for the party.

ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: The Scope of Strategic Planning KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: The Scope

CHAPTER 11 Strategy and Performance Excellence Teaching Notes This chapter centers on strategic management as the basis of all managerial activities relating to total quality management. Leadership is acknowledged as the "driver" for an effective TQ focus, but this chapter focuses on

Strategy and Performance Excellence 16 It began with the development of an order fulfillment process psyche that transcended departmental

consultation with each client. A casual dress code and work policies, and a number of perks for workers, such as health insurance, flextime, generous vacation and sick leave benefits, a 401(k) retirement plan, and competitive wages have, in the past, made

ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: The Value and Scope of Performance Measurement KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Moderate NAT: BUSPROG: Analytic TOP:

1 CHAPTER 12 Measurement and Knowledge Management For Performance Excellence Teaching Notes This chapter develops facets of measurement and knowledge management. These concepts may be somewhat difficult for students to grasp, at

Measurement and Knowledge Management for Performance Excellence 16 philosophy. The departments and areas submit an annual report to the Leadership Team that summarizes its performance, including relevant LEARNing Board measures, during the previous academic year. Measures developed

I. Westerfield Construction 1 time, and other resources were also provided to her by the executive team. The CSLC team used the tools prior to developing an action plan for implementation. They developed measures to monitor the effectiveness of the CSLC and drive continuous improvements. She decided to phase in the implementation

ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Leadership Competencies and Practices KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Moderate NAT: BUSPROG: Analytic TOP: A-Head: Leadership Competencies and Practices

Leadership for Performance Excellence 13 ANSWERS TO DISCUSSION QUESTIONS 1. By saying that leadership is the "driver," we try to emphasize the critical nature of leadership at every level for successful TQ. The answer to Review Question 3, above, elaborates on the practices

1 CHAPTER 13 Leadership for Performance Excellence Teaching Notes The keys to attaining an in-depth understanding of total quality organizations as integrated systems are to have basic knowledge of organizing

Quality in Banking: Los Alamos National Bank i By William M. Lindsay, Ph.D., P.E An earlier version was presented at: 3er. Congreso Internacional de Ingeniería Industrial y de Sistemas ITESM-Morelia Campus Morelia, Mexico September 17-21, 2008 excellence in an organization. The Los Alamos National Bank won the MBNQA

Building, and Sustaining Quality and Performance Excellence 13 understanding of purposes and goals and use of complementary measures and information for planning, tracking, analysis, and improvement at each of the three levels 6. Amazingly, many colleges and universities would not be classified by Senge as "learning

1 CHAPTER 14 Building, and Sustaining Quality and Performance Excellence Teaching Notes The keys to attaining an in-depth understanding of total quality organizations as integrated systems are to have

Can Six Sigma Work in Health Care? Colin David is the CEO of a Southwest Louisiana Regional Medical Center (SLRMC), a small nonprofit hospital with 150 beds and 825 employees, offering a wide range of outpatient and inpatient services. Colin had just returned from a health care conference during which

Case - Equipto, Inc. Equipto, Inc., a division of a Fortune 500 corporation, is located in the Midwest. The Industrial Motor Division (IMD) makes motor units that are part of the installation package for large-scale industrial systems. The company has two major competitors in the United States and

ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Organizational Culture and Change KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Organizational Culture and Change

Child Focus, Inc. i Child Focus, Inc. (CFI), whose mission is to join with communities in strengthening families and improving the quality of life for children, offers a wide variety of programs in Clermont, Hamilton, and Brown counties in Ohio. With an agency budget in excess of $11 million dollars, the

U.S. Water Resource Agency - Flagstaff District i navigation, flood-damage reduction, environmental protection, recreation, water supply, and other public benefits • protection of the region’s waterways and wetlands • support for emergency preparedness, natural-disaster relief, and recovery work worldwide • provision of a broad range of engineering and technical support for

Error Data Chapter 2 The Disciplinary Citation Case Data for Scatter Diagram for Package Errors 33 11 7 34 19 7 35 11 7 36 3 7 37 6 7 38 13 7 39 4 7 40 0 7 280 Operator Errors Avg. 1 8 7 2 0 7 3 3 7 413 7 16 0 7 17 4 7 18 6 7 19 15 7 20 22 7 21 5 7 22 4

Total Quality in Organizations 20 3. Deming's 14 points may be put into the six categories listed, but it should be realized that some of

1 CHAPTER 2 Total Quality In Organizations Teaching Notes This chapter introduces the concept of quality in production and service systems and develops the idea that quality is central to effective operation

boxes and weigh the packages as well as checking them for defects such as rips, leaks, and pinholes. Weights of packages near the bottom, mid and top of each skid (pallet) are inspected. If these packages conform to weight specifications, then the entire skid is accepted and sent

cartons, physical counts, sorting, and manual ticketing. Advance shipping notice capability provided the means of knowing exactly what was coming and when. Shipments arrived preticketed by suppliers with external shipping container marking. Entire containers were scanned for contents and matched to purchase orders. Case - Mercantile Stores During the 1980s,

The Nightmare on Telecom Street i H. James Harrington, a noted quality consultant, related the following story in Quality Digest magazine: I called to make a flight reservation just an hour ago. The telephone rang five times before a recorded voice answered. “Thank you for calling ABC Travel Services,”

ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: The Deming Philosophy KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Moderate NAT: BUSPROG: Analytic TOP: A-Head: The Deming Philosophy

Problem 3-06 Versele Sporting Goods Months Survey attribute 12345678910 11 12 1. Friendliness of employees 6.9 6.2 5.4 5.5 6.0 5.2 6.8 6.5 4.9 4.1 5.0 6.6 2. Knowledge of products 6.9 6.6 6.2 5.4 4.8 6.3 5.7 6.8 6.7 6.5 5.8 5.9 3. Helpfulness in selecting the right product 5.4 4.9 5.0 6.8

Focusing on Customers 34 Relative Relative Attribute Importance Computer A Computer B Performance CPV- A CPV- B Speed 20% 7.5 7.8 -0.3 1.50 1.56

Problem 3-08 NPS's for Angelina's Bea uty Emporium WEEK NPS Score 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

Problem 3-09 Premier Computer Designs Relative Relative Attribute Importance Computer A Computer B Performance CPV-A CPV-B Speed 20% 7.5 7.8 -0.3 1.50 1.56 Active memory 25% 8.5 8.0 0.5 2.13 2.00 Portability 15% 6.5 5.3 1.2 0.98 0.80 Reliability 30% 9.1 8.8 0.3 2.73 2.64 Price 10% 3.1 5.2 -2.1 0.31 0.52 SUM 7.64 7.52 Note: Last three columns added

1 CHAPTER 3 Focusing on Customers Teaching Notes This chapter focuses on customer satisfaction. Until a few years ago, this was a topic that was thought to be only a “marketing” activity and out of the area of responsibility of quality, human

24 9132 6 25 10 133 6 26 10 134 6 27 9135 7 28 10 136 8 29 9137 7 30 10 138 8 31 10 139 8 32 10 140 7 33 10 141 8 34 10 142 6 35 10 143 6 36 10 144 8 37 9145 7 38 10 146 8 39 9147 8 40 10 148 8 41 9149 8 42 10

Focusing on Customers 21 22. A customer survey for a pharmacy asks customers to rate the pharmacy according to the following: • Friendly

ANS: F PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: Customer Satisfaction and Engagement KEY: Bloom's: Knowledge ANS: F PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG:

Case: American Parkinson’s Disease Association Center i • To establish fundraising chapters in strategic areas throughout the country. • To publish and distribute a quarterly newsletter, educational booklets, audio- visual and other educational and supportive materials about PD. • To sponsor educational conferences for professionals, patients, caregivers, and families throughout

restaurant manager received your letter and asked me to respond as Total Quality Lead. Looking back at our records, we did not show a reservation on the books for your family. I have addressed your comments with the appropriate department head so that others will not have to experience the

Case: Cincinnati Veterans Administration Medical Center i such areas as urology, orthopedics, vascular surgery and neurosurgery. The Mental Health Service provides psychiatric and psychological support care for patients with a variety of programs including care for patients with post-traumatic stress disorder, substance abuse, and serious mental illnesses. The Neurology Service

16 23. 360-degree feedback is a method whereby a group of individuals who frequently interact with an employee or team participate in both the goal setting process and the performance appraisal process. The group may include suppliers,

1 CHAPTER 4 Workforce Focus Teaching Notes This chapter provides an overview of one of the most important sub-systems in TQ – Workforce Focus – which is called the human resources area in

ANS: T PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic STA: DISC: HRM TOP: A-head: The Evolution of Workforce Management KEY: Bloom's: Knowledge ANS: F PTS: 1 DIF: Difficulty:

ANS: F PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: Process Management KEY: Bloom's: Knowledge ANS: T PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic

Process Management 20 improved;” “The boss won’t let us change it;” “Government regulations prohibit that from being changed (maybe they do, but no one has ever

Supply Chain for Banana Production Test samples Monitor growth & quality Cut banana stalks from trees Cut bananas into bunches of 5-6 bananas Quality OK? Color? Wash & inspect banana clusters Place sticker & package bunches Load into containers & ship

1 CHAPTER 5 Process Management Teaching Notes This chapter investigates the concept of process and techniques used to design and manage processes. It emphasizes the fact that it is essential to design products and services to meet customer

Stuart Injection Molding Company 1 conducts market research to understand customer needs and handles customer complaints; the project engineering department performs design reviews; the manufacturing department conducts in-process inspection for the purpose of maintaining control and coordinates continuous improvement processes. Maintenance, supplier relations, receiving, and human resources departments support these functions.

P6-11 Statistics Problem 6-11 Twenty First Century Laundry Summary Statistics Using Excel Data Analysis Tool - "Descriptive Statistics" Note that the data must be put into a unidimensional array in order to run the summary statistics Summary Data in Original Data Ascending Order 69 3 1 72 93 380 6 5 36 142 264 44 15 77

19 13 20 17 21 9 22 18 23 12 24 11 25 14 26 15 27 9 28 12 29 8 30 15 Example 6.9 Mean 13.133 Observation Value Median 13 116 Mode 12 915 10 17 11 13 12 9 13 16 14 13 15 18 16 10 17 17 18 12 213 Range 10 312 Sample variance 8.257 414 Sample standard deviation 2.874 510 612 714 810 Example 6.10 Good Good Good Defective Good Defective Good Good Good Defective Good Good

Problem 6-5 Los Alamos Green Tea Probability Calculations Using the Normal Distribution - Template Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses

Problem 6-8 Piedra Cretebuilders Probability Calculations Using the Normal Distribution - Template Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the cumulative

Problem 6-4 Fruitayuda, Inc Probability Calculations Using the Normal Distribution - Template Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the cumulative

1 CHAPTER 6 Statistical Methods in Quality Management Teaching Notes This chapter describes concepts of statistics, statistical thinking, statistical methodology, sampling, experimental design, and process capability. Students should be

Problem 6-20 Rabbitfoot Bank, Inc. Rabbitfoot Industry Data 33.9 40.0 34.8 39.6 Industry Data Rabbitfoot Bank 35.0 38.5 39.1 40.0 Mean 39.4750 Mean 38.0967 40.1 42.4 Standard Error 0.4134 Standard Error 0.4701 37.7 38.6 Median 40.1000 Median 38.2500 39.0 42.3 Mode 40.0000 Mode 39.1000 36.3 41.6 43.0 40.9 t-Test: Two-Sample Assuming Equal Variances 39.1 40.9 35.9 40.3 Rabbitfoot Industry Data 40.5 39.8 Mean 38.0967

Problem 6-2 Numeros Verdes Partners, P.S.C. COUNTIF FUNCTION Answers the question: Is the bill overdue? TRUE FALSE COUNTIF (RANGE, CRITERIA) --> COUNTED ITEMS 50 150 PERCENT OF TOTAL 25% 75% FALSE TRUE FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE

Problem 6-7 Wayback Cleaning Co. Probability Calculations Using the Normal Distribution - Template Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the

Problem 6-24 Paintfast Manufacturing Co. 22 Factorial Experiment Enter data ONLY in yellow-shaded cells. Description Name Low level High level Treatment Factor 1 Factor 2 Response=Dryability A Low Low 78 B High Low 91 C Low High 85 D High High 87 Main Effects Factor 1 7.50 Factor 2 1.50 Interaction Factor 1 x Factor 2 -5.50 75 80 85 Factor 2 High Factor 2

Problem 6-25 Sival Electronics 23 Factorial Experiment Enter data only in yellow-shaded cells. Interaction Charts Calculations Base Case Factor 1 Factor 2 Average Description Name Level 1 Level 2 L L 10.5 Factor 1 Signal Low High H L 13 L H 12 Main Effects H H 11.5 Signal 4.00 Material 0.50 Temperature -0.50 16 18 Interactions Signal x Material 1.50 Signal x Temperature -5.50 Material

Chapter 6 - Problems 15-19 Confidence Intervals Problem 6-15 (a) T.INV.2T for t-value, 2-tailed distribution, 95% conf. 2.131 CONFIDENCE.T interval for Students t-distribution 1.4920 Problem 6-15 (b) T.INV.2T for t-value, 2-tailed distribution, 99% conf. 2.947 CONFIDENCE.T interval for Students t-distribution 2.0627 Problem 6-16 NORM.S.INV for z-value, 2-tailed distribution, 90% conf. 1.282 Problem 6-17 T.INV for t-value, 1-tailed distribution, a = 0.05

Problem 6-9 Machined Part Probability Calculations Using the Normal Distribution - Template Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the cumulative

Problem 6-21 Softswift Software Developers Sub 1 Sub 2 Sub 3 Anova: Single Factor 86 90 89 73 85 82 SUMMARY 69 77 74 Groups Count Sum Average Variance 77 80 70 Sub 1 10 767 76.7 75.5667 86 92 88 Sub 2 10 840 84 63.5556 72 71 66 Sub 3 10 757 75.7 69.3444 88 86 72 67

Problem 6-23 Sival Electronics One-Way ANOVA Supplier 1 Supplier 2 Supplier 3 Finish 1 11.9 6.8 13.5 Finish 2 10.3 5.9 10.9 Anova: Single Factor SUMMARY Groups Count Sum Average Variance Supplier 1 5 54.6 10.92 4.762 Supplier 2 5 35.6 7.12 0.697 Supplier 3 5 64.1 12.82 1.812 ANOVA Source of Variation SS df MS F P-value F crit Between Groups 84.233 2 42.117 17.377

Problem 6-22 Rockglass, Inc. 600 3.2 650 1.8 Regression Statistics 700 3.9 Multiple R 0.96085 900 4.7 Observations 15.00000 950 5.3 1000 5.5 ANOVA 1050 5.4 df SS MS F Significance F 1100 6.3 Regression 1 42.66603571 42.666 156.361 1.27331E-08 1150 7.0 Residual 13 3.547297619 0.27287 1200 6.8 Total 14 46.21333333 1250 7.7 1300 8.1 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower

Problem 6-6 Kiwi Oil, Inc Probability Calculations Using the Normal Distribution - Template Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the

Problem 6-14 Wherehousing, Inc. Normal Distribution Probability Template Enter data only in yellow-shaded cells Mean, m50 Standard deviation, s0.5 x -value 48.50 Calculated z-value -3.0 F(x) = P(X ≤ x)0.00135 x z F(x) 48.50 -3.00 0.00135 48.55 -2.90 0.00187 48.65 -2.70 0.00347 49.20 -1.60 0.05480 49.25 -1.50 0.06681 49.30 -1.40 0.08076 49.35 -1.30 0.09680 49.40 -1.20 0.11507 49.45 -1.10 0.13567 49.50 -1.00 0.15866 49.55 -0.90 0.18406 49.60 -0.80 0.21186 49.65 -0.70

Statistical Methods in Quality Management 21 15. The distribution center manager at internet distributor CyberAuto Warehouse wants to find a confidence interval for the average time required for an associate to fill an order for

ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Basic Probability Concepts KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Basic Probability Concepts

The HMO Pharmacy Crisis i series,’ whatever that means. But get this, he will only identify the change points and send me a list. He says it’s my job to figure out what they mean and how to respond. I don’t know much about statistics. The only thing I remember from my

ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Product Development KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Product Development KEY:

Ch 7 - Quality in Practice Applying QFD in in Managed Healthcare Direction of Rate of Co. Rate of Absol. % Font Use of Gloss. Q&A Tbl. of Lang. Improvement Import. Now Plan Improv. Wgt. Improve. size Update Photos colors Terms Sect. Contnt. Frindly. Ease-use 4.5 3.2 4.5 1.4 6.3 25.2% 3 1 3

Problem 7-20 Widetred Tire Co. Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the cumulative normal distribution Enter the mean of the distribution

Problem 7-21 Livelong, Inc. Computer Monitors Exponential Probability Distribution Template Enter data only in yellow-shaded cells. Failure rate per hour (λ) = 0.00095 Hours 5000 Calculated probability 0.991348 T = x Cum. Failure Hours Probability F(x) 0 0.000000 250 0.211403 500 0.378115 3250 0.954384 3500 0.964027 3750 0.971632 4000 0.977629 4250 0.982358 4500 0.986088 4750 0.989029 5000 0.991348 Table for chart construction T = x Cum. Failure Hours Probability 0 0.000000 250

Problem 7-22 Satellite Radio Reliability Exponential Probability Distribution Enter data only in yellow-shaded cells. Failure rate per hour (λ) = 0.000015 Hours 12000 Calculated probability 0.164730 T = x Cumulative Failure Reliability Hours Probability F(x) 1-F(x) 0 0.000000 1.00000 137500 0.872864 0.12714 150000 0.894601 0.10540 162500 0.912621 0.08738 175000 0.927560 0.07244 187500 0.939945 0.06005 200000 0.950213 0.04979 212500 0.958725 0.04127 225000 0.965782 0.03422 237500 0.971632 0.02837 250000 0.976482

Problem 7-13 MicroKeeb Co. Computer Chip Design Descriptive Statistics Original Data Sorted Data 2.001 1.998 Descriptive Statistics 2.000 1.998 2.001 1.999 Mean 2.00008 1.998 1.999 Standard Error 0.00021 1.999 1.999 Median 2.00000 2.000 1.999 Mode 2.00000 2.000 2.000 Standard Deviation 0.00104 2.000 2.002 2.000 2.000 2.002 1.999 2.000 2.001 2.002 1.998 1.999 2.001 2.000 2.000 2.000 1.999 2.001 2.001 2.000 2.000 2.002 2.000 2.000

Example 7.4 Process A Process B Nominal 0.5 Nominal 0.5 k125000 k125000 Dimension, x Probability, f(x) (x - E[X])^2 Dimension, x Probability, f(x) (x - E[X])^2 0.47 0 0.0009 0.47 0.02 0.0009 0.48 0.2 0.0004 0.48 0.03 0.0004 0.49 0.2 0.0001 0.49 0.15 0.0001 0.5 0.2 0 0.5 0.6 0 0.51 0.2 0.0001 0.51 0.15 0.0001 0.52 0.2 0.0004 0.52

1 CHAPTER 7 Design for Quality and Product Excellence Teaching Notes The precise manner in which a person or team approaches product design, solving problems to achieve product excellence, or developing product reliability is

Problem 7-17 Call-Nowait handling time Descriptive Statistics Using Excel Functions Data Sorted data 16.5 4.3 Mean 6.0160 27.3 4.6 Median 6.2 By inspection of sorted data 10 7.1 5.9 11 6.5 6.0 12 6.2 6.1 13 7.2 6.2 14 7.4 6.2 15 4.6 6.3 16 5.0 6.4 17 4.3 6.5 18 6.9 6.5 19 6.1 6.6 20 4.7 6.7 21 5.5 6.9 22 6.7 7.1 23 6.4

Problem 7-12 AutoCM, Inc. Descriptive Statistics Original Data Sorted Data Descriptive Statistics Bin Frequency 24.9349 24.8672 24.850 0 25.0666 24.8824 Mean 25.0056 24.875 1 25.0323 24.9180 Standard Error 0.0080 24.900 1 25.0666 24.9196 Median 25.0123 24.925 3 24.9842 24.9210 Mode #N/A 24.950 2 24.9210 24.9285 Standard Deviation 0.0566 24.975 9 24.9962 24.9745 24.8824 24.9758 25.0906 24.9788

Problem 07 - 30 National Partamiento Co. Months in Service Number Installed Number Failed Number Not Failed Cumulative Failures Month x Failures Months in Service Monthly Failure Rate Cumulative Failure Rate 1 24440 1 24439 1 1 1 0.00004092 0.00004092 Regression on number of units failed by month 2 24439 3 24436 4 6 2 0.00012275 0.00016367 10 24331 30 24301 139 300

Problem 7-19 Supercelluar Battery Life-Reliability Enter data only in the shaded cells This spreadsheet is designed to calculate the probability of values equal to, or less than, a desired x value, given the mean and standard deviation of a normally distributed variable. It uses the cumulative normal distribution Enter the mean of the distribution

Design for Quality and Product Excellence 32 26. Bestronics has a three-step process for processing customer sales. First, the cashier must look up the customer’s loyalty card on the company’s information system. Second, the cashier enters the

Kingyro $0.567 85.3 124.0 23 Antonio's $0.542 90.0 158.3 16 Georgio’s Targets $0.51/oz. 70/oz 110/ oz. 13% Total Sodium = 605 mg. Kingyro 35 4 5 2 3 4 Antonio's 4 4 3 3 4 5 3 Speedy svc. - staff 4 4 4 4 Speedy svc. - kitchen 5 4 3 4 Menu board

Problem 7-16 BearingPort Airplane Co. Calculation of Taguchi Loss Values Value Loss ($) Process P Probability Weighted Loss ($) Process Q Probability Weighted Loss ($) 0.20 96.00 00.00 0.02 1.92 0.21 54.00 0.12 6.48

Design for Quality and Product Excellence 21 ANSWER – CONTINUED 5. The Importance and Competitive Evaluation of customer requirements can be read from the survey results in the data tables in spreadsheet Prob07-05.xlsx that are provided, and placed

QIP - Applying SPC to Pharmaceutical Product Manufacturing Descriptive Statistics for Capability Study Original Data 4.959 4.955 4.943 4.954 4.967 4.945 4.958 4.957 Sorted Data 4.943 Median 4.9555 4.945 Mode #N/A 4.945 Standard Deviation 0.0083 4.946 Sample Variance 0.0001 4.948 Kurtosis -0.2063 4.953 Minimum 4.9354 4.953 Maximum 4.9668 4.954 Sum 173.4035 4.954 Count 35.0000 4.955 Largest(1) 4.9668 4.955 Smallest(1) 4.9354 4.956 4.956 4.935 Descriptive Stats - Pharmaceutical Products 4.937 4.939

Bloomfield Tool Co. Gauge Repeatability and Reproducibility This spreadsheet is designed for up to three operators, three trials, and ten parts. Enter data ONLY in yellow shaded cells. Number of operators 2Upper specification limit 0.10 Average range 0.0072 Number of trials 2Lower specification limit 0.05 Range of operator averages 0.0018 Number of parts

Chapter 8 Knight Industries 26 69 62.9 13.2 27 74 62.6 13.2 28 72 63.4 13.2 29 77 63.2 13.3 30 73 63.0 13.3 31 77 62.6 13.2 37 79 62.7 13.2 Spec. Limits 60-80 60-65 12.6-13.5 38 79 62.6 13.1 39 79 62.3 13.2 40 76 62.4 13.2 41 79 63.6 13.3 74.146 63.056 13.261 4.569 0.511 0.080 Batch Viscosity Percent Solids Lb./Gal. 174 64.2 13.3 269 62.6

Bloomfield Tool Co. Gauge R&R Data Sample Operator 1 Operator 2 1 0.0650 0.0600 0.0650 0.0550 2 0.1000 0.1050 0.1050 0.0950 6 0.0850 0.0800 0.0750 0.0700 7 0.0600 0.0700 0.0550 0.0640 8 0.0680 0.0750 0.0670 0.0720 9 0.0800 0.0910 0.0870 0.0820 10 0.0700 0.0805 0.0865 0.0830 3 0.0850 0.0950 0.0820 0.0940 4 0.0550 0.0580 0.0485 0.0525 5 0.0920 0.0880 0.0990 0.0900

Case - Hydraulic Lift Co. The Hydraulic Lift Company (HLC) manufactures freight elevators and automotive lifts used in garages and service stations. Figure 1 shows a simplified diagram of a hydraulic lift. The check valve is an important component in the system. Its purpose is to control the flow of

Knight Industries Viscosity Measures X and Moving Range Chart This spreadsheet is designed for up to 50 observations and a moving range from 2 to 5. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in

CBT Company Packing Slip Errors This spreadsheet is designed for up to 75 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Sample Number of Unit Defects Standard Sample Nonconformances Size per unit Deviation LCLu CL UCLu 4 3

60 Cpl 0.586070683 60 Cpk 0.586070683 60 60 60 60 70 70 70 70 75 75 75 75 75 75 80 80 80 80 80 Hydraulic Lift Company Situation 1 Original Data Column1 85 45 80 60 85 70 80 50 60 80 Data 50 100 80 60 85 90 75 50 60 90 45 Mean 73.1 85 80 60 85 65 80 45 60 70 70 45 Standard Error 1.313834699 70 90 80 60 70

5. Define each of the following control chart patterns. Include a sketch of each pattern with your definition. a. One point outside the control limits b. Sudden shift in the process average c. Cycles d. Trends e. Hugging the center line f.

22 74 62.7 13.3 23 70 62.9 13.3 24 75 62.9 13.2 25 65 62.8 13.2 26 69 62.9 13.2 27 74 62.6 13.2 28 72 63.4 13.2 29 77 63.2 13.3 30 73 63.0 13.3 31 77 62.6 13.2 32 79 62.6 13.2 33 79 62.5 13.2 34 79 62.5 13.3 35 66 62.3 13.1 36 77 63.2 13.3 37 79 62.7 13.2 38 79

CHAPTER 08—MEASURING AND CONTROLLING QUALITY TRUE/FALSE 1. A nonconforming unit of work is one that has no defects or errors. 2. Attribute measurement requires a much larger sample size than variable measurement to obtain the same amount of statistical information. ANS:

Chapter 8 Hallenvale Hospital Case Fraction Nonconforming (p) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (p-bar) Avg. sample size Approximate Control Limits Using Sample Fraction Standard Average Sample Size Calculations Sample

Problem 8-16 Precision Parts, Inc. Gauge Repeatability and Reproducibility This spreadsheet is designed for up to three operators, three trials, and ten parts. Enter data ONLY in yellow shaded cells. Number of operators 3 Upper specification limit 1.8 Average range 0.117 Number of trials 3Lower specification limit 1.4 Range of operator averages 0.058 Number of parts

Chapter 8 – Measuring and Controlling Quality 101 ANSWERS TO CASE QUESTIONS I. CONTROL OF TFE’S AT HALLENVALE HOSPITAL 1. A control chart constructed from these data is shown in Figure 8.7. (Note that

Chapter 8 – Measuring and Controlling Quality 41 Variance Ratios EV% of Total Variance = 100 𝐸𝑉2 𝑇𝑉2 = 100 0.1012 01362 = 55.15% [54.17% - rounding error] AV% of Total Variance = = 100

Problem 8-33 Constant Hope Hospital This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in the

Problem 8-35 El Toro Grande Restaurante This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in

Prob. 8-19 Statistics 23.996 23.995 24.004 23.994 24.001 24.006 23.986 24.001 Problem 8-19 Kermit Theatrical Productions Data Column1 24.029 24.003 Mean 24.0014 24.020 Standard Error 0.0009671 23.991 Median 24.001 24.002 23.998 23.993 24.016 24.010 23.991 24.008 24.015 23.990 24.013 24.010 23.995 23.997 23.988 23.995 24.013 23.990 Page 1 24.008 Mode 24 23.996 Standard Deviation 0.0096707 23.991 Sample Variance 9.352E-05 24.000 Kurtosis 0.5313187 24.005 Skewness 0.0527081 24.011 Range 0.058 23.989 Minimum 23.971 24.023 Maximum 24.029 24.020 Sum 2400.135 24.004 Count 100 24.004 Confidence Level(95.0%) 0.0019189 Prob. 8-19 Statistics 23.984 24.000 23.994 23.971 24.008 24.011 24.015 23.998 23.997 24.009 23.995 24.008 23.996 24.010 24.002 24.005 23.999 24.000 23.991 24.007 24.007 23.995 24.000 23.984 24.001 23.996 24.013 23.999 23.999 24.005 23.985 24.002 23.994 24.010 23.988 24.003 Page 2 23.992 23.994 23.997 23.995 23.994 24.001 24.000 24.001 Prob. 8-19 Statistics 24.009 24.005 24.009 24.018 24.007 Page 3 24.003 23.998 23.983 24.001 24.003 24.006 23.996 24.000 24.013 24.009 24.019 Problem 8-19 Kermit Theatrical Productions Frequency Distribution

Chapter 8 – Measuring and Controlling Quality 61 Minimum -1.310 Maximum 1.290 Sum 2.710 Count 250 Bin Frequency -1.35 0 -0.95 13 The values appear to be slightly skewed

Quality Measurements Bin Sample 1 2 3 4 5 Mean 0.55 Bin Frequency 10.682 0.689 0.776 0.798 0.714 0.732 0.60 0.55 1 20.787 0.860 0.601 0.746 0.779 0.755 0.65 0.60 1 16 0.816 0.817 0.768 0.716 0.649 0.753 17 0.826 0.777 0.721 0.770 0.809 0.781 18 0.828 0.829 0.865 0.778 0.872 0.834 19 0.805 0.719 0.612 0.938 0.807

Chapter 8- Problem 13a Aussieburgers, Ltd. Descriptive Statistics and Frequency Chart This spreadsheet is designed for up to 100 pieces of data Enter data ONLY in yellow-shaded cells. Note: The data and cell boundaries below were sorted in ascending order to more accurately show the increasing values on the histogram as contrasted with

Problem 8-34 Fujiyama Electronics This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in the grid

Chapter 8- Problem 12a Genetic Research Thermometers Descriptive Statistics and Frequency Chart This spreadsheet is designed for up to 100 pieces of data Enter data ONLY in yellow-shaded cells. Note: The data and cell boundaries below were sorted in ascending order to more accurately show the increasing values on the histogram as contrasted

Problem 8-38 Gotham City Police Department X and Moving Range Chart This spreadsheet is designed for up to 75 observations and a moving range from 2 to 5. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your

Prob. 8-29 Hawkeye Magnetronics This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in the grid

Problem 8-24 Hawkeye Magnetronics Run Chart - Showing Sample Means Over a Time Period See Run Chart Tab for this chart Number of observations 90 Grand Average Sample Mean Observation Values DATA 22 0.36 20 0.35 0.39 0.37 0.37 23 0.39 21 0.40 0.37 0.37 0.38 24 0.40 22 0.35 0.38 0.35 0.36 25 0.40 23 0.35 0.42 0.41 0.39 26 0.41

Problem 8-28 Hermitage DNA Labs, LLC Sample Observations -1.31 -1.29 1 0.23 -0.04 0.44 -0.79 -0.07 -1.29 Descriptive Statistics Bin Frequency 2 -0.14 -0.83 0.81 0.21 -1.31 -1.21 -1.35 0 3 0.16 -1.29 0.77 -0.28 -0.11 -1.18 Mean 0.011 -0.95 13 4 0.28 0.44 0.44 0.50 -0.31 -1.04 Standard Error 0.035 -0.55 32 5 -0.44 -1.03 -0.11 -0.47

Prob08-06.xlsx Problem 8-06 Great Press Printing Company Cost Quality Cost Total $000's QUALITY COSTS Prevention 5.31 Total Quality Cost 100.00 Costs by Category Customer complaint remakes $27,000 Ext. Fail. Printing plate revisions 28,000 Int. Fail. Other waste 38,000 Int. Fail. Correction of typographical errors 210,000 Int. Fail. Press downtime 285,000 Int. Fail. Bindery waste 51,000 Int. Fail. Gauging 100,000 Appraisal Proofreading 450,000

Problem 8-26 J. McWilliams Swim Club This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data

Prob. 8-18 Statistics 0.578 0.579 0.579 Problem 8-18 Mach4 Tool Co. 0.573 Column1 0.573 0.573 Mean 0.5755 0.574 Standard Error 0.0003 0.576 0.576 0.577 0.577 0.577 Page 1 0.574 Median 0.5750 0.574 Mode 0.5750 0.574 Standard Deviation 0.00169 0.575 Sample Variance 0.0000 0.575 Kurtosis -0.1800 0.575 Skewness 0.4443 0.575 Range 0.0060 0.575 Minimum 0.5730 0.575 Maximum 0.5790 0.576 Sum 14.3880 0.576 Count 25.0000 0.576 Confidence Level(95.0%) 0.0007 0.576 Problem 8-18 Mach4 Tool Company Frequency Distribution and Histogram This spreadsheet is designed

Chapter 8 – Measuring and Controlling Quality 81 37. Calculate the process capability statistics for the outside diameters of the bottles made on the injection molding machine at the Moby Molding Co.

Problem 8-36 Moby Molding Company This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in the

Problem 8-15 Frankford Brake Systems Gauge Repeatability and Reproducibility This spreadsheet is designed for up to three operators, three trials, and ten parts. Enter data ONLY in yellow shaded cells. NOTE: 0's were filled into the template for the third operator and formulas for the part averages were modified. Number of operators 2Upper

Prob08-08.xlsx Problem 8-08 Repack Solutions Inc. Quality Costs and Percentages Customer complaint rework 40,000 Error correction 43,000 Int. Fail. Correcting erroneous orders before shipping 43,000 Other waste 42,500 Int. Fail. Quality training of associates 30,500 Typo corrrections 13,500 Int. Fail. Quality improvement projects 20,500 Subtotal 581,500 Other waste 42,500 Correction of typographical errors--pick tickets 13,500 Checking boxes 715,000 Appraisal Income.

Problem 8-20 River City Parts Co. Sample Means and Standard Deviations 1.75134 2.00100 1.24558 1.71195 1.99752 1.24992 1.74831 2.00100 1.25209 1.77861 2.00100 1.24992 1.75740 1.98708 1.24992 1.73619 1.99404 1.24558 1.74225 1.98708 1.24992 Part 1 Part 2 Part 3 1.74831 2.01144 1.25426 1.75740 2.00448 1.24775 1.73619 1.99752 1.24341 1.74528 1.99752 1.25643 1.74831 2.00100 1.24775 1.76346 1.99404 1.24341 1.74528 2.00796 1.24558 1.76952 2.00448 1.23907 1.70892 2.00448 1.24124 1.74528 1.98360 1.24775 1.73922 2.00100

Prob. 8-17 Statistics Problem 8-17 Mach4 Tool Co - Machining Process Original Data 0.575 Range 0.0300 0.574 Minimum 0.5560 0.575 Maximum 0.5860 0.575 Sum 14.3500 0.577 Count 25 0.577 0.577 0.577 0.556 Column1 0.556 0.562 0.564 0.564 0.572 0.562 0.572 0.572 0.572 0.573 0.575 0.572 Sample Variance 0.0000 0.573 Kurtosis 1.3730 0.575 Skewness -0.9603 0.577 0.580 0.579 0.581 0.583 0.586 0.575 Page 1 0.564 Mean 0.5740 0.575 0.574 0.575 0.575 0.577 0.564 Standard Error 0.0013 0.577

Prob08-05.xlsx Problem 8-2 SALES AND QUALITY COSTS Problem 8-05 Costcutin Company Total Sales $1,537,280 $933,600 $1,397,120 Costs as % of Sales 0.21 0.18 0.12 $ 000's % of Quality Costs by Product $Qual. Cost - A $Qual. Cost - B $Qual. Cost - C Product A. Product B Product C External failure 96.85$

Problem 8-30A Rapid Check Kiters Calculation of control limits with 20 samples. This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7.

Problem 8-25 River Bottom Fire Department This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in

Problem 8-22 River Bottom Fire Department Histogram and Run Charts See Histogram and Run Chart Tabs for Results Number of observations 180 Grand Average Sorted DATA Observation Values 1 3.81 Sample 1 2 3 4 5 6 Means Descriptive Statistics 2 3.82 1 3.81 3.93 3.89 3.91 4.08 4.01 3.94 3 3.85 2 4.08 3.82 3.98 4.21 4.00 4.06 4.03

Chapter 8 – Measuring and Controlling Quality 112 IV. Skyhigh Airlines The solution to this case involves a fairly straight-forward sequential analysis of the system that can be obtained by using the following model (note each

Prob08-11.xlsx Problem 8-11 Beechmount Software Corporation Quality Costs and Percentage Percent Cumulative % Cost Returns 38.92% 38.92% 360,000 Inspection costs (extra)--outgoing 65,000 Customer returns 360,000 Training and system improvement costs 28,000 System downtime 88,000 Total Quality Costs $925,000 Total Costs 925,000 Original Data Category Annual Loss Rework $200,000 Rejected disks (loaded) 30,000 Rejected disks (blank) 18,000 Inspection costs (extra)--incoming 136,000 Rework 21.62% 60.54% 200,000 Insp.

Problem 8-14 NEW Gauge, Inc. Gauge Repeatability and Reproducibility This spreadsheet is designed for up to three operators, three trials, and ten parts. Enter data ONLY in yellow shaded cells. Number of operators 3Upper specification limit 1.6 Average range 0.028 Number of trials 2Lower specification limit 1.4 Range of operator averages 0.013 Number of

1 CHAPTER 8 Measuring and Controlling Quality Teaching Note This chapter presents the basic concepts of statistical process control. These include purposes, control charting techniques, design and analysis of charts,

Prob08-10.xlsx Problem 8-10 Stateside Metrology Repairs, Inc. Quality Costs and Percentage Percent Cumulative % Cost Customer returns 36.36% 36.36% $120,000 Workstation downtime 18.18% 54.55% 60,000 Rework costs 16.67% 71.21% 55,000 Original Data Inspection costs -- incoming 20,000 Workstation downtime 60,000 Training/system improvement 35,000 Rework costs

Chapter 8 – Measuring and Controlling Quality 21 Answer 6. Original Data: Cost Element Amount Customer complaint remakes $ 27,000 Printing plate revisions

Prob08-07.xlsx Problem 8-07 Miami Valley Aircraft Service Co. $000's QUARTERLY QUALITY COSTS External failure 4.76 4.62 3.88 3.54 Internal failure 16.67 16.67 11.84 10.73 Appraisal 4.29 6.15 6.33 3.90 Prevention 1.90 2.56 2.65 3.90 Total Quality/Labor Cost 27.62 30.00 24.69 22.07 1 2 3 4 Total labor 21,000 19,500 24,500 20,500 % Total Labor/Base 100 92.86 116.67 97.62 PERCENTAGES QTRLY.

Prob08-09.xlsx Problem 8-9 Nosoco Paper Company Quality Costs and Percentages Rejected paper 375,000 Odd lot 66,000 Inspection 25,000 Customer complaints 103,000 Wasted materials 31,000 Quality improvement training 10,000 Total Quality Cost $655,000 Percent Cumulative % Cost Rejected paper 57.25% 57.25% 375,000 Customer complaints 15.73% 72.98% 103,000 Original Data Category Annual Loss Downtime $30,000 Testing costs 15,000 Odd lot 10.08% 83.05% 66,000 Wasted materials

Problem 8-23 Palma State Bank Run Chart - Showing Sample Means Over Time Number of observations 125 Grand Average Sample Mean DATA Observation Values Sample 1 12.09 1 2 3 4 5 Means 2 12.20 1 11.86 12.09 12.05 12.25 12.18 12.09 3 12.26 2 11.57 13.21 12.24 11.95 12.02 12.20 11 12.83 10 11.56 14.01 12.80 11.42 12.87 12.53 12

Problem 8-32 PCDrives Process Capability Analysis - X-Bar and R-charts Note: This spreadsheet has been modified to use the sample statistics, without requiring the sample data. The process capability calculations relied upon the estimated standard deviation = R-bar/d 2 This spreadsheet is designed for up to 50 samples, each of a constant sample size

Hydraulic Lift Company Situation 1 85 45 80 60 85 70 80 50 60 80 50 100 80 60 85 90 75 50 60 90 85 80 60 85 65 80 45 60 70 70 70 90 80 60 70 70 90 100 70 70 75 45 60 80 75 70 65 100 70 60 60 70

Montvalley Short-Haul Lines, Inc. Billing Study II Average Number of Defects Per Unit (u) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (u-bar) 0.3227 Number Sample Defects

Montvalley Short-Haul Lines, Inc. Initial U-Chart (Base Case) Average Number of Defects Per Unit (u) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (u-bar) 0.6270 Number Sample Defects

Data Montvalley Short-Haul Lines, Inc. Error Category % of Total Cumulative Errors Errors Percent 0 2300 24.35% 24.35% Page 1 8264 21.43% 45.78% 1159 12.91% 58.69% 6151 12.26% 70.94% 4147 11.93% 82.87% 587 7.06% 89.94% 762 5.03% 94.97% 362 5.03% 100.00% Totals 1232 100.00% Chart 80.00% 100.00% 120.00% 250 300 350 Montvalley Short-Haul Lines, Inc. Pareto Diagram Page 2 0.00% 20.00% 40.00% 60.00% 0 50 100 150 200 2 8 1 6 4 5 7 3 Frequency of Billing Errors Error Categories Errors Cumulative Percent

Knight Industries Solids Process Capability Analysis This spreadsheet is designed to handle up to 150 observations. Enter data ONLY in yellow-shaded cells. 363.700 62.900 62.900 62.500 463.600 63.400 62.900 62.500 562.400 62.600 62.800 62.300 663.400 63.300 62.900 63.200 763.200 64.100 62.600 62.700 863.000 62.700 63.400 62.600 963.800 63.800 63.200 62.300 10 63.500 63.900 63.000 62.400 DATA 12345678910 11 12 13 14 15 164.200

Chapter 8 Morelia Mortgage Co. - Remove outliers This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your

wanted to be sure that it was needed. He decided to do a gauge R&R test and selected two experienced inspectors to perform the test using 15 identical parts, whose dimensions had been verified. Table 1 shows data provided by the supervisor. Thus, data were from two operators, two gauges,

Chapter 8 Morelia Mortgage Co. - Additional samples added This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter

Knight Industries i Weight solids: 60–65 percent Weight per gallon: 12.6–13.5 The Excel file C08 KnightIndData.xlsx (available in the Instructor Reserve case folder) contains data for a series of batches that were produced. Using appropriate SPC charts or other statistical tools, evaluate how well the process is in control and its capability

1 2 3 4 5 9 14 15 16 17 18 19 20 21 22 23 24 385.000 80.000 60.000 85.000 65.000 80.000 45.000 60.000 70.000 70.000 470.000 90.000 80.000 60.000 70.000 70.000 90.000 100.000 70.000 70.000 575.000 45.000 60.000 80.000 75.000 70.000 65.000 100.000 70.000 60.000 660.000 70.000 70.000 80.000 60.000 80.000 65.000 100.000 60.000 100.000 760.000 70.000 80.000 70.000 50.000 90.000 80.000 65.000 80.000 90.000 860.000 50.000 50.000 90.000 70.000

Knight Industries Solids Measures X and Moving Range Chart This spreadsheet is designed for up to 50 observations and a moving range from 2 to 5. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in

Chapter 8 Morelia Mortgage Co. - Original data This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your

Montvalley Short-Haul Lines, Inc. Billing Study Part II Fraction Nonconforming (p) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Sample Fraction Standard Sample Value Size Nonconforming LCLp CL UCLp Deviation 5

Montvalley Short-Haul Lines, Inc. Base Case Table 1 MVSH Defective Billing Data Table 2 MVSH Distribution of Billing Errors No. of Error Category Day No. Defective Bills Day Bills 1 2 3 4 5 6 7 8 110 154 463326210 36 2 9 2 76 582644310 42 313 367 78354627 42 410 489 520 2957314 65 515 576 713

Montvalley Short-Haul Lines, Inc. Initial p-chart (Base case) Fraction Nonconforming (p) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Sample Fraction Standard Sample Value Size Nonconforming LCLp CL UCLp Deviation 110

Chapter 9 - Case Freadilunch 1 Vacant Tables Day During the Freadilunch Restaurant "Rush Hours" 11:00 11:15 11:30 11:45 12:00 12:15 12:30 12:45 1:00 1:15 1:30 1:45 2:00 Mon. -Wk. 1 10 124301182659 Wk. 29322110353447 Wk. 36441020433368 Wk. 4 11 210102275536 Tues. Wk. 1 9 3 2 1 2 3 2 3 2 4 3 7 10 Wk. 27510121503667 Wk.

Case: The PIVOT Initiative at Midwest Bank – Part II 1 1 Appreciation is expressed to one of the author’s students, Michael Wolf, who wrote the paper on which this case is based, as part of the requirements for MGT 699, Total Quality Management, 2002, at Northern Kentucky University, and

Aircraft Parts order Flowchart New or refurbished? On-hand? Aircraft parts order needing to be filled Call to order Refurbished No Yes Phone, fax, or e- mail

Problem 9-10 Aircraft Parts Distributors EMPLOYEES PROCESSING METHOD Experience Number of orders per time period COMPUTER ENTRY PROCEDURES Empowerment to contact client Sequencing of work Training on the equipment Error check method Training on how

Prob09-19.xlsx Problem 9-19 Lauren Elizabeth Apparel Company QUALITY ERRORS AND PERCENTAGES Percent Cumulative % Frequency Electronic charge errors 52.20% 52.20% 5220 Shipping errors 23.10% 75.30% 2310 Billing errors 9.95% 85.25% 995 Delivery errors 7.95% 93.20% 795 Long delays

Prob09-13.xlsx Problem 9-13 ElecktronTech Student Trainees in Electronics Time (minutes) Times Percent Cumulative % Freq Sorted Values 92.17% 2.00% 1 9 10 6.52% 8.00% 310 11 10.87% 18.00% 510 811 15 918 16 10 10 16 11 10 16 12 16 16 13 12 16 14 19 16 15 15 16 16 18 17 17 17 17 18 18 17 19 19 17 20 20 17 21 17 17 22 11 18 23

Process Improvement and Six Sigma 21 3.5 22750.1 5.1 159.1 3.6 17864.4 5.2 107.8 3.7 13903.4 5.3 72.3 5. Nanospark Electronics manufactures 100,000 circuit boards per

Prob09-16.xlsx Lack of press time 245 Design department delays 80 Paper not in stock 70 Lack of proper order information 49 Lost order 26 Press setup delays 205 Problem 9-16 Deuce Printing Company QUALITY ERRORS AND PERCENTAGES Percent Cumulative % Frequency No press time 34.75% 34.75% 245 Original Data Error category Frequency Customer change delays 30 Setup delays 29.08% 63.83% 205 Design delays 11.35% 75.18% 80 No

Problem 9-15 Plastic Gear for PrintHeads, Inc. 3.76 3.76 3.75 3.78 3.79 3.71 3.76 3.76 3.75 3.78 3.79 3.71 3.76 3.76 3.81 3.78 3.79 3.72 3.76 3.76 3.73 3.78 3.79 3.72 3.76 3.77 3.81 3.78 3.79 3.72 3.76 3.77 3.81 3.78 3.80 3.72 3.76 3.77 3.83 3.79 3.70 3.72 3.76 3.77 3.81 3.79 3.70 3.72 3.76 3.77 3.69 3.79 3.70

Process Improvement and Six Sigma 41 • Average amount in accounts receivable divided by average number of sales. • Percentage of accounts received past due. • Sum of the dollar amount

Problem 9-20 Monterey Fiesta Mexican Restaurant Raw Data Breadstick Week Orders Margaritas 1860 1330 Correlation Matrix 2850 1350 Week Orders Margaritas 3800 1290 Week 1 4850 1350 Orders 0.071102 1 5880 1360 Margaritas -0.06291 0.968071 1 6780 1250 7815 1275 Sorted Data 8780 1250 Orders Margaritas 18 870 1380 780 1250 19 890 1390 780 1250 20 910 1380 800 1250 21 940 1400 800 1290 22

Input Data Problem 9-12 Lakerside United Bank Cash Bank Cash Strapping Errors Errors vs. Weeks of Employment Average Weeks Weekly on Job Errors 314.7 616.3 917.1 12 17.0 13 11.1 16 13.7 21 16.7 Page 1 23 14.4 25 10.8 26 11.3 29 2.6 46 3.4 58 4.9 64 4.4 67 4.3 70 5.1 75 4.4 85 3.4 90 4.6 96 5.5 15.0 20.0 Weeks on the Job Lakerside United Bank Cash Strapping Errors 0.0 5.0 10.0 Weeks 3 9 13 21 25 29 58 67

Raw Data Problem 9-17 DeltaWidgets, Inc. Production Rate vs. Defects Production Rate Defects 13 9 16 10 Page 1 20 16 25 17 28 18 32 22 36 25 38 20 41 20 43 12 48 12 50 16 53 11 56 10 59 9 0 25 30 010 20 30 40 50 60 Production Rate Production Rate vs. Defects 5 10 15 20 Defects

Problems 9-02 and 9-03 Calculations for dpmo and sigma levels Prob. 9-2 NORM.S.INV(1 - Number of Defects/Number of Opportunities) + SHIFT For a probability of 98.7% good items, we find NORM.S.INV(0.987) + SHIFT = 3.226212 Prob. 9-3 Alpha Beta Gamma Combined This problem requires finding 3 defect rates, plus a combined rate. See columns Defect rate 0.0100

Problem 9-14 Packman Shipping - Packing Times Scatter Diagram This spreadsheet is designed for up to 40 single values. Enter data ONLY in yellow-shaded cells. Enter the number of values in cell E6. Then enter your data below. To recalculate the sheet, press F9 key. If X-axis values differ from those shown, then substitute

Problem 9-18 Buenosdientes Candy Company Scatter Diagram This spreadsheet is designed for up to 25 single values. Enter data ONLY in yellow-shaded cells. Enter the number of values in cell E6. Then enter your data below. If X-axis value differ from those shown, then substitute and clear unneeded ones. Number of VALUES (<= 30)

Problem 9-9 Placewrite, Inc. EMPLOYEES PROCESSING METHOD Experience Number of clients per processor Empowerment to contact client Sequencing of work Training on the equipment Error check method Training on how to handle errors in copy Instruction on resume writing Format used by clients Delivery of instructions to processor CLIENT PROCEDURES Client dissatisfaction

Problem 9-04 Calculations for dpmo levels Sigma Level dpmo Sigma Level dpmo 3.0 66807.2 4.6 967.6 Use the formula: = (1 – NORM.DIST(4,1.5,1,TRUE))*1000000 = 6209.7 dpmo 3.1 54799.3 4.7 687.1 3.2 44565.5 4.8 483.4 3.3 35930.3 4.9 336.9 3.4 28716.6 5.0 232.6 3.5 22750.1 5.1 159.1 3.6 17864.4 5.2 107.8 3.7 13903.4 5.3 72.3 3.8 10724.1 5.4 48.1 3.9 8197.5 5.5 31.7 4.0

1 CHAPTER 9 Process Improvement and Six Sigma Teaching Notes This chapter brings process improvement and the Six Sigma concept into a sharp focus, and builds on the need to integrate a performance management framework with operational requirements

ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Process Improvement Methodologies KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Process Improvement Methodologies KEY:

National Furniture Store C-E Diagram for Special Order Errors SALES ASSOCIATE ORDER PREPARATION Lack of complete information from customer Incorrect FAX bin Lack of additional customer info to office No request for office to re-FAX order Lost or misplaced order Wrong merchandise ordered Store not notified when order is due there REGIONAL

Raw data Freadilunch Case - Sheet 2 Checksheet Data for Customer Concerns During the Readilunch Restaurant "Rush Hours" Day Mon. 11:00 11:30 12:00 12:30 1:00 1:30 2:00 Total Take-out probs. 2 4 3 5 4 1 2 21 Long table wait 0 5 7 6 3 2 1 24 Inefficient service 1 4 5 6 4 5

Janson Medical Clinic Analysis of Complaint Data Wait for doctor 18.06% 18.06% 13 Ease of appointment 16.67% 34.72% 12 Ease of phoning 13.89% 48.61% 10 Convenient hours 9.72% 58.33% 7 Courtesy of receptionist 9.72% 68.06% 7 Friendliness of phone receptionist 6.94% 75.00% 5 Responsive care via phone 6.94% 81.94% 5 Comfortable waiting 5.56% 87.50% 4 Physician listens 4.17% 91.67%

At the regional office, the special order department receives the fax from the store, reviews it, and informs the store if additional information is needed. When all the information is complete, they process the order. Sometimes they lose or misplace the form after it arrives on the fax machine, order

financial impact analysis and equipment purchasing. The assistant vice president and team supervisor, were subject matter experts from within CPD. Another project coordinator was brought on board for her bank wide knowledge and overall project support. The project analyst for CPD and five other areas was in charge of the

Case - Implementing Six Sigma at GE Fanuc i to see a lot of good data driving decision making. . . . We have ingrained our tool sets within our people, so Six Sigma is a philosophy and an outlook that allows us to examine a broken process, get to a solution,

Fraction Nonconforming (p) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (p-bar) Avg. sample size Approximate Control Limits Using Sample Fraction Standard Average Sample Size Calculations Sample Value Size Nonconforming

This spreadsheet is designed for up to 75 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (u-bar) Sample Number of Unit Defects Standard Sample Nonconformances Size per unit Deviation LCLu CL UCLu 24 583 0.0602 0.030178 00.0756

Binomial Probability Distribution Enter data only in yellow-shaded cells. Number of successes, x3 x f(x) F(x) 00.10737 0.10737 10.26844 0.37581 20.30199 0.67780 30.20133 0.87913 40.08808 0.96721 50.02642 0.99363 60.00551 0.99914 70.00079 0.99992 80.00007 1.00000 90.00000 1.00000 10 0.00000 1.00000 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Sample size, n (up to 50) 10 Probability of a success, p0.2 f(x) = P(X = x)0.2013 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Poisson Probability Distribution Template Enter data only in yellow-shaded cells. Number of outcomes,

Number Nonconforming (np) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Each sample must have a constant sample size; enter this in cell C6. Click on the sheet tab to display the control chart (some rescaling may be needed). Sample size

Confidence Interval for Population Mean, Standard Deviation Known Alpha 0.05 Standard deviation 0.5 Upper 100.4633 Sample size 36 Sample average 100.3 Confidence Interval 95% Error 0.1633 Lower 100.1367 Confidence Interval for Population Mean, Standard Deviation Unknown Alpha 0.05 Sample standard deviation 0.7 Sample size 16 Sample average 100.3 Confidence Interval 95% t-value 2.1314 Error 0.3730 Lower 99.9270 Upper 100.6730 Confidence Interval for a Proportion Alpha 0.01 Sample proportion 0.42 Sample size 200 Confidence Interval 99% z-value

Process A Process B Nominal 0.5 Nominal 0.5 k125000 k125000 Dimension, x Probability, f(x) (x - E[X])^2 Dimension, x Probability, f(x) (x - E[X])^2 0.47 0 0.0009 0.47 0.02 0.0009 0.48 0.2 0.0004 0.48 0.03 0.0004 0.49 0.2 0.0001 0.49 0.15 0.0001 0.5 0.2 0 0.5 0.6 0 0.51 0.2 0.0001 0.51 0.15 0.0001 0.52 0.2 0.0004 0.52 0.03

Copyright 2000: James R. Evans. For use exclusively with The Management and Control of Quality, 5th Edition or higher. This software may not be used for any commercial purpose, modified, or or otherwise distributed without written permission from the author. This Excel template allows you to simulate control charts having

22 Factorial Experiment Copyright © 2013 Cengage Learning Interaction Charts Calculations Enter data only in yellow-shaded cells. Factor 1 Factor 2 Average Description Name Low level High level L L #DIV/0! Factor 1 H L #DIV/0! Factor 2 L H #DIV/0! Factor 3 H H #DIV/0! Treatment Factor 1 Factor 2 Factor 3 Response Factor 1 Factor

22 Factorial Experiment Enter data ONLY in yellow-shaded cells. Description Name Low level High level Treatment Factor 1 Factor 2 Response ALow Low 84 B High Low 90.5 CLow High 88.5 D High High 81 Main Effects Factor 1 -0.50 Factor 2 -2.50 Interaction Factor 1 x Factor 2 -7.00 82 84 86 88 90 92 Factor 2 High Factor 2 Low 76 78 80 Low High Factor 1 Temperature 100 degrees

This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (c-bar) Standard deviation Number of Sample Nonconformances LCLc CL UCLc 35 #N/A #N/A #N/A 36 #N/A #N/A #N/A 37 #N/A #N/A #N/A 38 #N/A

SUMMARY OUTPUT Regression Statistics Multiple R 0.999967043 R Square 0.999934087 Adjusted R Square 0.999925848 Standard Error 0.027282724 Observations 10 ANOVA df SS MS F Significance F 4 4.071249331 0.018750669 5 5.092354294 0.017645706 6 6.044063774 0.035936226 7 7.194046063 0.005953937 8 8.294460149 0.005539851 9 9.603060685 -0.013060685 10 10.42589284 -0.015892839 0 2 4 0 2 4 6 8 10 12 Actual (Y) Source (X) Actual (Y) Predicted Actual (Y) RESIDUAL OUTPUT Observation Predicted Actual (Y) Residuals 1 1.067416284 0.022583716 2

This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in the grid below. Click

This spreadsheet is designed for up to 50 samples, each of a constant sample size from 2 to 10. Enter data ONLY in yellow-shaded cells. Enter the number of samples in cell E6 and the sample size in cell E7. Then enter your data in the grid below. Click

1 2 3 4 5 6 7 8 9 10 11 12 13 40 A B C D E F G H I J K L M N O P Q R S T U V W Process Capability Analysis Frequency Distribution and Histogram This spreadsheet is designed to handle up to 150 observations. Enter data ONLY in yellow-shaded cells. Data Minimum 10.500 Nominal specification 10.7 Data Maximum 10.900 Upper

U-Bolt Data 10.80 10.80 10.70 10.70 10.65 10.60 10.55 10.65 10.75 10.75 10.65 10.60 10.50 10.80 10.65 10.65 10.70 10.85 10.80 10.70 10.75 10.75 10.80 10.80 10.80 10.70 10.75 10.75 10.70 10.70 10.65 10.60 10.70 10.65 10.75 10.80 10.60 10.70 10.60 10.60 10.75 10.65 10.60 10.80 10.85 10.70 10.65 10.75 10.90 10.75 10.75 10.70 10.65 10.55 10.80 10.65 10.60 10.60 10.85 10.65 10.75 10.70 10.65 10.75 10.70 10.85 10.60 10.85 10.65 10.65 10.75 10.65 10.70 10.65 10.85 10.70 10.75 10.85 10.85 10.75 10.75 10.85 10.80 10.50 10.65 10.75 10.75 10.80 10.75 10.70 10.65 10.80 10.70 10.75 10.65 10.85 10.60 10.90 10.85 10.75 10.85 10.75 10.80 10.70 10.75 10.65 10.85 10.70 10.65 10.80 10.65 10.70 10.65 10.70 10.60 10.65 10.85 10.80 10.60 10.70

1 2 3 4 5 6 7 34 A B C D E F G H I J K L M N O P Q R S T U V W X Y Frequency Distribution and Histogram This template is designed to handle up to 150 observations. Enter data only in yellow cells. Data Minimum 10.500 DATA 12345678910 11 12 13

Fraction Nonconforming (p) Chart This spreadsheet is designed for up to 50 samples. Enter data ONLY in yellow-shaded cells. Click on the sheet tab to display the control chart (some rescaling may be needed). Average (p-bar) Avg. sample size Approximate Control Limits Using Sample Fraction Standard Average Sample Size Calculations Sample Value Size Nonconforming

Gauge Repeatability and Reproducibility This spreadsheet is designed for up to three operators, three trials, and ten parts. Enter data ONLY in yellow shaded cells. Number of operators 3 Upper specification limit 1Average range 0.029 Number of trials 2 Lower specification limit 0.5 Range of operator averages 0.056 Number of parts 10 Range of part averages 0.525 Data Part Part