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 of quality (see Power Point slides for use in your lectures) and then often show a video. One of the

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 are compared, then it is often easier to make a value-based

Case: Is Quality Good Marketing or Is Good Marketing Quality 1 first time and that there is always something that can be improved. They are never totally satisfied with the present level of quality. This commitment to the pursuit of excellence is passed down to every employee. The franchisor's quality motto is

variety of home-cooked cuisines than found in currently available businesses. In developing their business model, they realized that a TQ-focused management infrastructure would be vital to success. Here are some of the ideas they arrived at. variety of home-cooked cuisines than found in currently available businesses. In developing their business

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

companys success. He realizes that the industry is highly competitive, with such national chains as T.G.I. Fridays as well as other local restaurants. Staffing will be a challenge, as the turnover in this industry is traditionally quite high, particularly among high school and college students who will comprise a large

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 20 four dimensions implied in these comments are addressed below. a. The applicant presents limited evidence of systematic evaluation and refinement of several key leadership approaches that may support operational excellence and enhance sustainability. These include approaches for innovation, performance leadership, creation

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

TecSmart Electronics 1 Deming philosophy provided the foundation to carry the company into the twenty-first century, the current CEO decided to pursue a Baldrige focus and began a process of self-assessment against the criteria to identify opportunities for improvement. Deming philosophy provided the foundation to carry the company into the twenty-first century,

Now, lets list things that have to be done in order to get ready for the party, suggested Amber. They quickly produced the following list (not in any order): Pick date. Plan menu. Get food delivered. Estimate costs. Locate and book a hall. Determine budget. Select music. Hire a

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

1 process of envisioning the organizations future and developing the necessary goals, objectives, and action plans to achieve that future. To explore the goal of strategy development, which is envisioning the future for purposes of decision making and resource allocation. Effective strategy development requires a systematic process that involves participation

Strategy and Performance Excellence 16 It began with the development of an order fulfillment process psyche that transcended departmental thinking regarding purchasing, forming, selection and shipping. Management and union leadership were engaged to explain and communicate this new psyche throughout the plants. The business then developed performance measures for the

price, because the president, Trendy Art, believes that CDGs experienced staff should emphasize high quality and creativity instead of price. They accomplish their primary objectives 97 percent of the time. price, because the president, Trendy Art, believes that CDGs experienced staff should emphasize high quality and creativity instead of price.

ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: The Value and Scope of Performance Measurement KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: The Value and Scope of Performance Measurement KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head:

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 first. This may especially be true if there are

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 by Coyote for their LEARNing Board are shown below. The Associated Outcomes,

I. Westerfield Construction 1 executive and middle managers, whose purpose was to review and become personally involved in selected change initiatives. Communications among the team, middle managers, and employees had improved significantly, largely because of frequent open forums that were initiated by the CEO. Deters concluded that the firms strategy supported its

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

Leadership for Performance Excellence 13 easily transfer their knowledge and learning to environmental processes. In addition, there is evidence that achieving superior environmental performance can be a significant driver of higher quality. easily transfer their knowledge and learning to environmental processes. In addition, there is evidence that achieving

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 principles and organizational cultures and to develop and use a sustainable model

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 Ingeniera Industrial y de Sistemas ITESM-Morelia Campus Morelia, Mexico September 17-21, 2008 Award criteria. Applications to various facets of performance excellence in banking, including

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 of quality (individual, process, and organizational). Integration goes beyond alignment and is achieved when the individual components

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 basic knowledge of organizing principles and organizational cultures and to develop and

framework. Colin was quite excited. However, he knew that changing the culture in a hospital was indeed difficult. However, he felt that if he could accomplish that, SLRMC could truly become a nationally recognized leader in the industry. In discussing the concept, the executive management team was also excited at

philosophy. With much fanfare at the corporate level, the new TQ program, called Quality or Else, was rolled out. It soon became known by its initials QOE. Initial skepticism surfaced within various divisions about whether such a program would help to arrest the progress of Equiptos competitors, especially a rapidly

ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Organizational Culture and Change KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Organizational Culture and Change KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Organizational Culture and Change KEY: Bloom's:

Child Focus, Inc. i schools, foster care, independent living, case management, outpatient individual, family, and group therapy, diagnostic testing, psychiatric services, and professional training. CFIs 240 employees have a broad range of professional education including high school, associates, bachelors, masters, Ph.D., R.N., and M.D. degrees. All staff share the authority, dedication, and

U.S. Water Resource Agency - Flagstaff District i new infrastructure and projects; (2) management of the operation and maintenance of existing infrastructure; (3) the performance of reimbursable work for others; (4) administration of regulatory requirements; (5) provision of emergency response and recovery; and (6) the delivery of defense, environmental, and restoration programs.

Error Data Chapter 2 The Disciplinary Citation Case Data for Scatter Diagram for Package Errors 5 3 76 0 7721 78 0 79 8 710 12 711 3 712 4 713 8 714 4 715 2 7 5 3 76 0 7721 78 0 79 8 710 12 711 3 712 4 713 8 714

Total Quality in Organizations 20 the quality philosophy in one area of work and ignore it in another. If each person is responsible for their own quality, then you don't need many of the rules, regulations, and external controls that have been the "norm" in many organizations for decades.

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 of these systems. Students should be encouraged to develop

the consumer. QAs primary concern is for controlling product weight, appearance, and shelf life throughout the manufacturing operations. Production operators are held accountable for their cuts on specific meat products. The cuts must be performed according to quality assurance specifications in order to obtain high yields. (Yield is the percentage

products that offer superior quality and value." In 1983 Mercantile initiated a program for processing incoming merchandise at regional distribution centers. This was seen as more efficient than receiving merchandise in the individual stores. In 1986 the company remodeled and expanded existing stores to accommodate a greater selection of fashion

The Nightmare on Telecom Street i take care of you. More music. Then yet another message: Our operators are still busy, but please hold. Your business is important to us. More bad music. Finally the sweet voice returned, stating, To speed up your service, enter your 19-digit customer service number. I frantically

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

Problem 3-06 Versele Sporting Goods Months Survey attribute 12345678910 11 12 4. Store layout 3.7 5.0 4.3 5.8 3.2 4.9 4.1 4.7 3.9 4.5 4.7 6.25. Product availability 3.1 3.0 5.4 3.3 2.9 3.1 4.7 3.8 3.1 4.9 4.4 3.56. Product quality 6.6 7.0 6.2 5.4 7.0 6.5 6.4 5.9 6.9 6.8 6.1

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 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

5 4 4 8 10 812 10 6 4 4 12 10 6 4 4 6 4 10 6 4 12 10 6 4 4 4 4 8 10 8 4 6 6 10 8 6 8 4 4 6 10 4 8 4 4 4 6 10 8 4

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 2.00Portability 15% 6.5 5.3 1.2 0.98 0.80Reliability 30% 9.1 8.8 0.3 2.73 2.64Price 10% 3.1 5.2 -2.1 0.31 0.52SUM 7.64 7.52Note: 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

Customer Number 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75NPS Rating 8 10 10 7 8 9 9 10 662910 7 8 9 10 6 7 10 10 7 8 8 10Customer Number

Focusing on Customers 21 for bad comments on the survey will do as this employee did, or else try to find a way to hide or destroy the unfavorable comment cards. for bad comments on the survey will do as this employee did, or else try to find a way

ANS: T PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: Customer Satisfaction and Engagement KEY: Bloom's: Knowledge ANS: T PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: Customer Satisfaction and Engagement KEY: Bloom's: Knowledge ANS: T PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head:

Case: American Parkinsons Disease Association Center i increased in recent years when celebrities Muhammad Ali and Michael J. Fox developed the disease. Patients with PD are commonly referred to neurologists. However, due to the complex nature of the disease and its management, it is common for neurologists themselves to refer patients to

they had made a reservation and expected to be seated promptly. The hostess told them, I believe that you made a reservation, but I cant seat you until all the people on the reservation list are seated. You are welcome to go to the lounge for complimentary coffee and punch

Case: Cincinnati Veterans Administration Medical Center i 2. Develop a united team of competent, caring, empowered employees committed to providing service that exceeds expectations. 3. Foster unprecedented labor/management trust and cooperation. 4. Create a proactive organization that is responsive, streamlined, and personal. 2. Develop a united team of competent, caring, empowered employees

16 There is no one right answer to the question of whether conventional performance reviews should be eliminated, or not. Obviously, a TQ-oriented organization must face the question of what they reward and how they will do so. Performance appraisals are most effective in a TQ environment when

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 many organizations. The workforce area continues to undergo wrenching change as organizations

ANS: F PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: The Evolution of Workforce Management KEY: Bloom's: Knowledge ANS: F PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-head: The Evolution of Workforce Management KEY: Bloom's: Knowledge ANS: F PTS: 1 DIF: Difficulty: Easy NAT: BUSPROG: Analytic

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

Process Management 20 improved; The boss wont let us change it; Government regulations prohibit that from being changed (maybe they do, but no one has ever looked up the regulation!) PROBLEMS 1. The process of making a batch of paint in a paint factory consists of the following

Quality OK? Color?Wash & inspectbanana clustersPlace sticker &package bunchesLoad into containers & ship to U.S. on cargo shipsDiscardRottenRipen - treatwith gasGreenRipeSort bananasBox - per customer specsBox - per customer specsBox - per customer specsRetail customers Quality OK? Color?Wash & inspectbanana clustersPlace sticker &package bunchesLoad 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 needs.

Stuart Injection Molding Company 1 responsible for the majority of implementation issues, such as maintaining measuring and test equipment, verifying process capability, performing inspection, selecting methods for monitoring process performance, and auditing the system. responsible for the majority of implementation issues, such as maintaining measuring and test equipment, verifying process capability, performing

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

213 Range 10312 Sample variance 8.257414 Sample standard deviation 2.874510612714810 213 Range 10312 Sample variance 8.257414 Sample standard deviation 2.874510612714810 213 Range 10 312 Sample variance 8.257 414 Sample standard deviation 2.874 510 612 714 810 19 1320 1721 922 1823 1224 1125 1426 1527 928 1229 830 15 19 1320 1721 922 1823 1224 1125 1426 1527 928 1229

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 encouraged to take a big picture perspective on this framework,

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 35.7 34.9 Standard Deviation 2.4806 Standard Deviation 2.575040.2 33.0 Sample Variance 6.1534 Sample Variance 6.630743.9 41.4

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 Factor 1 Drying agent 10 mg. 20 mg.Factor 2 Temperature 80 degrees 100 degrees Factor 1 Drying agent 10 mg. 20 mg.Factor 2 Temperature 80 degrees 100 degrees Factor 1 Drying agent 10 mg. 20 mg. Factor

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 Factor 2 Material Silicon Gold L H 9.5Factor 3 Temperature Low High H H 15Treatment Signal

Chapter 6 - Problems 15-19 Confidence Intervals Problem 6-15 (a) T.INV.2T for t-value, 2-tailed distribution, 95% conf. 2.131CONFIDENCE.T interval for Students t-distribution 1.4920Problem 6-15 (b)T.INV.2T for t-value, 2-tailed distribution, 99% conf. 2.947CONFIDENCE.T interval for Students t-distribution 2.0627Problem 6-16NORM.S.INV for z-value, 2-tailed distribution, 90% conf. 1.282Problem 6-17T.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 63.555672 71 66 Sub 3 10 757 75.7 69.344488 86 7267

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 Finish 3 9.5 8.1 12.3Finish 4 8.7 7.2 14.5Finish 5 14.2 7.6 12.9 Finish 3 9.5 8.1 12.3Finish 4 8.7 7.2 14.5Finish 5 14.2 7.6 12.9 Finish 3 9.5 8.1 12.3 Finish 4 8.7 7.2 14.5 Finish 5 14.2 7.6

Problem 6-22 Rockglass, Inc. 600 3.2 650 1.8 Regression Statistics 700 3.9 Multiple R 0.96085 750 2.8 R Square 0.92324800 4.2 Adjusted R Square 0.91734850 4.8 Standard Error 0.5223778 750 2.8 R Square 0.92324800 4.2 Adjusted R Square 0.91734850 4.8 Standard Error 0.5223778 750 2.8 R Square 0.92324 800 4.2 Adjusted R Square 0.91734 850 4.8 Standard Error 0.52237 7 8 900

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 48.70 -2.60 0.0046648.75 -2.50 0.0062148.80 -2.40 0.0082048.85 -2.30 0.0107248.90 -2.20 0.0139048.95 -2.10 0.0178649.00 -2.00 0.0227549.05 -1.90 0.0287249.10 -1.80 0.0359349.15 -1.70 0.044570.000000.100000.200000.3000048.5048.6548.7548.8548.9549.0549.1549.2549.3549.4549.5549.6549.7549.8549.9550.0550.1550.2550.3550.4550.5550.6550.7550.8550.9551.0551.1551.2551.3551.45 48.70

Statistical Methods in Quality Management 21 The Cumulative Probabilities for the Standard Normal Distribution table (Appendix A) can also be used as a cross-check. The Cumulative Probabilities for the Standard Normal Distribution table (Appendix A) can also be used as a cross-check. The Cumulative Probabilities for the Standard Normal

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

The HMO Pharmacy Crisis i but you work there every day. Youre in a much better position to find out how to fix the problem. Just tell me what to do, and Ill do it. But what about the statistical consultant you hired to analyze the data on inaccurate prescriptions? asked Dover.

ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Product Development KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Product Development KEY: Bloom's: Knowledge ANS: T DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Product Development KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Easy

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 () = 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 750 0.5095831000 0.6132591250 0.6950171500 0.7594921750 0.8103362000 0.8504312250 0.8820512500 0.9069862750 0.9266493000 0.9421560.0000.2000.4000.6000.8001.0000 1000 2000 3000 4000 5000 6000ProbabilityHours to FailureCum.

Problem 7-22 Satellite Radio Reliability Exponential Probability Distribution Enter data only in yellow-shaded cells. Failure rate per hour () = 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 12500 0.170971 0.8290325000 0.312711 0.6872937500 0.430217 0.5697850000 0.527633 0.4723762500 0.608394 0.3916175000 0.675348 0.3246587500 0.730854 0.26915100000 0.776870 0.22313112500 0.815019 0.18498125000 0.846645 0.153350.2000.4000.6000.8001.0001.200ProbabilityCumulative

2.002 2.000 Sample Variance 0.000001.999 2.000 Kurtosis 0.018202.000 2.000 Skewness -0.170851.998 2.000 Range 0.004001.999 2.000 Minimum 1.998002.001 2.000 Maximum 2.002002.000 2.000 Sum 50.002002.000 2.000Count 25.00000 2.002 2.000 Sample Variance 0.000001.999 2.000 Kurtosis 0.018202.000 2.000 Skewness -0.170851.998 2.000 Range 0.004001.999 2.000 Minimum 1.998002.001 2.000 Maximum 2.002002.000 2.000 Sum 50.002002.000 2.000Count 25.00000 2.002 2.000

0.49 0.2 0.0001 0.49 0.15 0.00010.5 0.2 0 0.5 0.6 00.51 0.2 0.0001 0.51 0.15 0.00010.52 0.2 0.0004 0.52 0.03 0.00040.53 0 0.0009 0.53 0.02 0.0009Expected Value, E[X] 0.5 Expected Value, E[X] 0.5Variance, Var[X] 0.0002 Variance, Var[X] 0.00009Expected Loss 25.00 Expected Loss 11.25 0.49 0.2 0.0001 0.49 0.15 0.00010.5 0.2 0

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 not as critical as doing it in

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 35.5 4.7 Mode 5,5;6.2;6.5 By inspection of sorted data (tri-modal)45.1 4.855.9 5.0 Range 3.166.0 5.1 Variance (pop.) 0.770176.6 5.5 Variance (sampl.) 0.802284.8 5.5 Stnd Dev (pop.) 0.877695.6 5.6 Stnd Dev

Problem 7-12 AutoCM, Inc. Descriptive Statistics Original Data Sorted Data 25.0366 24.9349 Sample Variance 0.0032 25.000 725.0170 24.9513 Kurtosis -0.1981 25.025 925.0501 24.9621 Skewness -0.2635 25.050 624.9683 24.9639 Range 0.2508 25.075 725.0390 24.9683 Minimum 24.8672 25.100 325.0114 24.9685 Maximum 25.1180 25.125 225.0369 24.9723 Sum 1250.2795 More 025.0894 24.9732 Count 50.000025.0219 24.973501234524.85024.87524.90024.92524.95024.97525.00025.02525.05025.07525.10025.125MoreFrequencyBinFrequency 25.0366 24.9349 Sample Variance

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 3 24436 6 24430 10 18

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 RaRbRccRd = (0.98)(0.97) (0.99)(0.99) = 0.932 b) A configuration that meets the 0.98 reliability requirement is difficult, but can be attained by providing backup components for a. b. and c. In other words a, b, and c must all be in

Kingyro 4.25 7.5 640 930 23Antonio's 3.25 6 540 950 16* Lower is better Kingyro 4.25 7.5 640 930 23Antonio's 3.25 6 540 950 16* Lower is better Kingyro 4.25 7.5 640 930 23 Antonio's 3.25 6 540 950 16 * Lower is better Kingyro $0.567 85.3 124.0 23Antonio's $0.542 90.0 158.3 16Georgios Targets $0.51/oz.

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 0.03 1.62 0.22 24.00 0.12 2.88 0.15 3.60 0.23 6.00 0.12 0.72 0.15 0.900.24 0.00 0.28 0.00 0.30 0.000.25 6.00 0.12 0.72 0.15 0.900.26 24.00 0.12

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 under their respective columns Georgios Giant Gyros technical requirements

QIP - Applying SPC to Pharmaceutical Product Manufacturing 4.965 4.956 4.952 4.953 4.954 4.960 4.953 4.9664.963 4.953 4.935 4.948 4.961 4.939 4.960 4.9374.959 4.945 4.962 4.943 4.956 4.955 4.962 4.9624.960 4.946 4.959 4.965 4.956 4.952 4.953 4.954 4.960 4.953 4.9664.963 4.953 4.935 4.948 4.961 4.939 4.960 4.9374.959 4.945 4.962 4.943 4.956 4.955

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 10

Chapter 8 Knight Industries 773 63.2 13.3879 63.0 13.3968 63.8 13.510 69 63.5 13.411 77 63.0 13.312 79 63.1 13.313 74 62.9 13.2 773 63.2 13.3879 63.0 13.3968 63.8 13.510 69 63.5 13.411 77 63.0 13.312 79 63.1 13.313 74 62.9 13.2 773 63.2 13.3 879 63.0 13.3 968 63.8 13.5 10 69 63.5 13.4 11 77 63.0

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 30.0850 0.0950 0.0820 0.094040.0550 0.0580 0.0485 0.052550.0920 0.0880 0.0990 0.0900 30.0850 0.0950 0.0820 0.094040.0550 0.0580 0.0485 0.052550.0920 0.0880 0.0990 0.0900 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 6 0.0850 0.0800 0.0750 0.0700 7 0.0600 0.0700 0.0550 0.0640 80.0680 0.0750 0.0670

$2,200 per month worth of parts had been scrapped over the past three years (see Figure 2). This amount translates to about 14 percent of total production of the parts, a scrap rate that is considered unacceptable. About half of the defective items were scrapped due to inability of the

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 the grid

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). Average (u-bar)0.0934 Average (u-bar)0.0934 Average (u-bar) 0.0934 Sample Number of Unit Defects Standard Sample Nonconformances Size per unit Deviation LCLu CL UCLu 4 3

50 Standard Deviation 13.13834699 60 70 80 70 50 90 80 65 80 9050 Sample Variance 172.6161616 60 50 50 90 70 60 80 70 80 7050 Kurtosis -0.335665194 70 70 75 70 80 80 80 70 90 8050 Skewness -0.080246952 90 80 80 60 90 70 75 75 80

c. Set up the control chart. 2. Data collection a. Record the data. b. Calculate relevant statistics: averages, ranges, proportions, and so on. c. Plot the statistics on the chart(s). 3. Determination of initial control limits a. Compute the upper and lower control limits. b. Draw the center line

4 75 63.6 13.35 62 62.4 13.26 69 63.4 13.27 73 63.2 13.38 79 63.0 13.39 68 63.8 13.510 69 63.5 13.4 4 75 63.6 13.35 62 62.4 13.26 69 63.4 13.27 73 63.2 13.38 79 63.0 13.39 68 63.8 13.510 69 63.5 13.4 4 75 63.6 13.3 5 62 62.4 13.2 6 69

CHAPTER 08MEASURING AND CONTROLLING QUALITY TRUE/FALSE 1. A nonconforming unit of work is one that has no defects or errors. ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP: A-Head: Measurement for Quality Control KEY: Bloom's: Knowledge ANS: F DIF: Difficulty: Easy NAT: BUSPROG: Analytic TOP:

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 Value Size

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 10

Chapter 8 Measuring and Controlling Quality 101 c. For a 2.5 shift and a 0.90 probability, use n = 3 (rounded to next higher value). c. For a 2.5 shift and a 0.90 probability, use n = 3 (rounded to next higher value). c. For a 2.5

Chapter 8 Measuring and Controlling Quality 41 % of Tolerance Equipment variation = 100 0.1010.12 = 84.17% Operator variation = 100 0.0120.12 = 10.00% R & R variation = 100 0.1010.12 = 84.17% Part variation = 100 0.0920.12 = 76.67% % of Tolerance Equipment variation = 100 0.1010.12

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 grid below.

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 the grid

Prob. 8-19 Statistics 24.008 Mode 2423.996 Standard Deviation 0.009670723.991 Sample Variance 9.352E-0524.000 Kurtosis 0.531318724.005 Skewness 0.052708124.011 Range 0.05823.989 Minimum 23.97124.023 Maximum 24.02924.020 Sum 2400.13524.004 Count 10024.004 Confidence Level(95.0%) 0.0019189 24.008 Mode 2423.996 Standard Deviation 0.009670723.991 Sample Variance 9.352E-0524.000 Kurtosis 0.531318724.005 Skewness 0.052708124.011 Range 0.05823.989 Minimum 23.97124.023 Maximum 24.02924.020 Sum 2400.13524.004 Count 10024.004

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 -0.55 32 -0.15 51 0.25 67 0.65 51 1.25 35 1.65 1 -0.55 32 -0.15 51 0.25 67 0.65 51 1.25 35 1.65 1 -0.55 32 -0.15

Quality Measurements Bin 30.780 0.667 0.838 0.785 0.723 0.759 0.70 0.65 1040.591 0.727 0.812 0.775 0.730 0.727 0.75 0.70 1450.693 0.708 0.790 0.758 0.671 0.724 0.80 0.75 4060.749 0.714 0.738 0.719 0.606 0.705 0.85 0.80 3170.791 0.713 0.689 0.877 0.603 0.735 0.90 0.85 3780.744 0.779 0.660 0.737 0.822 0.748 0.95 0.90 1490.769

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 decreasing

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 below. Click

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 with

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 data in

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 below. Click

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 60.37 40.36 0.37 0.40 0.3870.36 50.43 0.37 0.36 0.3980.34 60.37 0.40 0.33 0.3790.41 70.34 0.38 0.36 0.3610 0.36 80.34 0.33 0.36 0.3411 0.40 90.44 0.37 0.42

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 Internal failure 612000 Printing plate revisions 28,000 Int. Fail.Appraisal 592000 Quality improvement projects 13,000 Prevent.Prevention 69000 Gauging 100,000 AppraisalTotal Quality Cost 1300000 Other waste 38,000 Int. Fail.Correction of typographical errors 210,000 Int. Fail.Proofreading 450,000 Appraisal Internal failure 612000 Printing plate revisions 28,000

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 in the

Prob. 8-18 Statistics 0.574 Median 0.57500.574 Mode 0.57500.574 Standard Deviation 0.001690.575 Sample Variance 0.00000.575 Kurtosis -0.18000.575 Skewness 0.44430.575 Range 0.00600.575 Minimum 0.57300.575 Maximum 0.57900.576 Sum 14.38800.576 Count 25.00000.576 Confidence Level(95.0%) 0.00070.576 0.574 Median 0.57500.574 Mode 0.57500.574 Standard Deviation 0.001690.575 Sample Variance 0.00000.575 Kurtosis -0.18000.575 Skewness 0.44430.575 Range 0.00600.575 Minimum 0.57300.575 Maximum 0.57900.576

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. (from Prob. 8-36). Use 0.12 as the upper tolerance limit and - 0.10 as the lower

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 grid below.

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 specification

Prob08-08.xlsx Problem 8-08 Repack Solutions Inc. Quality Costs and Percentages Pkg. waste 5.48% 82.02% 80,000 Int. Fail. Prevention $63,500Income. insp. 4.11% 86.13% 60,000 Appraisal External Failure $40,000Error correction 2.95% 89.08% 43,000 Int. Fail.Other waste 2.91% 91.99% 42,500 Int. Fail. Total $1,460,000Cust. complaints 2.74% 94.73% 40,000 Ext. Fail.Qual. train. assoc. 2.09% 96.82% 30,500 Prevent.Improvement proj.

Problem 8-20 River City Parts Co. Sample Means and Standard Deviations 1.75134 2.01492 1.245581.73316 2.00448 1.249921.75134 2.00448 1.239071.71498 2.00100 1.258601.75437 2.00796 1.256431.73619 2.00100 1.252091.73922 1.98708 1.25426 1.75134 2.01492 1.245581.73316 2.00448 1.249921.75134 2.00448 1.239071.71498 2.00100 1.258601.75437 2.00796 1.256431.73619 2.00100 1.252091.73922 1.98708 1.25426 1.75134 2.01492 1.24558 1.73316 2.00448 1.24992 1.75134 2.00448 1.23907 1.71498 2.00100 1.25860 1.75437 2.00796 1.25643 1.73619 2.00100 1.25209 1.73922

Prob. 8-17 Statistics Problem 8-17 Mach4 Tool Co - Machining Process Original Data 0.564 Mean 0.5740 0.575 0.574 0.575 0.575 0.5770.564 Standard Error 0.0013 0.577 0.577 0.577 0.577 0.5800.572 Median 0.5750 0.579 0.581 0.583 0.586 0.5750.572 Mode 0.57500.572 Standard Deviation 0.0067 0.564 Mean 0.5740 0.575 0.574 0.575 0.575 0.5770.564 Standard Error 0.0013 0.577 0.577 0.577

Prob08-05.xlsx Problem 8-2 SALES AND QUALITY COSTS Problem 8-05 Costcutin Company Total Sales $1,537,280 $933,600 $1,397,120Costs as % of Sales 0.21 0.18 0.12$ 000's % of Quality Costs by Product$Qual. Cost - A $Qual. Cost - B $Qual. Cost - C Product A. Product B Product CExternal failure 96.85$ 31.93$ 16.77$ 30% 19% 10%Internal

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. Then enter

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 the grid

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 X = 1 person hour, and the fractions are additional fractions of person-hours required

Prob08-11.xlsx Problem 8-11 Beechmount Software Corporation Quality Costs and Percentage Percent Cumulative % Cost Returns 38.92% 38.92% 360,000 Rework 21.62% 60.54% 200,000Insp. - In. 14.70% 75.24% 136,000Sys. Downtime 9.51% 84.76% 88,000Insp. - Out 7.03% 91.78% 65,000Rej. disks (load.) 3.24% 95.03%30,000Trng./sys. improve. 3.03% 98.05% 28,000Rej.disks (blank) 1.95% 100.00% 18,000 Rework 21.62% 60.54% 200,000Insp. - In. 14.70% 75.24% 136,000Sys.

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 parts

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, managerial requirements for implementation of SPC, and SPC considerations within service

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 Inspection - out 12.12% 83.33%40,000Training/improvement 10.61% 93.94%35,000Inspection - in 6.06% 100.00%20,000Total Costs $330,000 Inspection - out 12.12% 83.33%40,000Training/improvement 10.61% 93.94%35,000Inspection - in 6.06% 100.00%20,000Total Costs $330,000 Inspection - out 12.12% 83.33% 40,000 Training/improvement 10.61%

Chapter 8 Measuring and Controlling Quality 21 Answer 6. Original Data: Cost Element Amount Customer complaint remakes $ 27,000 Printing plate revisions 28,000 Quality improvement projects 13,000 Gauging 100,000 Other waste 38,000 Correction of typographical errors 210,000 Proofreading 450,000 Quality planning 56,000 Press downtime 285,000 Bindery waste

Prob08-07.xlsx Problem 8-07 Miami Valley Aircraft Service Co. $000's QUARTERLY QUALITY COSTS External failure 1,000 900 950 725Internal failure 3,500 3,250 2,900 2,200Appraisal 900 1,200 1,550 800Prevention 400 500 650 800Total Quality Cost 5800 5850 6050 4525 External failure 1,000 900 950 725Internal failure 3,500 3,250 2,900 2,200Appraisal 900 1,200 1,550 800Prevention 400 500 650

Prob08-09.xlsx Problem 8-9 Nosoco Paper Company Quality Costs and Percentages Odd lot 10.08% 83.05% 66,000Wasted materials 4.73% 87.79% 31,000Downtime 4.58% 92.37% 30,000Inspection 3.82% 96.18% 25,000Testing costs 2.29% 98.47% 15,000Quality Imprv. Trng. 1.53% 100.00% 10,000Total Costs 655,000 Odd lot 10.08% 83.05% 66,000Wasted materials 4.73% 87.79% 31,000Downtime 4.58% 92.37% 30,000Inspection 3.82% 96.18% 25,000Testing costs 2.29% 98.47% 15,000Quality

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 4 12.73 3 12.31 11.96 12.22 12.43 12.36 12.265

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

75 45 60 80 75 70 65 100 70 6060 70 70 80 60 80 65 100 60 10060 70 80 70 50 90 80 65 80 9060 50 50 90 70 60 80 70 80 7070 70 75 70 80 80 80 70 90 8090 80 80 60 90

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 Standard Sample of Defects Size

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 Standard Sample of Defects

Data Montvalley Short-Haul Lines, Inc. Error Category % of Total Cumulative Errors Errors Percent 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% 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% 8264 21.43% 45.78% 1159 12.91% 58.69% 6151 12.26% 70.94% 4147 11.93% 82.87% 587

Knight Industries Solids Process Capability Analysis This spreadsheet is designed to handle up to 150 observations. Enter data ONLY in yellow-shaded cells. Nominal specification 62.5 Cp 1.631091 Nominal specification 62.5 Cp 1.6311Upper tolerance limit 65 Cpl 1.993909 Upper tolerance limit 65 Cpl 1.9939Lower tolerance limit 60 Cpu 1.268273 Lower tolerance limit 60 Cpu 1.2683Cpk

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 data in

PV = Rp x K3 and TV = (R&R)2 + (PV)2 The Rp value is obtained by calculating the range of the sample averages in a gauge study. The K3 value depends on the number of parts measured in the study. Some of these values are found in Table 2.

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 your data

Knight Industries i agitated vessel that contains a milling medium (small steel or titanium dioxide balls of consistent size), which reduces it to a specified particle size. Finally, the paint is removed from the mill and allowed to cool, then tested. Solvent is lost during the milling stage because of elevated temperatures;

1 2 3 4 5 678Nominal specification 75 Cp 0.634Upper tolerance limit 100 Cpl 0.586Lower tolerance limit 50 Cpu 0.682AverageStandard deviation73.10013.138 678Nominal specification 75 Cp 0.634Upper tolerance limit 100 Cpl 0.586Lower tolerance limit 50 Cpu 0.682AverageStandard deviation73.10013.138 6 7 8 Nominal specification 75 Cp 0.634 Upper tolerance limit 100 Cpl 0.586 Lower tolerance limit 50 Cpu 0.682 Average Standard deviation 73.100 13.138 9 1415161718192021222324385.000 80.000 60.000 85.000 65.000

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 the grid

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 data in

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). Average (p-bar) 0.305 Average (p-bar) 0.305 Average (p-bar) 0.305 Sample Fraction Standard Sample Value Size Nonconforming

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). Average (p-bar) 0.630 Average (p-bar) 0.630 Average (p-bar) 0.630 Sample Fraction Standard Sample Value Size Nonconforming

Wk. 4 10 154300186489Thurs. Wk. 1 10 5330205827512Wk. 26451222433368Wk. 36723204713164Wk. 4 11 101001172486Fri. Wk. 1 2 1 3 4 0 0 0 1 2 2 7 5 9Wk. 23342111323367Wk. 36041220003168Wk. 44220101202486Avg. Vacant Tables7.25 2.75 2.65 1.65 1.2 1.25 1.1 2.75 3.7 3.45 3.9 6.4 7.5Grand Average 3.50 3.50 3.50 3.50

Case: The PIVOT Initiative at Midwest Bank Part II 1 and to identify and agree on key CTQs that impact the customer. During the process, it was difficult for everyone, especially the subject matter experts (SMEs), to ignore their perceptions in speculating about possible causes of errors, which became known as

Check for in-stock info. ? Assigned to picker?Place label &package orderEnter order into computerNewOrder placed in queue for fillingFill orderFill orderSr. warehouse tech. checks accuracyExpedited orderNoYesMove to shipping for pickupSr.warehouse tech.Apprentice Check for in-stock info. ? Assigned to picker?Place label &package orderEnter order into computerNewOrder placed in queue for

Problem 9-10 Aircraft Parts Distributors EMPLOYEES PROCESSING METHOD Experience Empowerment to contact client Sequencing of work Training on the equipmentError check methodTraining on how to handle errors in orders Instructions to cust. svc. rep on order entryFormat used by type of items orderedDelivery of instructions to warehouseInaccurateshipments Empowerment to contact client Sequencing of work

Prob09-19.xlsx Problem 9-19 Lauren Elizabeth Apparel Company Billing errors 9.95% 85.25% 995Delivery errors 7.95% 93.20% 795Long delays 6.80% 100.00% 680Total 10000 Billing errors 9.95% 85.25% 995Delivery errors 7.95% 93.20% 795Long delays 6.80% 100.00% 680Total 10000 Billing errors 9.95% 85.25% 995 Delivery errors 7.95% 93.20% 795 Long delays 6.80% 100.00% 680 Total 10000 QUALITY ERRORS AND PERCENTAGES Percent Cumulative %

Prob09-13.xlsx Problem 9-13 ElecktronTech Student Trainees in Electronics Time (minutes) 12 8.70% 26.00% 41013 15.22% 40.00% 71114 6.52% 46.00% 31115 6.52% 52.00% 31116 15.22% 66.00% 71117 13.04% 78.00% 61118 15.22% 92.00% 71219 6.52% 98.00% 31220 2.17% 100.00% 112 12 8.70% 26.00% 41013 15.22% 40.00% 71114 6.52% 46.00% 31115 6.52% 52.00% 31116 15.22% 66.00% 71117 13.04%

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 3.8 10724.1 5.4 48.1 3.9 8197.5 5.5 31.7 4.0 6209.7 5.6 20.7 4.1 4661.2 5.7 13.3 4.2 3467.0 5.8 8.5 4.3 2555.1 5.9 5.4 4.4 1865.8 6.0

Prob09-16.xlsx Setup delays 29.08% 63.83% 205Design delays 11.35% 75.18% 80No paper 9.93% 85.11% 70Order info error 6.95% 92.06% 49Cust. chg, delays 4.26% 96.31% 30Lost order 3.69% 100.00% 26Total 705 Setup delays 29.08% 63.83% 205Design delays 11.35% 75.18% 80No paper 9.93% 85.11% 70Order info error 6.95% 92.06% 49Cust. chg, delays 4.26% 96.31% 30Lost

Problem 9-15 Plastic Gear for PrintHeads, Inc. 3.71 3.77 3.81 3.71 3.71 3.673.68 3.77 3.82 3.71 3.71 3.683.69 3.77 3.74 3.77 3.74 3.683.73 3.71 3.82 3.77 3.80 3.693.73 3.74 3.80 3.77 3.80 3.693.76 3.72 3.82 3.76 3.80 3.693.76 3.72 3.81 3.76 3.73 3.703.81 3.72 3.83 3.76 3.73 3.70 3.71 3.77 3.81 3.71 3.71

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 in accounts receivable, multiplied by the number of days since the sale, divided by the total

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 9750 1160 645 102010 710 1140 650 100011 740 1140 675 108012 675 1080 710 114013 720

Input Data 23 14.425 10.826 11.329 2.646 3.458 4.964 4.467 4.370 5.175 4.485 3.490 4.696 5.5 23 14.425 10.826 11.329 2.646 3.458 4.964 4.467 4.370 5.175 4.485 3.490 4.696 5.5 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 Problem 9-12 Lakerside United Bank Cash Bank Cash Strapping Errors Errors vs. Weeks

Raw Data Problem 9-17 DeltaWidgets, Inc. Production Rate vs. Defects Production Rate Defects 13 9 16 10 20 1625 1728 1832 2236 2538 2041 2043 1248 1250 1653 1156 1059 9 20 1625 1728 1832 2236 2538 2041 2043 1248 1250 1653 1156 1059 9 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 Page

For a probability of 98.7% good items, we find NORM.S.INV(0.987) + SHIFT = 3.226212Prob. 9-3Alpha Beta Gamma CombinedThis problem requires finding 3 defect rates, plus a combined rate. See columns Defect rate 0.0100 0.0107 0.0192 0.0121to the right. This assumes a 1.5 sigma shift. Sigma level 3.8263 3.8003 3.5706 3.7533 For

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 and

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) 25 12345678910

Problem 9-9 Placewrite, Inc. EMPLOYEES PROCESSING METHOD Experience Sequencing of work Training on the equipmentError check methodTraining on how to handle errors in copyInstruction on resume writingFormat used by clientsDelivery of instructions to processorCLIENT PROCEDURESClient dissatisfaction Sequencing of work Training on the equipmentError check methodTraining on how to handle errors in copyInstruction on resume writingFormat

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 5.0 232.63.5 22750.1 5.1 159.13.6 17864.4 5.2 107.83.7 13903.4 5.3 72.33.8 10724.1 5.4 48.13.9 8197.5 5.5 31.74.0 6209.7

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 in

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

National Furniture Store C-E Diagram for Special Order Errors SALES ASSOCIATE ORDER PREPARATION No request for office to re-FAX orderLost or misplaced order Wrong merchandise orderedStore not notified when order is due thereREGIONAL OFFICEORDER PROCESSINGOrder errors No request for office to re-FAX orderLost or misplaced order Wrong merchandise orderedStore not notified when order is

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 3

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% 7Courtesy of receptionist 9.72% 68.06% 7Friendliness of phone receptionist 6.94% 75.00% 5Responsive care via phone 6.94% 81.94% 5Comfortable waiting 5.56% 87.50% 4Physician listens 4.17% 91.67% 3Respectful

associate puts the order form in a bin for the office manager to fax to the special order department at the regional office. When the office manager faxes the special order forms from the bin, she files them in a notebook. associate puts the order form in a bin for

offered through a network of retail banking centers located primarily within Ohio and Kentucky. Midwest Bank also has a growing presence in Florida with 13 retail banking centers. Commercial banking products and services are offered through nine regional offices. Customers can also access this Midwests financial and banking products and

Case - Implementing Six Sigma at GE Fanuc i and sale of factory automation products, which serve the automotive, food-processing and packaging, paper, pharmaceutical, robotics, chemical, and energy markets. The headquarters and main manufacturing plant is at its Charlottesville facility, and includes more than 500,000 square feet of floor space divided among

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 Deviation LCLp

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 6 5 87 0.0575 0.029476 00.0756 0.1647 3

Binomial Probability Distribution Enter data only in yellow-shaded cells. Number of successes, x3 Sample size, n (up to 50) 10Probability of a success, p0.2f(x) = P(X = x)0.2013 Sample size, n (up to 50) 10Probability of a success, p0.2f(x) = P(X = x)0.2013 Sample size, n (up to 50) 10 Probability of a success, p0.2 f(x)

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 100 Average (np-bar) Standard deviation Number Sample Nonconforming

Sample size 36Sample average 100.3Confidence Interval95%Error 0.1633Lower 100.1367 Sample size 36Sample average 100.3Confidence Interval95%Error 0.1633Lower 100.1367 Sample size 36 Sample average 100.3 Confidence Interval 95% Error 0.1633 Lower 100.1367 Confidence Interval for Population Mean, Standard Deviation Known Alpha 0.05 Standard deviation 0.5 Upper 100.4633 Confidence Interval for Population Mean, Standard Deviation Unknown Alpha 0.05 Sample standard deviation Sample size 16Sample average 100.3Confidence Interval 95%t-value 2.1314Error

0.49 0.2 0.0001 0.49 0.15 0.00010.5 0.2 0 0.5 0.6 00.51 0.2 0.0001 0.51 0.15 0.00010.52 0.2 0.0004 0.52 0.03 0.00040.53 0 0.0009 0.53 0.02 0.0009Expected Value, E[X] 0.5 Expected Value, E[X] 0.5Variance, Var[X] 0.0002 Variance, Var[X] 0.00009Expected Loss 25.00 Expected Loss 11.25 0.49 0.2 0.0001 0.49 0.15 0.00010.5 0.2 0

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 a variety

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 3

22 Factorial Experiment Enter data ONLY in yellow-shaded cells. Description Name Low level High level Factor 1 Temperature 100 degrees 120 degreesFactor 2 Reacton time 60 minutes 75 minutes Factor 1 Temperature 100 degrees 120 degreesFactor 2 Reacton time 60 minutes 75 minutes Factor 1 Temperature 100 degrees 120 degrees Factor 2 Reacton time 60

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 6 5 0 1.8 5.82492247 3 0 1.8 5.82492248 1 0 1.8 5.82492249 2

SUMMARY OUTPUT Regression Statistics Multiple R 0.999967043 R Square 0.999934087 Adjusted R Square 0.999925848 Standard Error 0.027282724 Observations 10 ANOVA Regression 1 90.33725522 90.33725522 121364.4291 5.16118E-18Residual 8 0.005954776 0.000744347Total 9 90.34321Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 0.02648404 0.018447595 1.435636437 0.189027821 -0.016056191 0.069024271 -0.016056191 0.069024271Source (X) 0.991364042 0.002845689 348.3739787 5.16118E-18 0.984801871

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 on sheet

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 on sheet

1 2 3 4 5 6 7 8 9 10 11 12 13 1415161718192021222324252627282930313233343536373839410.600 10.650 10.650 10.700 10.600 10.750 10.800 10.850 10.650 10.650 10.700 10.600Lower limit 10.450510.700 10.750 10.700 10.750 10.550 10.700 10.850 10.700 10.750 10.600 10.750 10.700Upper limit 10.950610.600 10.900 10.850 10.750 10.650 10.650 10.600 10.750 10.750 10.600 10.650 10.650Cell width 0.050710.600 10.750 10.800 10.700 10.600 10.850 10.850 10.850 10.800 10.850 10.850

U-Bolt Data 10.6010.7010.6010.6010.7510.6510.6010.8010.8510.7010.6510.7510.9010.7510.75 10.6010.7010.6010.6010.7510.6510.6010.8010.8510.7010.6510.7510.9010.7510.75 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 10.8010.8010.7010.7010.6510.6010.55 10.8010.8010.7010.7010.6510.6010.55 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 10.6510.8510.6010.9010.8510.7510.8510.7510.80 10.6510.8510.6010.9010.8510.7510.8510.7510.80 10.65 10.85 10.60 10.90 10.85 10.75 10.85 10.75 10.80 10.7010.6510.5510.8010.6510.6010.6010.8510.6510.75 10.7010.6510.5510.8010.6510.6010.6010.8510.6510.75 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 10.7010.6010.6510.8510.8010.6010.70 10.7010.6010.6510.8510.8010.6010.70 10.70 10.60 10.65 10.85 10.80 10.60 10.70 10.70 10.75 10.65 10.85 10.70 10.65 10.80 10.65 10.70 10.65

1 2 3 4 5 6 7 89101112131415161718192021222324252627282930313233410.600 10.650 10.650 10.700 10.600 10.750 10.800 10.850 10.650 10.650 10.700 10.600510.700 10.750 10.700 10.750 10.550 10.700 10.850 10.700 10.750 10.600 10.750 10.700 Enter smallest and largest limits for the frequency distribution in the yellow cells below.610.600 10.900 10.850 10.750 10.650 10.650 10.600 10.750 10.750 10.600 10.650 10.650710.600 10.750 10.800

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 Deviation LCLp

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 #