Archives: Solution Manual

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 II. Clifton […]

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 […]

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, […]

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, […]

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 b. […]

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 award which […]

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 […]

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 […]

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 […]

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 month. A random sample of 1,000 boards is inspected every week for three characteristics. […]

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 managing […]

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 […]

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 6.80% 100.00% […]

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 […]

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 […]

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 % […]

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 […]

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 […]

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 […]

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 order by customer Check for in-stock info. ? Assigned to picker? Place label & […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 if the control limits are removed, […]

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 […]

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 to the left, so the mean is smaller than the median […]

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 𝐴𝑉2 𝑇𝑉2 = 100 0.0122 […]

Chapter 8 – Measuring and Controlling Quality 21 Answer 6. Original Data: Cost Element Amount Customer complaint remakes $ 27,000 Printing plate revisions 28,000 The spreadsheet data and pie chart (see spreadsheet Prob08-06.xlsx in the Instructor materials for details) for […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]

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 […]