Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table:Year1234578 91011Registrations (000)5.05.05.04.011.09.06.011.013.016.017.0This exercise contains only parts a and b.a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responsesto one decimal place):Year4678101112Forecast4.676.6788.678.671013.3315.33b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, andregistrations in the other 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12are (round your responses to one decimal place):Year45678101112Forecast

Formula used- 3-period moving average forecast=At-1+At-2+At-33 3-period weighted moving average…

Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table: Year 1 2 3 4 5 6 7 8 9 10 11 Registrations (000) 5.0 5.0 5.0 4.0 11.0 9.0 6.0 11.0 13.0 16.0 17.0 This exercise contains only parts a and b. a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to one decimal place): Year 4 5 6 7 8 9 10 11 12 Forecast 4.67 6.67 8 8.67 8.67 10 13.33 15.33 b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registrations in the other 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to one decimal place): Year 4 5 6 7 8 10 11 12 Forecast

Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table: Year 1 2 3 4 5 6 7 8 9 10 11 Registrations (000) 5.0 5.0 5.0 4.0 11.0 9.0 6.0 11.0 13.0 16.0 17.0 This exercise contains only parts a and b. a) Using a 3-year moving average, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to one decimal place): Year 4 5 6 7 8 9 10 11 12 Forecast 4.67 6.67 8 8.67 8.67 10 13.33 15.33 b) Using a 3-year weighted moving average in which the registrations in the most recent year are given a weight of 2, and registrations in the other 2 years are each given a weight of 1, the forecasted registrations (in thousands) for years 4 through 12 are (round your responses to one decimal place): Year 4 5 6 7 8 10 11 12 Forecast

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 22P: The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six...

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At the beginning of each week, a machine is in one of four conditions: 1 = excellent; 2 = good; 3 = average; 4 = bad. The weekly revenue earned by a machine in state 1, 2, 3, or 4 is 100, 90, 50, or 10, respectively. After observing the condition of the machine at the beginning of the week, the company has the option, for a cost of 200, of instantaneously replacing the machine with an excellent machine. The quality of the machine deteriorates over time, as shown in the file P10 41.xlsx. Four maintenance policies are under consideration: Policy 1: Never replace a machine. Policy 2: Immediately replace a bad machine. Policy 3: Immediately replace a bad or average machine. Policy 4: Immediately replace a bad, average, or good machine. Simulate each of these policies for 50 weeks (using at least 250 iterations each) to determine the policy that maximizes expected weekly profit. Assume that the machine at the beginning of week 1 is excellent.
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The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
Mr. Ferdinand is a well known entrepreneur who sells fresh organic beef to persons in the local community. Customers love the quality of meet that Mr. Ferdinand sells so they tend to recommend his product to other persons. Recently, Mr. Ferdinand has been running out of beef to sell to his customers. One customer suggests to him that he must use past sales records to forecast demand for his beef so that he can meet his customers demand. Using a numerical example, demonstrate to Mr. Ferdinand how he can use a three week and a four-week weighted moving average to forecast demand for his beef. Show how Mr. Ferdinand can test the accuracy of his forecast. Explain to Mr. Ferdinand two ways in which he can benefit from doing forecast for his beef.
Exercise # 2 –Calculating MADt, Revised MADt,Error,and the Revised Error :Has the Forecast Improved ? How much has it improved in % terms ? Franklin Henry forecasts demand for an auditing and accounting services group, for an estate planning group ,and a business consulting group.His group forecasts for the preceding six-month(M) period, which were made six months prior to that time, are each based on a level, patternless projection.The following data table shows these forecasts(F) and the actual demand (AD) for each group : Estate Planning Auditing and Accounting Business Consulting (Client –days) (Client –days) (Client –days) M AD F M AD F M AD F 1 120 130 1 260 230 1 120 120 2 80 130…
Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table: Year 1 2 3 4 5 6 7 8 9 10 11 Registrations (000) 5.00 6.00 3.00 5.00 10.00 8.00 8.00 9.00 11.00 16.00 17.00 Part 2 a) Calculate the forecasted registrations for years 2 through 12 using exponential smoothing, with a smoothing constant (α) of 0.40 and a starting forecast of 6.00 for year 1 (round your responses to one decimal place): Year 1 2 3 4 5 6 7 8 9 10 11 12 Forecast (000) 6.00 enter your response here enter your response here enter your response here enter your response here enter your response here enter your response here enter your response here enter your response here enter…
Three years ago CMSV admissions office began using exponential smoothing with a smoothing constant of 0.25 to forecast the number of applications for admissions each year. Based on the previous experience, this process was begun with an initial estimate of 5000 applications. The actual number of applications turned out to be 4600 in the first year and grew to 5300 in the second year and 6000 last year. Determine the forecasts that were made for each of the past three years. Calculate MSE and MAD for these three years and determine the forecast for the next year.
The number of heart surgeries performed at HeartvilleGeneral Hospital has increased steadily over the past sev-eral years. The hospital’s administration is seeking the bestmethod to forecast the demand for such surgeries in year 6.The data for the past 5 years are shown. The hospital’s administration is considering the followingforecasting methods. Begin error measurement in year 3 soall methods are compared for the same years.i. Exponential smoothing, with a = 0.6. Let the initial fore-cast for year 1 be 45, the same as the actual demand.ii. Exponential smoothing, with a = 0.9. Let the initial fore-cast for year 1 be 45, the same as the actual demand.iii. Trend projection with regression.iv. Two-year moving average.v. Two-year weighted moving average, using weights 0.6 and0.4, with more recent data given more weight.vi. If MAD is the performance criterion chosen by the admin-istration, which forecasting method should it choose?vii. If MSE is the performance criterion chosen by the…
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A restaurant wants to forecast its weekly sales. Historical data (in dollars) for 15 weeks are shown in this Excel file (also shown in Problem 6 on page 243 in the textbook). Answer the following questions related to this information: What is the MSE for the two- and three-period moving average models? Compare the results. Find the best number of periods for the moving average model based on MSE.
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