Operations Management: Processes and Supply Chains, Student Value Edition Plus MyLab Operations Management with Pearson eText -- Access Card Package (12th Edition)
12th Edition
ISBN: 9780134855424
Author: Lee J. Krajewski, Manoj K. Malhotra, Larry P. Ritzman
Publisher: PEARSON
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Chapter 10, Problem 12P
Gemini Inc. is using the Sales and Operations Planning with Spreadsheets Solver in OM Explorer to develop a 5-nioiith production plan. Use the provided derived outputs and calculated costs to specify the model’s inputs. (Hint: Don’t forget that the
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The following table shows the actual sales of upholstered chairs for a furniture manufacturer and the forecastsmade for each of the last 8 months. Calculate CFE, MSE, s, MAD, and MAPE for this product.
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Bradley's Copiers sells and repairs photocopy machines. The manager needs weekly forecasts of service calls so that he can schedule service personnel. Use the actual demand in the first period for the forecast for the first week so error measurement begins in the second week. The manager uses exponential smoothing with α = 0.5 Forecast the number of calls for week 6, which is next week. WEEK ACTUAL SERVICE CALLS 1 28 2 32 3 40 4 27 5 30
The actual demand of a product for six months are summarized in the table below:
Month (t)
Demand Dt
1
80
2
90
3
70
4
100
5
70
6
90
Find three months weighted moving averages by assuming the weights, W1 =1, W2 = 0.4 and W3 = 0.5
ii.
Compute the mean forecast error
iii.
Compute the mean square error (MSE)
iv.
Compute the mean absolute deviation (MAD).
v.
Mean absolute percent error (MAPE)
Chapter 10 Solutions
Operations Management: Processes and Supply Chains, Student Value Edition Plus MyLab Operations Management with Pearson eText -- Access Card Package (12th Edition)
Ch. 10 - List the types of costs incurred when employees...Ch. 10 - Prob. 2DQCh. 10 - Prob. 3DQCh. 10 - Explain why management should be concerned about...Ch. 10 - The Barberton Municipal Division of Road...Ch. 10 - Bob Canton’s golf camp estimates the following...Ch. 10 - Prob. 3PCh. 10 - Prob. 4PCh. 10 - Management at the Kerby Corporation has determined...Ch. 10 - Gretchen’s Kitchen is a fast-food restaurant...
Ch. 10 - Prob. 7PCh. 10 - Tax Prep Advisers, Inc. has forecasted the...Ch. 10 - Climate Control, Inc. makes expedition-quality...Ch. 10 - Prob. 11PCh. 10 - Gemini Inc. is using the Sales and Operations...Ch. 10 - Gerald Glynn manages the Michaels Distribution...Ch. 10 - Cara Ryder manages a ski school in a large resort...Ch. 10 - Prob. 15PCh. 10 - Prob. 17PCh. 10 - The Hickory Company manufactures wooden desks....Ch. 10 - Prob. 19PCh. 10 - The Mowry Machine Shop still has five jobs to be...Ch. 10 - If we use the same number of workers in each...Ch. 10 - Prob. 2AMECh. 10 - Prob. 3AMECh. 10 - Suppose the overtime cost is $3,300. What happens...Ch. 10 - Suppose the undertime cost is the same as the...Ch. 10 - Prob. 6AMECh. 10 - Prob. 1VCCh. 10 - Does Starwood employ a chase, level, or mixed...Ch. 10 - How would staffing for the opening of a new hotel...Ch. 10 - Explain the alternatives available to Darlene Fry...Ch. 10 - Prob. 2C
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- The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?arrow_forwardThe file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?arrow_forwardThe file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?arrow_forward
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