Operations Management
11th Edition
ISBN: 9780132921145
Author: Jay Heizer
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 4, Problem 42P
CEO John Goodale, at Southern Illinois Power and Light, has been collecting data on demand for electric power in its western subregion for only the past 2 years. Those data are shown in the table below.
To plan for expansion and to arrange to borrow power from neighboring utilities during peak periods, Goodale needs to be able to forecast demand for each month next year. However, the standard
| DEMAND IN MEGAWATTS | |||
| MONTH | LAST YEAR | THIS YEAR | |
| January | 5 | 17 | |
| February | 6 | 14 | |
| March | 10 | 20 | |
| April | 13 | 23 | |
| May | 18 | 30 | |
| June | 15 | 38 | |
| July | 23 | 44 | |
| August | 26 | 41 | |
| September | 21 | 33 | |
| October | 15 | 23 | |
| November | 12 | 26 | |
| December | 14 | 17 | |
- a. What are the weaknesses of the standard forecasting techniques as applied to this set of data?
- b. Because known models are not appropriate here, propose your own approach to forecasting. Although there is no perfect solution to tackling data such as these (in other words, there are no 100% right or wrong answers), justify your model.
- c. Forecast demand for each month next year using the model you propose.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
pleae solve all parts within 30 minutes.
CEO John Goodale, at Southern Illinois Power and Light,has been collecting data on demand for electric power in itswestern subregion for only the past 2 years. Those da ta areshown in the ta ble below.To plan for expansion and to arrange to borrow powerfrom neighboring utilities during peak periods, Goodaleneeds to be able to forecast demand for each month nextyear. However, the standard forecasting models discussed inthis chapter will not fit the data observed for the 2 years.a) What are the weaknesses of the standard forecasting techniquesas applied to this set of data?b) Because known models a re not appropriate here, proposeyour own approach to forecasting. Although there is noperfect solution to tackling data such as these (in otherwords, there are no 100% right or wrong answers), justify your model.c) Forecast demand for each month next year using themodel you propose.
The worksheet Hudson Demand Case Data in MindTap provides the number of visits over one year from January to December (52 weeks). Chart the data and explain the characteristics of the time series. How would you forecast future demand for customer visits? What criteria will you use to determine a “good” forecast? What methods would you use, and why? What is your final recommendation with respect to a forecasting method?
Note:-
Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism.
Answer completely.
You will get up vote for sure.
Chapter 4 Solutions
Operations Management
Ch. 4 - What is a qualitative foretasting model, and when...Ch. 4 - Identify and briefly describe the two general...Ch. 4 - Identify the three forecasting time horizons....Ch. 4 - Briefly describe the steps that are used to...Ch. 4 - A skeptical manager asks what medium-range...Ch. 4 - Explain why such forecasting devices as moving...Ch. 4 - What is the basic difference between a weighted...Ch. 4 - What three methods are used to determine the...Ch. 4 - Research and briefly describe the Delphi...Ch. 4 - What is the primary difference between a...
Ch. 4 - Define time series.Ch. 4 - What effect does the value of the smoothing...Ch. 4 - Explain the value of seasonal indices in...Ch. 4 - Which forecasting technique can place the most...Ch. 4 - In your own words, explain adaptive forecasting.Ch. 4 - What is the purpose of a tracking signal?Ch. 4 - Explain, in your own words, the meaning of the...Ch. 4 - What is the difference between a dependent and an...Ch. 4 - Give examples of industries that are affected by...Ch. 4 - Give examples of industries in which demand...Ch. 4 - Prob. 21DQCh. 4 - The following gives the number of pints of type B...Ch. 4 - 4.2 a. Plot the above data on a graph. Do you...Ch. 4 - Refer to Problem 4.2. Develop a forecast for years...Ch. 4 - A check-processing center uses exponential...Ch. 4 - The Carbondale Hospital is considering the...Ch. 4 - The monthly sales for Yazici Batteries, Inc., were...Ch. 4 - The actual demand for the patients at Omaha...Ch. 4 - Daily high temperatures in St. Louis for the last...Ch. 4 - Lenovo uses the ZX-81 chip in some of its laptop...Ch. 4 - Data collected on the yearly registrations for a...Ch. 4 - Use exponential smoothing with a smoothing...Ch. 4 - Consider the following actual and forecast demand...Ch. 4 - As you can see in the following table, demand for...Ch. 4 - Following are two weekly forecasts made by two...Ch. 4 - Refer to Solved Problem 4.1 on page 138. a. Use a...Ch. 4 - Solved example 4.1 Sales of Volkswagens popular...Ch. 4 - Refer to Solved Problem 4.1. Using smoothing...Ch. 4 - Consider the following actual (At) and forecast...Ch. 4 - Income at the architectural firm Spraggins and...Ch. 4 - Question 4.20 Resolve Problem 4.19 with =.1 and ...Ch. 4 - Question 4.21 Refer to the trend-adjusted...Ch. 4 - Question 4.22 Refer to Problem 4.21. Complete the...Ch. 4 - Question 4.23 Sales of quilt covers at Bud Baniss...Ch. 4 - Question 4.24 Mark Gershon, owner of a musical...Ch. 4 - Question 4.25 The following gives the number of...Ch. 4 - Prob. 26PCh. 4 - Question 4.27 George Kyparisis owns a company...Ch. 4 - Question 4.28 Attendance at Orlandos newest...Ch. 4 - Question 4.29 North Dakota Electric Company...Ch. 4 - Lori Cook has developed the following forecasting...Ch. 4 - Prob. 31PCh. 4 - Question 4.32 The following data relate the sales...Ch. 4 - Question 4.33 The number of internal disk drives...Ch. 4 - Question 4.34 The number of auto accidents in...Ch. 4 - Question 4.35 Rhonda Clark, a Slippery Rock,...Ch. 4 - Accountants at the Tucson firm, Larry Youdelman,...Ch. 4 - Sales of tablet computers at Ted Glickmans...Ch. 4 - Question 4.38 City government has collected the...Ch. 4 - Dr. Lillian Fok, a New Orleans psychologist,...Ch. 4 - Using the data in Problem 4.39, apply linear...Ch. 4 - Bus and subway ridership for the summer months in...Ch. 4 - CEO John Goodale, at Southern Illinois Power and...Ch. 4 - Emergency calls to the 911 system of Durham, North...Ch. 4 - Using the 911 call data in Problem 4.43, forecast...Ch. 4 - The following are monthly actual and forecast...Ch. 4 - Thirteen students entered the business program at...Ch. 4 - Question 4.47 Storrs Cycles has just started...Ch. 4 - Question 4.48 Dave Fletcher, the general manager...Ch. 4 - Question 4.49 Boulanger Savings and Loan is proud...Ch. 4 - Case study Southwestern University: (B) This...Ch. 4 - Case study Southwestern University: (B) This...Ch. 4 - Southwestern University: (B) This integrated case...Ch. 4 - For its first 2 decades of existence, the NBAs...Ch. 4 - For its first 2 decades of existence, the NBAs...Ch. 4 - For its first 2 decades of existence, the NBAs...Ch. 4 - For its first 2 decades of existence, the NBAs...Ch. 4 - Forecasting at Hard Rock Cafe Video Case With the...Ch. 4 - Forecasting at Hard Rock Cafe Video Case With the...Ch. 4 - Forecasting at Hard Rock Cafe Video Case With the...Ch. 4 - Forecasting at Hard Rock Cafe Video Case With the...Ch. 4 - Forecasting at Hard Rock Cafe Video Case With the...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, operations-management and related others by exploring similar questions and additional content below.Similar questions
- Under what conditions might a firm use multiple forecasting methods?arrow_forwardThe file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.arrow_forwardScenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing expenditures for packaging materials with Jeff Joyner. Ben was particularly disturbed about the amount spent on corrugated boxes purchased from Southeastern Corrugated. Ben said, I dont like the salesman from that company. He comes around here acting like he owns the place. He loves to tell us about his fancy car, house, and vacations. It seems to me he must be making too much money off of us! Jeff responded that he heard Southeastern Corrugated was going to ask for a price increase to cover the rising costs of raw material paper stock. Jeff further stated that Southeastern would probably ask for more than what was justified simply from rising paper stock costs. After the meeting, Ben decided he had heard enough. After all, he prided himself on being a results-oriented manager. There was no way he was going to allow that salesman to keep taking advantage of Coastal Products. Ben called Jeff and told him it was time to rebid the corrugated contract before Southeastern came in with a price increase request. Who did Jeff know that might be interested in the business? Jeff replied he had several companies in mind to include in the bidding process. These companies would surely come in at a lower price, partly because they used lower-grade boxes that would probably work well enough in Coastal Products process. Jeff also explained that these suppliers were not serious contenders for the business. Their purpose was to create competition with the bids. Ben told Jeff to make sure that Southeastern was well aware that these new suppliers were bidding on the contract. He also said to make sure the suppliers knew that price was going to be the determining factor in this quote, because he considered corrugated boxes to be a standard industry item. Is Ben Gibson acting legally? Is he acting ethically? Why or why not?arrow_forward
- Scenario 3 Ben Gibson, the purchasing manager at Coastal Products, was reviewing purchasing expenditures for packaging materials with Jeff Joyner. Ben was particularly disturbed about the amount spent on corrugated boxes purchased from Southeastern Corrugated. Ben said, I dont like the salesman from that company. He comes around here acting like he owns the place. He loves to tell us about his fancy car, house, and vacations. It seems to me he must be making too much money off of us! Jeff responded that he heard Southeastern Corrugated was going to ask for a price increase to cover the rising costs of raw material paper stock. Jeff further stated that Southeastern would probably ask for more than what was justified simply from rising paper stock costs. After the meeting, Ben decided he had heard enough. After all, he prided himself on being a results-oriented manager. There was no way he was going to allow that salesman to keep taking advantage of Coastal Products. Ben called Jeff and told him it was time to rebid the corrugated contract before Southeastern came in with a price increase request. Who did Jeff know that might be interested in the business? Jeff replied he had several companies in mind to include in the bidding process. These companies would surely come in at a lower price, partly because they used lower-grade boxes that would probably work well enough in Coastal Products process. Jeff also explained that these suppliers were not serious contenders for the business. Their purpose was to create competition with the bids. Ben told Jeff to make sure that Southeastern was well aware that these new suppliers were bidding on the contract. He also said to make sure the suppliers knew that price was going to be the determining factor in this quote, because he considered corrugated boxes to be a standard industry item. As the Marketing Manager for Southeastern Corrugated, what would you do upon receiving the request for quotation from Coastal Products?arrow_forwardThe owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?arrow_forwardThe file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). 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_forward
- 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?arrow_forwardThe 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_forwardData collected on the yearly demand for 50-pound bags of fertilizer at Wallace Garden Supply are shown in the following table: YEAR DEMAND FOR FERTILIZER (1,000S OF BAGS) 1 4 2 6 3 4 4 5 5 10 6 8 7 7 8 9 9 12 10 14 11 15 a) Forecast demand again with a weighted moving average in which sales in the most recent year are given a weight of 2 and sales in the other 2 years are each given a weight of 1?arrow_forward
- Sales of tablet computers at Marika Gonzalez's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: Week 2 3 1 Demand 19 23 29 4 36 5 6 7 8 9 10 26 28 35 24 24 30 a) The forecast for weeks 2 through 10 using exponential smoothing with a = 0.60 and a week 1 initial forecast of 19.0 are (round your responses to two decimal places): Week 1 2 3 4 Demand 19 23 29 36 Forecast 19.0 5 6 7 26 28 35 8 9 10 24 30 24 b) For the forecast developed using exponential smoothing (a=0.60 and initial forecast 19.0), the MAD = sales (round your response to two decimal places). c) For the forecast developed using exponential smoothing (x=0.60 and initial forecast 19,0), the tracking signal = (round your response to two decimal places).arrow_forwardSales of tablet computers at Ted Glickman's electronics store in Washington, D.C., over the past 10 weeks are shown in the table below: Week 1 2 3 4 5 6 7 8 9 10 Demand 20 23 29 38 25 29 36 24 26 29 a) The forecast for weeks 2 through 10 using exponential smoothing with α = 0.50 and a week 1 initial forecast of 20.0 are (round your responses to two decimal places): Week 1 2 3 4 5 6 7 8 9 10 Demand 20 23 29 38 25 29 36 24 26 29 Forecast 20.0 b) For the forecast developed using exponential smoothing (α = 0.50 and initial forecast 20.0), the MAD = sales (round your response to two decimal places). c) For the forecast developed using exponential smoothing (α = 0.50 and initial forecast 20.0), the tracking…arrow_forwardTwo forecasting models were used to predict the future value of a time series. These are shown in the accompanying table, together with the actual values. Forecast Value Ft Model 1 Model 2 Actual Yt 7.5 6.3 6 6.3 6.7 6.6 5.4 7.1 7.3 8.2 7.5 9.4 Compute MAD and MPE for each model and determine which was more accurate.arrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,
Purchasing and Supply Chain ManagementOperations ManagementISBN:9781285869681Author:Robert M. Monczka, Robert B. Handfield, Larry C. Giunipero, James L. PattersonPublisher:Cengage Learning
Contemporary MarketingMarketingISBN:9780357033777Author:Louis E. Boone, David L. KurtzPublisher:Cengage Learning
MarketingMarketingISBN:9780357033791Author:Pride, William MPublisher:South Western Educational Publishing
Practical Management Science
Operations Management
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:Cengage,
Purchasing and Supply Chain Management
Operations Management
ISBN:9781285869681
Author:Robert M. Monczka, Robert B. Handfield, Larry C. Giunipero, James L. Patterson
Publisher:Cengage Learning
Contemporary Marketing
Marketing
ISBN:9780357033777
Author:Louis E. Boone, David L. Kurtz
Publisher:Cengage Learning
Marketing
Marketing
ISBN:9780357033791
Author:Pride, William M
Publisher:South Western Educational Publishing
Single Exponential Smoothing & Weighted Moving Average Time Series Forecasting; Author: Matt Macarty;https://www.youtube.com/watch?v=IjETktmL4Kg;License: Standard YouTube License, CC-BY
Introduction to Forecasting - with Examples; Author: Dr. Bharatendra Rai;https://www.youtube.com/watch?v=98K7AG32qv8;License: Standard Youtube License