Operations and Supply Chain Management 9th edition
9th Edition
ISBN: 9781119320975
Author: Roberta S. Russell, Bernard W. Taylor III
Publisher: WILEY
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Chapter 12, Problem 22P
A group of business students at Tech organized a student club called Prism to Develop and disseminate content about business majors through a website they created. They would like the college and various academic departments to use their club website to provide information about, and promote, their majors, and to post possible job opportunities from potential employers. In order to convince the college administration and department heads to use their site, they want to develop a
Develop a linear trend line forecast, an exponential smoothing forecast (α = .60) and a three-month weighted moving average (with the most recent month weighted by .50, the next closest month by .30 and the final month by .20). Indicate which one seems to be the most accurate forecast model and the forecast for week 25.
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The purpose of this assignment is to give you an opportunity to create a forecast based on data and interpret the results. You will also gain more practice with Minitab.
Forecasting future commodity prices can be important for many production operations. One such commodity is the price of aluminum ($US per metric ton). For this assignment, you are asked to use Minitab to create two forecasts based upon the data provided below. First, create a one period forecast based upon a three month moving average. Second, create a one period forecast based upon exponential smoothing (using a smoothing weight of 0.95). Compare the two forecasts based upon the Mean Absolute Deviation (MAD) measure of accuracy. Interpret your results.
The Data:
Month
Price
Jan-18
2,209.73
Feb-18
2,181.79
Mar-18
2,069.24
Apr-18
2,254.69
May-18
2,299.67
Jun-18
2,237.62
Jul-18
2,082.24
Aug-18
2,051.51
Sep-18
2,026.46
Oct-18
2,029.86
Nov-18
1,938.51
Dec-18
1,920.38
Jan-19
1,853.72
Feb-19…
Following are two weekly forecasts made by two different methods for the number of gallons of gasoline, in thousands, demanded at a local gasoline station. Also shown are actual demand levels, in
thousands of gallons:
Week
1
2
3
4
Forecast
Method 1
0.95
1.08
0.95
1.20
Actual
Demand
0.72
0.98
1.00
0.97
Week
1
2
3
4
Forecast
Method 2
0.82
1.19
0.92
1.15
Actual
Demand
0.72
0.98
1.00
0.97
The MAD for Method 1= 0.153 thousand gallons (round your response to three decimal places).
The mean squared error (MSE) for Method 1 = thousand gallons² (round your response to three decimal places).
Consider the following time series data.
Choose the correct time series plot.
What type of pattern exists in the data?
Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. Round your answers to two decimal places.
= 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for week 7. Round your answers to two decimal places.
Compare the three-week moving average forecast with the exponential smoothing forecast using
Use trial and error to find a value of the exponential smoothing coefficient that results in a smaller MSE than what you calculated for = 0.2. Find a value of for the smallest MSE. Round your answer to three decimal places.
Chapter 12 Solutions
Operations and Supply Chain Management 9th edition
Ch. 12 - List some of the operations and functions in a...Ch. 12 - What is the difference between quantitative...Ch. 12 - Describe the difference between short- and...Ch. 12 - Prob. 4QCh. 12 - Why is accurate forecasting so important to...Ch. 12 - Discuss the relationship between forecasting and...Ch. 12 - Prob. 7QCh. 12 - Describe the Delphi method for forecasting.Ch. 12 - What is the difference between a trend and a cycle...Ch. 12 - How is the moving average method similar to...
Ch. 12 - In the chapter examples for time series methods,...Ch. 12 - What effect on the exponential smoothing model...Ch. 12 - How does adjusted exponential smoothing differ...Ch. 12 - What determines the choice of the smoothing...Ch. 12 - How does the linear trend line forecasting model...Ch. 12 - Of the time series models presented in this...Ch. 12 - What advantages does adjusted exponential...Ch. 12 - Describe how a forecast is monitored to detect...Ch. 12 - Explain the relationship between the use of a...Ch. 12 - Selecting from MAD, MAPD, MSE, E, and E, which...Ch. 12 - What is the difference between linear and multiple...Ch. 12 - Define the different components (y, x, a, and b)...Ch. 12 - A company that produces video equipment, including...Ch. 12 - The Hartley-Davis motorcycle dealer in the...Ch. 12 - The manager of the I-85 Carpet Outlet needs to be...Ch. 12 - The LawnPlus Fertilizer Company distributes...Ch. 12 - Graph the demand data in Problem 12.3. Can you...Ch. 12 - The chairperson of the department of management at...Ch. 12 - The manager of the Excom Service Station wants to...Ch. 12 - The Intrepid mutual fund of growth stocks has had...Ch. 12 - The Oceanside Hotel is adjacent to City Coliseum,...Ch. 12 - Mary Hernandez has invested in a stock mutual fund...Ch. 12 - Globetron manufactures components for use in small...Ch. 12 - The Bee Line Caf is well known for its popular...Ch. 12 - For the demand data in Problem 12.11, develop a...Ch. 12 - Develop a seasonally adjusted forecast for the...Ch. 12 - Backstreets Pizza delivery service has randomly...Ch. 12 - The Willow River Mining Company mines and ships...Ch. 12 - The Great Northwest Outdoor Company is a catalog...Ch. 12 - Townside Food Vending operates vending machines in...Ch. 12 - The town aquatic center has an indoor pool that...Ch. 12 - Develop an adjusted exponential smoothing forecast...Ch. 12 - During the past five months the emergency room at...Ch. 12 - At its craft store and through its website, the...Ch. 12 - A group of business students at Tech organized a...Ch. 12 - Temco Industries has developed a forecasting model...Ch. 12 - Monitor the forecast in Problem 12.23 for bias...Ch. 12 - Develop a statistical control chart for the...Ch. 12 - Monitor the adjusted exponential smoothing...Ch. 12 - Develop an exponential smoothing forecast with =...
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