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Forecast

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The management of American Auto Parts is having a meeting to plan the operating budgets for next year. The Senior Vice-President of Sales, Mr. Tom Tremellon, knows that a large portion of the company's income is derived from sales of their 1-year, 3-year, and lifetime batteries. He has asked you, Manager of Production, to prepare an estimate of the revenues the company can expect in each of the four quarters of next year from 1-year, 3-year, and lifetime batteries, as well as the total revenue from all these batteries. Please show all work in EXCEL.

To prepare your forecast, you have contacted Mr. Beasley in the Accounting Department, who has provided you with the following sales figures (in $ thousands) in each quarter of the previous 4 years:

YearQuarter Total 1-Year 3-Year Lifetime
2006 1 1050 205 325 520
2 600 123 180 297
3 575 150 155 270
4 982 190 285 507
2007 1 1010 206 310 494
2 652 115 190 347
3 537 150 160 227
4 990 210 300 480
2008 1 998 200 300 498
2 650 138 205 307
3 550 101 150 299
4 932 175 275 482
2009 1 1070 200 315 555
2 632 125 191 316
3 525 110 160 255
4 980 200 288 492

Use exponential smoothing to develop simple, trend (Holt's), and seasonal models (both simple and trend) for forecasting the quarterly revenue from 1-year batteries. Do the same for 3-year and lifetime batteries, and also for the total revenue. Use the RMSE performance measure for selecting the best model in each case.

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Solution Summary

This solution is comprised of detailed analysis and step-by-step calculations of various Forecast Techniques like Exponentially Smoothed Forecast, Trend Projection etc. in EXCEL. The solution provides students with a clear perspective of the given problems and the related aspects of forecast analysis.

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Moving Average Forecast, Exponential Smoothing Forecast, Adjusted Exponential Smoothing Forecast, MAD, MAPD, Cumulative Error, Comparison among different Forecast Techniques and Regression Analysis

1. The saki motorcycle dealer in Minneapolis wants to make an accurate forecast of demand for the Saki Super TXII motorcycle during the next month.
Because the manufacturer is in Japan, it is difficult to send motorycles back or reorder if the proper number is not ordered a month ahead.
From sales records, the dealer has accumulated the following data for the past year:

Month Motorcyle Sales
January 9
February 7
March 10
April 8
May 7
June 12
July 10
August 11
September 12
October 10
November 14
December 16

a) Compute a 3-month moving average forecast of demand for April thru January of the next year
b) Compute a 5-month moving average forecast for June thru January
c)Compare the two forecasts computed in (a) and (b) using MAD-which should be used next year January?

2. The chairperson of the department of management at State University wants to forecast the number of students
who will enroll in production and operations mgmt next semester-- in order to determine how many sections to schedule.
The chair has acumulated the following enrollment data for the past 8 semesters:

Semester Students enrolled
1 400
2 450
3 350
4 420
5 500
6 575
7 490
8 650

a) Compute a three semester moving average forecast for semesters 4 thru 9
b)compute the exponentially smoothed forecast for the enrollment data α = 0.20
c) compare the two forecasts using MAD-which is more accurate?

3. Whistle Stop Café is well known for its homemade ice cream, made in a small plant in back of café. People drive
all the way from Atlanta and Macon to buy the ice cream. The two women who own the cafe want to develop a forecasting model
so they can plan their ice cream production operation and determine the number of employees they need to sell ice cream in the café.
They have accumulated the following sales records for their ice cream for the past 12 quarters:

Year Quarter Ice Cream sales(gallons)
2003 1 350
2 510
3 750
4 420
2004 5 370
6 480
7 860
8 500
2005 9 450
10 550
11 820
12 570

Develop an adjusted exponential smoothing model with α = 0.50 and β = 0.50 to forecast demand and assess
its accuracy using cumulative error(e) and average error. Does there appear to be any BIAS in the forecast?

4. Aztec Industries has developed a forecasting model that was used to forecast during a 10-month period.
The forecasts and actual demand were as follows:

Month Actual Demand Forecast Demand
1 160 170
2 150 165
3 175 157
4 200 166
5 190 183
6 220 186
7 205 203
8 210 204
9 200 207
10 220 203

Measure the accuracy of the forecast by using MAD, MAPD, and cumulative error.
Does forecasting method appear to be accurate?

5. The manager of Ramona Hotel believes that how well the local Blue Sox professional baseball team is has an impact on the occupancy rate at the hotel during summer months.
Following are the number of victories for the Blue Sox( in a 162-game schedule) for the past 8 years and the hotel occupancy rates:

Year Blue Sox Wins Occupancy Rate in %
1 75 83
2 70 78
3 85 86
4 91 85
5 87 89
6 90 93
7 87 92
8 67 91

Develop a linear regression model for these data and forecast the occupancy rate for the next year if Blue Sox wins 88 games.

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