FORECASTING
Casestudy 1
Revenue
SouthwesternUniversity football game Attendance
Question1
model
Theforecasting technique applied, in this case, is the quantitativemethod. I have forecasted the expected revenue of the year 2014 and2015 by use of linear regression model in the Excel functions. In theselection of this technique, I have weighed various factors thatcould have an impact on the variety or the historical trend of thegame attendance. The forecasting technique applied has a highaccuracy level, and this is why I have considered using it. The mainpurpose of this forecasting is to present an overview of the expectedrevenue from the football game attendance. I have preferred the useof quantitative model since this approach mainly concerns solely withthe available data and ignores the fickleness of the participants andconcentrate on the underlying data to predict the future trend.
Question2
Thefollowing are the expected revenues for the year 2014 and 2015 (seethe workings in the excel spreadsheet attached)
Year 
Game 
Total attendance 
Price 
Revenue 

1 
2 
3 
4 
5 

2014 
48,453 
51,571 
46,983 
35,583 
49,148 
231,739 
20 
$4,634,779 
2015 
51,496 
53,237 
48,019 
37,708 
51,878 
242,338 
21 
$5,089,094 
Fromthe workings, the expected revenue is $4,634,779. This amount isarrived at by multiplying the expected attendance of each game by theticket price.
Questionthree
School’soptions
Salesforecasting refers to the prediction of the number or set of numbersthat corresponds to a future occurrence. It is an important tool ofmanagement as it helps in decision making especially when building upproduction budgets. Sales forecasting helps the managers in coming upwith close estimations of the expected demand. It is, therefore,important to ensure a high level of accuracy when predicting thefuture sales level. As forecast horizon shortens, the forecastaccuracy increases. Since the case of the firm discussed hereembraces monthly forecasting, we also expect that the accuracy isrelatively high.
Casestudy 2
Question1
Thetrend
Thetrend has shown that the revenue decreases from January to Septemberand then starts increasing over the year (see the graph). Thisimplies that the sales of the Glass Slipper is seasonal, and it ishigh at the beginning and the end of each year. The regressionanalysis has shown that the revenue is at the lowest point during themonth of September after that it starts rising gradually.
Questiontwo
Thetrend line
Thetrend developed for the year 2014 through regression analysis is moresimilar to that of question one above. The trend line has a negativeslope just like that of the previous analysis. However, the slope ofthis line is constantly sloping downward because the forecastinganalysis used, in this case, is a linear regression.
Question3
Multiplicativemodel
Thisis an estimation of the future trend of revenue by taking intoaccount the four components of a time series (Douglass, William &Wathen, 2011). These components include the trend, the cyclicalvariation the seasonal variation and the irregular variation.
SecularTrend T this is a longterm direction of a time series. It shows thegeneral movement of the activities of the firm by clearly indicatingthe down swings and the upswings.
Cyclicvariation C – this is a rise or fall in time series for one year.Normal operations of any business are faced with recession,depression and after that recovery.
Seasonalvariation – this shows the pattern of change in time series withina year. In this case, the firm has a pattern that is repeating itselfeach year.
Irregularvariation – this represents the unpredictable fluctuations in theexpected trend.
Toobtain the forecasted values for the year 2014, we can usemultiplicative model. This is done by multiplying the four componentsof the time series that is
Y= T × C × S × I
Seethe table below
Period 
Monthly Revenue ($1000s) 

month 
2011 
2012 
2013 
Centered Moving Average 
Ratio to CMA 
seasonal index 
2014 Sales Deseasonalized 

1 
January 
438 
444 
450 

2 
February 
420 
425 
438 

3 
March 
414 
423 
434 
441 
0.984871 
0.984871407 
434 
4 
April 
318 
331 
338 
403 
0.838017 
0.838016529 
338 
5 
May 
306 
318 
331 
368 
0.900272 
0.900271985 
331 
6 
June 
240 
245 
254 
308 
0.825569 
0.825568797 
254 
7 
July 
240 
255 
264 
283 
0.932862 
0.932862191 
264 
8 
August 
216 
223 
231 
250 
0.925234 
0.925233645 
231 
9 
September 
198 
210 
224 
240 
0.934631 
0.934631433 
224 
10 
October 
225 
233 
243 
233 
1.044413 
1.044412607 
243 
11 
November 
270 
278 
289 

12 
December 
315 
322 
335 
Theresulting slope regression analysis is quite different from the oneobtained with multiplicative model. This is because this model tendsto more accurate by taking into account any contributing factor thatcould affect the future revenue.
References
DouglassA., William G. & Wathen S. (2011). StatisticalTechniques in Business and Economics fifteenth edition.California: McGraw Hill International.
HeizerJ. and Render B. (2014). OperationManagement 11^{th}edition.Upper Saddle River Pearson.