MGT303

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For your convenience, an excel (with multiple worksheets) file is posted together with this homework on blackboard. You can find the data you need to answer all questions. Moreover, you are encouraged to use excel to complete the computations and find the answer.

 

 

1. The monthly sales for Telco Batteries, Inc., were as shown in the excel sheet. Please use 4-month weighted moving average method to forecast the demands from month 5 to month 19. The weight associated to last month’s sales is 40%; the weight associated to the sales two months ago is 30%; the weight associated to the sales three months ago is 20%; the weight associated to the sales four months ago is 10%. Once you have the forecasted sales from month 5 to month 19, calculate the following measures of the forecast error. a. Mean Absolute Deviation (MAD); b. Mean Squared Error (MSE); and c. Mean Absolute Percent Error (MAPE).

 

2. The AMC theatre in sunset plaza observes the daily tickets sales for the past four weeks. Please see the data in the excel sheet. The manager of the theatre has forecasted that next week’s total ticket sale will be 2390. Based on this forecast and the seasonal index method, how many tickets will be sold on each day next week?

 

3. The excel file has a sheet that contains data for the past 30 weeks of sales for product X. Based on the data, answer the following questions a. Plot the data. Just by looking at the plot (that is, without doing any calculation), do you think a 3-week simple moving average would be a better forecast, or a 6-week simple moving average? Just by looking at the plot, do you think an exponential smoothing with value of α = 0.2 would be better forecast or would α = 0.6 be better? Explain your reasoning. (Note: do the two comparisons separately; don’t compare moving average with exponential smoothing) b. Apply the four forecasts (3-week and 6-week simple moving average and exponential smoothing with value of α = 0.2 and α = 0.6), and plot the forecasted sales along with the actual sales you plotted in (a). For exponential smoothing, assume the forecast for the first week is 600. c. For each of the above four forecasts, compute the MAD for forecasting before week 25. In addition, compute the MAD for the last five weeks. Compare those numbers to see if they confirm your answer to part (a). If you plan to hand in Excel, please make sure to show enough work and highlight the answers. And you also need to send me the Excel file by email. Total Marks: 15+10+15=40 *The following question is a bonus that worth 10 points. Submission is optional. *

 

4. Data on the bus and subway ridership for the summer months in London, England, have been recorded for the past 16 years. See excel file for the data sheet. a. Please use trend projection method to forecast the ridership (how many) for the summer months in the year 17. b. Now, the bus and subway ridership for the summer months is believed to be tied heavily to the number of tourists visiting the city. The excel data sheet also provides the data on the number of tourists. Please use linear regression model to forecast the ridership (how many) for the summer months in a year when the estimated number of tourists visiting is 18 millions.

 

 

1.Weights:0.4Last month 
 0.3Two months ago 
 0.2Three months ago 
 0.1Four months ago 
      
 MonthSales   
 120   
 221   
 315   
 414   
 513   
 616   
 717   
 818   
 920   
 1020   
 1121   
 1223   
 1321   
 1414   
 1513   
 1618   
 1721   
 1816   
 1917   
      
      
      

2.

       
  Tickets Sold 
 Week 1Week 2Week 3Week 4 
 Sun399413401378 
 Mon169147155157 
 Tue201222194208 
 Wed198217235204 
 Thu311300299309 
 Fri554591563570 
 Sat597611600617 
       
       

 

3.

WeekDemand 
1        574 
2     1,004 
3        717 
4     1,076 
5     1,076 
6        932 
7     1,291 
8     1,219 
9        932 
10     1,219 
11     1,219 
12     1,076 
13     1,650 
14     1,650 
15     1,434 
16     1,219 
17     1,291 
18     1,578 
19     1,793 
20     1,721 
21     1,578 
22     1,865 
23     1,434 
24     1,793 
25     1,865 
26     1,506 
27     1,650 
28     1,506 
29     1,721 
30     1,506 
   

 

4. a.

    
Trend Projection 
    
 Year (summer months)Ridership (in millions) 
  
 11.5 
 21 
 31.3 
 41.5 
 52.5 
 62.7 
 72.4 
 82 
 92.7 
 104.4 
 113.4 
 121.7 
 131.5 
 142 
 152.3 
 161.9 
    
    

 

b.

      
 Linear Regression  
      
  Year (summer months)Number of Tourists (in million)Ridership (in millions) 
   
  171.5 
  221 
  361.3 
  441.5 
  5142.5 
  6152.7 
  7162.4 
  8122 
  9142.7 
  10204.4 
  11153.4 
  1271.7 
  1371.5 
  14102 
  15132.3 
  16191.9 
      
     
  • 10 years ago
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