Reference no: EM131215930

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:	0.4	Last month
		0.3	Two months ago
		0.2	Three months ago
		0.1	Four months ago
	Month	Sales
	1	20
	2	21
	3	15
	4	14
	5	13
	6	16
	7	17
	8	18
	9	20
	10	20
	11	21
	12	23
	13	21
	14	14
	15	13
	16	18
	17	21
	18	16
	19	17

2.

		Tickets Sold
		Week 1	Week 2	Week 3	Week 4
	Sun	399	413	401	378
	Mon	169	147	155	157
	Tue	201	222	194	208
	Wed	198	217	235	204
	Thu	311	300	299	309
	Fri	554	591	563	570
	Sat	597	611	600	617

3.

Week	Demand
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)
	1	1.5
	2	1
	3	1.3
	4	1.5
	5	2.5
	6	2.7
	7	2.4
	8	2
	9	2.7
	10	4.4
	11	3.4
	12	1.7
	13	1.5
	14	2
	15	2.3
	16	1.9

b.

	Linear Regression
		Year (summer months)	Number of Tourists (in million)	Ridership (in millions)
		1	7	1.5
		2	2	1
		3	6	1.3
		4	4	1.5
		5	14	2.5
		6	15	2.7
		7	16	2.4
		8	12	2
		9	14	2.7
		10	20	4.4
		11	15	3.4
		12	7	1.7
		13	7	1.5
		14	10	2
		15	13	2.3
		16	19	1.9