For problems 1 and 2, use the following set of seasonal factors. Use 1200 for the starting deseasonal forecast (meaning, as of January 1, 2013) for problem 1. Seasonal factors for use in this problem set:
1. In January '13, seasonal actual demand came in at 1650 cases. Setting ? = 0.3, what will the new smoothed forecast be? What will the new seasonal forecasts be for February, April, July, and December?
2. In February '13, seasonal actual demand came in at 410 cases. What will the new smoothed forecast be? What are the new seasonal forecasts for March, April, July, and December?
3. March Demand comes in at 395. What will be the new smoothed (deseasonal) forecast? What are the new seasonal forecasts for April, July, and December?
4. April demand comes in at 745. What will be the new smoothed (deseasonal) forecast? What are the new seasonal forecasts for May, July, December?
Discussion Question: You have three years of historical sales data for the product that you forecasted in problems 1 through 4.
Based upon this data, do you think that the starting deseasonal forecast (as of January 1, 2013) of 1200 for problem 1 is reasonable?
Do you see any year-to-year trends? What about the given seasonality factors? (HINT: calculate the monthly seasonal factors for each of the past three years. Plot these factors on a graph.
Use a different color for each year. Do they look consistent? Do you notice any trends?)
HISTORICAL DEMAND FOR CASES OF SMART CARDS (USED IN DIGITAL CAMERAS)
|
Month
|
2010
|
2011
|
2012
|
|
Jan
|
1500
|
1510
|
1960
|
|
Feb
|
300
|
350
|
340
|
|
Mar
|
400
|
480
|
450
|
|
AN
|
600
|
630
|
760
|
|
May
|
1500
|
1700
|
1760
|
|
Am
|
1400
|
1540
|
1410
|
|
Jul
|
1000
|
1380
|
1600
|
|
Aug
|
1400
|
1540
|
1410
|
|
Sep
|
900
|
980
|
980
|
|
Oct
|
500
|
530
|
630
|
|
Nov
|
500
|
530
|
630
|
|
Dec
|
2000
|
2330
|
2270
|
|
Total
|
12000
|
13500
|
14200
|