Business
QUESTIONS – Homework #2 – Forecasting
BANA 3000, Operation Management – Spring 2019
Instructions.All questions are multiple choice.Submit answers through Canvas on or before the due date. No answers are accepted after the due date. If no answers are submitted by the due date, the grade will be zero.Take care and note that answers through Canvas may only be submitted once. Therefore, download the Word file with the questions. Obtain all the answers. Then submit all your answers only once through Canvas. |
Forecasting |
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Find support for the material in this assignment at the website: MDHarper.comAt the MDHarper.com website, click the “Operations Management” tab.Search under the “Forecasting” category.View Video-1 (10:40): IntroductionView Video-2 (3:18): Moving Average Part 1View Video-3 (8:31): Moving Average Part 2View Video-4 (6:38): Moving AverageView Video-5 (9:33): RegressionView Video-6 (3:29): Seasonal Index Part 1View Video-7 (6:31): Seasonal Index Part 2View Video-8 (9:19): DecompositionView Video-9 (11:43): Exponential SmoothingView Video-10 (9:45): Forecast AccuracyView Video-11 (22:05): Worked ProblemsView Video-12 (8:24): Decomposition by FilteringView Video-13 (7:44): Decomposition by BlockingTotal of 12 hours and 20 minutes of video instruction.. . . |
Part 1. [Video-1] Question 1. How many of the statements are true?
(A) 0 (B) 1 (C) 2 (D) 3 (E) 4
Statement 1. The periodicity of a seasonal component in a monthly time series is twelve.
Statement 2. A stationary time series will always have a trend component.
Statement 3. An annual time series will never have a seasonal component
Statement 4. A seasonal component is a type of cyclical component.
Part 2. [Video-1] Questions 2-5.
Question 2. Consider the annual time series.
Using a two-point moving average, what is the forecast for time period 9?
(A) 236 (B) 224 (C) 175 (D) 218 (E) none of the above
Years | Series | |
1 | 150 | |
2 | 163 | |
3 | 183 | |
4 | 184 | |
5 | 200 | |
6 | 206 | |
7 | 209 | |
8 | 227 | |
Question 3. Consider the monthly time series.
Using exponential smoothing with a parameter of 0.84, what is the forecast for time period 9?
(A) 236 (B) 224 (C) 175 (D) 218 (E) none of the above
Months | Series | |
1 | 150 | |
2 | 163 | |
3 | 183 | |
4 | 184 | |
5 | 200 | |
6 | 206 | |
7 | 209 | |
8 | 227 | |
Question 4. Consider the weekly time series.
Using simple linear regression, what is the forecast for time period 9?
(A) 236 (B) 224 (C) 175 (D) 218 (E) none of the above
Weeks | Series | |
1 | 150 | |
2 | 163 | |
3 | 183 | |
4 | 184 | |
5 | 200 | |
6 | 206 | |
7 | 209 | |
8 | 227 | |
Question 5. Consider the quarterly time series.
Using the seasonal index approach, what is the forecast for time period 9?
(A) 236 (B) 224 (C) 175 (D) 218 (E) none of the above
Quarters | Series | |
1 | 150 | |
2 | 163 | |
3 | 183 | |
4 | 184 | |
5 | 200 | |
6 | 206 | |
7 | 209 | |
8 | 227 | |
Part 3. [Videos 1-8, 11-13] Questions 6-8
An international distribution company of containers has operated a 5.8-million unit capacity warehouse in Manila to service customers between the Asian continent and the North American continent for the past four years. Liann Able, VP of logistics, sent the quarterly time series, which are the number of units in thousands serviced through the warehouse, to three of her analysts and asked them to bring forecasts to the next planning meeting for discussion. Liann convened the meeting and asked each analyst to report.
Quarter 1 | Quarter 2 | Quarter 3 | Quarter 4 | |
Year 1 | 3082 | 4958 | 1742 | 938 |
Year 2 | 3174 | 5106 | 1794 | 966 |
Year 3 | 3266 | 5254 | 1846 | 994 |
Year 4 | 3358 | 5402 | 1898 | 1022 |
Question 6. [Video-1, Video-2, Video-3, Video-4] Amy assumed a stationary time series with only a random component. Amy reported using a three-point moving average. What did Amy report as the forecast in thousands for the first quarter of year five?
(A) 3450 (B) 3220 (C) 3542 (D) 2774 (E) none of the above Question 7. [Video-1, Video-6] Bob assumed a stationary time series with seasonal and random components. What did Bob report as the forecast in thousands for the first quarter of year five? (A) 3450 (B) 3220 (C) 3542 (D) 2774 (E) none of the above Question 8. [Video-1, Video-8] Carl assumed a non-stationary time series with linear trend, seasonal, and random components. What did Carl report as the forecast in thousands for the first quarter of year six? (A) 3450 (B) 3220 (C) 3542 (D) 2774 (E) none of the above |
Part 4.
Question 9. [Video-10] Assume the time series is an annual time series. For a constant forecast of 212 for all the years, what is the tracking signal with a window of three (Tk3) for the forecast of 223 for year 9?
(A) +0.75 (B) +0.25 (C) –2.00 (D) –8.00 (E) none of the above
Years | Series | |
1 | 150 | |
2 | 163 | |
3 | 183 | |
4 | 184 | |
5 | 200 | |
6 | 206 | |
7 | 209 | |
8 | 227 | |
Part 5. [Select the videos most helpful]
Question 10. At the end of their fiscal year, a consulting company wants a monthly forecast for the number of contracts next year. They collected monthly data from the last five years.
Year 1 | Year 2 | Year 3 | Year 4 | Year 5 | |
Month 1 | 315 | 393 | 471 | 549 | 627 |
Month 2 | 295 | 367 | 439 | 511 | 583 |
Month 3 | 264 | 330 | 396 | 462 | 528 |
Month 4 | 291 | 363 | 435 | 507 | 579 |
Month 5 | 265 | 331 | 397 | 463 | 529 |
Month 6 | 201 | 249 | 297 | 345 | 393 |
Month 7 | 219 | 273 | 327 | 381 | 435 |
Month 8 | 168 | 210 | 252 | 294 | 336 |
Month 9 | 98 | 122 | 146 | 170 | 194 |
Month 10 | 147 | 183 | 219 | 255 | 291 |
Month 11 | 266 | 332 | 398 | 464 | 530 |
Month 12 | 387 | 483 | 579 | 675 | 771 |
If you were working for my consulting company, which set of forecasts would I accept as the best monthly forecasts for next year?
Forecast (A) | Forecast (B) | Forecast (C) | Forecast (D) | (E) none of the above | |
Month 1 | 612 | 917 | 705 | 471 | |
Month 2 | 527 | 786 | 655 | 439 | |
Month 3 | 436 | 653 | 594 | 396 | |
Month 4 | 522 | 781 | 651 | 435 | |
Month 5 | 437 | 655 | 595 | 397 | |
Month 6 | 238 | 353 | 441 | 297 | |
Month 7 | 294 | 440 | 489 | 327 | |
Month 8 | 176 | 265 | 378 | 252 | |
Month 9 | 58 | 87 | 218 | 146 | |
Month 10 | 131 | 196 | 327 | 219 | |
Month 11 | 438 | 656 | 596 | 398 | |
Month 12 | 926 | 1387 | 867 | 579 |