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

Find support for the material in this assignment at the website: MDHarper.com

At the MDHarper.com website, click the “Operations Management” tab.

Search under the “Forecasting” category.

View Video-1 (10:40): Introduction

View Video-2 (3:18): Moving Average Part 1

View Video-3 (8:31): Moving Average Part 2

View Video-4 (6:38): Moving Average

View Video-5 (9:33): Regression

View Video-6 (3:29): Seasonal Index Part 1

View Video-7 (6:31): Seasonal Index Part 2

View Video-8 (9:19): Decomposition

View Video-9 (11:43): Exponential Smoothing

View Video-10 (9:45): Forecast Accuracy

View Video-11 (22:05): Worked Problems

View Video-12 (8:24): Decomposition by Filtering

View Video-13 (7:44): Decomposition by Blocking

Total 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

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