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[>Operations Management homework help](https://www.sweetstudy.com/fields/business-finance-operations-management) **files (2)** – ## WeekTwoAssignment2InstructionsHM.docx – ## EKGMachines-Week2Assignment2.xlsx **## WeekTwoAssignment2InstructionsHM.docx** | | | | — | — | | | | **Turnitin™** | | | …
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[>Operations Management homework help](https://www.sweetstudy.com/fields/business-finance-operations-management)
**files (2)**
– ## WeekTwoAssignment2InstructionsHM.docx
– ## EKGMachines-Week2Assignment2.xlsx
**## WeekTwoAssignment2InstructionsHM.docx**
| | |
| — | — |
| |
| **Turnitin™** | |
| **This assignment will be submitted to Turnitin™.** |
| **Instructions** | |
| **Assignment 2 of 2:**<br>**Nelson Fabricators sells a portable EKG machine. The sales manager requires a weekly forecast of the portable EKG machine so that he can schedule production. The manager uses exponential smoothing with α = 0.30.**<br>**Data template:** [**EKG Machines – Week 2 Assignment 2 template.xlsx**](https://navigate.nu.edu/content/enforced/11242-SCM630-020416-Master/EKG%20Machines%20-%20Week%202%20Assignment%202%20template.xlsx)<br>**1. Forecast the number of machines at the end of week 17.**<br>**2. Calculate the bias and MAD.**<br>**3. Recreate your forecast with α = 0.70. Compare the MAD to your previous answer. Which forecast is better?**<br>**4. Using SOLVER, compute the forecast with the minimum MAD by changing α. What characteristic of the data directly influences your optimal answer? Briefly discuss the benefit of optimizing α.**<br>**Provide your solution and responses in the attached template, using ONLY the space provided.** |
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**Week Two Assignment 2**
****
## image1.png
**## EKGMachines-Week2Assignment2.xlsx**
## Sheet1
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| Nelson Fabricators — portable EKG machines |
| | Alpha (a) = | 0.58 | | Note: Starting condition impacts solution. See SOLVER to verify. |
| Week | Actual Production | Forecast | | Error | Abs Error |
| 1 | 434 | 434 | | | | | Assumption for initial conditions |
| 2 | 613 | 434 | | -179 | 179 |
| 3 | 640 | 537 | | -103 | 103 |
| 4 | 531 | 596 | | 65 | 65 |
| 5 | 758 | 559 | | -199 | 199 |
| 6 | 711 | 673 | | -38 | 38 |
| 7 | 790 | 695 | | -95 | 95 |
| 8 | 807 | 750 | | -57 | 57 |
| 9 | 646 | 783 | | 137 | 137 |
| 10 | 714 | 704 | | -10 | 10 |
| 11 | 881 | 710 | | -171 | 171 |
| 12 | 746 | 808 | | 62 | 62 |
| 13 | 702 | 772 | | 70 | 70 |
| 14 | 945 | 732 | | -213 | 213 |
| 15 | 977 | 854 | | -123 | 123 |
| 16 | 750 | 925 | | 175 | 175 |
| 17 | | |
| | | | | Bias | MAD |
| | | | a = 0.3 | | | (was =average(f7…f21) |
| | | | a = 0.7 | | | (was =average(f7…f21) |
| | | | a = optimal |
| | | ANSWERS: | | There is an upward trend in the data, so a higher alpha is more responsive to this trend. (A trend-adjusted method would be even better.) |
| | | | | Optimization provides the alpha which mimizing your historical forecasting error, and therefore is assumed to provide the best future forecast (assuming the underlying conditions remain the same as before). |
EKG Machines
Nelson Fabricators
Actual Production 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 613 640 531 758 711 790 807 646 714 881 746 702 945 977 750 Forecast 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 434 537 596 559 673 695 750 783 704 710 808 772 732 854 925 0
Week
Machines Produced
## oleObject1.bin
## image1.emf
## image2.png
**## EKGMachines-Week2Assignment2.xlsx**
## Sheet1
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| Nelson Fabricators — portable EKG machines |
| | Alpha (a) = | 0.58 | | Note: Starting condition impacts solution. See SOLVER to verify. |
| Week | Actual Production | Forecast | | Error | Abs Error |
| 1 | 434 | 434 | | | | | Assumption for initial conditions |
| 2 | 613 | 434 | | -179 | 179 |
| 3 | 640 | 537 | | -103 | 103 |
| 4 | 531 | 596 | | 65 | 65 |
| 5 | 758 | 559 | | -199 | 199 |
| 6 | 711 | 673 | | -38 | 38 |
| 7 | 790 | 695 | | -95 | 95 |
| 8 | 807 | 750 | | -57 | 57 |
| 9 | 646 | 783 | | 137 | 137 |
| 10 | 714 | 704 | | -10 | 10 |
| 11 | 881 | 710 | | -171 | 171 |
| 12 | 746 | 808 | | 62 | 62 |
| 13 | 702 | 772 | | 70 | 70 |
| 14 | 945 | 732 | | -213 | 213 |
| 15 | 977 | 854 | | -123 | 123 |
| 16 | 750 | 925 | | 175 | 175 |
| 17 | | |
| | | | | Bias | MAD |
| | | | a = 0.3 | | | (was =average(f7…f21) |
| | | | a = 0.7 | | | (was =average(f7…f21) |
| | | | a = optimal |
| | | ANSWERS: | | There is an upward trend in the data, so a higher alpha is more responsive to this trend. (A trend-adjusted method would be even better.) |
| | | | | Optimization provides the alpha which mimizing your historical forecasting error, and therefore is assumed to provide the best future forecast (assuming the underlying conditions remain the same as before). |
EKG Machines
Nelson Fabricators
Actual Production 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 613 640 531 758 711 790 807 646 714 881 746 702 945 977 750 Forecast 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 434 537 596 559 673 695 750 783 704 710 808 772 732 854 925 0
Week
Machines Produced
## oleObject1.bin
## image1.emf
## image2.png
**## WeekTwoAssignment2InstructionsHM.docx**
| | |
| — | — |
| |
| **Turnitin™** | |
| **This assignment will be submitted to Turnitin™.** |
| **Instructions** | |
| **Assignment 2 of 2:**<br>**Nelson Fabricators sells a portable EKG machine. The sales manager requires a weekly forecast of the portable EKG machine so that he can schedule production. The manager uses exponential smoothing with α = 0.30.**<br>**Data template:** [**EKG Machines – Week 2 Assignment 2 template.xlsx**](https://navigate.nu.edu/content/enforced/11242-SCM630-020416-Master/EKG%20Machines%20-%20Week%202%20Assignment%202%20template.xlsx)<br>**1. Forecast the number of machines at the end of week 17.**<br>**2. Calculate the bias and MAD.**<br>**3. Recreate your forecast with α = 0.70. Compare the MAD to your previous answer. Which forecast is better?**<br>**4. Using SOLVER, compute the forecast with the minimum MAD by changing α. What characteristic of the data directly influences your optimal answer? Briefly discuss the benefit of optimizing α.**<br>**Provide your solution and responses in the attached template, using ONLY the space provided.** |
| | |
| |
**Week Two Assignment 2**
****
## image1.png
**## EKGMachines-Week2Assignment2.xlsx**
## Sheet1
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| Nelson Fabricators — portable EKG machines |
| | Alpha (a) = | 0.58 | | Note: Starting condition impacts solution. See SOLVER to verify. |
| Week | Actual Production | Forecast | | Error | Abs Error |
| 1 | 434 | 434 | | | | | Assumption for initial conditions |
| 2 | 613 | 434 | | -179 | 179 |
| 3 | 640 | 537 | | -103 | 103 |
| 4 | 531 | 596 | | 65 | 65 |
| 5 | 758 | 559 | | -199 | 199 |
| 6 | 711 | 673 | | -38 | 38 |
| 7 | 790 | 695 | | -95 | 95 |
| 8 | 807 | 750 | | -57 | 57 |
| 9 | 646 | 783 | | 137 | 137 |
| 10 | 714 | 704 | | -10 | 10 |
| 11 | 881 | 710 | | -171 | 171 |
| 12 | 746 | 808 | | 62 | 62 |
| 13 | 702 | 772 | | 70 | 70 |
| 14 | 945 | 732 | | -213 | 213 |
| 15 | 977 | 854 | | -123 | 123 |
| 16 | 750 | 925 | | 175 | 175 |
| 17 | | |
| | | | | Bias | MAD |
| | | | a = 0.3 | | | (was =average(f7…f21) |
| | | | a = 0.7 | | | (was =average(f7…f21) |
| | | | a = optimal |
| | | ANSWERS: | | There is an upward trend in the data, so a higher alpha is more responsive to this trend. (A trend-adjusted method would be even better.) |
| | | | | Optimization provides the alpha which mimizing your historical forecasting error, and therefore is assumed to provide the best future forecast (assuming the underlying conditions remain the same as before). |
EKG Machines
Nelson Fabricators
Actual Production 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 613 640 531 758 711 790 807 646 714 881 746 702 945 977 750 Forecast 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 434 537 596 559 673 695 750 783 704 710 808 772 732 854 925 0
Week
Machines Produced
## oleObject1.bin
## image1.emf
## image2.png
**## WeekTwoAssignment2InstructionsHM.docx**
| | |
| — | — |
| |
| **Turnitin™** | |
| **This assignment will be submitted to Turnitin™.** |
| **Instructions** | |
| **Assignment 2 of 2:**<br>**Nelson Fabricators sells a portable EKG machine. The sales manager requires a weekly forecast of the portable EKG machine so that he can schedule production. The manager uses exponential smoothing with α = 0.30.**<br>**Data template:** [**EKG Machines – Week 2 Assignment 2 template.xlsx**](https://navigate.nu.edu/content/enforced/11242-SCM630-020416-Master/EKG%20Machines%20-%20Week%202%20Assignment%202%20template.xlsx)<br>**1. Forecast the number of machines at the end of week 17.**<br>**2. Calculate the bias and MAD.**<br>**3. Recreate your forecast with α = 0.70. Compare the MAD to your previous answer. Which forecast is better?**<br>**4. Using SOLVER, compute the forecast with the minimum MAD by changing α. What characteristic of the data directly influences your optimal answer? Briefly discuss the benefit of optimizing α.**<br>**Provide your solution and responses in the attached template, using ONLY the space provided.** |
| | |
| |
**Week Two Assignment 2**
****
## image1.png
**## EKGMachines-Week2Assignment2.xlsx**
## Sheet1
| | | | | | | | |
| — | — | — | — | — | — | — | — |
| Nelson Fabricators — portable EKG machines |
| | Alpha (a) = | 0.58 | | Note: Starting condition impacts solution. See SOLVER to verify. |
| Week | Actual Production | Forecast | | Error | Abs Error |
| 1 | 434 | 434 | | | | | Assumption for initial conditions |
| 2 | 613 | 434 | | -179 | 179 |
| 3 | 640 | 537 | | -103 | 103 |
| 4 | 531 | 596 | | 65 | 65 |
| 5 | 758 | 559 | | -199 | 199 |
| 6 | 711 | 673 | | -38 | 38 |
| 7 | 790 | 695 | | -95 | 95 |
| 8 | 807 | 750 | | -57 | 57 |
| 9 | 646 | 783 | | 137 | 137 |
| 10 | 714 | 704 | | -10 | 10 |
| 11 | 881 | 710 | | -171 | 171 |
| 12 | 746 | 808 | | 62 | 62 |
| 13 | 702 | 772 | | 70 | 70 |
| 14 | 945 | 732 | | -213 | 213 |
| 15 | 977 | 854 | | -123 | 123 |
| 16 | 750 | 925 | | 175 | 175 |
| 17 | | |
| | | | | Bias | MAD |
| | | | a = 0.3 | | | (was =average(f7…f21) |
| | | | a = 0.7 | | | (was =average(f7…f21) |
| | | | a = optimal |
| | | ANSWERS: | | There is an upward trend in the data, so a higher alpha is more responsive to this trend. (A trend-adjusted method would be even better.) |
| | | | | Optimization provides the alpha which mimizing your historical forecasting error, and therefore is assumed to provide the best future forecast (assuming the underlying conditions remain the same as before). |
EKG Machines
Nelson Fabricators
Actual Production 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 613 640 531 758 711 790 807 646 714 881 746 702 945 977 750 Forecast 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 434 434 537 596 559 673 695 750 783 704 710 808 772 732 854 925 0
Week
Machines Produced
## oleObject1.bin
## image1.emf
## image2.png
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