B Case Due to a dramatic decline in the number of visitors due to COVID-19, theme park Magic World has suffered a massive loss in 2020 which forced the company to put on hold planned renovations of current attractions and construction of new attractions. 70% of health experts believe that life will become normal after the general population gets vaccinated, the remaining 30% argue that the vaccines will not be effective for new virus mutations and therefore wearing masks and limiting gatherings may still be enforced for few years. If life gets back to normal, Magic World expect revenues to match pre-COVID figures, so renovations and additions should be implemeted. Keeping projects on hold may sound prudent but may result in lower number of visitors if life gets back to normal. Assume the probability of life getting back to normal is 0.7. The company is considering 4 decision alternatives: 1. Keep Renovations and Additions on Hold, with payoffs $-20 (loss) if life gets back to normal and $0 if it doesn't 2. Implement Renovations - Keep Additions on Hold, with payoffs $120 if life gets back to normal and $-30 (loss) if it doesn't 3. Keep Renovations on Hold - Implement Additions, with payoffs $160 if life gets back to normal and $-80 (loss) if it doesn't 4. Implement both Renovations and Additions, with payoffs $180 if life gets back to normal and $-110 (loss) if it doesn't. Tasks 1 point Construct the payoff table and, using the Expected Value approach, obtain the optimal Decision and corresponding Expected Value. Show work in yellow area below 1 point Conduct a sensitivity analysis using Excel's Data Table, with similar columns to the ones we developed in class: - First column: Prob of Life Back to Normal, with values ranging from 0 to 1 by. 1 increments -Second column: Optimal Decision -Third column: Expected Value Place Sensitvity Table inside the yellow area below Retail Sales: Restaurants and Other Eating Places, Millions of Dollars, Monthly, Month Not Seasonally Adjusted 2016-01-01 44115 2016-02-01 45139 2016-03-01 49100 2016-04-01 48893 2016-05-01 49634 2016-06-01 47822 2016-07-01 49313 2016-08-01 48397 2016-09-01 46129 2016-10-01 47710 2016-11-01 46022 2016-12-01 50012 2017-01-01 46310 2017-02-01 45767 2017-03-01 52336 2017-04-01 50738 2017-05-01 52376 2017-06-01 51553 2017-07-01 51746 2017-08-01 51505 2017-09-01 48963 2017-10-01 49932 2017-11-01 48090 2017-12-01 53549 2018-01-01 47797 2018-02-01 47540 2018-03-01 55328 2018-04-01 52271 2018-05-01 55993 2018-06-01 56191 2018-07-01 56477 2018-08-01 56692 2018-09-01 51571 2018-10-01 52993 2018-11-01 51060 2018-12-01 55178 2019-01-01 49940 2019-02-01 49623 2019-03-01 57793 2019-04-01 55206 2019-05-01 59164 2019-06-01 58310 2019-07-01 58679 2019-08-01 59318 2019-09-01 53944 2019-10-01 55762 2019-11-01 54039 2019-12-01 57086 Assume today's date is Dec 31, 2019 (pre-COVID!!). To answer the following questions, use data provided in sheet 'Restaurant Sales Data'. 1 point 1 point 1 point 1 point 1 point Obtain the seasonal indexes for Jan to Dec based on the past FOUR years (2016-2019) in yellow cells below. Obtain the Seasonally Adjusted Actual for Jan '19 to Dec '19 in orange cells below. Using only 2019 data and Exponential Smoothing with Trend (a=0.3, B=0.6), forecast Sales for Jan '20 in light blue cell below. Obtain MSE for 2019 in gray cell below. Using Excel's Solver, obtain the optimal parameters (a and B) that minimize MSE for 2019. GENERATE SOLVER'S ANSWER REPORT Seasonal Index Seasonall y Adj Actual 2019 Actual 2016 Actual 2017 Actual 2018 Actual 2019 Forecast 2020 1 point Using the seasonal indexes obtained and your forecast for Year 2020, forecast monthly Sales for Year 2020 (enter values in appropriate orange cells above) B Case Due to a dramatic decline in the number of visitors due to COVID-19, theme park Magic World has suffered a massive loss in 2020 which forced the company to put on hold planned renovations of current attractions and construction of new attractions. 70% of health experts believe that life will become normal after the general population gets vaccinated, the remaining 30% argue that the vaccines will not be effective for new virus mutations and therefore wearing masks and limiting gatherings may still be enforced for few years. If life gets back to normal, Magic World expect revenues to match pre-COVID figures, so renovations and additions should be implemeted. Keeping projects on hold may sound prudent but may result in lower number of visitors if life gets back to normal. Assume the probability of life getting back to normal is 0.7. The company is considering 4 decision alternatives: 1. Keep Renovations and Additions on Hold, with payoffs $-20 (loss) if life gets back to normal and $0 if it doesn't 2. Implement Renovations - Keep Additions on Hold, with payoffs $120 if life gets back to normal and $-30 (loss) if it doesn't 3. Keep Renovations on Hold - Implement Additions, with payoffs $160 if life gets back to normal and $-80 (loss) if it doesn't 4. Implement both Renovations and Additions, with payoffs $180 if life gets back to normal and $-110 (loss) if it doesn't. Tasks 1 point Construct the payoff table and, using the Expected Value approach, obtain the optimal Decision and corresponding Expected Value. Show work in yellow area below 1 point Conduct a sensitivity analysis using Excel's Data Table, with similar columns to the ones we developed in class: - First column: Prob of Life Back to Normal, with values ranging from 0 to 1 by. 1 increments -Second column: Optimal Decision -Third column: Expected Value Place Sensitvity Table inside the yellow area below Retail Sales: Restaurants and Other Eating Places, Millions of Dollars, Monthly, Month Not Seasonally Adjusted 2016-01-01 44115 2016-02-01 45139 2016-03-01 49100 2016-04-01 48893 2016-05-01 49634 2016-06-01 47822 2016-07-01 49313 2016-08-01 48397 2016-09-01 46129 2016-10-01 47710 2016-11-01 46022 2016-12-01 50012 2017-01-01 46310 2017-02-01 45767 2017-03-01 52336 2017-04-01 50738 2017-05-01 52376 2017-06-01 51553 2017-07-01 51746 2017-08-01 51505 2017-09-01 48963 2017-10-01 49932 2017-11-01 48090 2017-12-01 53549 2018-01-01 47797 2018-02-01 47540 2018-03-01 55328 2018-04-01 52271 2018-05-01 55993 2018-06-01 56191 2018-07-01 56477 2018-08-01 56692 2018-09-01 51571 2018-10-01 52993 2018-11-01 51060 2018-12-01 55178 2019-01-01 49940 2019-02-01 49623 2019-03-01 57793 2019-04-01 55206 2019-05-01 59164 2019-06-01 58310 2019-07-01 58679 2019-08-01 59318 2019-09-01 53944 2019-10-01 55762 2019-11-01 54039 2019-12-01 57086 Assume today's date is Dec 31, 2019 (pre-COVID!!). To answer the following questions, use data provided in sheet 'Restaurant Sales Data'. 1 point 1 point 1 point 1 point 1 point Obtain the seasonal indexes for Jan to Dec based on the past FOUR years (2016-2019) in yellow cells below. Obtain the Seasonally Adjusted Actual for Jan '19 to Dec '19 in orange cells below. Using only 2019 data and Exponential Smoothing with Trend (a=0.3, B=0.6), forecast Sales for Jan '20 in light blue cell below. Obtain MSE for 2019 in gray cell below. Using Excel's Solver, obtain the optimal parameters (a and B) that minimize MSE for 2019. GENERATE SOLVER'S ANSWER REPORT Seasonal Index Seasonall y Adj Actual 2019 Actual 2016 Actual 2017 Actual 2018 Actual 2019 Forecast 2020 1 point Using the seasonal indexes obtained and your forecast for Year 2020, forecast monthly Sales for Year 2020 (enter values in appropriate orange cells above)