Question 16You work for BMW as a planner. BMW is expecting to increase its annual sales for BMW- X5 in year 2021 by 10 % comparedto year 2020 annual sales. Find the quarterly forecast for year 2021, given the following data:(You need to consider the Quarterly seasonal effect)QuarterActual Car Sales (2019)Actual Car Sales (2020)Quarter 18000088000Quarter 2100000110000Quarter 3120000132000Quarter 49000099000000Attach you solutions.Write your results of the 1st Q Forecasted sales in Field.

BMW-X5 sales are increasing by 10% every quarter looking at as per the given data. let us clarify…

Answered: You work for BMW as a planner. BMW is…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Mr Brown a small-scale manufacturer of musical instruments thinks that the demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. He has collected the data shown in the table attached; Using the least squares regression method derive a forecasting equation. Determine the estimate for guitar sales if Maroon 5 performed on TV nine times last month?
You work for BMW as a planner. BMW is expecting to increase its annual sales for BMW- X5 in year 2021 by 10 % compared to year 2020 annual sales. Find the quarterly forecast for year 2021, given the following data : (You need to consider the Quarterly seasonal effect) Quarter Actual Car Sales (2019) Actual Car Sales (2020) Quarter 1 80000 88000 Quarter 2 100000 110000 Quarter 3 120000 132000 Quarter 4 90000 99000
Below is a table containing data on product demand for the most recent five months along with the forecasts that had been made for those 5 previous months. Use the 3-period weighted moving average method to forecast the number of sales to expect next month. Use the following weights: 0.4 for the most recent, 0.3 for the 2nd most recent, and 0.3 for the 3rd most recent. Month Demand Forecast 1 308 337 2 388 341.4 3 344 368 4 400 342.8 5 341 378.4
Given the following data, use least-squares regression to derive a trend equation: Period Demand 1 8 where y = Demand and x = Period. 2 9 3 7 4 8 5 9 O 6 13 The least-squares regression equation that shows the best relationship between demand and period is (round your responses to two decimal places): >=0+0x.
Sales of Volkswagens have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Year Sales 1 460 2 502 3 518 4 575 5 580 Part 2 a) Forecasted sales for year 6 using the trend projection (linear regression) method are enter your response here sales (round your response to one decimal place).
After using your forecasting model for six months, you decide to test it using MAD and a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD May June July August September October Period FORECAST 450 500 550 615 635 710 a. Find the tracking signal. (Negative values should be indicated by a minus sign. Round your answers to 2 decimal places.) May June July August September October ACTUAL 495 545 395 460 675 625 Tracking Signal 1.00 2.00 (0.80) (2.20) (2.38) (3.50)
The most naive forecast can is quite valuable in leading to an organization’s success because it is most widely understood by senior managers. True or False
st Info Period 1 2 3 4 5 6 7 8 9 10 K Develop two exponential smoothing forecasts for periods 2 through 11. For the first forecast, use a = 0.3. For the second, use α = 0.7. Assume that your forecast for period 1 was 205. Click the icon to view the time series data. Find the exponential smoothing forecast for each period when a = 0.3 (enter your responses rounded to one decimal place). Demand 222 249 222 228 235 155 161 153 163 152 X Period 1 2 Demand 222 249 Forecast (α = 0.3) 205
We are using Winter’s method and monthly data toforecast the GDP. (All numbers are in billions of dollars.)At the end of January 2005, Lt 600 and Tt 5. We aregiven the following seasonalities: January, 0.80; February,0.85; December, 1.2. During February 2005, the GDP is ata level of 630. At the end of February what is the forecastfor the December 2005 level of the GDP? Use a b g 0.5.
Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 Sales $3,400,000 $4,650,000 $3,300,000 $2,900,000 What would be the forecast for next year's sales using the naïve approach? Answer is complete but not entirely correct. Next year's sales $ 3,550,000 2020 $3,500,000
Given: Year Demand 1 7 2 9 3 5 4 9 5 Predict the value for Year 5: 2 year moving average What is MSE for 2 year moving average? 2 year moving average using 0.6 (weight for the oldest period) and 0.4(weight for most recent period Exponential smoothing, =0.2 and forecast for Year 1 = 5 Linear trend Which forecast method has the least amount of error using MAD? What is the coefficient?
Given the following data, use least-squares regression to derive a trend equation: Period Demand where y Demand and x = Period. 1 10 2 12 3 5 4 13 5 12 6 11 D The least-squares regression equation that shows the best relationship between demand and period is (round your responses to two decimal places):

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