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Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
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Simple exponential smoothing with α= 0.3 is being used to
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- The Yummy Ice Cream Company projects the demand for ice cream by using first-order exponential smoothing. Last week the forecast was 100,000 gallons of ice cream, and 90,000 gallons was actually sold. Using alpha=.1, prepare a forecast for next week. Calculate the forecast using Alpha=.2 and Alpha=.3 for this problem. Which values of Alpha gave the best forecast, assuming actual demand for next week ends up being 95,000 gallons?arrow_forwardMark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances 3 3 8 5 8 6 Demand for Guitars 2 5 6 4 11 6 This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): Y = + c) The estimate for guitar sales if Maroon 5 performed on TV 11times = sales (round your response to two decimal places). d) The correlation coefficient (r)for this model = (round your response to four decimal places). The coefficient of determination (r2) for this model = (round your response to…arrow_forwardAmazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options. F t+1 = αDt + (1-α) Ft increase value of alpha decrease value of alpha stay with the same alpha decrease the expected forecast for the current month (Ft) On hearing our forecast, our clients probe - how sure are we about the forecast? Which measure of errror (among those stated below) should we best use to respond? a. Root MSE (RMSE) b. Mean absolute percentage error (MAPE) c. Mean forecast error (Avg. error) d. Cumulative forecast error (CFE)arrow_forward
- The number of cases of merlot wine sold by the Connor Owen winery in an eight-year period is as follows: CASES OF MERLOT WINE 276 362 YEAR 2005 2006 2007 2008 2009 2010 2011 2012 404 462 364 506 416 382 Using an exponential smoothing model with an alpha value of 0.30, estimate the smoothed value calculated as of the end of 2012. Use the average demand for 2005 through 2007 as your initial forecast for 2008, and then smooth the forecast forward to 2012. (Round your intermediate calculations and final answer to the nearest whole number.) Answer is complete but not entirely correct. Forecast for 2012 372arrow_forwardIn a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows: Month Sales 1 16 2 22 3 18 4 20 5 23 Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?arrow_forwardMark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances Demand for Guitars Y = This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): + 4 2 X 5 7 5 9 7 6 7 6 10 7arrow_forward
- Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances 3 4 8 6 7 7 Demand for Guitars 4 6 7 5 10 6 This exercise contains only parts b, c, and d. Part 2 b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): Y = enter your response here + enter your response herexarrow_forwardThe following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. Use time series decomposition to forecast quarterly sales for the next year. (Do not round intermediate calculations. Round your answers to the nearest whole number.) Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.arrow_forwardDevelop a forecast for the next month using a four-month moving average method. Use the Excel Functions SLOPE and INTERCEPT to write the linear regression prediction equation with Months as the independent variable and sales as the dependent variable. Use the prediction equation to estimate the number of sales in month 9.arrow_forward
- A company wants to generate a forecast for unit demand for year 2017 using exponential smoothing. The actual demand in year 2016 was 120. The forecast demand in year 2016 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2017 forecast value?arrow_forwardHarlen Industries has a simple forecasting model: Take the actual demand for the same month last year and divide that by the number of fractional weeks in that month. This gives the average weekly demand for that month. This weekly average is used as the weekly forecast for the same month this year. This technique was used to forecast eight weeks for this year, which are shown in the following tables along with the actual demand that occurred. The following eight weeks show the forecast (based on last year) and the demand that actually occurred (picture attached): Calculate the MAD and tracking signal for each week.arrow_forwardAfter using your forecasting model for six months, you decide to test it using a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD FORECAST ACTUAL May 450 500 June 500 550 July 550 400 August 600 500 September 650 675 October 700 600 Find the tracking signal of each montharrow_forward
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