Please consider the following information and make forecasts for January, February, March, and April of Year 2. Use second-order polynomial regression to make forecasts.Adjusted seasonal indexActual sales Deseasonalized sales836825Year 1Year 2JanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberJanuaryFebruaryMarchApril0.720.751:221.4811.230.70.780.921.131.281.330.720.751.221.481220131910201184685641875100810681141

Answered: Please consider the following…

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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4.23 Sales of vegetable dehydrators at Bud Banis's dis- count department store in St. Louis over the past year are shown below. Management prepared a forecast using a combination of exponential smoothing and its collective judgment for the 4 months (March, April, May, and June of 2010): Management's Forecast 2009-2010 Month Unit Sales 100 July August September October November 93 96 110 124 December 119 92 January February March 83 101 120 96 114 April May 89 110 June 108 108 a) Compute MAD and MAPE for management's technique. b) Do management's results outperform (i.e., have smaller MAD and MAPE than) a naive forecast? Which forecast do you recommend, based on lower forecast error?
The following represents the actual demand and two different sets of forecasts using different methods. Compute the MAD for each forecasting method and determine the best method for this set of data. Year-Qtr Actual demand Forecast methos 1 MAD1 (A-F) Forecast Method 2 MAD2 (A-F) 2020-3 98 97 89 2020-4 108 110 106 2021-1 83 68 70 2021-2 100 101 102 2021-3 95 86 87 2021-4 90 94 92 2022-1 71 68 72 2022-2 104 105 106 2022-3 84 91 88 2022-4 80 93 91 2023-1 78 68 70 2023-2 120 118 122
Please help with the correct answers in details: Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 4 5 6 Value 19 12 17 10 18 15 Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy. (a) mean absolute error MAE = (b) mean squared error MSE = (c) mean absolute percentage error (Round your answer to two decimal places.) MAPE = % (d) What is the forecast for week 7?
A forecast for the first six months of the year revealed a tendency to underpredict the actual demand for the revitalized Hubig’s Pies plant in the Marigny. Month Actual Forecast January 675 600 February 720 700 March 640 620 April 510 495 May 480 410 June 565 535 What is the mean squared error of this forecast? a. 1,694 b. 1,873 c. 2,075 d. 1,469
Types of Forecasts that might be needed in IKEA
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Given the table below, complete the missing cell values applying the Exponential Smoothing method of forecasting and Mean Absolute Deviation. Answer also the other 2 related questions below the table. Numbers with decimal should take 2 decimal places. Actual Quarter Tonnage Unloaded 1 2 3 4 5 6 7 8 175 160 170 160 160 170 180 200 Forecast for a=3 175 175.0 170.5 167.2 165.1 166.6 170.6 Sum of absolute deviation = Absolute Deviation for a = .3 Other questions: (1) What is the MAD for a = .3? (2) Which smoothing constant would you prefer? 0.00 15.00 0.50 10.35 4.93 13.45 29.41 80.89 Forecast for a = .6 175 166.0 168.4 163.4 161.3 166.5 174.6 Absolute Deviation for a = .6 0.00 15.00 4.00 8.40 3.36 8.66 13.46 25.38
Use regression or simple exponential smoothing with the following data. For exponential smoothing, assume the forecast for year 1 was the same as the actual and use an alpha of 0.7. (use excel to show formauls used) Year Sales 1 290 2 340 3 400 4 410 5 400 1. What is the tracking signal for the forecast?
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Sales of hair dryers at the Walgreens stores in Youngstown, Ohio, over the past 4 months have been 100, 110, 120, and 130 units (with 130 being the most recent sales). Develop a moving-average forecast for next month, using these three techniques:a) 3-month moving average.b) 4-month moving average.c) Weighted 4-month moving average with the most recent month weighted 4, the preceding month 3, then 2, and the oldest month weighted 1.d) If next month’s sales turn out to be 140 units, forecast the following month’s sales (months) using a 4-month moving average.
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?
Here are the actual tabulated demands for an item for a nine-month period (January through September). Your supervisor wants to test two forecasting methods to see which method was better over this period. MONTH ACTUAL January 120 February 145 March 146 April 171 May 154 June 182 July 138 August 135 September 146 a. Forecast April through September using a three-month moving average. b. Use simple exponential smoothing with an alpha of 0.20 to estimate April through September, using the average of January through March as the initial forecast for April. c-1. Calculate MAD for Three-month moving average and Exponential smoothing. c-2. Use MAD to decide which method produced the better forecast over the six-month period.

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