You are given the following actual demand values for the past 18 months. Using theexponential smoothing forecasting technique with an Alpha of 0.34, determine the forecastfor time period 20 (January):MonthJuneJulyAugustSeptemberOctoberNovemberDecemberJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberTime12346789101112131415161718Actual Demand Forecast58525558474655574753424552474246525758.0055.9655.6356.4453.2350.7752.2153.8451.5152.0248.6147.3848.9548.2946.1546.1048.11 51.1353.13O 5748.11

Forecasting refers to predicting what will happen in the future using old and current information.…

Answered: You are given the following actual…

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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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
Use exponential smoothing with trend adjustment to forecast demand for period 11. Let α = 0.5, � = 0.3, and let the initial trend value be 12 and the initial forecast be 200
Demand for oil changes at Garcia's Garage has been as follows: Number of Oil Changes January 44 February 49 March 66 IT April 59 May 53 June 58 59 63 Month July August a. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For Janu The forecasting model is given by the equation Y=+x. (Enter your responses rounded to two decimal places) J Show Transcribed Text >. Use the model to forecast demand for September, October, and November. Here, X=9, 10, and 11, respectively. (Enter your responses rounded to two decimal places.) Forecast for the number of Oil Changes Show Transcribed Text Month September October November can you do all the parts please corrrect
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 index Actual sales Deseasonalized sales 836 825 Year 1 Year 2 January February March April May June July August September October November December January February March April 0.72 0.75 1:22 1.48 1 1.23 0.7 0.78 0.92 1.13 1.28 1.33 0.72 0.75 1.22 1.48 1220 1319 1020 1184 685 641 875 1008 1068 1141
The demand for a product of a company in each of the last twelve months is shown in the table below; Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Demand 37 40 41 37 45 50 43 47 56 52 55 54 Use the table to compute the following forecast demand for the company; a. Four months simple moving average b. Four months weighted moving average using the weight of 40%, 30% and 20% and 10%respectively.
2
Consider the following yearly demand values for a particular product. Year Demand 1 360 389 3 410 4 381 368 374 (a) Produce forecast for year 4,5,6, and 7 by using 3-week moving average. (b) Produce forecast for year 4,5,6, and 7 by using 3-week weighted moving average where weights are as follows; wt–3 = .1, wt-2 = .3, and wt-1 = .6. (c) Produce forecast for year 4,5,6, and 7 by using exponential smoothing with a = .2. Please assume that the forecast for the first year is 360.
One forecasting model was used to forecast demand for a product. The forecasts and the demand are shown in the table below. Use two decimals in your answer. Actual Forecast 1 40 41 2 35 38 3 38 35 4 33 30 The Mean Absolute Deviation (MAD) is O 0.5 2.5 O 1.48 7.01
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?
Use simple exponential smoothing with a=0.6 to forecast chargers sales for February through May. Assume that the forecast for January was 24 chargers. *** Month January Charger Sales 42 February 33 March 28 April 59 May ? a) What will be the forecast for May? b) Calculate the MSE.

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