Problem 5You are trying to determine what forecast method to use. Based upon the followinghistorical data, calculate the following forecast and specify what procedure you wouldutilize.MAYJUNEMONTH JANUARY FEBRUARY MARCH APRILDEMAND 626567687173MONTH JULY AUGUST SEPTEMBER OCTOBER NOVEMBER DECEMBERDEMAND 767878808485a. Calculate the simple three-month moving average for periods 4 to 12.b. Calculate the weighted three-month moving average for periods 4 to 12. Useweights of 0.50, 0.30, and 0.20 for the most recent, second most recent, andthird most recent periods, respectively.c. Calculate the single exponential smoothing forecast for periods 2 to 12 using aninitial forecast (F₁) of 61 and a of 0.30.d. Calculate the exponential smoothing with trend component forecast for periods 2to 12 using an initial trend forecast (T₁) of 1.8, and initial exponential smoothingforecast (F₁) of 60, an a of 0.30 and ō of 0.30.e. Calculate the MAD for the forecasts made by each technique in periods 4 to 12.Which forecasting method provides the best forecasts?

monthDemand Jan62Feb65Mar67Apr68May71Jun73Jul76Aug78Sep78Oct80Nov84Dec85

Answered: a. Calculate the simple three-month…

Practical Management Science

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Chapter2: Introduction To Spreadsheet Modeling

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

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a) Apply moving average and exponential smoothing to generate two forecasts of the closing price for the entire data period. Set appropriate values for the forecast parameters (e.g., k and a). b) Plot and compare the actual and predicted data values you calculated in part (b). Provide one chart that displays actual, moving average, and exponential smoothing lines. Hint: Calculate MSE, MAD, or MAPE to compare the different forecasts. Develop your own forecast from scratch. Do NOT use Excel’s Data Analysis or Forecast Sheets.
4. The sales (in millions of dollars) for an 18-month period are as follows. Month Sales Month Sales 1 600 600 775 10 775 3 600 11 600 4 650 12 575 700 13 625 800 14 650 550 15 600 8 775 a. Compare a three-month moving average forecast with an exponential smoothing forecast. Use a = 0.1. Which provides the better forecasts based on MSE? b. Find the forecast for the next month using the best forecast method. 2.
eBook Problem 6-05 Consider the following time series data. 3 16 Week 1 Week 2 Value 18 13 a. Choose the correct time series plot (1) € (!!!) Time Series Value Time Series Value 28642986420 284H2G86420 1 4 11 2 {B} 2 TH 3 Week (t) Week (t) 5 6 € (iv) Time Series Value Time Series Value 28642 NO 28642 NO 1 2 2 4 3₁ Week (t) Week (t) 5 6
Forecast bias is useful to determine a. Seasonality b. Trends c. if forecast error is truly random d. the exponential smoothing factor
14 Community General Hospital finds itself treating many bicycle accident victims. Data from the last seven 24-hour periods is shown below: Day Bicycle Victims 1 8 2 14 3 8 4 14 5 18 6 15 With an alpha value of 0.31 and a starting forecast in day 3 equal to the 21 , what is the exponentially smoothed forecast for day 4? (Round to two decimal places) 15 What is the exponentially smoothed forecast for day 5? (Round to two decimal places)
Which time-series forecasting method works best if the company assumes that product demand will decrease over time? A. Weighted moving average B. Linear trend C. Moving average D. Exponential smoothing
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
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.
Since the beginning of the year, the purchase manager at a department store has been recording sales data to be used to compute 4-period moving averages to forecast sales for an upcoming month. Sales data for the months of January through September are reported in the table below. Month January February March April May June July August September Sales 21 20 39 22 29 14 42 28 27 Computed forecast values are not rounded. Compute the four-period moving average forecast for October. value as a whole number by rounding. Specify the Compute the mean absolute deviation (MAD) for the four-period moving average forecasts. Specify the MAD as a whole number by rounding. Compute the mean squared error (MSE) for the four-period moving average forecasts. Specify the MSE as a whole number by rounding. Compute the mean absolute percentage error (MAPE) for the four-period moving average forecasts. Specify the MAPE as a whole number by rounding. CHECK ANSWER
If month one has 240,000 demands, month two 250,080 demands, month three 325,00 demands, month four 370,000 demands, month five 420,000 demands and month six 509,00 demands. Use a forecast for the first month of 240,000, an initial trend forecast of 50,000, and smoothing parameters of 0.35 for both demand smoothing and trend smoothing. Compute the forecasts and trends using double exponential smoothing
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The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-26, where x = 0.5 and y = 0.8. Then find the MAD for periods 13-24. Click the icon to view the monthly use of natural gas data. Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26. (Type integers or decimals rounded to two decimal places as needed.) Forecast Period 13 14 15 16 17 18 Natural Gas Usage Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Period Gas Usage 1 2 8699 A WI 3 4 5 7 9 10 11 12 13 14 1516718192021 22 23 24 241 221 155 137 54 34 29 27 30 40 90 201 229 238 242 136 34 34 28 26 27 40 86 187

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