A large Portland manufacturer wants to forecast demand for a piece of pollution-control equipment. A review of past sales ($A_t$), as shown below, indicates that an increasing trend is present. Smoothing constants are assigned the values of $\alpha = 0.20$ and $\beta = 0.4$. The firm assumes the initial forecast for month 1 ($F_1$) was 9.00 units and the trend over that period $T_4$ was 2.00 units.Using trend-adjusted exponential smoothing, Forecasts ($F_t$), Trend ($T_t$), and Forecasts Including Trend ($FIT_t$) for months 1 through 4 have already been developed and are provided below. Continue with the process and determine $F_t$, $T_t$, and $FIT_t$ for months 5 and 6 (round your responses to two decimal places):\[\begin{array}{|c|c|c|c|c|}\hline\text{Month (t)} & \text{Actual Demand (A_t)} & \text{Forecast (F_t)} & \text{Trend (T_t)} & \text{Forecast Including Trend (FIT_t)} \\\hline1 & 10.0 & 9.00 & 2.00 & 11.00 \\2 & 16.0 & 10.80 & 1.92 & 12.72 \\3 & 23.0 & 13.38 & 2.18 & 15.56 \\4 & 20.0 & 17.05 & 2.78 & 19.83 \\5 & 25.0 & - & - & - \\6 & 22.0 & - & - & - \\7 & 30.0 & - & - & - \\8 & 26.0 & - & - & - \\9 & 38.0 & - & - & - \\10 & - & - & - & - \\\hline\end{array}\]

A forecasting technique called "Trend-Adjusted Exponential Smoothing" is used to make predictions…

Answered: A large Portland manufacturer wants to…

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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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.
Use simple exponential smoothing with a -0.8 to forecast electric scooter sales at Guelph- Humber Inc. for July. Assume that the forecast for May was for 45 electric scooters. Round your answer to 1 decimal Electric Scooter Sales Month May 42 June 47 July 45 August 40 Weekly demand for medical masks for the last five weeks at Chopper Drug Store has been as follows: 92, 96, 100, 100 and 102 (listed from the oldest to most recent). Suppose a naive forecast was used to forecast demand. What would the MAD be for this situation? Enrollment in a particular Yoga class at Guelph Fitness Centre for the last five months has been 80, 86, 88, 88 and 90(listed from the oldest to most recent). Suppose a two-month mov average was used to forecast enrollment. What would the MSE be for this situation?
The monthly sales for Yazici Batteries, Inc., were as follows: Month Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Sales 21 20 15 12 15 16 15 17 19 21 20 24 Using a method of trend projection, the forecast for the next month (Jan) = ______ sales (round your response to two decimal places).
A firm uses simple exponential smoothing with trend adjustment to forecast demand, "a = 0.6" and "B=0.6". The Forecast including Trend (FIT) for the 1st month of 2022 was 434 units whereas the actual demand turned out to be 488 units, the trend over that period was 26. Calculate the demand forecast including trend for the 2nd month.
The number of cases of merlot wine sold by the Connor Owen winery in an eight-year period is as follows: YEAR CASES OF MERLOT WINE 1 270 2 356 3 398 4 456 5 358 6 500 7 410 8 376 Using an exponential smoothing model with an alpha value of 0.20, estimate the smoothed value calculated as of the end of year 8. Use the average demand for years 1 through 3 as your initial forecast for year 4, and then smooth the forecast forward to year 8. Note: Round your intermediate calculations and final answer to the nearest whole number.
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
4) A small manufacturing firm has recorded a time series of the cost per unit for the firm's leading product over the past eight years. Year Cost/Unit($) 1 2 3 4 5 6 7 8 20 24.5 28.2 27.5 26.6 30 31 36 a) If you are not given a base value or trend, what would be the forecast for the year 10 made at the end of year 8 (a=0.2, ß=0.4). b) Forecast for the year 9 using 1) 5-month weighted average using 0.1,0.1,0.1,0.2,0.3, with the heaviest weight applied to the recent month 2) Exponential smoothing using a =0.2 and forecast of 21 for year 5 3) If a value was not provided in the question above, what value of a would you choose?
The following are monthly actual and forecast demand levels for May through December for units of a product manufactured by the Deborah Bishop Company in Des Moines: Month May June July August September October Actual Demand 105 82 110 112 108 106 130 120 Forecast Demand 100 108 97 98 108 102 105 107 November December For the given forecast, the tracking signal = MADS (round your response to two decimal places).
Data collected on the yearly registrations for a Six Sigma seminar at the Quality College are shown in the following table: Year Year Forecast (000) 1 2 4.00 7.00 1 5.00 3 5.00 Registrations (000) a) Calculate the forecasted registrations for years 2 through 12 using exponential smoothing, with a smoothing constant (a) of 0.40 and a starting forecast of 5.00 for year 1 (round your responses to one decimal place): 2 3 4.6 5.56 4 4.00 4 5.34 11 9 10 7 8 6 5 9.00 9.00 6.00 10.00 13.00 14.00 12.00 5 4.80 6 7 6.48 7.49 8 6.89 9 8.14 10 10.08 11 11.64 12 11.79 b) Mean absolute deviation based on the forecast developed using the exponential smoothing method (with a smoothing constant (a) = 0.40 and a starting forecast of F₁ = 5.00) is response to one decimal place). registrations (round your
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With the multiplicative seasonal method of forecasting: the seasonal amplitude is a constant, regardless of the magnitude of average demand. there can be only four seasons in the time-series data. the times series cannot exhibit a trend. seasonal factors are multiplied by an estimate of average demand to arrive at a seasonal forecast.
The accompanying dataset shows the monthly number of new car sales in the last two years. Implement the Holt-Winters multiplicative seasonality model with trend. Find the best combination of a, ß, and y to minimize MSE. a = 0.2, B = 1, y =0.2; a = 0.6, ß = 0.4, y = 0.6 E Click the icon to view the new car sales data. First find the MSE for each set of values, a, B, and y. (Round to the nearest whole number as needed.) MSE 0.2 1 0.2 0.6 0.4 0.6

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