A dry cleaner uses exponential smoothing to forecast equipment usage at its main plant. August usage was forecasted to be 45 percent of capacity, actual usage was 52 percent of capacity. A smoothing constant of .05 is used. a. Prepare a forecast for September. (Round your final answer to 2 decimal places.) Forecast for September percent of capacity b. Assuming actual September usage of 52 percent, prepare a forecast for October usage. (Round your answer to 2 decimal places.) Forecast for October percent of capacity

If we use a simple exponential smoothing method with ??=0.3, ??11=100 and ??12=120, what is your forecast for next year? A. 120 B. 126 C. 128 D. None of the above 10. Which one is NOT part of the reasons to keep inventory? A. To allow flexibility in production scheduling B. To meet variation in product demand C. To maintain dependence of operations D. To provide a safeguard for variation in raw material delivery time

You have a data set that includes time period and past sales data, and you want to use a time series forecasting technique. Which of the following forecasting methodologies is considered a time series forecasting technique? a.Group of answer choices b.Delphi method c.Simple movement smoothing d.Weighted moving average e.Simulation f.Exponential averaging

4. What is the difference between trend and seasonality in time series data? 5. Here are the errors associated with a particular forecast over the past 5 months, in chronological order: 5, 10, -15, 0, 8. In which month was the forecast perfectly accurate? In which month was the forecast the least accurate? In which month or months was the forecast too high? (Noteshaper Ramp Up # 23) 6. Tutoring Center needs to allocate tutors this week for office appointments, so it needs to forecast the number of students who will seek appointments. The director has gathered the following time series data recently: Time Period Code Student Appointments Jan 6 - 10 95 Jan 13 - 17 80 Jan 20 - 24 65 Jan 27 – 31 4 50 a) What is the naïve forecast for the number of student appointments for Time Period 5 (Feb 3– 7)? b) What is the 2 week moving average for Time Period 5? c) What is the 3 week moving average for Time Period 5? d) What is the forecast for Time Period 5 using exponential smoothing with alpha =…

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

Here are the errors associated with a particular forecast over the past five months, in chronological order: 2, 5, 0, -5, -10. Which of the following statements is (are) true? I. The forecast was too high during the fifth month. II. The mean error over these five months is -2. III. The forecast was perfectly accurate during one of the months. a. I only Ob. Il only O. Il only d. I and III Oe.I, Il and II

Assume that the data in columns A and B report the new constructions of residential homes, per month, in thousands, from 2015 to 2020 in the state of Oregon. a) Chart the data. Discuss any apparent trend and seasonal patterns. b) Use the "Tahoe Salt" method with linear trend to forecast new constructions for the 12 months of 2021. c) Add your model values and forecast to the data chart. d) Compute the MAD of your model. e) Compute and plot the tracking signal in a new chart. f) Discuss the quality of this model. What is the flagrant flaw? g) Suggest an approach that would yield better results in terms of forecast values, MAD, and tracking signal. An appropriate narrative argument is sufficient (i.e., one or two sentences). You do not need to do calculations for this part.

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

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Exponential Smoothing, Exponentially Smoothed MAD, and Tracking SignalThe XYZ Company was flooded by a thunderstorm and lost part of its forecasting data.Positions in the table that are marked [a], [b], [c], [d], [e], and [ f ] must be recalculated from the remaining data.