A company has the following demand history by seasons over a two-year period. The production planner deseasonalizes the data first. Then, using the exponential smoothingmethod with a = 0.30 and F= 80, she forecasts the demand for the first season of 2021. Finally, the planner reseasonalizes her forecast. What is her final forecast for the firstseason of 2021?20192020Season 18488Season 27671Season 39397Season 44651Season 57773O a. 87.50O b. 77.57Oc. 79.87O d. 75.98O e. 86.43

In this question, the production planner would deseasonalize the data set, to make such…

Answered: A company has the following demand…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

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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Forecasting is predicting the future; therefore, it is not concerned at all with the past. True False
The following are sales revenues for a large utility company for years 1 through 11. Forecast revenue for years 12 through 15. Because we are forecasting four years into the future, you will need to use linear regression as your forecasting method. Note: Enter your answers in millions. YEAR 1 2345 5 6 7 8 9 10 11 Period 12 13 14 15 REVENUE (MILLIONS) $4,865.7 5,072.3 5,507.7 5,738.2 5,495.4 5,198.7 5,087.0 5,111.5 5,559.7 5,740.4 5,868.4 Forecast
The observed demand from the four quarters of Year 1 can also be used to developinitial deseasonalized forecast estimates that are required to start making forecasts withthe exponential smoothing models. The first real forecast will be for Q1 of Year 2, andthe use of simple exponential smoothing requires an estimated deseasonalized forecastfrom Q4 of Year 1 to use as a starting point. The estimated deseasonalized “forecast” forQ4 of Year 1 should be obtained as the average deseasonalized quarterly demand fromYear 1. The estimate can also be used as the starting base forecast component for Q4 ofYear 1 for trend-adjusted exponential smoothing. The initial deseasonalized trendcomponent estimate for Q4 of Year 1 should be assumed to be zero.
Use the data set below to answer the following questions: [Gunakan set data di bawah untuk menjawab soalan-soalan berikut:] Table 1: Monthly sales unit IJadual 1: Unit jualan bulanau) Month Sales Units [Bulan] 1 [Unit Jualan] 1100 2 1200 3 1450 4 1800 2100 2700 i) Calculate the three-period simple moving average forecasts for months 4, 5 and 6. (Hitunskau rawalan pALata bergerak nudak weNEgUrakan empat-tenroh untuk bulau 5 dan 6.) Calculate the four-period weighted moving average forecasts for months 5 and 6 usıng weights of 0.05, 0.15, 0.30, and 0.50 from the earliest period to the latest period, respectively.
The actual number of patients at Providence Emergency Medical Clinic for the first six weeks of this year follows: The value of the forecast is Week Actual No. of Patients 45 49 IT 56 40 44 55 Clinic administrator Dana Schniederjans wants you to forecast patient numbers at the clinic for week 7 by using this data. You decide to use a weighted moving average method to find this forecast. Your method uses four actual demand levels, with weights of 0.333 on the present period, 0.250 one period ago, 0.250 two periods ago, and 0.167 three periods ago. a) What is the value of your forecast? patients (round your response to two decimal places). 123456
The following table shows predicted product demand using your particular forecasting method along with the actual demand that occurred: FORECAST ACTUAL 1,480 1,530 1,380 1,480 1,680 1,580 1,730 1,780 1,630 1,680 Compute the tracking signal using the mean absolute deviation and running sum of forecast errors. Note: Negative values should be indicated by a minus sign. Round your "Mean Absolute Deviation", "Tracking Signal" to 2 decimal places and all other answers to the nearest whole number. Period Forecast Actual Deviation RSFE Absolute Deviation Sum of Absolute MAD TS Deviation 1 1,480 1,530 -50 -50 50 50 50.00 -1.00 2 1,380 1,480 -100 -150 100 150 75.00 -2.00 3 1,680 1,580 100 -50 100 250 83.33 -0.60 4 1,730 1,630 100 50 100 350 87.50 0.57 5 1,780 1,680 100 150 100 450 90.00 1.67
can you make an operations management analysis for this forecasting
A forecaster is assessing two different models for demand. The output from each model and the actual demand data appears in the table. Use MAD to compare the two models. Which model does a better job of forecasting? Demand Model 1 52 52 54 56 60 56 58 58 52 57 53 57 Model 2 21 55 54.7 51.9 54.5 52.0 53.8 55.8 59.6 56.4 52 53.5 53.5 55.5 56.5 57.5 57.8 58.0 58.0 56.0 52.6 56.3 56.6 55.0 53.4 O Model 2 is 9% better O Model 1 is 8% better O Model 2 is 15% better O Model 1 is 5% better
The most naive forecast can is quite valuable in leading to an organization’s success because it is most widely understood by senior managers. True or False
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
In the Delphi technique, the members of the panel of experts don’t meet and discuss, yet they must reach a certain level of agreement in their forecasts. True False
My problem asks me to Forecast using a "simple 4-month moving average" but when I calcualte that, I get a Forecast for November of 1750. This is exactly 125 off of the answer given in the problem. I am wondering if the answer lies in an Additive Seasonal variation of 125 to the regular forecast of part a. But I am uncertain if I am doing that math correctly. I am looking for understanding of how to solve the problem that will arrive at the answers contained in the presented information. I would be greatful for an example of how to do this. Here is the problem as it was presented: Historical demand for the best-selling product is provided in the table below: Month Units Sold May 1,500 June 1,400 July 1,800 August 1,500 September 1,900 October November 1,800 ??? Forecasting for November, the logistics manager has calculated (a) the simple 4-month moving average [1875 units] and Mean Absolute Deviation [125 units] and (b) a…

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