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Please help explain the following: Please type answers because it is sometimes hard to read submitted handwriting. Using the data provided, produce forecasting models using

Please help explain the following:

Please type answers because it is sometimes hard to read submitted handwriting.

Using the data provided, produce forecasting models using the following:

  • Simple Moving Average (on a 4-month moving average)
  • Weighted Moving Average (4 month moving average with weights = 0.4, 0.3, 0.2. 0.1)
  • Exponential Smoothing (? = 0.3) (assume forecast for January is the same as the actual demand)

image text in transcribedimage text in transcribedimage text in transcribed
Provided Data for Forecasting Modules Demand January 1750 February 1700 March 1650 April 1600 May 1625 June 1650 July 1650 August 1750 September 1750 October 1600 November 1550 December 1500. Demand Data Chart of Last Two Years 1800 1750 1700 1650 Demand Data Chart of the Last Two Years + 1600 Demand Data (last 2 years) 1550 150 1450 1400 1350 January February March Apni May tuly August aber ctobe November December nuary February Mare Apri May june july Aug sber sept Octo November Decemberv Questions Questions to be addressed 1. 1v'v'hile the MAD can be used to determine which forecasting method is the most accurate, normally, the MAPE is a better measure. So, compare the MAPE of the three methods and determine which is the best model leg. the most accurate}. 2. Base on the chosen model, what should you recommend to your boss as the forecasted demand forJanuary? 3. Produce a line chart that shows all three of the forecast and the actual demand. 4. Review the chart and the data. Do you think that the forecasts have given you an accurate result? Do you notice any trends in the data that could affect the January forecast? If so, what would you recommend to your boss? Forecasting Models - Simple Moving Average [on a 4 month moving average]I - Weighted Moving Average [4 month moving average with weights = 0.4, 0.3, 0.2. 0.1} - Exponential Smoothing {:1 = 0.3} {assume forecast for January is the same as the actual demand}

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