A check-processing center uses exponential smoothing to forecast the number of incoming checks each month. The number of checks received in June was 38 million, while the forecast was 43 million. A smoothing constant of 0.20 is used. a) Using exponential smoothing and given alpha, the forecast for the month of July = nothing million checks received (round your response to one decimal place). b) If the check-processing center received 46 million checks in the month of July, then using exponential smoothing and given alpha, the forecast for the month of August = nothing million checks received (round your response to one decimal place). c) Exponential smoothing may not be an appropriate method to use for the check-processing center because:

Please refer to the explanation section

Explanation:

June number of checks received = 38 000 000

June Forecast = 43 000 000

a = 0.20

A. July forecast =  38 000 000 x 0.20 + 43 000 000 x (1 - 0.20)

    July forecast =  7600 000 +34400 000 = 42 000 000 checks received

B. August forecast = 46 000 000 x 0.20 + 42 000 000 x (1 - 20)

August forecast = 9200 000 + 33600 000 = 42800 000

C. Exponential Smoothing relies substantially on past Forecasts. even if  the actual checks changed substantially along the way exponential smoothing only reflects 20% (0.20 ) of these changes in their forest which may lead to a huge deviation between forecast and actual results. The exponential smoothing method doesnot reflect the effect of each and every variable that may actually affect the number of checks received. a linear functions with all variables that affect checks received might yield better results

i would say their habit of breaking down.