Dubai-fly airline operates flights from Dubai to Milan. The demand for airline seats varies from month to month. There are few peak seasonal demands, for instance, just before and after Christmas and during August and September when schools reopen after summer vacation. Generally, anticipating higher demand, airline fixes higher fares. Every airline has a revenue management section, the analysts of the section forecast the demand and adjust the price accordingly. On the first of every month, Dubai-fly consolidates the number of seats sold in the previous month (time series data) in every sector it operates flight. Based on the last 6 years’ data, the airline wants to forecast its demand for the next January (first month of 7th year) considering the forthcoming post-Christmas demand. The data tabulated are the number of seats sold (in hundreds).

a. Develop a seasonally adjusted forecast model for these data for monthly passenger load. Forecast demand for each month for the 7th year by using linear trend line estimate for the airline demand. Do the data appear to have a seasonal pattern? What is the forecast for the next January?
b. Develop an adjusted exponential smoothing forecast (ESF) (α = 0.25 and β = 0.30) for the yearly demand for the airline. Compare its accuracy with that of the linear trend line model in part (a).

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Month January February March April May June July August September October November December 1 58 33 17 36 47 65 37 26 29 33 27 16 2 66 43 24 37 54 73 32 30 21 49 37 22 3 68 39 26 40 57 59 43 34 26 38 43 20 Year 4 62 46 32 33 51 67 44 32 25 47 45 19 5 75 55 22 31 60 78 36 38 28 43 42 26 6 68 49 29 36 53 73 48 36 30 49 48 30