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A recent report prepared by The Boeing Company indicated the total number of commercial aircraft in the world’s airline fleet in 2009 was 18,890 and was expected to grow to 36,300 by 2029. The estimated market value (in 2009 USD) of the increase which was based on several factors including, expected aircraft retirements, freighter conversions, and new aircraft deliveries will be $3.59 trillion (Boeing, 2010).
A similar report released by Airbus Industries considering the same factors projected the size of the world fleet in 2029 to be 36,303. It may seem remarkable that competing companies could independently formulate 20 year industry forecasts with almost identical results, but the explanation for the similarity is quite simple: similar approaches are employed in the data analysis to develop strategic forecasting. Business intelligence tools such as correlation analysis and econometric modeling are used to perform in-depth studies of past market conditions and then use the results of those studies to develop a better understanding of current market conditions, forecast future market demands and develop strategic plans for the future (Airbus, 2009).
Generally speaking, forecasting is an extremely important activity. According to Thomas J. Gallagher, Managing Director of CIBC World Markets Global Aerospace, no endeavor has a greater impact, for good or bad, on the overall success of a corporation than the practice of forecasting. When successful, forecasting integrates the abilities, decisions, and informed perspectives of the entire corporation and converts them into a comprehensive view of the future and a cohesive set of expectations. A successful forecast illuminates, while just below the surface it provides an accurate description of the complexity and intricacy of the reality it attempts to portray. Poor forecasting overwhelms its victims by numbing them with large data arrays of indeterminate relevance, ill-considered assumptions, and undisciplined clumps of emotion. A poor forecast offers confusion, misdirection, and disappointment to those whom its developers were seeking to enlighten. Good forecasting on the other hand achieves lasting value, credibility, and more importantly, clarity and transparency. Furthermore, all of these attributes can be acquired with a good forecast methodology without compromising accuracy (Gallagher, 1998).