Forecasting White Swans is not trivial, but at least there is a quite advanced arsenal -in form of advanced mathematical forecasting models- that we may employ so as to accurately forecast phenomena that tend to be observable at regular frequencies. Call it time-series models, econometric models, computational intensive approaches as in Artificial Neural Networks... there is always a way to get a fair extrapolation of what is going to happen either in the form of a point forecast, or a density forecast: the latter being comprised of a prediction interval and a level of confidence associated with your belief that the forecasted value will actually lay in the forecasted range. However when it comes to "Black Swans" then we have a whole new level of a game - much harder: the question now becomes from "how many White Swans we will see tomorrow?" to "when the next Black Swan will be seen?" and "if so... will he be alone this time...?". And in the lack of sufficient quantitative information judgmental forecasting approaches may have to be used this time. The story of this paper unfolds by adopting two different perspectives on how to approach this problem: (a) a technical one on potentially useful mathematical OR/MS tools and techniques that could help us determine the forecasting horizon of our problem, that is the period ahead that we will reasonable expect at least one "Black Swan" to appear, and (b) a historical one on why persistently and consistently fail to anticipate and forecast "Black Swans." What has history to teach us on how we can do a better job on that front? The paper concludes with a series of examples from various disciplines where the suggested techniques and ideas could be successfully employed.