Operational Research (OR) is the ‘science of better’. People constantly try to get
better, in practically all aspects of their personal and professional life, and thus OR is de facto a ubiquitous science. What might however not be that clear, is that the way we improve is driven by the very OR science, and the scientific results that constitute the respective body of literature, theory and practice. Of all the tasks, that can be related to the OR science, the one that more frequently we do, is forecasting.
We do constantly try to estimate what is coming next, and we drive our decisions for each and every situation based on these forecasts: from where to put our money to who will be the best surgeon to operate us. Within the broad boundaries of OR, forecasting stands out as the most ubiquitous sub-discipline. In the forecasting literature, a lot of attention has been given to modelling fast-moving time series and building causal models; however, very limited attention has been given to intermittent time series and intermittent demand forecasting. In this research, we advocate for the broader use of intermittent demand forecasting methods for forecasting special events, as a simpler, faster, and robust alternative to more complex non-OR models. Furthermore, in a foresight context, we argue for a novel way of deciding the strategic planning horizon for phenomena prone to appearance of special events.