Forecasting for demand relies on two dominant methods: deterministic (results determined by present conditions) and probabilistic (take into account degrees of confidence, uncertainty, and room for error). This continuing post by Christopher Dyke describes the use of these two methods, and how companies decide which one to use: Post-Gulf War I, the oil industry started to incorporate probabilistic forecasting so it could account for decisions that OPEC made concerning the levels of oil production (Abramsia & Finizza 1995). These decisions involved the probability that OPEC would increase, decrease, or maintain supply levels. The most popular modeling technique for this type of forecast is the Monte Carlo simulation. So, if probabilistic forecasting is such a flexible tool for more accurately planning procurement, production, and logistics demand, why don’t more companies use it? There are three main factors that discourage many companies from leveraging this capability: First, retraining and a new understanding of forecasting techniques and analysis. Second, a paradigm shift in the way S&OP is conducted. And third, a full understanding of what impacts their demand. The article goes on to explain a shift that occurred, where companies attempted to fully understand forecasting and utilize it in a way that provides an more informed conclusion for business to move forward on.