The data output of the probabilistic forecasting engine are distributions of probabilities. While risk analysis tends to be an afterthought in traditional forecasting approaches, Lokad is bringing the case front and center with probabilistic forecasts. This is exactly what probabilistic forecasts are about: properly balancing real-world decisions when facing an uncertain future. Yet, the chances are, if you are a supply chain practitioner, you have been doing "intuitive" probabilistic forecasting for years already: think of all the situations where your basic forecasts had to be revised up or down, because the risks were just too great. From a practitioner’s perspectiveProbabilistic forecasts might sound very intimidating and technical. Thus, when it comes to preparing for the worst, probabilistic forecasts provide a powerful way of quantitatively balancing the risks (while traditional forecasts remain blind to the latter). Instead of being stuck in a wishful thinking perspective, where forecast figures are expected to materialize, probabilistic forecasts remind you that everything is always possible, just not quite equally probable. These probabilistic forecasts provide an entirely new way of looking at the future. Every level of demand gets its estimated probability until the probabilities become so small that they can safely be ignored. The probability of 0 (zero) units of demand is estimated, the probability of 1 unit of demand is estimated, of 2 units of demand, and so on. Simply put, a probabilistic forecast of demand does not merely give an estimate of the demand, but assesses the probabilities of every single future. Lokad has developed a radically new way of tackling forecasts, namely probabilistic forecasts. The core challenge is to handle the tough cases the ones that disrupt your supply chain, and drive everybody nuts. However, the core forecasting business challenge is not to do well on the easy cases, where everything will be going well even considering a crude moving average. When the demand is exactly where it was expected to be, everything goes smoothly. As all executives know, businesses should hope for the best, but prepare for the worst. In supply chain management, costs are driven by extreme events: it's the surprisingly high demand that generates stock-outs and customer frustration, and the surprisingly low demand that generates dead inventory and consequently costly inventory write-off. Methods like safety stock analysis are supposed to handle uncertainty, but in practice, safety stock analysis is merely an afterthought. The origin of our probabilistic forecastsĭoes your forecasting engine handle seasonality, trends, days of week?ĭo you use external data to refine the forecasts?Įmbracing uncertaintyIn our experience, no amount of fine-tuning the existing forecasting models, and no amount of R&D to develop better models - in the traditional sense - can fix this problem.
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