Currently on the market there are several players working in demand prediction, but most of the existing companies rely on standard statistical forecasting algorithms. This kind of software performs a statistical fit of existing statistical models on past data and can automatically select the statistical formula with for the prediction. It is important to notice that this decision is made based on the past. Most of these models do not leverage existing Machine Learning techniques in order to do demand forecasting. While you can make predictions based on statistical models (called statistical inference), the statistical models are usually built for inference about the relationships and variables and not for having the most accurate predictions. One clear example is that all statistical models are interpretable – a human can look at the model and infer some information about the process and about the data. Machine Learning, on the other hand, has the goal of having the most accurate predictions. Many Machine Learning models are not interpretable – it is not evident what the weights on the neurons on an Artificial Neural Network mean. By using Machine Learning there is much to be improved on prediction accuracy for demand planning. Although using Machine Learning would make the most sense, Machine Learning is a complex topic, requiring a deep knowledge and understanding of the field to select the best models to be trained and deployed. After using the models for prediction, it is important to monitor performance since a model performance tend to decay over time, via a process called concept drift.
With our technology StreamWiser, we streamline the process of model monitoring and performance tuning. Our technology is able to automatically decide which models are best for a given prediction – and adjust in real time to changes of scenario. You can consider StreamWiser to be your Data Science department focused solely on getting the best predictions possible.
Let our tech do the heavy lifting of demand prediction – training models, selecting models, monitoring performance – so that you can focus on what really adds value: your business.