Continuous execution of system dynamics models on input data stream

Abstract

This article describes a new approach for system dynamics models execution. In most cases when model execution is involved it is performed on a set of static and known data, which are sent to the model as an input. And it is expected, that on the model output modeler will get a set of other system or event characteristics, computed by the model based on the input parameters. This approach still has the widest usage, but it is not the only one scenario, which is demanded by different industries. With growing popularity of concepts such as Internet of Things, demand in modeling based solutions, which take as input continuous data streams, has grown significantly. In comparison with stand-alone client-side modeling systems, cloud-based solutions, such as sdCloud, became a reasonable answer to such industry request. Such systems can provide an ability of continuous execution of system dynamics models. In other words, these systems are ready to accept an incoming data stream and perform model execution that will result in streaming modeling results back to the end-user. Running system dynamics models in parallel with the process it is describing allows to perform predictive modeling of the system status in the future, and it also allows to find additional hidden external impacts to the model. For example, such approach can be a base for predictive maintenance of complicated technical systems, because it allows computing nearest maintenance time more efficient.