StandPI enables the efficient usage of Crowdsourcing Delivery for the loading industry. Therefore, internal and external system parameters will be continuously monitored and these real-time data will be further processed by a machine learning algorithm. By the means of this algorithm, the matching of the loader’s product supply and the dynamically available transportation capacities concerning Crowdsourcing Delivery will be optimized. Eventually, in contrast to the nowadays commonly used sequentially controlling and optimization of the transportation and inner logistics systems, the aim of this research project is a self-learning controlling, which acts at the interface of this system, concerning a cross-system optimization. Hence, consistent exploitation of the remaining capacities of vehicles en route will significantly contribute to economical, ecological and social sustainability concerning physical distribution.