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.