  Reverse Vending Machines (RVMs) are a proven instrument for facilitating
closed-loop plastic packaging recycling. A good customer experience at the RVM
is crucial for a further proliferation of this technology. Bin full events are
the major reason for Reverse Vending Machine (RVM) downtime at the world leader
in the RVM market. The paper at hand develops and evaluates an approach based
on machine learning and statistical approximation to foresee bin full events
and, thus increase uptime of RVMs. Our approach relies on forecasting the
hourly time series of returned beverage containers at a given RVM. We
contribute by developing and evaluating an approach for hourly forecasts in a
retail setting - this combination of application domain and forecast
granularity is novel. A trace-driven simulation confirms that the
forecasting-based approach leads to less downtime and costs than naive emptying
strategies.
