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The increasing number of product returns and the associated costs force enterprises to avoid returns before they are even made. Therefore, customers should only submit an order if the return probability is low. Consequently, we designed a generic meta-model for real-time prediction of product returns in ERP systems (RCS). According to the Design Science Research approach, we defined the requirements based on the literature. Our meta-model supports customers with real-time recommendations to adapt their cart based on interactions with the website and ERP data to reduce the return rate. A feedback system evaluates data and gives individual suggestions to influence customers’ behaviour. According to the evaluation step, we showed the feasibility of a generic RCS. Next, we integrated the first module of the RCS into a cloud-based ERP system supplier. We conducted a simulation-based evaluation for website adaptions and feature extraction of customer interactions.



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