If consumer demand and retail supply plans are not aligned, retailers need to either solve logistical issues with expensive ad hoc solutions or mark down oversupplied goods (Fisher and Raman 2010). Both approaches lead to a deterioration of the profit base. Consequently, retailers need efficient modeling and decision-making techniques. An analytics orientation at many retail organizations provides a great opportunity for modelers and nudging retail managers towards more quantitative decision-making (Kopalle 2010; Hu¨bneretal.2010). A comprehensive operations planning framework that integrates consumer behavior is required to maintain and increase retailers’ profit both directly (e.g., reduced stockouts) and indirectly (e.g., higher customer satisfaction). While consumer integration into supply chain management (SCM) gains further importance for retailing business practice, only few analytical explanations with consumer integrationfor retail DSCP have been put forth. Retail practice and research lack a holistic framework that is based on an integral planning perspective at the entire retail supply chain (SC) and comprehensive quantitative decision support systems (DSS).