DSCP is very complex. While retail managers strive to follow the industry mantra “retail is detail”, most retail managers have little time to consider the details of different planning aspects. Also, not every detail that has to be considered at the actual execution can be reflected in the planning process. One core proposal in DSCP is to abstract from reality and to use models as a basis for plans. Analytical models emerge as the most promising solutions to many of the DSCP problems, especially as the advances in computing capabilities allow solving larger problems (Kopalle 2010). Retail research literature is rich on DSSs for single planning problems (see for example the literature reviews of Levy et al. 2004; Levy and Grewala 2007; Kopalle 2010 or Akkerman et al. 2010). Also Agrawal and Smith (2009b) andFisher and Raman (2010) describe various retail planning problems. However, they stick to isolated planning problems and do not provide an integrated planning framework or analyze the interdependence of them. That is why it is not surprising, that practitioners often complain about the limited practical value or limited scope of DSS, and challenge the possibility of integrating them into current systems (Kuhn and Sternbeck 2011). Current publications on retail demand and supply chain management do not provide a comprehensive planning architecture, as they deal mainly with isolated planning problems. Fisher and Raman (2010, p.127) note that “Retailers have three tactics at their disposal for matching supply with demand: accurate forecasting, supply flexibility, and inventory stock pilling”. We want to broaden this perspective as retail research and practice lack a holistic framework taken into account: