SAF OptimalClusterSet for Lowering Costs with Cluster Specific Parameterization

Retailers frequently have their own rules according to stores are classified. However, this individual grouping practice often lacks a sound and viable statistical basis to draw from. Such clusters, which only appear to be homogenous, cause substantial parameterization expenditures in automated ordering processes and lead to cost-driving excess or insufficient inventories, if in fact they are not homogenous.

To address this potential problem, SAF developed an innovative solution: SAF OptimalClusterSet. With this service SAF consolidates units (stores, items) that are comparable from a fulfillment-on-demand point of view in uniform groups (clusters). One cluster reference store is identified for each cluster.

Cluster specific parameterization is performed on the basis of these reference stores. This approach reduces expenditures incurred in the initial parameterization phase, as well as those of subsequent parameter maintenance.