Assessing cross-docking performance using a novel network DEA model regarding sustainability and undesirable factors under uncertainty

Although evaluating warehouses' efficiency using different approaches has been gaining prestige in recent years, this study is the first attempt to assess the efficiency of cross-docking systems. For this purpose, a comprehensive structure of a cross-docking system is examined, where a broad range of components affecting its performance are considered, including inbound and outbound doors, various means of transportation, inspection, kitting, storage, retrieval, and staging operations. In addition, an extensive set of key performance indicators (KPIs) are introduced for each component regarding automation, digitization, resiliency, sustainability, and lean concerns. More importantly, a novel network data envelopment analysis model is proposed to evaluate the efficiency of the cross-docking system with respect to undesirable factors. Furthermore, a new hybrid uncertainty approach is offered to handle the uncertainties in introduced KPIs and quantify some qualitative judgments. Finally, a case study is examined to illustrate the effectiveness of the proposed model and uncertainty approach. The obtained results reveal that the simultaneous exploitation of pre-distribution and post-distribution policies can increase the efficiency of the examined system by 36%. Also, modifying the layout to eliminate redundant transportation operations can lead to an 18% increase in efficiency.