There has been considerable interest worldwide in last few years in the growth of third party logistics (3PL) providers. 3PL distribution center (DC) enables firms to achieve reduced operating costs, increased revenues, and to focus on their core competence. This research aims to find the key performance indicators through a survey of a set of DCs and then evaluate their efficiency over the period 2005-2007 using data envelopment analysis (DEA) models based on selected performance indicators as inputs and outputs. Three inputs and two outputs for all DCs from the surveyed performance indicators were selected in this study. DEA is a non-parametric linear programming technique used to evaluate the efficiency of decision making units (DMUs) where multiple inputs and outputs are involved. We adopted both the input-oriented CCR model and the BCC model that were designed to derive weights instead of being fixed in advance and handle positive inputs/outputs. A Malmquist productivity index (MPI) analysis further evaluates efficiency change and productivity growth between two time points. Our empirical results show that scale inefficiency is the major reason for the inefficient DMUs. For the future research, more DC data should be collected and different DEA models could be applied for other benchmark studies.
Proceedings of the International Material Handling Research Colloquium
Ting, Ching-Jung and Fang, Hsueh-Lin, "Using Data Envelopment Analysis to Evaluate the Performance of Third Party Distribution Centers" (2010). 11th IMHRC Proceedings (Milwaukee, Wisconsin. USA – 2010). 37.