DEA-ADALINE: an approach to improve the relative efficiency of 3PLs providers

Abstract

PurposeBenchmarking is an approach to understand and evaluate the current position of different entities like third-party logistics (3PLs) service providers with respect to the best practices. The purpose of this paper is to develop an integrated approach to benchmark the 3PLs and minimize the transportation cost, operational cost, total carbon emissions and improve vehicle capacity utilization rate and demand management in an efficient manner.Design/methodology/approachConventional data envelopment analysis (DEA) is extended to develop an integrated approach involving DEA and adaptive linear neuron network (ADALINE). The proposed methodology is dichotomized to the benchmarking of DMUs using Charnes, Cooper, Rhodes ratio and, in the process, helps to suggest ways to reduce inefficiencies.FindingsThe approach also supports the decision makers to understand ways to increase the efficiency of decision-making units (DMUs) that are relatively inefficient for a set of 3PLs considered in this study. This is one of the potential researches as it studies how to improve the efficiency of inefficient DMUs.Research limitations/implicationsThe proposed approach would help the decision makers to better understand the complexities associated with the low-performing 3PLs. The application of the proposed DEA-ADALINE methodology is illustrated using a range of data set for 3PLs.Originality/valueThe paper examines and explains the value added to the conventional DEA model for obtaining the relative efficiencies with reference to the most efficient DMUs without any loss to the original characteristics of DEA and ADALINE.