Logistics efficiency in small and medium enterprises: A logistics, data envelopment analysis combined with artificial neural network (DEA-ANN) approach

Abstract

This article introduces a novel method for measuring logistics efficiency in small and medium enterprises (SME's) for use with logistics, data envelopment analysis - artificial neural network (DEA-ANN). This method has never been used in logistics before. The research was conducted in Querétaro, Mexico. The sample included 92 SME's, using a questionnaire of 38 questions, 37 of these questions was related to logistics practices and one was about the monthly logistic costs. The database was used to perform a DEA, taking into account the 37 questions of logistics practices as inputs and the logistics cost as outputs; a model of undesirable outputs was used because it is a cost where increases are always undesirable for any enterprise. With the information from DEA, an ANN was created that features a prediction of the index of logistics efficiency; the results with the neural network were very satisfactory, 0.84 R 2 . While modeling was rather accurate, this highlights the inefficiency detected in a large proportion of SME's, further representing problems for these types of firms. Logistic inefficiency may be the reason why many SME's go bankrupt.