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

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 (DEAANN). 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.