Selection of an automated solution in logistics using DEA models

Abstract

Digitization and automation are much-discussed topics, mainly in the area of connecting industry and artificial intelligence, known as Industry 4.0. Currently, choosing a suitable automated solution is a big problem for many industrial companies and their customers. There are many methods for evaluating the efficiency of individual solutions. One of them is data envelopment analysis (DEA), which compares a set of admissible solutions formed by homogeneous production units, also referred to as “decision-making units” (DMUs). This paper aims to measure the efficiency of logistics solutions using DEA models assuming constant returns to scale (CCR). The application of CCR models to a specific case demonstrates the possibility of solving the problem of choosing the most suitable solution. The results of CCR models are compared, and based on the achieved results, the company's previous decision is also evaluated. In the end, the (in)appropriateness of using these models was pointed out due to the large number of effective DMUs, and the use of a different model was recommended for future logistics solutions evaluation.