Python Implementation of the Value-Based DEA Method

AbstractThis paper is aimed at presenting the Python implementation of the Value-Based Data Envelopment Analysis (VBDEA) method, which was designed to evaluate the efficiency of decision-making units (DMUs). This methodological framework explores the links between data envelopment analysis (DEA) and multi-criteria decision analysis (MCDA) and proposes a new perspective on the use of the additive DEA model using concepts from the multi-attribute value theory (MAVT). One of the major strengths of VBDEA over typical DEA methodologies is that it offers information on the main reasons behind DMUs’ (in)efficiency. Additionally, this approach allows straightforwardly ranking of efficient and inefficient DMUs, since it relies on a super-efficiency model. Because of the use of value functions, besides allowing the incorporation of the decision-maker (DM)’s preferences, this methodology easily handles negative or null data. In this context, we illustrate the Python implementation of the method by reproducing the main results obtained by (Gouveia et al., Or Spectrum 38:743–767, 2016), when these authors evaluated the performance of 12 health units in a Portuguese region incorporating management preferences given by real DMs.