Abstract

The direction in research of the efficiency of decision-making units in this paper is an efficient→multi-inefficient→multi-efficient unit. So, the general purpose of this paper is twofold: (1) identification of 'hidden' inefficient units within a multi-set, among efficient units of the basic set, and (2) achieving the efficiency in such identified inefficient units. This indicates (warns of!) a negative efficient→inefficient process, so as to provide a timely response and thereby prevent multi-inefficiency. The specific goal is to assess the efficiency of the Serbian railway passenger stations, first within the basic set of the Passenger Transport Section Belgrade, then in the multi-set of the Passenger Transport Sections, and finally in the superset, the Passenger Transport Sector. This is achieved by means of the multi-set DEA (Data Envelopment Analysis) method, which is a system for: (i) relative efficiency assessment, in the first iteration, through the basic set analysis, and (ii) decrease in efficiency of potentially inefficient units, in subsequent iterations, through the multi-set analysis. The result is that the efficient stations Požarevac and Pančevo Bridge are at the initial level, and the (newly) efficient Požarevac, Novi Sad and Inđija at the final level. The best practice station remains the Požarevac Station, which is multi-efficient, and therefore the role model to inefficient stations. The conclusion is drawn that the solution resulting from the multi-set DEA analysis is more realistic, and less relative, because it applies to a wider analysed set of decision-making units, i.e., a larger coverage when considering the issue. This is important for fitting into the new era of growing globalization, and therefore our recommendation is the integral multi-set, as opposed to the individual single set approach.

Highlights

  • A number of same-type organisational units within a single organisation jointly accomplish the objective of the organisation, thereby contributing to a higher or lesser extent

  • The highest span is of DMU25 which is on the verge of efficiency (0.256134/1), with the achieved 25.6% of the goal

  • After reading the papers of the first and subsequent authors on the subject of the DEA method, it can be learnt that efficiency is a relative feature, as it varies depending on the data analysed

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Summary

Introduction

A number of same-type organisational units within a single organisation jointly accomplish the objective of the organisation, thereby contributing to a higher or lesser extent. From a more complex mathematical point of view, it is a ratio between the weighted sum of multiple outputs and weighted sum of multiple inputs For this purpose, the Data Envelopment Analysis, (DEA) was created in 1978 by Charnes, Cooper and Rhodes as a method of calculating the efficiency of the so-called Decision Making Units, abbreviated DMU), (Charnes et al, 1978). The Sensitivity analysis of a single same set of decision making units, but applying different input/output data and opposite DEA models, results in the same efficiency (Vuković, 2016). The stated Multiset DEA analysis of the same data of decision-making units in a number of different, ever bigger sets, results in smaller or equal efficiency, so some efficient units become inefficient. The Multiset DEA analysis of units is used for predicting inefficient results, which meant increasing the set of decision-making units by adding a new set. If, according to Marjanović (Vešović et al, 2007), the basic goals of the company are survival, facilitation of survival (efficiency of operation) and progress, within the context of the multiset approach: 1. The current state of the set indicates: the survival (the organisation is operating with both efficient and inefficient units)

Higher targeted state of the set obtained by Multiset DEA analysis marks
Passenger Transport Section Užice
Findings
Conclusion
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