Big data efficiency analysis: Improved algorithms for data envelopment analysis involving large datasets

2021 ◽  
pp. 105553
Author(s):  
Andreas Dellnitz
Author(s):  
Matthias Klumpp ◽  
Dominic Loske

Although resources are scarce and outputs incorporate the potential to save human lives, efficiency measurement endeavors with data envelopment analysis (DEA) methods are not yet commonplace in the research and practice of non-government organizations (NGO) and states involved in humanitarian logistics. We present a boot-strapped DEA window analysis and Malmquist index application as a methodological state of the art for a multi-input and multi-output efficiency analysis and discuss specific adaptions to typical core challenges in humanitarian logistics. A characteristic feature of humanitarian operations is the fact that a multitude of organizations are involved on at least two levels, national and supra-national, as well as in two sectors, private NGO and government agencies. This is modeled and implemented in an international empirical analysis: First, a comprehensive dataset from the 34 least developed countries in Africa from 2002 to 2015 is applied for the first time in such a DEA Malmquist index efficiency analysis setting regarding the national state actor level. Second, an analysis of different sections in a Rohingya refugee camp in Bangladesh is analyzed based on a bootstrapped DEA with window analysis application for 2017, 2018, and 2019 quarter data regarding the private NGO level of operations in humanitarian logistics.


2017 ◽  
Vol 13 (1) ◽  
pp. 1
Author(s):  
Natelda R Timisela ◽  
Ester D Leatemia ◽  
Febby J Polnaya ◽  
Rachel Breemer

The current research aimed to analyze the relative efficiency level of enbal (sago starch) agro-industries. The relative efficiency analysis on 32 DMUs of enbal agro-industries showed that 40,63% of the industries were efficient and 59.38% were inefficient. Every efficient DMU became the reference for the inefficient DMUs based on the suggested quality. Each DMU of the enbal agro-industries has not reached a good efficiency level, which was indicated by the average relative efficiency scale of 0.886. This was a relatively low value, and improvements on the use of production input were needed. The analysis result on the DMUs of the enbal agro-industries which were on constant return to scale position were 40,62%. This showed that enbal agro-industries actors have applied production input efficiently, for the production increase was equal to the use of input. In other words, the use of input was more proportional. The DMUs of enbal agro-industries which were on decreasing return to scale position were 15,63%. This showed that the use of production input had been unsuitable so that the output decreases and the production cost increased. Meanwhile, the DMUs that were on increasing return to scale position were 43,75%. This showed that the industry actors who used certain production input would create efficient DMUs. On the other hand, the input excess would possibly decrease the output. As a result, the industry actors should be concerned about the use of production input in order to establish business efficiency.


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