From the process of installation of DEA-Solver to the computation of the analysis, the DEA process undergoes a various set of challenges. The first challenge incurs at the time of the installation itself, as it requires to be manually included in MS Excel.
Overcome common challenges in GM (1,1) modelling using MS Excel
GM (1,1) modelling is a popular grey forecasting method because of its computational efficiency. There are many challenges in GM (1,1) modelling, but they are solvable using MS Excel. This article is a detailed guide on how to overcome these challenges.
Slacks based measure or SBM analysis in DEA
Slacks based measure or SBM analsysis is a non-radial model to solve the problem in the “additive model” developed by Charnes, Cooper, & Rhodes in 1978. This model can discriminate between efficient and inefficient Decision-Making Units (DMU).
MALMQUIST index summaries interpretations from DEA
This article, discusses and interprets the rest of the results from Malmquist DEA. Furthermore, the analysis of Malmquist index summaries for both output and input frontiers are interpreted.
Interpreting cost efficiency or cost DEA of banks using DEAP
Differences between Multi-stage and Cost- data envelopment analysis (DEA) was also discussed. However, the article will only interpret the results from cost efficiency analysis from the constant returns to scale (CRS) frontier.
Executing and interpreting cost efficiency of banks through VRS cost DEA
In the previous article, discussed and interpreted the findings of cost efficiency using constant returns to scale (CRS) Cost Data Envelopment Analysis (DEA).
Executing cost DEA of banks using Data Envelopment Analysis Program (DEAP)
The cost efficiency analysis or cost data envelopment analysis or cost DEA is evaluated when information on prices and costs are available from the source of the data collected for input and output variables (Cooper, Seiford, & Zhu, 2011).
MALMQUIST productivity Index test of the healthcare sector in India
Malmquist productivity index evaluates the efficiency change over time as mentioned by Färe, Grosskopf, & Margaritis, (2011). However, Malmquist productivity index literature has been uneven with some authors assuming constant returns to scale and others allowing for variable returns to scale.