Data Envelopment Method in Decision Making

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Table of Contents

Introduction
Literature review
Conclusion
Works Cited

Introduction

Organizations such as those in the manufacturing sector and the service industry such as finance and education among others endeavor to determine the efficient and productive use of their resources based on the productive efficiency of decision-making units (DMUs) to solve linear programming and performance-related problems. Here, the data envelopment analysis (DEA) constitutes the technique that has widely been adopted to solve optimization problems that have different variables and effects on the economic performance of those institutions.

Typically, the data envelopment analysis (DEA) is a linear programming technique for measuring the relative performance of organizational units where the presence of multiple inputs and outputs makes comparisons difficult (Riccardi, Oggioni and Toninelli 2).

This study aims to investigate the use of data envelopment analysis (DEA) to evaluate their use in the relative efficiency of decision-making units (DMUs) in the manufacturing industry with a specific focus on the Malmquist Taiwanese productivity of semiconductor packaging and testing firms (Pishvaee and Torabi 12). The key areas to focus on including the measurement or frontier forward shift, technical change, and frontier backward shift for two conservative periods.

The objectives of this study include evaluating the DEA Malmquist productivity index as a geometric mean of two Malmquist productivity indices. The second objective is to evaluate the efficiency of the DEA method in decision making.

Literature review

The DEA Malmquist productivity index used to measure the efficiency of the decisions

Liu and Wang conducted a study to determine the packaging and testing of semiconductors efficiency using the Malmquist productivity indices (1). In this study, three metrics were used in the measurement, which include the frontier backward shift, the measurement of technical change, and the forward frontier shift (Sueyoshi and Goto 8). The study focused on determining the efficiency of the decisions made using decision-making units (DMUs) based on the DEA Malmquist productivity index.

A review of the use of the efficient frontier techniques will be used to evaluate the efficiency of the technology using a set of DMU or DEA. In this case, n DMU could be placed under comparisons using slack variables for the r^th input and outputs (Li 21). The study will investigate the measurement of the DMUs using the technical efficiency change (TEC) and technology frontier shift (FS) which are the key components factored into the study.

Data Envelopment Analysis used to benchmark the quality and delivery of services

An investigation will be conducted on the context of a study by Liu and Wang which shows that 15 companies were used in the study to calculate their performance using the output value of the employees, net profit after tax, profitability ratio, and the growth rate variables. Further analysis in the same area of study will focus on different industries in Saudi Arabia where Data Envelopment Analysis (DEA) has been used to benchmark the quality and delivery of services in the process of providing them to people demanding the services in different capacities (Li 21).

This study takes different input variables to determine the efficiency of the decision-making process and the results that are obtained due to the course of actions that are taken. The study was conducted in the Qassim District to determine the performance and efficiency of the systems in use in different companies. This area of investigation will factor the use of the constant return to scale (CRS) and the variable return to scale (VRS) models and determine the input and output variables of interest.

Data Envelopment Analysis used in solving linear programming problems

The Data Envelopment Analysis (DEA) method was reviewed for its appropriateness to be used in solving linear programming problems based on the elements of the extended theory in decision making. It will provide the background for using the method to make optimal solutions (Malana and Malano 19). The DEA method can be applied to solve the linear programming problem in the packaging and testing of semiconductors efficiency. However, the linear programming and the data envelopment analysis (DEA) will be compared in the context of the efficiency in decision making in the sense of the size of data to analyze and the efficiency of the methods.

Data Envelopment Analysis used to benchmark the performance

This study will be conducted to investigate the use of Data Envelopment Analysis (DEA) to benchmark the performance of King Khalid University Health Care units and the training of medical personnel. Malana and Malano claim that the hospital aims to use its advanced medical equipment to offer high-quality medical training and education using its group of teaching hospitals in different areas of the medicine (21). Besides, the hospital endeavors to provide medical services to the local communities, inpatients, and outpatients to address their medical needs.

Investigations on the use of the non-parametric methods of Data Envelopment Analysis (DEA) to establish the efficiency of resources were based on the use of the Constant Return to Scale model (CCR) (Malana and Malano 6). The goal was to determine the efficiency of decision-making units (DMU). Another model that was used in the investigation to evaluate the DMU includes the Variable Return to Scale (VRS). In this study, data were collected from 20 hospitals with multiple inputs made from the hospital staff and patients. Ratios were calculated and a DMU of less than 100% was deemed to be inefficient.

In the same area of interest, an analysis of the literature on the relative efficiency of service delivery was conducted using the Data Envelopment Analysis. The CharnesCooperRhodes (CCR) and BankerCharnesCooper (BCR) models were used to investigate the performance of the management of the financial resources of the hospital. The data used for the study was collected from 10 hospitals and analyzed by formulating hypotheses that were used to either reject or accept the results of the study. Different input and output parameters were used (Malana and Malano 5). The CharnesCooperRhodes (CCR) model was assigned values on a scale of 0 to 1. On the other hand, the BankerCharnesCooper (BCC) was used to model a hospitals financial production process. Each hospital was treated as an entity that could take two inputs and two outputs, which include the total deposits, total loan advances, net investments, and total expenses.

Conclusion

In conclusion, the researcher will analyze the data using the input-orientation approach by using the same input data for both models (Malana and Malano 21). The results were interpreted based on DMUs. In this case, a DMU that was less than 1 showed that the bank was inefficient and a DMU greater than 1 showed that the bank was efficient. An evaluation of the results showed that more than 10% of the banks in Saudi Arabia were inefficient. However, the relative efficiency of the bank was shown to depend on its size.

Works Cited

Li, Deng-Feng. Linear programming method for MADM with interval-valued intuitionistic fuzzy sets. Expert Systems with Applications 37.8 (2010): 5939-5945. Print.

Liu, Fuh-Hwa Franklin, and Peng-Hsiang Wang. DEA Malmquist productivity measure: Taiwanese semiconductor companies. International Journal of Production Economics 112.1 (2008): 367-379. Print.

Malana, Naeem M., and Hector M. Malano. Benchmarking productive efficiency of selected wheat areas in Pakistan and India using data envelopment analysis. Irrigation and drainage 55.4 (2006): 383-394. Print.

Pishvaee, Mir Saman, and S. Ali Torabi. A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems 161.20 (2010): 2668-2683. Print.

Riccardi, R., G. Oggioni, and R. Toninelli. Efficiency analysis of world cement industry in presence of undesirable output: application of data envelopment analysis and directional distance function. Energy Policy 44 (2012): 140-152. Print.

Sueyoshi, Toshiyuki, and Mika Goto. Efficiency-based rank assessment for electric power industry: a combined use of data envelopment analysis (DEA) and DEA-discriminant analysis (DA). Energy Economics 34.3 (2012): 634-644. Print.

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