چکیده:
Background & Purpose: Analysis and forecasting of efficiency in organizations in order to evaluate the performance of units and planning to improve the performance of units is very important. The purpose of this study is to analyze and predict the relative efficiency of the country's social security branches. For this purpose, in general, in this study, a framework has been created to estimate the future value of the efficiency of units using artificial neural networks. Methodology: In this research, using non-parametric data envelopment analysis method and game theory method, cover the research gaps in measuring cost efficiency and technical efficiency in a two-tier supply chain in terms of price stability and price instability. Findings: First, branch efficiency was calculated using data envelopment analysis method and then performance classification was performed. Conclusion: In this study, based on the past performance of the units and calculating their cost efficiency in consecutive years, the future performance of the units was predicted using the time series function. Conclusion: Managers should implement a data collection and processing system in the organization and regularly perform clustering and performance forecasting for the coming months and years, based on which to improve and optimize inputs and outputs. Keywords: Efficiency, Data Envelopment Analysis, Data Mining, Neural Networks
خلاصه ماشینی:
161-179 Research Article Efficiency Analysis and Prediction with a Data Envelopment Analysis and Data Mining Approach Mohammad Hossein Alimadadi 1, Mehrzad Navabakhsh 2, Ashkan Hafez Alkotob 3 Abstract Background and Objective: Analyzing and predicting efficiency in organizations for evaluating unit performance and planning for performance improvement is of great importance.
Methodology: In this research, using the non-parametric Data Envelopment Analysis method and Game Theory, research gaps in the field of measuring cost and technical efficiency in a two-tier supply chain under price stability and price instability conditions have been covered.
Conclusion: Managers should implement a type of data collection and processing system and regularly perform clustering and efficiency prediction for the coming months and years, and based on that, work on improving and optimizing inputs and outputs.
Färe and Grosskopf, based on the research of Shephard6, and Shephard and Färe presented a set of models to address structures that traditional Data Envelopment Analysis is unable to handle (Shephard et al.
In this research, using data mining techniques and neural networks and by discovering hidden patterns, efficiency for future years is predicted (Kao, 2014).
In this research, efficiency prediction for future years has been performed using data mining techniques and neural networks and by discovering hidden patterns.
Data Envelopment Analysis; it is a non-parametric method for measuring the relative efficiency of units with identical inputs and outputs and determining the efficiency frontier of decision-making units 1.
Discussion and Conclusion The presentation of models for calculating the efficiency of processes with network structures has attracted much attention from Data Envelopment Analysis researchers in recent years.
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