Evaluation of Economic Performance through Clustering and Vikor Model Case Study: The Urban Water and Wastewater Industry Holding

Document Type : Original Article

Authors

1 Senior Financial Expert

2 Head of Bureau for General Assemblies, TPWWC

3 Director General, Bureau of General Assemblies and Financial Control, NWWEC

Abstract

In the context of evaluating performance organizations apply the conventional techniques of analyzing indicators and quantitative and biased parameters. Although these methods provide useful information, but due to exclusion of some indicators, no clear and thorough image of the performance is provided and the results often lack the definite validity. The economic indicators cover different sets of performance. Therefore, application of scientific models to evaluate performance will have a great impact on evaluations as well as on clarifying the targeted policies and decisions. This research was undertaken to identify the economic performance of Water and Wastewater Companies on the basis of economic indicators and scientific models and in the context of divisional and top management decision making in the sector. After selecting the efficient economic indicator and setting their weights by Shannon Entropy Model, the performance of urban Water and Wastewater Companies were classified in 4 independent groups from the point of view of economic indicators by applying the statistical clustering method (hierarchal approach). Then the economic performance of the Water and Wastewater Companies were graded in the clusters with the aim of modeling and specifying plans to improve and enhance them. The results underlined the efficiency of quantitative models in generating definite data with high degree of reliability for strategic management and in particular for making decisions on planning and management control.

Keywords

References

 
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Journal of Water and Wastewater Science and Engineering
Volume 3, Issue 1
April 2018
Pages 47-57

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History

  • Receive Date: 03 June 2018
  • Revise Date: 04 August 2018
  • Accept Date: 18 August 2018

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