Perforrmnce indicators play some important roles in enterprise operation. Both researchers and managers have recently pointed out that the identification of correlation between different performmance indicators may le...Perforrmnce indicators play some important roles in enterprise operation. Both researchers and managers have recently pointed out that the identification of correlation between different performmance indicators may lead to a better understanding of business. However, it is becoming more and more difficult to measure and analyze these indicators since the fast growing number of performance indicators and the complex relationships between them The existing categories failed to reflect these changes in an adequate way, and the quantitative analysis methods for identifying the characters of those pefformance indicators are still worthy of investigation. The main objective of this paper is to propose a practical methodology for managing and analyzing performance indicators in enterprises, which focuses on building up a performance indicator system and discovering the characters of those performance indicators by applying complex network methods. The empirical results of a telecommunieation enterprise show that the proposed method can be effective in understanding the correlations between performance indicators.展开更多
The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （200...The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.展开更多
基金This work was partially supported by the Project of National Natural Science Foundation for Distinguished Young Scholars under Grants No.70901009,No.71202155，the Youth Research and Innovation Program in Beijing University of Posts and Telecommunications，the National Basic Research Program of China under Grant No.2012CB315805
文摘Perforrmnce indicators play some important roles in enterprise operation. Both researchers and managers have recently pointed out that the identification of correlation between different performmance indicators may lead to a better understanding of business. However, it is becoming more and more difficult to measure and analyze these indicators since the fast growing number of performance indicators and the complex relationships between them The existing categories failed to reflect these changes in an adequate way, and the quantitative analysis methods for identifying the characters of those pefformance indicators are still worthy of investigation. The main objective of this paper is to propose a practical methodology for managing and analyzing performance indicators in enterprises, which focuses on building up a performance indicator system and discovering the characters of those performance indicators by applying complex network methods. The empirical results of a telecommunieation enterprise show that the proposed method can be effective in understanding the correlations between performance indicators.
基金Hong Kong Grants Council Grants #622105 and #622307the National Basic Research Program of China (aka the 973 Program) under project No.2003CB517106.
文摘The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.
