Research on the grey relational evaluation method of core competencies of virtual enterprise members

Kybernetes ◽  
2008 ◽  
Vol 37 (9/10) ◽  
pp. 1250-1256 ◽  
Author(s):  
Dejun Chen ◽  
Zude Zhou ◽  
D.T. Pham
Keyword(s):  
Evaluation Method ◽  
Core Competencies ◽  
Virtual Enterprise ◽  
Grey Relational

Designing a Virtual Enterprise Architecture using Structured System Dynamics

2006 ◽  
Vol 25 (1) ◽  
pp. 13-29
Author(s):  
Nikitas A. Assimakopoulos ◽  
Anastasios N. Riggas
Keyword(s):  
System Dynamics ◽  
Enterprise Architecture ◽  
Business Processes ◽  
Core Competencies ◽  
Virtual Enterprise ◽  
Business Goals ◽  
Starting Point ◽  
Efficient Integration ◽  
Structured System ◽  
The Impact

The starting point for establishing a Virtual Enterprise is a set of existing enterprises which might contribute with some of their functionalities (core competencies) to the formation of the virtual entity. The most important issue, in this formation, is the rapid integration of the business processes of the participating companies. The architecture of the VE must assist companies desiring to enter into a virtual relationship by defining the functions and interfaces of critical business processes, thus allowing for a more rapid and efficient integration of the expertise which will be contributed by each partner in the virtual enterprise. While the integration of computer and communication technologies are no doubt critical issues, the successful attainment of the business goals of the virtual enterprise often depends on its ability to align the business processes and practices of partner enterprises. Focus of this paper, is the presentation of Structured System Dynamics (SSPS) multi-methodology for the design and the evaluation of a Virtual Enterprise Architecture. SSPS uses Systems Thinking and System Dynamics principles as launch pad for its approach. The Systemic Methodologies of Problem Structuring Methodology (PSM) and SAST are also integrated in this new Multi-Methodology. SSPS is a new practical and scientific tool in designing and evaluating a VE architecture providing the ability to determine the impact, reliability, success of the Architectures' models created, refine them and identify potential process improvements. A framework for the rapid and efficient integration of the business processes of the participating companies in the virtual enterprise is provided. For this multi-methodology, a real-life application is also presented for a Virtual Enterprise that constructs Wireless Payment Mechanisms.

Comparative Study on Indoor Air Quality Evaluation Method

2012 ◽  
Vol 178-181 ◽  
pp. 267-271
Author(s):  
Xing Yang Leng ◽  
Ping Jiang
Keyword(s):  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Index Method ◽  
Comprehensive Evaluation Method ◽  
Comprehensive Index Method ◽  
Grey Relational

This paper reviewed six kinds of indoor air quality (IAQ) evaluation methods, by which the same IAQ status were evaluated. Besides, the relations and distinctions of six methods were also analyzed. The results indicate that the evaluation results of different methods on the same sample are not identical. Fuzzy comprehensive evaluation method, pollution loss rate method and comprehensive index method are relatively accurate, and improved grey relational analysis method has the largest error.

Environments for VE Integration

2010 ◽  
pp. 1020-1029
Author(s):  
Maria Manuela Cunha
Keyword(s):  
Best Practices ◽  
Core Competencies ◽  
Virtual Enterprise ◽  
Market Opportunity ◽  
Competitive Response ◽  
Maximum Performance ◽  
Business Alignment ◽  
Market Requirements ◽  
Operation Phase ◽  
Collaborative Outsourcing

Most definitions of virtual enterprise (VE) incorporate the idea of extended and collaborative outsourcing to suppliers and subcontractors in order to achieve a competitive response to market demands (Webster, Sugden, & Tayles, 2004). As suggested by several authors (Browne & Zhang, 1999; Byrne, 1993; Camarinha- Matos & Afsarmanesh, 1999; Cunha, Putnik, & Ávila, 2000; Davidow & Malone, 1992; Preiss, Goldman, & Nagel, 1996), a VE consists of a network of independent enterprises (resources providers) with reconfiguration capability in useful time, permanently aligned with the market requirements, created to take profit from a specific market opportunity, and where each participant contributes with her best practices and core competencies to the success and competitiveness of the structure as a whole. Even during the operation phase of the VE, the configuration can change to assure business alignment with the market demands, traduced by the identification of reconfiguration opportunities and constant readjustment or reconfiguration of the VE network to meet unexpected situations or to keep permanent competitiveness and maximum performance (Cunha & Putnik, 2002, 2005a, 2005b)

Applying a Core Competencies Approach in Virtual Enterprise Formation

2011 ◽  
pp. 213-228
Author(s):  
Carlos Frederico Bremer ◽  
Jairo Eduardo Moraes Siqueira ◽  
Luis Fernando Moraes Marques
Keyword(s):  
Strategic Planning ◽  
Core Competencies ◽  
Virtual Enterprise ◽  
Practical Case ◽  
Formation Stage ◽  
New Process

The process of core competencies identification has been incorporated by the enterprises within strategic planning. The virtual enterprise, which is a form of cooperation between enterprises, is one of the most benefited with this new process, mainly in its formation stage. The identified core competencies, which are deployed in products, process and technology, may support a more agile gathering of the virtual enterprise partners. This chapter presents a method to identify core competencies, supported by a practical case of successful virtual enterprise formation, where the method was applied and validated.

Quality management framework for a virtual enterprise network: a multi‐agent approach

2003 ◽  
Vol 13 (4) ◽  
pp. 300-309 ◽  
Author(s):  
H.C.W. Lau ◽  
Christina W.Y. Wong ◽  
Eric W.T. Ngai ◽  
I.K. Hui
Keyword(s):  
Core Competencies ◽  
Virtual Enterprise ◽  
Enterprise Networks ◽  
Key Factors ◽  
Enterprise Network ◽  
Management Framework ◽  
Agent Model ◽  
Geographically Dispersed ◽  
Multi Agent ◽  
To Come

Globalization and the borderless marketplace have created an opportunity for companies to come together to work on projects that could otherwise not be completed through the efforts of one or two companies acting independently. The formation of virtual enterprise networks combines the advantages of the various core competencies of members of the network to deliver customer satisfaction. Virtual enterprise networks consist of several firms that can be geographically dispersed, and that might be technically disparate in terms of system platforms. Coordination among members of the network and the the transfer of quality products and services among members of the network are the key factors in achieving success. The proposed framework presented in this paper utilizes a multi‐agent model to monitor quality within a virtual enterprise network to ensure that the processes among members run smoothly and efficiently.

scholarly journals DEA Cross-Efficiency Ranking Method Based on Grey Correlation Degree and Relative Entropy

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 966 ◽  
Author(s):  
Si ◽  
Ma
Keyword(s):  
Relative Entropy ◽  
Evaluation Method ◽  
Ideal Solution ◽  
Analysis Method ◽  
Relational Analysis ◽  
Correlation Degree ◽  
Efficiency Ranking ◽  
Grey Correlation Degree ◽  
Grey Relational ◽  
The Ideal

The ranking of decision-making units (DMUs) is one of the most significant issues in efficiency evaluation. However, the calculation results from the traditional DEA method sometimes include multiple efficient DMUs or multiple DMUs with the same efficiency value, in which case the approach is weak in distinguishing among these DMUs. Therefore, this study proposes a DEA cross-efficiency ranking method based on the relative entropy evaluation method and the grey relational analysis method. First, the approach uses the cross-efficiency matrix as the decision matrix of multiple criteria decision-making (MCDM), and the relationship between DMU and the ideal solution is analyzed by the grey relational analysis method and the relative entropy evaluation method. Then, the degree of the criteria is determined by Shannon entropy, and the weighted grey correlation degree and the weighted relative entropy are obtained. Finally, with the comprehensive relative closeness degree between the DMU and the ideal solution, we can sort all the DMUs accordingly. In a comparative analysis, it shows that this method analyzes the similarity between DMUs and the ideal solution from the information distance and the similarity of the data sequence curve, and has certain advantages for analyzing the ranking of DMUs.

Application of the Big Data Grey Relational Decision-Making Algorithm to the Evaluation of Resource Utilization in Higher Education

2018 ◽  
Vol 14 (2) ◽  
pp. 43-55 ◽  
Author(s):  
Ji Huan ◽  
Ren Bo
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Big Data ◽  
Performance Evaluation ◽  
Resource Utilization ◽  
Evaluation Method ◽  
Entropy Weight ◽  
Decision Algorithm ◽  
Higher Educational ◽  
Grey Relational

In this article, the authors apply the big grey relational decision-making algorithm to improve performance evaluation effectiveness of the higher educational resources utilization. First, they discuss the performance evaluation indexes in higher education. Second, they propose the big data grey relational decision algorithm. Third, they establish the mathematical models of entropy weight and grey evaluation method. Finally, the authors carry out an evaluation simulation analysis on four cities as researching objects. The results show that the big data grey relational decision-making algorithm is an effective method for evaluating the higher educational resource utilization.

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