Managing risk for business processes: A fuzzy based multi-agent system

Abstract

Abstract. Risk management for business processes is critical to the survival and performance of organizations, and has gained the interest of practitioners and academics. In this article, a fuzzy based multi-agent system (FMAS) is proposed as a practical solution to assess the process risk state and provide recommendations for the continuous improvement of business processes. To represent uncertain and ambiguous information obtained from experts, fuzzy theory is introduced to the FMAS. A fuzzy neural network is employed to implement an analysis agent capable of detecting the risk state of business processes within a firm. Furthermore, based on the predictions yielded by the analysis agent, the recommendation agent is then able to identify the possible deviations in the inconsistent process and provide suggestions for improvement. The effectiveness of the proposed FMAS is validated by its implementation in nine firms during twelve months, and the results are presented.

Automatic Verification and Diagnosis of Security Risk Assessments in Business Process Models

“…Other approaches, such as that described [63], try to determine the level of risk based on a business process state. Feng et al [63] apply fuzzy mechanisms to study the level of risk of a business process state. nonconformance is also carried out.…”

Automatic Verification and Diagnosis of Security Risk Assessments in Business Process Models

“…However, the approach pays no attention to risk or to the previous risk assessment of the model because these authors focus only on presenting the mechanisms to set up and generate specific security configurations in the model. Other approaches, such as that described [63], try to determine the level of risk based on a business process state. Feng et al [63] apply fuzzy mechanisms to study the level of risk of a business process state.…”

“…Other approaches, such as that described [63], try to determine the level of risk based on a business process state. Feng et al [63] apply fuzzy mechanisms to study the level of risk of a business process state. To provide a clear picture of all aforementioned research, a comparative study of the most relevant approaches related to the topic of this paper is given.…”

A new belief Markov chain model and its application in inventory prediction

“…Tough evidence theory has some open issues, such as conflict management [16,58], dependent evidence combination [53] and determination of basic probability assignment [28], it has a wide applications like fault diagnosis [27], supplier chain management [13,17], decision making [15,22,31,38] and risk evaluation which matters a lot in reality [20,24,30], due to its efficiency to model and fuse uncertain information. The following is some basic concepts of D-S evidence theory.…”

“…supplier chain management [13,17], decision making [15,22,31,38] and risk evaluation which matters a lot in reality [20,24,30], due to its efficiency to model and fuse uncertain information. The following is some basic concepts of D-S evidence theory.…”