A Fuzzy Inference System for Predicting Human Error and its Application in Process Management

Human resources are essential in manufacturing and service industries, and one of the main issues regarding human resources is how to predict the risk of human errors in different circumstances. Human errors play a significant role in the overall performance of manufacturing and service industries....

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Bibliographic Details
Published inIISE Annual Conference. Proceedings p. 2032
Main Authors Khanmohammadi, Sina, Chou, Chun-An, Esfahlani, Farnaz Zamani, Hourani, Anas
Format Journal Article
LanguageEnglish
Published Norcross Institute of Industrial and Systems Engineers (IISE) 01.01.2014
Subjects
Behavior
Engineering
Fuzzy logic
Human error
Identification
Manufacturing
Medical errors
Motion capture
Principal components analysis
Risk
Service industries
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Summary:Human resources are essential in manufacturing and service industries, and one of the main issues regarding human resources is how to predict the risk of human errors in different circumstances. Human errors play a significant role in the overall performance of manufacturing and service industries. For example, according to the Institute of Medicine (IOM) report, called "To Err Is Human", 44,000 to 98,000 patients die each year as a result of human caused medical errors in health care service industry. In this paper, a new fuzzy inference system approach is proposed to predict the risk of human errors. A hierarchical fuzzy inference system consisting of different sub FISs is applied, where each FIS represents different levels of the system. The independent variables including personal and environmental factors are fed to sub FISs to determine the intermediate variables that affect the level of human errors. The outputs of these FISs are fed into a mathematical model to determine the level of human errors in different circumstances. An example is provided to demonstrate how the results of the model can be interpreted and used for identifying appropriate strategies to decrease the risk of human errors.