Healthy Skepticism Library item: 8433
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Publication type: Journal Article
Chevreau FR, Wybo JL, Cauchois D.
Organizing learning processes on risks by using the bow-tie representation.
J Hazard Mater 2006 Mar 31; 130:(3):276-83
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TGF-4GY89YR-4&_user=10&_coverDate=03%2F31%2F2006&_rdoc=1&_fmt=full&_orig=search&_cdi=5253&_sort=d&_docanchor=&view=c&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=18690bdb2028a8c20ea45827891f7061
Abstract:
The Aramis method proposes a complete and efficient way to manage risk analysis by using the bow-tie representation. This paper shows how the bow-tie representation can also be appropriate for experience learning. It describes how a pharmaceutical production plant uses bow-ties for incident and accident analysis. Two levels of bow-ties are constructed: standard bow-ties concern generic risks of the plant whereas local bow-ties represent accident scenarios specific to each workplace. When incidents or accidents are analyzed, knowledge that is gained is added to existing local bow-ties. Regularly, local bow-ties that have been updated are compared to standard bow-ties in order to revise them. Knowledge on safety at the global and at local levels is hence as accurate as possible and memorized in a real time framework. As it relies on the communication between safety experts and local operators, this use of the bow-ties contributes therefore to organizational learning for safety.
Keywords:
Aramis method; Risk analysis; Bow-tie diagram; Organizational learning; Experience feedback process
MeSH Terms:
Accidents, Occupational/prevention & control*
Causality
Chemical Industry/organization & administration
Chemical Industry/standards*
Decision Trees*
Drug Industry
Environmental Exposure/prevention & control
Equipment Failure
European Union
France
Guidelines
Hazardous Substances
Humans
Inservice Training*
Models, Organizational
Problem-Based Learning*
Risk Assessment/methods*
Safety Management/methods*
Substances:
Hazardous Substances