Preventing human error at an approved training organization using Dirty Dozen

Abstract

Following a recently-submitted review on a few human factor identification models (interpretations of Professor Edwards’ SHELL Model, Boeing’s Maintenance Error Decision Aid (MEDA), Professor Reason’s Swiss Cheese Model, and Dupont’s Dirty Dozen), researchers have unanimously agreed on choosing the Dirty Dozen model for this quantitative study before its official implementation in hangars and workshops at Universiti Kuala Lumpur – Malaysian Institute of Aviation Technology (UniKL MIAT). This study measures the levels of awareness and effectiveness of UniKL MIAT’s current human factor safety practices. A specifically-tailored, comprehensive, Dirty Dozen checklist is produced and distributed as survey questionnaire to 120 UniKL MIAT’s students. Data from all 48 questions related to all 12 domains of Dirty Dozen are analyzed. The results shows that out of all 12 domains, six (Lack of communication, Lack of teamwork, Norm, Pressure, Lack of attention, Stress) are marked with “Agreed” and the other half (Complacency, Lack of knowledge, Lack of resources, Distraction, Lack of authority, Exhaustion) are marked as “Not Sure” in terms of awareness and effectiveness of their current human factor’s safety practices. These results will be reviewed by the top management of the university to take preventive actions and improvements for future human factor safety implementations. As Dirty Dozen is known to be the simplest technique to measure human error, it is significantly appropriate to be applied as this experiment’s variable, especially for students who are still studying and have no industrial working experiences.

Authors

• A. Samad
• Muhd Khudri Johari
• S. Omar

Keywords

• Computer Science
• Engineering
• Education