Masters Theses
Date of Award
12-2017
Degree Type
Thesis
Degree Name
Master of Science
Major
Industrial Engineering
Major Professor
Rapinder Sawhney
Committee Members
John Kobza, H. Lee Martin
Abstract
The focus of this thesis is to develop a risk mitigation methodology for events which are less frequent. This will help to prevent accidents between personnel and material handling equipment inside a manufacturing environment. The emphasis is on mitigating risk associated with leading indicators of an incident so that the methodology is proactive in nature. While there are various risk prevention techniques available in the literature, the low frequency events are overlooked very easily. Following a failure to apply regular Risk Prioritization Number (RPN) a new Risk Prioritization Number is developed and validated. We call the new risk assessment method as Low Frequency(LF) technique and it uses the term ’Controllability’ as an alternative to ’Probability of occurrence’. The LF technique with its emphasis on scheduling and routing flexibility addresses this need. The four-phase methodology is presented to enhance the risk mitigation framework. The first phase defines the scope by estimating near miss and events pertained to a particular area. It also demarcates the region into nodes based on each and every entry and exit point to the region. The second phase involves data collection utilizing the historical data and expert’s opinion. The third phase maps the assessment of the collected data using analysis tool in MATLAB and Failure Mode and Effects Analysis (FMEA) to prioritize the risks. The fourth phase addresses the solution based on the prioritized risks from the previous phase. The developed framework was tested in a large manufacturing plant and the results prove that this framework identified 10% more risk which the company had not identified which had the possibility to cause accident which are less frequent.
Recommended Citation
Vijayabalan, Arun, "Risk Mitigation Framework Considering Low Frequency Events Involving Mobile Entities. " Master's Thesis, University of Tennessee, 2017.
https://trace.tennessee.edu/utk_gradthes/5016