Masters Theses
Date of Award
5-2018
Degree Type
Thesis
Degree Name
Master of Science
Major
Industrial Engineering
Major Professor
Rapinder S. Sawhney
Committee Members
John E. Kobza, Xueping Li
Abstract
Collaborative risk management techniques place management and workers equally while developing a safety culture in workplaces. Traditional risk awareness methods which are commonly carried out in workplaces, such as training and safety manuals, are inherently passive in nature. On the other hand, visual tools are active risk communication mechanisms which deliver specific risk information in a work area. The presented study places emphasis on risk awareness for workers through the assignment of visual tools, which is critical to the success of a collaborative framework. Traditionally, the assignment of visual tools to work area locations has been arbitrary, potentially causing the risk information to be ineffective. The framework presented in this study provides a systematic visual tool assignment method for safety managers in manufacturing work areas. This placement is based on the attributes of the work area. The use of multi-criteria decision making (MCDM) techniques such as Analytic Hierarchy Process (AHP) incorporates the expertise of safety managers for a successful visual tool assignment by considering work area and entity variables. Analysis of Variance (ANOVA) and Data Envelopment Analysis (DEA) reduce the number of variables that act as the criteria for AHP. The scenario-based case study indicate that these variables had an impact on the choice of visual tools. These scenarios are designed to depict multiple locations in a heavy manufacturing plant layout. The presented study is applicable to mobile entity interfaces in manufacturing industries. It can be applied to other manufacturing safety incident categories and industries which could benefit from visual communication of risk information in work areas.
Recommended Citation
Shah, Riddhi Pradeep, "An AHP based visual tool assignment model for accident avoidance in manufacturing workplaces. " Master's Thesis, University of Tennessee, 2018.
https://trace.tennessee.edu/utk_gradthes/5053