Department

University of Tennessee at Chattanooga. Dept. of Psychology

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

Workplace safety is not a new field of study, but the application of data analytics as a predictive tool is relatively new to the area. Beyond the obvious harm caused to workers, fatal and nonfatal injuries accrue a significant financial burden nationwide. Previous safety measures have focused largely on reactive tools. While these have done a significant job reducing nonfatal workplace injuries, they have done little to assuage fatal ones. Predictive analysis may be the next step in reducing workplace injury. By mining and processing safety-related data, predictive analysis can inform employers of high risk situations before they happen. By taking this preventative approach, employers have the chance to stop workplace incidents before their employees suffer harm. While these analyses have generally used national-level data sources and shown great success at reducing injury in some industries, such as construction, there has been little work done either in the manufacturing sector or at the company level. Using company-level data collected from a chemical manufacturing company, this team will create a predictive model for workplace incidents. Data collected from the 2017 and 2018 years will be used to train and validate a prediction model. That model will then be tested in 2019 data to determine its predictive efficiency.

Date

October 2020

Subject

Industrial and organizational psychology

Document Type

posters

Language

English

Rights

http://rightsstatements.org/vocab/InC/1.0/

License

http://creativecommons.org/licenses/by/4.0/

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Oct 24th, 12:00 AM Oct 24th, 12:00 AM

Generating a predictive model for injury rates for a chemical manufacturing company

Workplace safety is not a new field of study, but the application of data analytics as a predictive tool is relatively new to the area. Beyond the obvious harm caused to workers, fatal and nonfatal injuries accrue a significant financial burden nationwide. Previous safety measures have focused largely on reactive tools. While these have done a significant job reducing nonfatal workplace injuries, they have done little to assuage fatal ones. Predictive analysis may be the next step in reducing workplace injury. By mining and processing safety-related data, predictive analysis can inform employers of high risk situations before they happen. By taking this preventative approach, employers have the chance to stop workplace incidents before their employees suffer harm. While these analyses have generally used national-level data sources and shown great success at reducing injury in some industries, such as construction, there has been little work done either in the manufacturing sector or at the company level. Using company-level data collected from a chemical manufacturing company, this team will create a predictive model for workplace incidents. Data collected from the 2017 and 2018 years will be used to train and validate a prediction model. That model will then be tested in 2019 data to determine its predictive efficiency.