Citation

Iyer PS, Haight JM, Del Castillo E, Tink BW, Hawkins PW. J. Saf. Res. 2005; 36(4): 341-351.

Affiliation

The Pennsylvania State University, Department of Energy and Geo-Environmental Engineering, 224 Hosler Building, University Park, PA 16802-5000, United States.

Copyright

(Copyright © 2005, U.S. National Safety Council, Publisher Elsevier Publishing)

DOI

10.1016/j.jsr.2005.06.011

PMID

16197961

Abstract

INTRODUCTION/PROBLEM: Property damage incidents, workplace injuries, and safety programs designed to prevent them, are expensive aspects of doing business in contemporary industry. The National Safety Council (2002) estimated that workplace injuries cost $146.6 billion per year. Because companies are resource limited, optimizing intervention strategies to decrease incidents with less costly programs can contribute to improved productivity. METHOD: Systematic data collection methods were employed and the forecasting ability of a time-lag relationship between interventions and incident rates was studied using various statistical methods (an intervention is not expected to have an immediate nor infinitely lasting effect on the incident rate). RESULTS/SUMMARY: As a follow up to the initial work, researchers developed two models designed to forecast incident rates. One is based on past incident rate performance and the other on the configuration and level of effort applied to the safety and health program. Researchers compared actual incident performance to the prediction capability of each model over 18 months in the forestry operations at an electricity distribution company and found the models to allow accurate prediction of incident rates. IMPACT ON INDUSTRY: These models potentially have powerful implications as a business-planning tool for human resource allocation and for designing an optimized safety and health intervention program to minimize incidents. Depending on the mathematical relationship, one can determine what interventions, where and how much to apply them, and when to increase or reduce human resource input as determined by the forecasted performance.