A study on industrial accident rate forecasting and program development of estimated zero accident time in Korea

6Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

Abstract

To begin a zero accident campaign for industry, the first thing is to estimate the industrial accident rate and the zero accident time systematically. This paper considers the social and technical change of the business environment after beginning the zero accident campaign through quantitative time series analysis methods. These methods include sum of squared errors (SSE), regression analysis method (RAM), exponential smoothing method (ESM), double exponential smoothing method (DESM), auto-regressive integrated moving average (ARIMA) model, and the proposed analytic function method (AFM). The program is developed to estimate the accident rate, zero accident time and achievement probability of an efficient industrial environment. In this paper, MFC (Microsoft Foundation Class) software of Visual Studio 2008 was used to develop a zero accident program. The results of this paper will provide major information for industrial accident prevention and be an important part of stimulating the zero accident campaign within all industrial environments.

Cited by Powered by Scopus

Improvement of inspection system for reduction of small-scale construction site accident in korea

8Citations
N/AReaders
Get full text

A novel grey–fuzzy–Markov and pattern recognition model for industrial accident forecasting

6Citations
N/AReaders
Get full text

Modern Cause and Effect Model by Factors of Root Cause for Accident Prevention in Small to Medium Sized Enterprises

5Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Kim, T. G., Kang, Y. S., & Lee, H. W. (2011). A study on industrial accident rate forecasting and program development of estimated zero accident time in Korea. Industrial Health, 49(1), 56–62. https://doi.org/10.2486/indhealth.MS1174

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 5

50%

Professor / Associate Prof. 3

30%

Lecturer / Post doc 1

10%

Researcher 1

10%

Readers' Discipline

Tooltip

Computer Science 2

29%

Social Sciences 2

29%

Engineering 2

29%

Physics and Astronomy 1

14%

Save time finding and organizing research with Mendeley

Sign up for free