<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The inherent benefits of an accident prevention program are generally known only after an accident has occurred. The purpose of implementation of the program is to minimize the number of accidents and cost of damages. Allocation of resources to implement accident prevention program is vital because it is difficult to estimate the extent of damage caused by an accident. Accurate fatal accident predictions can provide a meaningful data that can be used to implement accident prevention program in order to minimize the cost of accidents. This paper forecast the fatal accidents of factories in India by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Accident data for the available period 1980 to 2013 was collected from the Labour bureau, Government of India to analyze the long term forecasts. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (0, 0, 1) is suitable model for prediction of fatal injuries. The number of fatal accidents is forecasted for the period 2014 to 2019. These results suggest that the policy makers and the Indian labour ministry must focus attention toward increasing fatal accidents and try to find out the reasons. It is also an opportunity for the policy makers to develop policies which may help in minimizing the number fatal accidents.</p></abstract>

The inherent benefits of an accident prevention program are generally known only after an accident has occurred. The purpose of implementation of the program is to minimize the number of accidents and cost of damages. Allocation of resources to implement accident prevention program is vital because it is difficult to estimate the extent of damage caused by an accident. Accurate fatal accident predictions can provide a meaningful data that can be used to implement accident prevention program in order to minimize the cost of accidents. This paper forecast the fatal accidents of factories in India by using Auto-Regressive Integrating Moving Average Method (ARIMA) model. Accident data for the available period 1980 to 2013 was collected from the Labour bureau, Government of India to analyze the long term forecasts. Different diagnostic tests are applied in order to check the adequacy of the fitted models. The results show that ARIMA (0, 0, 1) is suitable model for prediction of fatal injuries. The number of fatal accidents is forecasted for the period 2014 to 2019. These results suggest that the policy makers and the Indian labour ministry must focus attention toward increasing fatal accidents and try to find out the reasons. It is also an opportunity for the policy makers to develop policies which may help in minimizing the number fatal accidents.

Prediction of fatal accidents in Indian factories based on ARIMA

Production Engineering Archives

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{"article-title":"Prediction of fatal accidents in Indian factories based on ARIMA"}

The inherent benefits of an accident prevention program are generally known only after an accident has occurred. The purpose of implementation of the program...

Prediction of fatal accidents in Indian factories based on ARIMA

Publié en ligne: 14 nov. 2018

Pages: 24 - 30

Reçu: 05 mars 2018

Accepté: 09 avr. 2018

DOI: https://doi.org/10.30657/pea.2018.18.04

Mots clés
fatal accidents, prediction, stationarity, correllogram, auto regressive integrating, moving average method

© 2018 Rajaprasad SVS, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Prediction of fatal accidents in Indian factories based on ARIMA

Publié en ligne: 14 nov. 2018

Pages: 24 - 30

Reçu: 05 mars 2018

Accepté: 09 avr. 2018

DOI: https://doi.org/10.30657/pea.2018.18.04

Mots clésfatal accidents, prediction, stationarity, correllogram, auto regressive integrating, moving average method

© 2018 Rajaprasad SVS, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
fatal accidents, prediction, stationarity, correllogram, auto regressive integrating, moving average method