A Markovian Analysis of Industrial Accident Data in a Nigerian Manufacturing Company

Despite the effort of manufacturing company owners in Nigeria to curtail industrial accident occurrence, it still remains a daunting challenge. This study seeks to predict the drift of industrial accidents in manufacturing companies in Delta State, Nigeria in order to ensure the health and safety of staff in the workplace. Markov Chain (MC) model was used to analyse eleven year industrial accident data obtained from a primary source: Health, Safety, and Environment (HSE) department of a manufacturing company in the Delta state of Nigeria. The data was summarised as integrated safety data to give a better picture of the organisation safety culture. The data was analysed and found to have absorbing chain tendencies. It also possesses a note of stochastic regularity, which fits into an MC model. Accident data in the company were classified into five states, namely fatality, loss time accident, medical aid accident, first aid incident, and near-miss. The result from the study reveals that fatality and loss time accidents were absorbing states, while medical aid accidents, first aid incidents, and near-miss were found to be non-absorbing states. A worker who commits an error in the form of near miss 1000 times stands a chance of fatality 77 times out of this total time of work error. This study will serve as a guide to manufacturing company stakeholders on the need to create safety awareness among the workforce.