Markov Chain theoretic approach to modelling industrial safety: some results from Nigeria oil and gas industry

Öz

the oil and gas industry. This study seeks to unravel the hidden details enwrapped in the safety data which enable the development of a model that would be effective in predicting future occurrence of industrial accidents. The purpose of this study is to spotlight the epidemiological impact of industrial accidents which claim lives, maim personnel and lower productivity through loss of man-hours. The major strategy adopted consists of the examination of a 10-year unified industrial accident recorded from 2007 to 2016 in the oil and gas sector of the Niger Delta of Nigeria. The Markov Chain model was applied to determine how workers habituate among different positions in the company before getting entrapped in any of the absorbing states. The industrial accident records are examined for embedded Markov properties, namely: stochastic regularity, absorbing behavior and the long-run distribution amongst the various states. The statistical computations were carried out with the aid of Matlab (R2017a) software. The historical Health Safety and Environment (HSE) statistical data were found to have an absorbing chain property. Thirteen (13) transition states were defined and named as; fatality, medical treatment, first aid cases, Lost time injury frequency, restricted work cases, first aid case, near miss, lost time injury, environmental incident, fire incident, unsafe acts, unsafe condition and number of attendance of clinic. The result from the study also revealed that staff makes between 17-18 habituations before being trapped in an absorbing state. Remarkably, 99% of workers in the organization had severe medical treatment cases (MTCs) as a result of work-related occupational illness and injury. The implication of the study is that the safety policy outlook of this company should be reorganized to reduce its lost workdays due to minor injury and illness.

Anahtar Kelimeler

• Absorbing state
• HSE records
• Industrial accident
• Markov Chain
• Valued diagraph

Kaynakça

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