A Bayesian network for business uncertainties in coal mining industry

In recent years, business uncertainty in the coal mining industry has increased along with increasing global energy demand and carbon emission reduction policies. Not much research has been done on business uncertainty in the coal mining industry based on modelling. This study aimed to develop a model for business uncertainty in the coal mining industry using a Bayesian network approach. The Indonesian coal mining industry was utilized as a case study to develop the proposed model. Two dimensions and nine factors were identified and structured based on a literature review and rigorous interviews with three interviewees from three companies. Each of these three companies’ business uncertainties was assessed by a total of forty respondents. The three most significant business uncertainty factors found were: decreasing coal reserves, low accessibility and transportation options, and government policy ambiguity. Triggering factors were also identified to quantify the probabilities of the business uncertainty factors. This study showed the structure of business uncertainty, enabling managers to better prioritize business uncertainty in the coal mining industry. This study also provides valuable insight for managers in coal mining companies and Indonesian policymakers to strengthen Indonesia’s coal mining industry.