Text-document clustering-based cause and effect analysis methodology for steel plant incident data

ABSTRACT

The purpose of this study is to develop a text clustering-based cause and effect analysis methodology for incident data to unfold the root causes behind the incidents. A cause–effect diagram is usually prepared by using experts’ knowledge which may fail to capture all the causes present at a workplace. On the other hand, the description of incidents provided by the workers in the form of incident reports is typically a rich data source and can be utilized to explore the causes and sub-causes of incidents. In this study, data were collected from an integrated steel plant. The text data were analysed using singular value decomposition (SVD) and expectation-maximization (EM) algorithm. Results suggest that text-document clustering can be used as a feasible method for exploring the hidden factors and trends from the description of incidents occurred at workplaces. The study also helped in finding out the anomaly in incident reporting.

KEYWORDS:

• Cause and effect
• root causes
• text mining
• clustering analysis
• incident data

Acknowledgments

The authors would like to thank the Ministry of Human Resource Development (MHRD) & Ministry of Steel, GOI, New Delhi, India, and Tata Steel Limited, Jamshedpur, India for funding this research under Uchchatar Avishkar Yojana (UAY) for the project entitled ‘Safety Analytics: Save People at Work from Accidents and Injuries WAI’. The authors are thankful to the learned reviewers for their valuable suggestions in enriching the quality of the paper. The authors also gratefully acknowledge the support and cooperation provided by the management of the plant studied.

Disclosure statement

No potential conflict of interest was reported by the authors.