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Systematic Review of Near-Miss and Hazard Observation Data Utilization in Industrial Safety Management
Subject area: Management and Commerce · Area of research: Industrial Safety Data Management
Abstract
Near-miss and hazard observation data have emerged as critical sources of information for enhancing industrial safety management, providing early indicators of potential incidents before they escalate into accidents. This systematic review examines the current state of research on the collection, analysis, and utilization of near-miss and hazard observation data across diverse industrial sectors, including energy, manufacturing, construction, and chemical processing. The review identifies methodologies for capturing such data, ranging from manual reporting and digital logging systems to mobile and IoT-enabled platforms, and evaluates their effectiveness in informing risk assessment, safety interventions, and organizational learning. Findings indicate that systematic use of near-miss data supports predictive and preventive safety strategies, enabling organizations to identify recurrent hazards, prioritize risk mitigation efforts, and implement targeted operational controls. Integration of hazard observation data into safety management systems enhances situational awareness, facilitates feedback loops, and strengthens safety culture by promoting proactive reporting and continuous learning. The review also highlights challenges associated with data quality, underreporting, standardization, and integration across multi-contractor environments, emphasizing the need for robust governance structures and digital tools to support reliable data capture, analysis, and decision-making. Emerging trends include the application of data analytics, machine learning, and visualization techniques to transform near-miss data into actionable insights, enabling scenario-based risk assessment and real-time decision support. Cross-sector benchmarking and standardized frameworks for data categorization, severity scoring, and incident linkage further enhance the utility of these datasets in reducing workplace accidents and improving compliance. This review demonstrates that systematic utilization of near-miss and hazard observation data is a strategic enabler of proactive safety management, contributing to reduced incident frequency, enhanced operational reliability, and strengthened governance in industrial operations. Recommendations for future research include exploring predictive models, integrating multi-source datasets, and developing industry-wide standards to optimize the value of near-miss data for safety decision-making.
Keywords
Near-Miss Reporting, Hazard Observation, Industrial Safety Management, Predictive Risk Assessment, Safety Culture, Data Analytics, Occupational Risk Mitigation.
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How to cite this paper
@article{1714417,
author = {Oluwakemi Motunrayo Adegbuji, Oghenepawon David Obriki},
title = {Systematic Review of Near-Miss and Hazard Observation Data Utilization in Industrial Safety Management},
journal = {Iconic Research And Engineering Journals},
year = {2019},
volume = {3},
number = {2},
pages = {981-999},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1714417.pdf},
abstract = {Near-miss and hazard observation data have emerged as critical sources of information for enhancing industrial safety management, providing early indicators of potential incidents before they escalate into accidents. This systematic review examines the current state of research on the collection, analysis, and utilization of near-miss and hazard observation data across diverse industrial sectors, including energy, manufacturing, construction, and chemical processing. The review identifies methodologies for capturing such data, ranging from manual reporting and digital logging systems to mobile and IoT-enabled platforms, and evaluates their effectiveness in informing risk assessment, safety interventions, and organizational learning. Findings indicate that systematic use of near-miss data supports predictive and preventive safety strategies, enabling organizations to identify recurrent hazards, prioritize risk mitigation efforts, and implement targeted operational controls. Integration of hazard observation data into safety management systems enhances situational awareness, facilitates feedback loops, and strengthens safety culture by promoting proactive reporting and continuous learning. The review also highlights challenges associated with data quality, underreporting, standardization, and integration across multi-contractor environments, emphasizing the need for robust governance structures and digital tools to support reliable data capture, analysis, and decision-making. Emerging trends include the application of data analytics, machine learning, and visualization techniques to transform near-miss data into actionable insights, enabling scenario-based risk assessment and real-time decision support. Cross-sector benchmarking and standardized frameworks for data categorization, severity scoring, and incident linkage further enhance the utility of these datasets in reducing workplace accidents and improving compliance. This review demonstrates that systematic utilization of near-miss and hazard observation data is a strategic enabler of proactive safety management, contributing to reduced incident frequency, enhanced operational reliability, and strengthened governance in industrial operations. Recommendations for future research include exploring predictive models, integrating multi-source datasets, and developing industry-wide standards to optimize the value of near-miss data for safety decision-making.},
keywords = {Near-Miss Reporting, Hazard Observation, Industrial Safety Management, Predictive Risk Assessment, Safety Culture, Data Analytics, Occupational Risk Mitigation.},
month = {August},
doi = {https://doi.org/10.64388/IREV3I2-1714417}
}