Enhancing procedure quality: Advanced language tools for identifying ambiguity and high-potential violation triggers

Abstract

In high-risk industrial environments, the clarity and accuracy of Standard Operating Procedures (SOPs) are critical for ensuring safety and regulatory compliance. The presence of ambiguities in SOPs can lead to misunderstandings, errors, and increased risks. While violations of procedural directives can significantly contribute to catastrophic outcomes. This study introduces the development of sophisticated tools utilizing both rule-based and machine learning methodologies in Natural Language Processing (NLP) specifically designed to detect ambiguities and identify high-risk steps prone to non-malevolent violation in procedural documentation. By addressing these linguistic and procedural discrepancies, we aim to enhance the clarity and applicability of SOPs, ultimately improving adherence and reducing risks in complex operational settings. The tools leverages a blend of linguistic rules to systematically identify and categorize ambiguities, and machine learning techniques with historical data to identify procedural directives with high-risk potential when violated. This enhances the precision and practical application of SOPs in sectors such as nuclear, oil and gas, and chemical processing. Initial tests demonstrate the tools’ effectiveness and promising applicability. This approach not only aids in refining SOPs but also contributes to the broader objective of enhancing operational safety and efficiency. The research underscores the importance of integrating advanced NLP techniques with traditional safety management practices to address the inherent challenges of procedural documentation in complex industrial settings.