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Advances in Internal QHSE Audit Systems for Industrial Engineering Operations
Subject area: Science,Engineering and Technology · Area of research: Internal QHSE Audit
DOI: https://doi.org/10.64388/IREV4I4-1715499
Abstract
The increasing complexity of industrial engineering operations necessitates a comprehensive approach to Quality, Health, Safety, and Environment (QHSE) management. Internal QHSE audit systems have emerged as essential tools for organizations striving to meet regulatory standards, optimize operational efficiency, and ensure the well-being of employees and the environment. This paper explores the latest advances in internal QHSE audit systems, highlighting their role in enhancing performance, mitigating risks, and improving sustainability within industrial operations. The review examines the evolution of audit methodologies, from traditional manual assessments to modern automated and data-driven systems, focusing on the integration of artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies to enhance audit accuracy and effectiveness. Additionally, it explores the use of advanced analytics for real-time monitoring, predictive insights, and risk management. The paper also discusses challenges such as the integration of audit systems with existing operations, data privacy concerns, and the need for continuous innovation to address emerging industrial hazards. Through case studies and industry-specific applications, this review highlights best practices and successful implementation strategies that can guide future developments in QHSE audit systems. The paper concludes by offering recommendations for organizations looking to enhance their QHSE audit frameworks, emphasizing the need for a holistic, technology-driven approach to maintain high standards of safety, compliance, and sustainability.
Keywords
Internal QHSE Audits, Industrial Engineering Operations, Artificial Intelligence in Auditing, Risk Management, Real-Time Monitoring, Predictive Analytics.
How to cite this paper
@article{1715499,
author = {Stephen Francis Obogo, Oluwakemi Motunrayo Arumosoye, Oghenepawon David Obriki},
title = {Advances in Internal QHSE Audit Systems for Industrial Engineering Operations},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {4},
number = {4},
pages = {399-417},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715499.pdf},
abstract = {The increasing complexity of industrial engineering operations necessitates a comprehensive approach to Quality, Health, Safety, and Environment (QHSE) management. Internal QHSE audit systems have emerged as essential tools for organizations striving to meet regulatory standards, optimize operational efficiency, and ensure the well-being of employees and the environment. This paper explores the latest advances in internal QHSE audit systems, highlighting their role in enhancing performance, mitigating risks, and improving sustainability within industrial operations. The review examines the evolution of audit methodologies, from traditional manual assessments to modern automated and data-driven systems, focusing on the integration of artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT) technologies to enhance audit accuracy and effectiveness. Additionally, it explores the use of advanced analytics for real-time monitoring, predictive insights, and risk management. The paper also discusses challenges such as the integration of audit systems with existing operations, data privacy concerns, and the need for continuous innovation to address emerging industrial hazards. Through case studies and industry-specific applications, this review highlights best practices and successful implementation strategies that can guide future developments in QHSE audit systems. The paper concludes by offering recommendations for organizations looking to enhance their QHSE audit frameworks, emphasizing the need for a holistic, technology-driven approach to maintain high standards of safety, compliance, and sustainability.},
keywords = {Internal QHSE Audits, Industrial Engineering Operations, Artificial Intelligence in Auditing, Risk Management, Real-Time Monitoring, Predictive Analytics.},
month = {October},
doi = {https://doi.org/10.64388/IREV4I4-1715499}
}