Jul 2026· International Research Journal of Innovation in Science and Technology· Vol 1, pp. 51-59· 0 citations
TL;DR
The study highlights that systematic risk assessment enables early identification of critical vulnerabilities, supports informed engineering decision-making, and contributes to more resilient, reliable, and sustainable smart manufacturing systems.
Abstract
Industry 4.0 technologies, including the Internet of Things (IoT), cyber-physical systems (CPS), artificial intelligence (AI), and automation, have significantly transformed modern manufacturing by improving productivity, flexibility, and operational efficiency. However, the increasing connectivity and complexity of smart manufacturing systems have also introduced new technical, operational, cybersecurity, and safety-related risks that require systematic assessment and management. This paper presents a structured framework for risk identification, assessment, and mitigation using a qualitative risk matrix approach. The proposed methodology classifies manufacturing risks into major categories and evaluates them based on their probability of occurrence and potential impact to support effective risk prioritization. Appropriate mitigation strategies, including predictive maintenance, enhanced cybersecurity measures, system redundancy, real-time monitoring, and workforce training, are discussed to improve system reliability and operational safety. The study highlights that systematic risk assessment enables early identification of critical vulnerabilities, supports informed engineering decision-making, and contributes to more resilient, reliable, and sustainable smart manufacturing systems. The proposed framework provides a practical reference for manufacturing engineers and industrial practitioners implementing risk management within Industry 4.0 environments.
The increasing integration of Operational Technology (OT) and Information Technology (IT) systems within industrial environments has introduced significant cybersecurity challenges. Traditional risk assessment approaches often lack the adaptability and scalability needed to address evolving threat landscapes and complex asset interdependencies. This paper presents a semi-automated risk analysis tool designed to evaluate OT cybersecurity risks through a multi-layered approach that integrates asset inventories, governance-based questionnaires, and external threat intelligence databases such as MITRE ATT&CK and CVE. The tool applies fuzzy logic to map potential threats and vulnerabilities, and generates graphical outputs including risk level visualizations, threat distribution charts, and lifecycle-based exposure matrices. Through experimentation on an industrial platform and validation via intrusion testing, the tool demonstrated its capacity to identify high-risk assets and operational stages that require mitigation. The framework provides a practical foundation for structured, scalable, and governance-aligned OT risk assessments.
The chemical process industry is indispensable to modern society but involves inherent risks associated with toxic,
flammable, reactive and corrosive substances, high pressures and temperatures, complex process interactions and large
inventories. Although major accidents are relatively infrequent, their consequences may extend beyond the plant boundary
and affect workers, emergency responders, communities, infrastructure and the environment. This review re-examines
chemical-industry safety and security from an integrated process-safety and disaster-risk-management perspective, using
the author's earlier article as its foundation while substantially rewriting and expanding its content. The review covers
hazard identification, HAZOP, What-If analysis, FMEA, fault-tree analysis, Layer of Protection Analysis, quantitative risk
assessment, inherently safer design, management of change, safety instrumented systems, mechanical integrity, human
factors, emergency planning and community preparedness. It also considers natural-hazard-triggered technological
accidents, the Indian regulatory framework and international approaches including OSHA Process Safety Management and
ISO 45001. Recent developments in digitalization, sensors, predictive analytics, digital twins and artificial intelligence are
examined as opportunities for early warning and decision support, together with their cybersecurity and human-factor
implications. The review concludes that effective chemical safety cannot depend on a single safeguard. It requires a
continuously verified system integrating safer design, reliable equipment, competent people, strong safety culture,
emergency preparedness, physical and cyber security, and organizational learning.
Ashok Agarwal· International Journal of Inn...· 0 citations
This research aims to present the Framework for Enhancing Network Integrity eXcellence (FENIX) model, integrating technological, organizational, human, and economic dimensions to enhance risk assessment in complex socio-technical systems.
The FENIX model employs fuzzy logic, multi-criteria prioritization, and non-additive aggregation methods to enhance risk assessment in complex socio-technical systems. Its applicability is demonstrated through a case study involving the introduction of a new aircraft.
The findings indicate that technological risks can be reduced through diagnostic and monitoring systems, whereas operational and human factors continue to play a significant role. Training, communication, and fatigue management are essential for minimizing systemic vulnerability. The economic dimension highlights the feasibility of reconciling mitigation effectiveness with financial sustainability.
The methodology assists decision-makers by optimizing the RPN, improving RTO reliability, and incorporating ERI and EPNmetrics, thereby allowing organizations to align risk management strategies with operational resilience and financial constraints.
In contrast to conventional FMEA, the FENIX framework incorporates uncertainty, nonlinear interdependencies, and cost–benefit considerations, offering a more comprehensive and flexible approach to risk prioritization and resource allocation.
Giuseppe Caristi, Daniela Barba, Maria Frasca et al.· Management Decision· 0 citations
Abstract This study proposes a risk management framework for an IoT-based Social Manufacturing system using the House of Risk (HOR) method. In a social manufacturing system, all distributed and collaborative production activities are highly dependent on real-time internet connectivity. In this study, 27 risk events and 22 risk agents were identified through observation and interviews, as well as SCOR-based process mapping. The method used is HOR Phase 1 and HOR Phase 2 analysis. In HOR Phase 1, which calculates the Aggregate Risk Potential (ARP), it was found that 12 risk agents predominantly contributed 78.52% of the total system risk, with the highest ARP values related to production planning errors (1872), production delays (1813), and inaccurate customer order identification (1148). In HOR Phase 2, 21 preventive actions were evaluated using the Effectiveness-to-Difficulty Ratio (ETD), yielding a priority-based mitigation sequence. The most effective actions include more careful production planning, improved coordination with SMRs, early identification of production bottlenecks, comprehensive recording of customer requests, and selective SMR qualification. This research contributes to the risk management literature by applying HOR to Industry 4.0 and provides practical guidance for risk mitigation in IoT-based social manufacturing systems.
M. W. Sari, R. Hafid Hardyanto, Theofilus Bayu Dwi Nugroho et al.· Management Systems in Produc...· 0 citations
Cyber-attacks on critical infrastructure are increasing in scale and sophistication, yet cybersecurity practice remains dominated by technology-centric assessments that insufficiently represent human contributions to risk. In cyber-physical systems (CPS), non-malicious human actions -including slips, mistakes, workarounds, training gaps, and misaligned procedures- frequently create, amplify or fail to detect vulnerabilities.This paper presents an integrated socio-technical framework that combines Human Factors (HF) methods, safety analysis, and cybersecurity modelling within a Secure-by-Design approach. The framework models how human performance variability influences cyber vulnerability and safety outcomes, enabling structured, scenario-based risk assessment and the derivation of traceable engineering requirements. An illustrative application demonstrates how HF findings are translated into human error mechanisms, cyber effects, unsafe control actions, safety impacts, and prioritised Secure-by-Design controls. By operationalising HF methods as cybersecurity engineering tools, the approach reframes cybersecurity as a socio-technical reliability problem comparable to safety engineering.
Eylem Thron, Duncan Ki-Aries, Martin Freer et al.· AHFE International· 0 citations
Precision agriculture (PA) increasingly relies on advanced techniques with drone machine based-technique to enhanced agricultural productivity, resource efficiency, and sustainability. Despite these significant, the widespread deployment of interconnected advanced farming systems has presented vital cybersecurity (CS) vulnerabilities that threaten data integrity, operational continuity, and decision-making processes. This researcher study introduced a comprehensive framework for analyzing CS challenges in PA by systematically identification and classification key CS parameters and its sub-parameters, threats, risks, and their potential impacts across advanced technique and drone-enabled agricultural settings. This approach evaluates the severity of CS risks, highlights vital vulnerabilities in existing PA infrastructures, and emphasizes the necessity for domain-specific security mechanism tailored to advanced farming ecosystems. The findings focused on securing interconnected agricultural devices, protecting sensitive farming information, and mitigating emerging cyber threats remain major challenges for sustainable PA deployment. Further, in this study proposes a future research roadmap that integrates advanced security techniques and real-time mitigation mechanisms to improving the resilience, reliability, and trustworthiness of PA systems. The proposed comprehensive framework provides researchers and expert with a structured foundation for strengthening CS and supporting the secure, sustainable adoption of next-generation modern agriculture techniques.
Shubham Kumar, Mohammad Faisal· International journal of com...· 0 citations