Proactive Risk Research Articles

Practical drought risk assessment and management framework: A step toward sustainable modernization in agricultural water management

A proposed framework to assess and manage risks associated with Agricultural Water Off-farm Conveyance and Distribution Systems (AWOCDS), quantify potential failure due to drought-induced scenarios, and provide short to long-term recovery plans to mitigate drought impacts. It makes an intellectual contribution by creating a new proactive risk assessment focusing specifically on AWOCDS as a crucial part of agricultural water management. The risk assessment comprises two components − risk probability and consequence. The former is calculated by analyzing droughts via the streamflow drought index using the DrinC calculator. The latter is determined through AWOCDS’ performance evaluation using hydraulic simulation via HEC-RAS. Additionally, water distribution is assessed spatiotemporally using adequacy and equity indicators, and an integrated consequence is developed by applying the simple additive weighting method. AWOCDS’ rehabilitation and modernization projects worldwide include three practical practices in its risk management component. These practices involve modifying standard operating procedures (SOPs) by updating maintenance, upgrading manual-based operations, and developing a centralized Model Predictive Controller (MPC) for AWOCDS automatic operation. The proposed framework underwent testing in an arid region. Based on the risk assessment results, the AWOCDS operating system failure rate ranges from 70% to 96% during extreme-severe drought scenarios and 53% to 65% during moderate-mild drought scenarios. Risk management analysis shows that a short-term recovery plan reduces the drought risk by 4% monthly and 3% yearly. However, the risk of failure ranges from 50 to 94% in mild to extreme drought scenarios. Upgrading SOPs decreases impact by 4–11% in extreme, 4–7% in moderate-severe and 7–9% in mild-moderate drought. Automating the operating system with MPC reduces impact by 40–43% and 30–56% in mild-moderate and severe-extreme drought scenarios. This proactive-based strategy can help water managers plan for rehabilitation and improve surface water distribution reliability.

Impact of infectious diseases associated with passenger ships on ports: a systematic review

Abstract Background In an enclosed ship environment, close contact between passengers and crew members is inevitable. As a result, passenger ships can be a suitable environment for the transmission of infections between people. In this context, it is essential to establish effective prevention and control measures to protect the health of passengers and crew members while ensuring that shipping minimizes its contribution to the global spread of disease. The aim of this review is to provide knowledge on the impact of infectious diseases on board large passenger ships on the port, the port community and other land-based operations to better understand the effectiveness of the prevention, mitigation and management (PMM) measures implemented, in particular ship-to-shore communication. Methods A systematic literature review was conducted according to the PRISMA guidelines, searching three databases and including additional articles from hand searches up to November 2023. Peer-reviewed studies of infectious disease outbreaks on large passenger ships that described ship-shore interaction, port preparedness, impact on the port community, and impact on other modes of transportation were included. Results A total of 555 publications were initially identified. Finally, 22 articles were included. Most studies reported on COVID-19 outbreaks on cruise ships; other communicable diseases reported were influenza, gastroenteritis and varicella. The articles highlighted the importance of comprehensive management plans and proactive risk assessment during infectious disease outbreaks. Conclusions Effective stakeholder collaboration, ship-to-shore communication, and coordination of medical transport, isolation, and quarantine measures are essential components of infectious disease PMM in maritime passenger shipping and ship-to-shore operations. The findings suggest that further research is needed to improve the evidence and knowledge about this important issue. Key messages • Proactive risk assessment, comprehensive management plans and effective collaboration are critical to preventing, mitigating and managing infectious diseases on passenger ships. • Overall, very little evidence of effective practices and capacities related to the interaction of passenger ships with land-based operations, stakeholders and port communities.

Validation and clinical performance of a single test, DNA based endometrial cancer molecular classifier

ObjectivesWe have previously shown that DNA based, single test molecular classification by next generation sequencing (NGS) (Proactive Molecular risk classifier for Endometrial cancer (ProMisE) NGS) is highly concordant with the...

Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition

Integration of Proactive Building Fire Risk Management in the Building Construction Sector: A Conceptual Framework to Understand the Existing Condition

Implementing Loss Prevention by Identifying Trends and Insights to Help Policyholders Mitigate Risks and Reduce Claims

Loss prevention is a critical focus for both insurers and policyholders, as rising claim volumes lead to increased premiums and financial losses. This paper explores the implementation of loss prevention strategies through the identification of trends and insights derived from historical data, predictive analytics, and emerging technologies. By leveraging these insights, insurers can recommend proactive risk mitigation measures to policyholders, reducing the occurrence of claims and overall risks. The study begins by analyzing historical data trends across various sectors such as auto, property, and health insurance, identifying key factors that drive claims. Predictive analytics is then applied to forecast future risks, allowing insurers to develop personalized strategies for mitigating those risks. Emerging technologies like Internet of Things (IoT) devices and artificial intelligence (AI) are highlighted for their role in providing real-time data and early warning systems, which help to prevent incidents before they escalate into claims. This paper also outlines practical loss prevention strategies for policyholders, such as installing smart home devices to monitor potential hazards, encouraging safe driving habits through telematics, and promoting health and wellness programs to lower medical claims. These risk mitigation strategies are shown to provide significant return on investment (ROI) for policyholders by reducing claims frequency and severity. Through case studies and data analysis, the paper demonstrates that proactive loss prevention not only benefits policyholders by lowering their risk exposure but also helps insurers reduce claims costs and enhance profitability. In conclusion, this research highlights the importance of trend identification, predictive analytics, and emerging technologies in the future of loss prevention, offering a path forward for insurers and policyholders to work collaboratively in mitigating risks.

The Evaluation of Rainfall Warning Thresholds for Shallow Slope Stability Based on the Local Safety Factor Theory

Rainfall-induced shallow slope instability is a significant global hazard, often triggered by water infiltration that affects soil stability and involves dynamic changes in the hydraulic behavior of unsaturated soils. This study employs a hydro-mechanical coupled analysis model to assess the impact of rainfall on slope stability, focusing on the dynamic hydraulic behavior of unsaturated soils. By simulating the soil water content and slope stability under four different rainfall scenarios based on observational data and historical thresholds, this study reveals that higher rainfall intensity significantly increases the soil water content, leading to reduced slope stability. The results show a strong correlation between the soil water content and slope stability, with a 20 mm/h rainfall intensity threshold emerging as a reliable predictor of potential slope instability. This study contributes to a deeper understanding of slope stability dynamics and emphasizes the importance of proactive risk management in response to changing rainfall patterns while also validating current management practices and providing essential insight for improving early warning systems to effectively mitigate landslide risk.

The Vision Zero strategy and its goals

The relevance of studying the Vision Zero strategy is driven by the growing challenges in road traffic safety faced by modern societies. Despite advances in technology and infrastructure, the number of fatalities and injuries in road accidents remains a significant issue, requiring new approaches for resolution. In the context of increased attention to sustainable development and reducing the negative impact on the environment, Vision Zero offers innovative solutions aimed at harmonizing human safety with the ecological and economic demands of modern transportation systems. The Vision Zero strategy represents a modern and ambitious approach to road safety, aiming for the complete elimination of deaths and serious injuries on the roads. This study is dedicated to a comprehensive analysis of the main goals and principles of the Vision Zero strategy, as well as its application in the context of road safety within the European Union. The aim of the study is to thoroughly examine the key objectives of Vision Zero, such as achieving zero fatalities in traffic accidents and reducing serious injuries. Special attention is given to defining serious injuries and methods to reduce them, including both technical and infrastructural solutions. The paper discusses the core principles of the strategy, including ensuring the safety of vehicles, infrastructure, and driver behavior, as well as the principles of a systems approach and proactive risk management. The findings of the study confirm that the Vision Zero strategy is an effective tool for reducing the number of traffic accidents and serious injuries, due to its comprehensive approach to road safety. Specifically, the research shows that achieving Vision Zero’s goals has a significant socio-economic impact, including reduced medical costs and improved quality of life for citizens. The realization of these goals also promotes the implementation of new technologies and innovations in the field of safety, making transportation systems more efficient and reliable. By analyzing and justifying the importance of achieving the Vision Zero goals, the study underscores the need for a comprehensive approach to managing road safety and demonstrates the potential of this strategy to ensure a safer and more sustainable transportation environment in the European Union.

Influence of macroeconomic factors on construction costs: an analysis of project cases

This study explores the relationship between price increase and project cost increase under macroeconomic shocks by considering the hierarchical structure within price categories, diversification of the cost breakdown structure, infrastructure sector, delivery system, construction methods, and type of bid. The analysis of a large construction company with five large projects as embedded units of analysis reveals significant variations in the relative influence of specific factors across the projects. Unit price categories located higher in the hierarchical breakdown structure display lower vulnerability to macroeconomic changes due to their unique and group-specific influencing factors while price categories influenced by macroeconomic factors depart at lower values in the hierarchical structure. Projects with low-cost breakdown structure diversification can either mitigate or exacerbate cost increases depending on construction methods (off-site or on-site), price drivers of most representative materials (global or local), and delivery systems risk allocation. Under macroeconomic crisis periods, public entities may consider giving high priority to projects more reliant on materials driven by regional dynamics (e.g., concrete pouring) over those driven by global market conditions (e.g., steel, aluminum) while prioritizing integrated delivery systems (e.g., design-build) that promote enhanced collaboration, proactive risk management, aligned incentives, and adaptability for managing complexities and volatilities. Complementary, for projects with higher reliance on global market-driven materials, the results suggest that contractors should prioritize early procurement and acquisition of highly volatile resources in projects under macroeconomic crises.

Navigating tensions of sustainable supply chains in times of multiple crises: A systematic literature review

AbstractThe challenges posed by multi‐crisis environments, such as pandemics and geopolitical uncertainties, create significant opportunities for supply chains (SC) to enhance their sustainability, resilience, and agility, thereby developing a strategic advantage. However, seizing these opportunities is not without its challenges; critical tensions inevitably arise. This paper, based on a systematic review of 287 SC studies, identifies and discusses five key tensions: (1) increased trade‐offs between sustainability and efficiency; (2) competing priorities between SC sustainability and resilience; (3) mismatched sustainability expectations causing stakeholder pressure; (4) conflicting demands between global and sustainable operations; and (5) unintended consequences of technological innovation, disadvantaging stakeholders with limited resources and capabilities. To mitigate these tensions while leveraging on the opportunities, the study proposes several strategic pathways, including adopting circular economy principles, optimizing inventory and distribution management, employing holistic measurement and forecasting, balancing risk‐hedging with proactive risk management, fostering inclusive stakeholder engagement, balancing globalization and regionalization, and integrating advanced digital technologies alongside with SC ecosystem wide change management. These strategies offer practical guidance for building sustainable and crises‐ready SCs and set the stage for an extensive research agenda to further explore SC sustainability in multi‐crisis environments.

SUPPLY CHAIN RISK MANAGEMENT: STRATEGIC SOLUTIONS FOR REDUCING TRANSPORTATION AND LOGISTICS RISKS

This study investigates strategic approaches to mitigating risks in transportation and logistics within global supply chains, focusing on the integration of advanced technologies, flexibility, collaboration, and sustainability. By employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the study systematically reviews 37 key articles to provide a comprehensive understanding of modern risk management practices. The findings reveal the increasing reliance on technologies such as predictive analytics, the Internet of Things (IoT), and blockchain for enhancing visibility, monitoring, and decision-making. Flexibility in logistics networks, including alternative sourcing and diversified transportation routes, emerged as crucial for mitigating disruptions, while collaboration among supply chain partners, particularly through real-time information sharing, significantly reduces risk exposure. Additionally, the study highlights the growing integration of sustainability into risk management, addressing climate change and environmental risks. This research underscores the need for proactive, adaptable, and sustainable risk management strategies to maintain supply chain resilience in the face of evolving global challenges.

ENGINEERING MANAGEMENT PERSPECTIVES ON SAFETY CULTURE IN CHEMICAL AND PETROCHEMICAL PLANTS: A SYSTEMATIC REVIEW

This systematic review examines the influence of engineering management practices on cultivating and sustaining a strong safety culture in chemical and petrochemical plants. Given the high-risk nature of these industries, establishing a robust safety culture is essential for mitigating operational hazards, reducing risks, and preventing accidents that can have serious human and environmental impacts. The review synthesizes findings from 135 peer-reviewed studies, focusing on key elements such as leadership commitment, employee engagement, regulatory compliance, safety management systems, and the integration of advanced technologies like automation and artificial intelligence (AI). The results highlight that safety performance in these industries is closely linked to management's active involvement in promoting safety practices, clear communication strategies, and the continuous assessment and improvement of safety protocols. Engineering managers play a critical role in driving safety initiatives and fostering an organizational culture where safety is a shared responsibility. The review also underscores the importance of regulatory compliance as a foundation for safety, while emphasizing that the most successful organizations go beyond compliance to embed safety deeply into their operational practices. Technological advancements, particularly in automation and real-time monitoring, were found to significantly enhance safety outcomes by enabling proactive risk identification and mitigation. Overall, this review offers valuable insights and practical guidelines for engineering managers aiming to strengthen safety protocols, reduce risks, and enhance safety performance in high-hazard industrial environments.

Exploring key factors for long-term vessel incident risk prediction

Factor analysis acts a pivotal role in enhancing maritime safety. Most previous studies conduct factor analysis within the framework of incident-related label prediction, where the developed models can be categorized into short-term and long-term prediction models. The long-term models offer a more strategic approach, enabling more proactive risk management, compared to the short-term ones. Nevertheless, few studies have devoted to rigorously identifying the key factors for the long-term prediction and undertaking comprehensive factor analysis. Hence, this study aims to delve into the key factors for predicting the incident risk levels in the subsequent year given a specific datestamp. The majority of candidate factors potentially contributing to the incident risk are collected from vessels’ historical safety performance data spanning up to five years. An improved embedded feature selection method, which integrates Random Forest classifier with a feature filtering process, is proposed to identify key risk-contributing factors from the candidate pool. A dataset with information of 131,652 vessels collected from 2015 to 2023 is utilized for case study. The results demonstrate superior performances of the proposed method in incident prediction and factor interpretability. Comprehensive analysis is conducted upon the key factors, which could help maritime stakeholders formulate management strategies for incident prevention.

Stability in Sight: Leveraging Machine Learning for Proactive Political Risk Management in the United States of America

This paper explores the application of machine learning (ML) in political risk management, with a specific focus on recent trends in political violence in the United States OF America. The growing intersection of political polarization, disinformation, and societal unrest has created a volatile political climate, as evidenced by events such as the January 6 Capitol insurrection and rising threats to public officials. The paper argues that machine learning could play a critical role in mitigating such risks by analyzing large datasets, including social media interactions, political speeches, and public sentiment, to predict potential flashpoints of violence. Through predictive analytics, sentiment analysis, and anomaly detection, ML can enhance decision-making processes and provide timely interventions to avert violent incidents. Additionally, case studies demonstrate ML’s superiority over traditional methods in risk assessments. Despite the challenges associated with ML, such as data privacy concerns, algorithmic bias, and the complexity of political contexts, this paper argues that machine learning holds immense potential in transforming political risk management. By integrating diverse data sources and refining risk models, ML can significantly improve accuracy and efficiency in predicting and mitigating political risks. The paper concludes with recommendations for further integrating ML tools in political risk strategies to address the increasingly unstable political environment.

The Impact of Artificial Intelligence on Project Management Efficiency

This research paper examines the influence of Artificial Intelligence (AI) on the efficiency of project management, concentrating on the ways in which AI tools and techniques improve different facets of project management. The study initiates with a comprehensive examination of project management and the increasing significance of AI in various domains, emphasizing the shortcomings of conventional methodologies and the capacity of AI to tackle these issues. The study seeks to evaluate the impact of AI on project processes, pinpoint effective AI tools, and investigate the advantages and obstacles associated with AI integration. A thorough examination of existing literature investigates AI tools, including machine learning, natural language processing, and predictive analytics, along with their applications in planning, scheduling, resource allocation, risk management, communication, and performance monitoring. Case studies and empirical evidence illustrate the beneficial effects of AI on project outcomes, while also highlighting existing gaps in the literature that warrant further investigation. The mixed-methods approach integrates qualitative and quantitative data sourced from surveys, interviews, and case studies, employing both thematic and statistical analysis. The findings indicate that AI plays a crucial role in enhancing project efficiency through the reduction of timelines, improvement of cost control, optimization of resources, and facilitation of proactive risk management, in addition to enhancing communication and collaboration. Nonetheless, factors such as initial investment, skill requirements, resistance to change, and ethical considerations are acknowledged. The discussion outlines actionable strategies for project managers, highlighting the importance of training, incremental integration, ensuring data quality, and promoting innovation. The study concludes by offering recommendations for future research regarding the long-term effects of AI, conducting cross-industry analyses, and creating AI tools specifically designed for project management. This approach aims to provide empirical evidence and strategic insights into the integration of AI for enhanced project outcomes.

Risk Management in Production using Quality Risk Approach and FMEA

Abstract: The production process of 600ml AQUA at PT Tirta Investama (AQUA) Pandaan faces significant risks, including non-compliance with standards in bottle density, incorrect packaging labels, unreadable bottle codes, box failures, and barcode rework. These issues pose challenges in maintaining product quality and operational efficiency. Specific Background: With 247 bottles failing density standards and 18 packaging errors occurring daily, effective risk management is crucial. Knowledge Gap: Previous studies have not integrated Quality Risk Management (QRM) and Failure Mode Effect and Criticality Analysis (FMECA) to address production risks in the context of AQUA's operations. Aims: This study aims to identify the highest critical risk points and propose mitigation strategies. Results: Utilizing QRM and FMECA, 42 risks were identified, categorized into 20 acceptable, 21 tolerable, and 1 unacceptable risk, specifically related to pallets stuck on conveyors due to substandard quality. Novelty: The application of these integrated methodologies provides a systematic approach to identifying and mitigating risks in the production process, highlighting the necessity for in-depth inspections by logistics and machine operators. Implications: The findings underscore the importance of rigorous quality control and proactive risk management in manufacturing to enhance product quality and minimize operational disruptions, thus ensuring customer satisfaction and competitiveness in the market. Highlights: Effective risk management is crucial to ensure product quality and operational efficiency. Integration of QRM and FMECA identifies critical risk points in the production process. Regular inspections and quality control can significantly reduce unacceptable risks. Keywords: Risk Management, Production Process, QRM, FMECA, AQUA

LEVERAGING MANAGEMENT INFORMATION SYSTEMS FOR ENHANCING CREDIT RISK ASSESSMENT IN COMMERCIAL BANKS

This study explores the significant impact of Management Information Systems (MIS) on credit risk assessment in commercial banks, examining how these systems enhance decision-making accuracy, operational efficiency, and proactive risk management. By synthesizing findings from 50 peer-reviewed studies, the research reveals that banks using MIS experience a 20-30% reduction in non-performing loans and a 40-50% increase in loan processing speed due to automation and real-time data analysis. The integration of advanced technologies such as artificial intelligence (AI) and predictive analytics further improves credit risk forecasting accuracy by 15-20%, enabling banks to implement proactive risk mitigation strategies that reduce borrower defaults by up to 25%. However, the study also highlights significant challenges, particularly for smaller banks, which face high implementation costs and difficulties integrating MIS with legacy systems. Despite these challenges, the role of MIS in ensuring regulatory compliance, particularly under Basel III, and reducing overall credit exposure by 15-20% underscores its critical importance in modern credit risk management. The findings suggest that while MIS is essential for maintaining financial stability and competitiveness, scalable and cost-effective solutions are necessary for broader adoption across the banking industry.

A-193 Optimizing Emergency Laboratory Operations Through Six-Sigma Strategies for Enhanced Efficiency and Effectiveness: A Pilot Study

Abstract Background Quality assurance is vital in laboratory diagnostics to ensure patient safety and accurate results. Laboratories employ internal and external quality control measures, particularly focusing on the analytical phase. Sigma metrics, representing process variation, play a crucial role in assessing accuracy, with Six Sigma setting high standards for operational excellence. This study aims to enhance laboratory testing procedures by analyzing the analytical phase. In 2022, the Emergency laboratory at KFAFH, Jeddah, achieved optimal sigma performance, marking a significant improvement in analytical testing. http://www.westgard.com/sigma-vp-kfafh.htm. In 2023, The study also demonstrated a 30% productivity increase, driven by sustained efforts and Sigma metrics implementation, leading to cost reductions and improved quality. Real-time patient moving averages enhance quality assurance, extending coverage to the total testing process. The primary objectives include redesigning IQC procedures and optimizing resource utilization. This will be accomplished by reducing the frequency of controls, minimizing unnecessary reruns, optimizing resource allocation, and managing total costs, all while ensuring compliance and averting violations. Methods A retrospective-prospective study was conducted at the Emergency Laboratory of King Fahad Armed Forces Hospital in Jeddah, Saudi Arabia, from May 2021 to October 2023. The study assessed 841,558 tests for 131,785 emergency patients. Forty-seven analytes were evaluated for Internal Quality Control and External Quality Control (CAP), with monthly data collection. Chemistry and Hematology parameters were analyzed using Unity Real-Time software on Alinity-ci and Alinity-hq analyzers. Data was collected in three stages: baseline, pre-Sigma verification, post-Sigma implementation, and a sustainability phase. The methodology involved DMAIC model tools like flowcharts, cause & effect analysis, and sigma metric scales. Six Sigma process tools, including DPM and process capability analysis, were used. Interventions included Six Sigma metrics for QC performance, DPM for TAT process improvement, and patient moving average Real-time for proactive risk management in reducing analytical testing variation. Results Excellent performance (> 6 sigma) was achieved for 70% of chemistry test parameters for three control levels.: This indicates a high level of process capability and reliability in laboratory operations, ensuring accuracy and consistency in test results. The study successfully reduced process variation in Emergency Room (ER) laboratory Turnaround times (TATs), leading to an increase in process capability as measured on the Sigma scale, which improved from 1.35 to 3.1, indicating a more robust and efficient laboratory operation. Reducing the cost and efficiency of internal failure controls by 29% and at the same time saving in labor cost/workload marked a decrease in QC frequency from 7.0% to 0.18%, reaching the target goal of TATs and satisfaction. Conclusions The adoption of Six Sigma methodologies resulted in a notable decrease in QC runs, signaling cost savings. These high-quality methods streamline laboratory operations, improve cost-effectiveness for the healthcare system, and ultimately benefit patients and clinicians. Six Sigma methodologies play a crucial role in selecting appropriate QC designs for each parameter, ensuring efficient control while maximizing cost-effectiveness. Emphasizing the importance of suitable QC strategies and continuous technologist training is vital for mitigating analytical errors and enhancing turnaround time through comprehensive automation of the analytical phase.

New clinicopathological concept of endometrial carcinoma with integration of histological features and molecular profiles.

The dual-stratified pathway of endometrial carcinomas (ECs) has long been dominant. However, in 2013, The Cancer Genome Atlas (TCGA) defined four EC subgroups with distinctive prognoses. Inspired by TCGA, in 2018, the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE) provided four pragmatic molecular classifiers to apply surrogate immunohistochemical markers to TCGA subgroup categorization. These trends prompted the revision of 2020 WHO Classification of Female Genital Tumors, 5th edition (2020 WHO classification), in which four molecular subtypes are recognized: POLE-ultramutated; mismatch repair-deficient; p53-mutant; and no specific molecular profile. In the 2020 WHO classification, the diagnostic algorithm is characterized by prioritizing POLEmut over other molecular abnormalities. Following the 2020 WHO classification, Federation of International Gynecology and Obstetrics (FIGO) proposed a new staging system in 2023. The updated system focuses on diagnostic parameters, such as histological type and grade, lymphovascular space invasion, and molecular alterations. These new histomolecular diagnostic concepts of ECs are being accordingly introduced into the routine pathology practice. For the first time, the 2020 WHO classification includes mesonephric-like adenocarcinoma (MLA) as a novel histological entity, mimicking the conventional mesonephric adenocarcinoma, but is considered of Müllerian ductal origin.

Leveraging AI and Machine Learning to Optimize SAP-Driven Financial Operations: A New Paradigm in Risk Management and Fraud Detection

As the global business landscape continues to evolve, organizations are increasingly relying on advanced technologies to enhance their financial operations. The integration of Artificial Intelligence (AI) and Machine Learning (ML) with SAP-driven financial systems represents a transformative shift in how companies manage risk and detect fraud. This article explores how AI and ML can optimize SAP systems for proactive risk management and real-time fraud detection. It highlights the benefits, challenges, and best practices for implementing these technologies within SAP environments, drawing on industry case studies and expert insights

Reducing Data Uncertainties: Fuzzy Real-Time Safety Level Methodology for Socio-Technical Systems

This paper presents the fuzzy real-time safety level (Fuzzy RealTSL) methodology. It aims to address the data uncertainties resulting from a lack of sensors in complex sociotechnical systems and reduce the need for the determination of their safety level in real-time during their operation. To achieve this, the methodology utilizes: (1) safety constraints from STPA (systems theoretic process analysis) analysis and EWaSAP (early-warning-signs analysis process), (2) fuzzy logic as the mathematical backbone to identify the degree of confidence about the occurrence of unsafe system states, (3) a modified centroid point and spread ordering to enable ordering sequences of unsafe system states that can lead to accidents according to how detrimental they are to the system safety. The RealTSL methodology is presented through its step-by-step application to the panel alignment system of a solar park utilizing rotating solar arrays. This paper aims to open a new perspective on the STAMP literature for discussions of uncertainties from a lack of information about the system’s state and to make it easier to measure its safety level. Knowing the safety level of a system in real-time is crucial for the systems in question as it enables proactive risk management and enhances decision-making by providing immediate insights into potential hazards, thus safeguarding against accidents.