Proactive Risk Research Articles

A comprehensive risk management framework for NIMBY projects: Integrating social network analysis and risk transmission chains

Urban development projects often face local opposition, known as the NIMBY (Not in my back yard) effect, affecting infrastructure implementation and urban growth. This study aims to develop a systematic risk management approach for NIMBY projects. Employing grounded theory, 29 NIMBY cases in China were analyzed, identifying 17 risk factors across six categories. Social network analysis revealed risk interactions, highlighting four factors: government decision-making risk, government credibility risk, project completion risk, and online public opinion risk. Two risk transmission chains were delineated, each consisting of 4–5 interconnected risk factors, providing insights into risk propagation mechanisms. Simulation results indicated that targeted risk mitigation strategies based on these chains affected network efficiency differently compared to random approaches. The network efficiency reduced from 16.5% to 11.2% for the first chain-based strategy and to 13.4% for the second, while random interventions showed limited impact on network efficiency. Based on these findings, a NIMBY risk management system focusing on risk nodes and incorporating risk compensation mechanisms is proposed. This system addresses proactive governance, stakeholder engagement, and risk mitigation. The research contributes to the understanding and management of NIMBY risks, providing tools for policymakers and project managers. The findings suggest that this approach may affect NIMBY conflicts and community acceptance of urban development projects, potentially influencing urban development processes.

Proactive cervical cancer risk assessment using data-driven analytics

This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.

A novel data-driven approach for proactive risk assessment in shield tunnel construction

A novel data-driven approach for proactive risk assessment in shield tunnel construction

Bacterial Diseases: An Emerging Threat for Central European Forest and Urban Trees?

ABSTRACTBacterial tree diseases have been mainly studied in agriculture and horticulture. For forest trees, damage due to bacterial diseases is understudied. Moreover, bacterial tree diseases often appear in the context of so‐called complex diseases, which are dependent on other factors, such as multiple microorganisms, insects or abiotic factors which weaken the host. In recent years, outbreaks of bacterial tree diseases, such as Xylella fastidiosa in the Mediterranean region or acute oak decline (AOD) in the United Kingdom, raised the awareness of bacterial diseases on forest trees. In this review, we aim to summarise the current issues and available knowledge about bacterial diseases of forest trees in Central Europe. Furthermore, we identify potential bacterial pathogens that could gain importance in the future for central European forests. The methods used were a systematic literature search and the analysis of the data collected over the last 10 years on bacterial diseases by the Swiss forest protection service. We conclude that, on one side, complex bacterial diseases could increase in importance, especially considering ongoing climate change. Therefore, the bacterial community of diseased trees (the pathobiome) needs to be studied more in depth to understand the emergence of complex bacterial diseases. On the other side, host ranges of highly pathogenic invasive genera and species, such as Xylella, need to be investigated experimentally for common central European tree species and varieties, to implement proactive risk management strategies against bacterial diseases in forest trees. Finally, urban trees and green spaces should be monitored more closely, as they could serve as starting points for bacterial disease outbreaks in forests, similarly to other emerging diseases and pathogens.

Strategic approaches to enhancing credit risk management in microfinance institutions

Microfinance institutions (MFIs) play a critical role in providing financial services to underserved, low-income populations, yet face significant challenges in managing credit risk due to the nature of their clientele and loan structures. Enhancing credit risk management in MFIs is essential for ensuring financial stability, reducing default rates, and fostering sustainable growth. This review examines strategic approaches to credit risk management tailored to the microfinance context, where traditional credit assessment methods may be less effective. It explores innovative client profiling techniques, such as segmented risk analysis and customized credit scoring models, which leverage demographic and behavioral data to more accurately assess borrower reliability. The use of data-driven decision-making, particularly through predictive analytics and machine learning, is highlighted as a means to anticipate client repayment behaviors and improve risk forecasting. Further, real-time loan monitoring systems with early warning indicators offer MFIs proactive risk mitigation strategies, enabling prompt interventions to prevent defaults. Institutional improvements, including standardized risk frameworks, enhanced training for loan officers, and regular compliance checks, strengthen organizational resilience against credit risks. Portfolio diversification strategies, such as sectoral and geographic diversification, and the integration of group lending models, are also discussed as effective means of reducing risk concentration. By implementing these multifaceted, strategic approaches, MFIs can improve their credit risk management frameworks, better serve their clients, and achieve greater financial sustainability. The review emphasizes the importance of adapting to rapidly evolving risk factors and adopting innovative tools and practices to meet the unique challenges of microfinance. The findings advocate for continuous research and investment in advanced credit risk methodologies, ultimately contributing to the broader goal of financial inclusion.

Navigating complex mergers: A blueprint for strategic integration in emerging markets

This study explores strategic integration in mergers and acquisitions (M&A), focusing on emerging markets. As M&A activity in these regions grows, companies face unique challenges due to diverse regulatory environments, cultural dynamics, and operational complexities. This research aims to provide a comprehensive framework to navigate these complexities, identifying core integration components, external influencing factors, technological advancements, and associated challenges. Methodologically, the study synthesizes existing literature on M&A integration, analyzing key strategic approaches across cultural alignment, risk management, and operational harmonization. Key findings indicate that successful strategic integration requires technological and human-centered approaches, including tools like ERP systems, data analytics, and cloud computing, which streamline operations, and emphasize cultural alignment, leadership synergy, and proactive risk management. Additionally, the study underscores the impact of regulatory and economic factors on integration strategies, highlighting the need for adaptable frameworks tailored to the complexities of emerging markets. The study concludes that structured integration strategies and flexible, technology-driven frameworks foster long-term M&A success and competitive advantage. Recommendations include adopting sustainable integration models, enhanced cross-functional collaboration, and robust regulatory compliance strategies. Future research opportunities are outlined, particularly regarding open innovation and sustainable practices in emerging market M&A.

Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm

Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries.

Agile Software Development with Secure and Scrum-Centric Approach

The fulfillment of agile models is crucial for ensuring that a software development project's tasks are completed efficiently and collaboratively. The definitions of the project architecture are usually performed in these models. Factors such as delivery time, cost and maintenance are determined. In traditional methods, project stages are carried out sequentially. After one of the stages is completed, another one is performed. When an innovation or change is attempted at any point in the project, some problems occur. These problems can generally be solved with Scrum agile methods, where communication is highlighted and processes are performed more flexibly. It is an undeniable fact that Scrum and security issues can be brought together when incorporating the Scrum strategy into software development models. In such a case, models are used that allow Security and Scrum to work within a framework. In this study, scientific studies based on scientific evidence aimed at eliminating incompatibilities in software development systematics were analyzed. The distribution of the publication years, the relation of scrum and security, the citation topic, the bibliometric maps and co-citation report are used in these analysis. In the result of these analyses, bibliometric and statistical analysis results of studies in the literature on software development that includes security principles with the Scrum model were revealed. When the results obtained in the experiments were examined, it was concluded that it was possible to develop secure software with an architecture in which Scrum and Security models were used together. During the software development phase, it enables proactive risk management by blending scrum and security elements. It also allows teams to detect security vulnerabilities during the software development phase. These facilitate the creation of a more secure and durable software product.

Usability of Mobile Control Room and CCTV Systems for Crowd Control at Large-Scale Events with the Occurrence of a Large Number of People With Focus on the Czech Republic

This article deals with the issue of the use of modern instruments for crowd control, in particular the use of a mobile closed-circuit television system connected directly to the control room during largescale events. The article has two aims: to check the usability of this type of system especially from the perspective of increasing the safety of event visitors, and to identify the optimal technical solution for crowd monitoring and management and possible obstacles for deployment in the field. The article further focuses on the practical usability of the information obtained through the mobile camera system and the control centre for increasing the safety of event visitors from the perspective of security managers. The conducted investigation identified obstacles to the deployment of a mobile camera system as well as described the optimal parameters for the use of a mobile camera system, and it collected feedback from the security team participating in the security management of the event. The results of the contribution enable easier orientation in the selection of suitable hardware and software for crowd control; Key vulnerabilities in deploying mobile CCTV systems were identified, including issues such as data transmission stability over LTE networks and the financial costs of alternative solutions like satellite connections. Based on field testing and feedback from security teams, recommendations were made to improve the system’s effectiveness and adaptability, leading to more precise decision-making and proactive risk management at large-scale events.

Analysis of Mitigation for Landslide Disaster Management in Lumajang Regency, East Java Province

This research aims to analyze the mitigation of natural disaster management, specifically landslides, in Lumajang Regency, East Java Province. Natural disasters are undesirable events, making it crucial for the government and the community to understand the processes and factors leading to such disasters. One significant disaster that requires attention from both the government and the community in Lumajang is landslides, necessitating an understanding of their impacts and management strategies. The alternatives for addressing this issue can involve disaster mitigation through various stages, which are the focus of this study, aimed at reducing the ongoing risks associated with landslide vulnerabilities. This research is descriptive in nature, utilizing a quantitative approach, with subjects comprising individuals who can share their knowledge about the phenomena or cases being studied. The findings indicate that the Regional Disaster Management Agency (BPBD) of Lumajang Regency is responsible for disaster management, including landslides. However, their mitigation stages—such as mapping, inspections, investigations, monitoring, and socialization—have not been effectively implemented to reduce disaster risks. The BPBD tends to act only after a disaster occurs, resulting in a lack of proactive risk reduction through disaster mitigation. There is a pressing need for efforts from various stakeholders to enhance awareness of landslide dangers, including the establishment of an early warning system and community education regarding natural disasters through outreach and education before, during, and after landslide events. Keywords: Mitigation analysis, Disaster, Landslide

Contrasting forest management strategies: Impacts on biodiversity and ecosystem services under changing climate and disturbance regimes

Natural disturbances may compromise the past and ongoing efforts to increase carbon sequestration and halt biodiversity loss in boreal forests. Measures to minimize the effects of forest disturbances i.e., adaptive management, offer solutions to secure future timber yields. However, the consequences of adaptive management on biodiversity, the climate change mitigation potential of forests, and other ecosystem services are not well understood. In addition, the impact of climate change and disturbances on future forest-based mitigation potential is not well known. We compared the effects of forest management options emphasizing climate change mitigation or adaptation on boreal forests in changing climate and disturbance regimes in southern Finland. We used the process-based forest landscape and disturbance model iLand to dynamically model interactions between climate change and disturbances together with forest management and protection options, and examined the consequent effects on forest carbon storage, berry yields, recreation, and structural attributes important for biodiversity. Mitigation managements resulted in up to one-fifth higher carbon stocks, even after accounting for disturbances by wind and bark beetles, but halved annual harvests over the 80-year simulation period. Adaptive managements reduced bark beetle disturbances, but in some cases the disturbed volumes were even higher than under business-as-usual management due to increased wind damage. The effects of proactive risk management depended on the time horizon considered, the adaptive management option chosen and the climate change scenario. In general, the mitigation managements had positive effects on the biodiversity indicators studied, while the effects of adaptive management were mixed. Our results highlight the complex interactions between disturbance risk prevention, biodiversity, carbon sequestration and storage, and other ecosystem services. The results guide forest managers and policymakers to plan mitigation and adaptation strategies optimizing multiple benefits, and strengthening forest resilience in a changing climate.

Tropical Interbasin Interaction as Effective Predictors of Late-Spring Precipitation Variability in the Southern Great Plains

Abstract The southern Great Plains experience fluctuating precipitation extremes that significantly impact agriculture and water management. Despite ongoing efforts to enhance forecast accuracy, the underlying causes of these climatic phenomena remain inadequately understood. This study elucidates the relative influence of the tropical Pacific and Atlantic basins on April–May–June precipitation variability in this region. Our partial ocean assimilation experiments using the Community Earth System Model unveil the prominent role of interbasin interaction, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these interbasin contrasts. Our statistical analyses suggest that these tropical interbasin contrasts could serve as a more reliable indicator for late-spring precipitation anomalies than El Niño–Southern Oscillation. The conclusions are reinforced by analyses of seven climate forecasting systems within the North American Multi-Model Ensemble, offering an optimistic outlook for enhancing real-time forecasting of late-spring precipitation in the southern plains. However, the current predictive skills of the interbasin contrasts across the prediction systems are hindered by the lower predictability of the tropical Atlantic Ocean, pointing to the need for future research to refine climate prediction models further. Significance Statement Agriculture and infrastructure in the southern plains face challenges from severe late-spring precipitation extremes. Traditional predictors like El Niño–Southern Oscillation (ENSO) lose effectiveness during the critical spring-to-summer transition, creating a forecasting gap. This study introduces the concept of tropical interbasin interactions, known to enhance seasonal predictability for late-spring precipitation in the southern plains. Novel climate model experiments highlight contributions from the tropical Pacific and Atlantic, offering a promising predictability that potentially surpasses the limitations of ENSO-based predictions. These outcomes hold the potential for developing operational forecasts of late-spring precipitation anomalies in the southern plains, enabling proactive risk management.

Conceptual Trends in Internal Audit

The evolution of the economic environment amidst multidimensional changes, i.e. development, globalisation, economic crises, pandemics, stakeholder interest in credible, relevant and transparent information, has led to continuous changes in the framework for doing business. These changes have generated for entities operating in a globalised economic environment business opportunities, significant profits, financial downturns, but also exposure to diverse and complex risks and therefore proactive risk management. From this perspective, internal audit has gradually, over time, become the key actor supporting the entity in achieving its objectives, adding value as well as corporate governance. In this context, the objective of the research is to identify the internal audit trend and related conceptual connotations. The research approach includes a literature review of the Web of Science from Clarivate Analytics. The research involves a systematic analysis on internal auditing, conducted through a quantitative research methodology. The research sample includes 29 articles extracted from the Web of Science Core Collection database, covering the publication period 2005-2024. The sample was established after several stages of keyword filtering. This analysis focuses on identifying author affiliations, content of papers, research methods used, and citations and references to highlight significant trends and changes in the field of internal audit. The results demonstrate a concrete and consistent interest in the field of internal audit, highlighting the identification and analysis of significant trends and changes in internal audit practices and roles over time. In addition, it highlights a trend towards a more holistic approach to internal audit that goes beyond its traditional assurance role and extends to providing strategic advice and support to organisations. The research will thus contribute to the identification of future research directions and to a deeper understanding of the role and importance of internal audit in today's economic environment.

Stakeholder-led proactive risk assessment of medication safety in the pharmaceutical supply chain

Abstract Introduction In 2022/23, 1.08 billion medications were dispensed to patients using NHS England, at a cost of £19.04 billion1 . However, Elliot et al (2019)2 estimate every year there are 237 million medication errors at some point of the medication process, with a high associated cost. Increasingly, media is reporting medication shortages of critical medicines3 that can contribute to errors of omission. Medication errors suggests system, tasks and processes should be explored to understand if they have been poorly designed. This paper is a pre-Brexit study that examines the pharmaceutical supply chain with stakeholders. Aim To explore risk to medication safety in the pharmaceutical supply chain using qualitative interviews of stakeholders across the supply chain and proactive risk assessment method SHERPA (Systematic Human Error Reduction and Prediction Approach). Method 1. Qualitative interviews with process models System process models were developed (by literature, field observations and interviewees) and employed to elicit stakeholder perceptions about risk to medication safety in 31 qualitative interviews. Participants included pharmacists, wholesalers, pharmaceutical industry representatives, regulators and policymakers. Ethical approval was granted by Cambridge LREC to include healthcare professionals and 6 patients. Interviews were audio-recorded, transcribed and thematically analysed using N-Vivo. 2. Focus group for SHERPA Six stakeholders participated in the SHERPA focus group4 , including pharmacists and representatives from pharmaceutical manufacturing and wholesale industries. Interview participants were invited and those that agreed, attended. The SHERPA template was completed by the group. The session was audio-recorded, transcribed and thematically analysed using N-Vivo. Results The system models led to a greater understanding of the current system ‘as-is’ and created an overview of the supply chain. SHERPA is a validated method and provided the rigour to risk assess the supply chain process and identify sources of risk. However, the error mode terminology required adjustment to healthcare. Across all supply routes, the main source of risk was failure to supply medicines that caused errors of omission and the risk of counterfeit medicines. Failure to supply resulted from shortages that take place from trading activities that result in stockpiling or exporting of medicines. A lack of robust regulation and parallel imports increased the risk of counterfeit medicines. Stakeholders highlighted deficiencies in the supply chain and poor performance of stakeholders, as medication safety concerns. In order of frequency, the deficiencies include fragmented supply chain; complacency by stakeholders; poor communication and integration across supply chain; and end-user often regarded as the pharmacist, rather than the patient. Discussion and conclusion From the findings, strategies emerged as possible solutions. These included communication of shortages in each component of supply chain; robust counterfeit strategies; use of technology (automation/digital tools); and re-design tasks and processes in dispensaries and wards. Staff training, education, and revalidation of stakeholders is vital. Key limitations in this study include biases that can occur from recruitment strategy and small sample size. The SHERPA method posed terminology problems. In conclusion, this multi-method approach reveals risk and possible solutions to improve safety and reduce cost in the NHS. It is clear this study needs to be repeated to understand the impact of Brexit and current reconfiguration of NHS.

Hidden in Plain Sight: A Data-Driven Approach to Safety Risk Management for Highway Traffic Officers

Highway traffic officers (HTOs) are often exposed to life-threatening workplace incidents while performing their duties. However, scant research has been undertaken to address these safety concerns. This research explores case study data from highway incident reports (held by National Highways, a UK government company) and employs deep neural network (DNN) in unearthing patterns which inform safety decision makers on pertinent safety challenges confronting HTOs. A mixed philosophical stance of positivism and interpretivism was adopted to synthesise the findings made. A four-phase sequential method was implemented to evaluate the validity of the research viz.: (i) architectural design; (ii) data exploration; (iii) predictive modelling; and (iv) performance evaluation. The DNN model’s predictive performance is benchmarked against three other machine learning models, namely Support Vector Machines (SVM), Random Forest (RF), and Naïve Bayes (NB). The DNN model outperformed the other three models. Findings from the data exploration also show that most work operations undertaken by HTOs have a medium risk level with night shifts posing the greatest risk challenges. Carriageways and traffic management enclosures had the highest incident occurrence. This is the first study to uncover such hidden patterns and predict risk levels using a database specifically for HTOs. This study presents evidence-based information for proactive risk management for HTOs.

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.

Identifying high-risk central lines in critically ill children: a novel nurse-driven screening and mitigation intervention to reduce CLABSI

BackgroundDespite strong adherence to central line associated bloodstream infection (CLABSI) infection prevention bundles, the CLABSI rate in our academic pediatric intensive care unit (PICU) and pediatric cardiac intensive unit (PCICU) remained high. MethodsWe developed a novel screening tool that stratified patients' risk for CLABSI and considered risk-mitigation strategies. ResultsOf 1583 screenings, 30% were classified as high-risk, 27% as moderate-risk, and 43% as low-risk. With accurate screening, the tool was 100% sensitive to patients who developed CLABSI, with a negative predictive value of 100% for low-risk screens. The CLABSI rate declined from 1.83 per 1000 catheter-days to 0.98 and 1.02 in 2021 and 2022, respectively, with unprecedented consecutive months CLABSI free. Device utilization was stable across both units, declining by 22% in the PICU and rising in the PCICU with increased cardiac surgeries. Clinicians expressed increased awareness of patient CLABSI risk factors and mitigation strategies in surveys. DiscussionsThis novel screening tool effectively identified high-risk patients to target resources and promoted improvements in CLABSI-prevention processes in the PICU and PCICU. ConclusionsA novel nurse-driven CLABSI risk factor screening tool identified and focused resources on patients at high-risk for CLABSI, and increased awareness and proactive risk mitigation by clinicians.

Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy

Background and Purpose:With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy. Materials and Methods:The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS). A proactive Failure Mode and Effect Analysis (FMEA) was conducted by a multidisciplinary team, including physicians, physicists, dosimetrists, technologists, quality managers, and the research and development team. Failure modes categorised as ‘Not acceptable’ and ‘Tolerable’ on the risk matrix were further examined to find mitigation strategies. Results:In total, 39 failure modes were defined for the total workflow, divided over four steps. Of these, 33 were deemed ‘Acceptable’, five ‘Tolerable’, and one ‘Not acceptable’. Mitigation strategies, such as a case-specific Quality Assurance report, additional scripted checks and properties, a pop-up window, and time stamp analysis, reduced the failure modes to two ‘Tolerable’ and none in the ‘Not acceptable’ region. Conclusions:The pro-active risk analysis revealed possible risks in the implemented workflow and led to the implementation of mitigation strategies that decreased the risk scores for safer clinical use.

Synergizing Ai and ERP for Predictive Supply Chain Management and Quality Assurance in Healthcare

This article explores the transformative potential of integrating Artificial Intelligence (AI) with Enterprise Resource Planning (ERP) systems in healthcare, focusing on applications in predictive supply chain management and quality assurance. The healthcare industry faces increasing pressure to improve efficiency while maintaining high-quality care, and AI-ERP integration offers a compelling solution. By combining ERP's comprehensive data management capabilities with AI's predictive and analytical power, healthcare organizations can enhance operational efficiency, improve decision-making, and ultimately deliver better patient outcomes. The article examines current implementations, case studies, and future projections to provide a comprehensive overview of how this technological synergy can help healthcare providers navigate the complex challenges of modern healthcare delivery, from optimizing inventory levels to ensuring regulatory compliance and proactive risk management.

A probabilistic assessment of ship blackout incident with Fault Tree Analysis into (FTA) Bayesian Network (BN)

Blackouts in maritime activities can cause propulsion loss and dangerous maritime conditions. Bayesian risk analysis is applied to ship blackout incidents in this study to improve understanding and reduce risks. Using Fault Tree Analysis (FTA), a Bayesian Network (BN) model incorporates fuel quality, lubricating oil quality, sensor error, injector error, and mechanical defects to estimate blackout probability. The model analyses how hazards and their interactions affect this situation using probabilistic inference. Sensitivity analysis identifies variables that affect blackout probabilities and prioritises risk mitigation solutions. Based on prior and posterior probabilities, ‘Automatic Voltage Regulator Failure’ (0.03 prior, 0.17 posterior), ‘Rotor Mechanical Fault’ (0.03 prior, 0.15 posterior), and ‘High Cooling Water Temperature’ (0.03 prior, 0.13 posterior) are the top three blackout causes. Other significant variables include ‘Switchboard Line Failure,’ ‘Faulty Fuel Pump,’ ‘Rotor Open Circuit,’ and ‘Temperature Sensor Failure’ in relative amounts. Bayesian risk analysis can identify and minimise marine blackout concerns, giving decision-makers a comprehensive framework for informed decision-making and proactive risk management. This research emphasises blackout accidents’ importance, improving maritime transportation safety and reliability.