Proactive Risk Research Articles

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.

Identifying Critical Success Factors for Effective Positive Risk Management in Project Management

This study identifies critical success factors (CSFs) for positive risk management in project management. The research, focused on IT projects in Maharashtra, combines quantitative surveys and qualitative interviews from 150 project managers. The findings highlight proactive risk identification, active stakeholder involvement, strong team dynamics, and a supportive organizational culture as key success factors, leading to enhanced project outcomes. Key Words: Positive Risk Management, Project Management, Critical Success Factors, IT Projects, Proactive Risk Identification, Stakeholder Involvement

Framework model of syncretic management of innovative projects

The contemporary business landscape demands agility, innovation, and the seamless integration of diverse approaches. Traditional project management methods often struggle to adapt to this dynamic environment. This abstract introduces the framework model of syncretic management for innovative projects. Syncretic management emphasizes the unification of various elements to achieve a cohesive whole. In this context, it signifies a framework that blends methodologies and tools to optimize project outcomes. Key components include – Interdisciplinary Collaboration, Agile Methodologies, AI Integration. The syncretic management model offers several advantages – Enhanced Innovation, Improved Efficiency, Increased Adaptability. Reduced Risks: data-driven insights from AI facilitate proactive risk identification and mitigation strategies. Paper provides a concise overview of the syncretic management model for innovative projects. Subsequent sections will explore the core components of this framework in greater detail, delve into its practical applications, and discuss potential challenges and considerations for successful implementation.

FINANCIAL CRISIS AND INSTABILITY

Financial crises and instability are common occurrences in the world economy, and they have significant effects on both developed and developing nations. The numerous causes of financial crises and the ensuing instability in the financial markets are thoroughly examined. It explains the theoretical foundations of financial crises, going over important ideas including market bubbles, excessive leverage, and regulatory shortcomings The paper also looks into how regulations and governmental actions can either exacerbate or lessen financial crises, how financial institutions contribute to systemic risk propagation and how interconnected they are. It looks at how the spread of crises across borders and asset classes can aggravate their severity through the contagion effect. It also covers the socio-economic fallout from financial crises, such as social unrest, income inequality, and unemployment. The summary concludes by highlighting how crucial it is to have strong regulatory control and proactive risk management to avert and lessen future financial disasters. It promotes a comprehensive strategy that blends international collaboration, regulatory changes, and responsible risk-taking to promote long-term economic growth and stability in a global financial system that is becoming more linked.

Evaluating the Impact of Artificial Intelligence in Managing Construction Engineering Projects

This study evaluates the impact of utilizing artificial intelligence (AI) in managing construction engineering projects. With the increasing complexity and scale of construction projects, AI offers promising solutions to enhance efficiency, accuracy, and decision-making processes. The study investigates the potential benefits, challenges, and practical applications of AI through detailed case studies. The study employs a mixed-methods approach, combining qualitative and quantitative research methods. Data was collected through literature reviews and case studies where AI had been successfully implemented. The analyses included comparisons between projects that used AI and those that did not. The findings demonstrated significant improvements in project efficiency, cost estimation accuracy, and risk management. For instance, AI-powered systems reduced scheduling errors by 35%, leading to more accurate timelines. Additionally, the integration led to a 20% reduction in project durations due to improved resource allocation and proactive risk management. Furthermore, AI-supported systems contributed to a 25% improvement in stakeholder satisfaction. In terms of cost estimation, AI-powered estimation tools improved the accuracy of cost estimates by 30%, helping to reduce budget overruns by 40%. In risk management, AI-supported tools enhanced the accuracy of risk identification by 45%, leading to the early detection of potential issues and the development of effective mitigation strategies that reduced the impact of risks by 30%. Thanks to these improvements, project success rates increased by 20%. These results demonstrate that integrating AI into the management of engineering construction projects can lead to tangible improvements in project efficiency, cost accuracy, and risk management, thereby enhancing stakeholder satisfaction and contributing to more successful project outcomes.

The impact of artificial intelligence on regulatory compliance in the oil and gas industry

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

Irish Hip Fracture Database Frailty Index Predicts Delirium Incidence and Length of Stay in Older Adults Admitted with Hip Fractures

Abstract Background The Irish Hip Fracture Database (IHFD) Frailty Index was developed with the National Office for Clinical Audit has been shown to predict adverse outcomes in older adults with fractures in retrospective review of national data. Here we examined the tool prospectively in clinical practice and its ability to predict delirium and length of stay. Methods We prospectively collected data on all hip fracture patients admitted to an acute orthopaedic floor from October 2022 to May 2024. Patients aged < 60 years or those with atypical femoral or peri-prosthetic fractures were excluded. The 21-item frailty index was applied to all eligible patients. Delirium diagnosis was based on a composite of clinical diagnosis and a 4AT score ≥ 4. Regression analysis was used to examine the relationship between delirium and length of stay with Frailty Index, age and sex. Results A total of 260 patients were included in the study, of whom 72% were female. The mean age was 80 ± 10 years, and 7.7% were nursing home residents. Additionally, 24.2% of patients were discharged directly home, while 40.3% were discharged to rehabilitation. Delirium developed in 24.6% of patients, and 24.9% of patients had a LOS exceeding 30 days. Higher frailty index scores were significantly associated with both LOS (p = 0.001) and delirium (p = 0.013) in both unadjusted and adjusted analyses. There was no significant association between age or sex and LOS or delirium in adjusted analysis. Conclusion The IHFD Frailty Index facilitates a pro-active risk stratification that is based on comorbidities and functional status. IHFD Frailty index independently predicted delirium and length of stay while age did not. Further studies integrating this tool into everyday practice in the care of older patients with hip fractures is warranted.

Risk assessment of public-private partnership water projects in Indonesia

Public-private partnerships (PPPs) are vital for infrastructure development in developing countries, integrating private efficiency with public oversight. However, PPP models often face risks, particularly in Indonesia’s water sector, due to its unique geographical and regulatory challenges. This study aims to identify and evaluate risk factors specific to drinking water PPP projects in Indonesia. Using a quantitative approach, structured questionnaires were distributed to experts in the sector, and the data was analyzed using a fuzzy evaluation method. Risks were categorized into location, design and construction, financial, operational, revenue, and political. The study emphasizes that effective risk management, including identification, analysis, and mitigation, is essential for project success. It highlights the importance of stakeholder involvement and flexible risk management strategies. Comprehensive and proactive risk management is key to the success of drinking water infrastructure projects. The research suggests that an integrated and collaborative approach among stakeholders can enhance risk management effectiveness. These findings provide valuable insights for policymakers, project managers, investors, and other stakeholders, underscoring the necessity for adaptable regulatory frameworks and robust policy guidelines to improve the sustainability and efficacy of future water-related PPPs.

A risk assessment of a gas pressure reduction station system with confidence for dealing with imprecisions and unknowns

Process systems are sensitive and vital industrial facilities. Disturbances in their performance may cause harm to the environment,humans,or significant economic damage. In risk assessment of chemical process industries, the available data, information, and knowledge are typically rare, limited, and often unrealistic. This issue poses a challenge to conducting a credible quantitative risk assessment and effects the robustness of the results. To address these challenges, this work proposes a methodology based on the Dempster-Shafer theory of evidence as the reasoning framework. It incorporates risk identification, analysis, and mitigation phases to ensure a thorough analysis of risks and the integration of proactive risk reduction strategies. The approach aims to model the worst-case hazard scenario and assess associated risks using various methods such as FMECA, Bow-Tie, Credal Network, and Dempster-Shafer theory. The proposed approach models imprecision and data ambiguity using intervals and associated belief mass. This extension provides a basis for addressing the fundamental problem of prior ignorance about the distribution of the observed data, which is prevalent in data mining applications. A new approach is proposed that utilizes Belief and Plausibility curves, similar to a Cumulative Distribution Function, to propagate uncertainty, enhance criticality discrimination, and determine cumulated belief measures. This approach is applied in analyzing the failure modes identified in FMECA and is further extended through the credal network for comprehensive risk assessment. Results show how to express irrelevant and independent judgments, and how to work out with inferences in credal networks. This issue is often overlooked, but if properly addressed it represents the key to ultimately drawing reliable conclusions and fully utilizing the system's available data. A case study of the City Gate Station system was used to verify the application potential of the proposed approach.

Modifying supply chain strategies to address regulatory compliance challenges: Lessons from South Eastern Nigeria

Regulatory compliance was an essential component of contemporary corporate operations, guaranteeing conformity to laws, rules, and standards controlling different aspects of organisational activity. Businesses operating in South Eastern Nigeria encounter complex obstacles to regulatory compliance, such as currency depreciation, the dynamic nature of data protection legislation, and the increasing global integration of supply chains. This report offers valuable insights into the intricate process of adjusting supply chain strategies to address regulatory compliance difficulties in South Eastern Nigeria. The paper examined regulatory compliance difficulties and their effects on supply chain dynamics by conducting a thorough literature review and analysing current research. Devaluing currency led to changes in import/export levies, tariffs, and customs procedures, which need quick and adaptable actions to ensure compliance and competitiveness. In addition, the digitalization of corporate operations intensified the issues of ensuring data security and privacy compliance. This necessitated investing in technological solutions and adopting proactive risk management strategies. Despite these difficulties, firms in South Eastern Nigeria adopted a range of tactics, such as implementing compliance management systems, diversifying suppliers and markets, and engaging with regulatory agencies, to successfully modify their supply chain strategies. The results emphasised the significance of taking proactive actions, engaging in strategic planning, and fostering cooperation among relevant parties to effectively address regulatory compliance obstacles and maintain competitiveness in the global market. Keywords: Adherence to regulations, Techniques for managing supply chains, Depreciation of currency, Safeguarding data, Region of South Eastern Nigeria.

Enhanced hybrid LSTM and SLAR modeling for in-depth analysis of temporal and spatial patterns in compositional data for environmental monitoring

A novel methodology for analyzing compositional data (CoDa) integrates long short-term memory (LSTM) networks with spatial lag autoregressive (SLAR) models to simultaneously capture temporal and spatial patterns. The proposed approach leverages LSTM’s strengths in handling temporal relationships and SLAR’s ability to capture spatial correlations, employing the centered log-ratio (CLR) transformation to manage CoDa’s characteristics, making it suitable for advanced analysis. The Adam optimizer ensures efficient and stable model training, enhancing convergence speed and precision. The combined approach involves preprocessing CoDa with CLR, initializing LSTM layers, performing forward passes through LSTM, integrating the SLAR model, generating outputs, post-processing data, calculating loss, and performing backpropagation. The methodology’s effectiveness is demonstrated through a case study on pollutant emissions data from a waste incineration plant, focusing on key pollutants such as Dioxins and Furans, Heavy Metals (Hg, Pb, Cd), Nitrogen Oxides (NO), Sulfur Dioxide (SO), Particulate Matter (PM), and Carbon Monoxide (CO). The LSTM network, configured with two hidden layers, dynamically adjusts learning rates for faster convergence. Integrating SLAR improves spatial pattern accounting, significantly enhancing predictive accuracy. Comparative analysis shows substantial performance metric improvements over standard LSTM models, with the LSTM-SLAR model reducing errors and explaining a higher proportion of data variability. In process safety and risk engineering, the proposed methodology enhances pollutant emission monitoring and control, enables proactive risk management, ensures regulatory compliance, and improves risk assessment accuracy, making it invaluable for dynamic risk assessment in environmental monitoring and is broadly applicable to fields requiring detailed temporal and spatial analysis, demonstrating robustness and versatility in handling complex CoDa.

DIGITALIZATION AS A MEANS OF OVERCOMING CHALLENGES AND THREATS OF CONSTRUCTION PARTICIPANTS IN A MULTIPROJECT ENVIRONMENT

This article examines the potential of digitalization as a strategic solution to address the challenges and threats faced by construction actors in a multi-project environment.Construction activities are inherently complex, often characterized by the simultaneous management of several projects, each with its own characteristics and challenges. In a multi-project environment, participants such as designers, developers, and contractors face significant obstacles, including resource allocation conflicts, communication barriers, inefficiencies, and increased risk of delays and cost overruns. The advent of digital technology offers a transformative approach to these challenges, enabling more efficient management and coordination across multiple projects. The main objective of this paper is to explore how digital tools and technologies can mitigate the specific challenges faced by different actors during the construction of multiple projects. Analyzing key issues such as scheduling conflicts, miscommunication, data management complexity and risk management difficulties, the article highlights critical areas where digitization can have a significant impact.One of the most urgent problems in a multi-project environment is the effective allocation and planning of resources. Traditional management approaches often fail to cope with overlapping schedules and competing priorities, resulting in delays and inefficiencies. Digital tools such as Building Information Modeling (BIM) and Project Management Information Systems (PMIS) offer integrated platforms that facilitate seamless coordination and real-time updates. These systems provide centralized data stores and sophisticated scheduling algorithms that improve decision-making and reduce the likelihood of conflicts.Communication barriers are another major problem in multi-project environments that can lead to misunderstandings, errors, and delays that negatively impact project timelines and outcomes. Digital communication platforms, including instant messaging, video conferencing, and collaboration software, enhance real-time communication and ensure alignment among all stakeholders. These platforms enable the rapid exchange of critical information and facilitate the collaborative environment necessary to manage multiple projects simultaneously.Ensuring that all stakeholders have access to accurate and up-to-date information is critical to effective decision-making. Digitization solves this problem by providing advanced data management and analytics capabilities. Big data analytics and artificial intelligence can analyze large data sets to predict potential problems and optimize resource allocation. These technologies help identify trends, predict project outcomes, and facilitate informed decision-making, ultimately increasing project efficiency and effectiveness.Risk management is particularly challenging in a multi-project environment due to increased complexity and interdependence. Digital risk management tools offer a framework for identifying, assessing and mitigating risks across projects. These tools provide scenario analysis, risk tracking and automatic alerts, enabling proactive risk management and reducing the likelihood of project failure.

Business and financial cycle across regimes: Does financial stress matter?

Identifying the business and financial cycles and their regimes is crucial for understanding the economy's state and financial market dynamics. Using a bivariate four-regime Markov-switching framework, we assess the accuracy of a model that identifies the business cycle by combining financial and real variables and compare it to a model that only uses real variables. We find that a model that incorporates information from a financial stress index outperforms a model based solely on real variables, not only during a financial crisis, but also, and more interestingly, during economic downturns. The empirical evidence provides insights for policymakers and investors to improve economic policy formulation, investment strategies, strategic decision-making, and proactive risk management. The results are robust to various model specifications.

Risk Assessment of ZAP-X→ Radiosurgery System in a Quaternary Care Hospital Using FMEA Approach

Background and Aims: The overarching goal of this study is to conduct a robust risk assessment using the Failure Modes and Effects Analysis (FMEA) approach. By identifying potential failure modes and their potential impacts on patient care and hospital operations, this research seeks to provide actionable insights and mitigation strategies for the safe and effective implementation of the ZAP-X Radiosurgery System. The swift integration of advanced medical technologies, exemplified by the ZAP-X→ Gyroscopic Stereotactic Radiosurgery System, a computer-controlled machine that delivers radiation to an intended target in the brain, necessitates a comprehensive proactive risk assessment before the launch of the system to ensure the safety of patients, staff and operational processes. Method: Applying the FMEA methodology, we meticulously mapped all processes and sub-processes associated with the ZAP-X Radiosurgery System implementation. Our multidisciplinary team, through a discussion on ‘what can go wrong’, identified failure modes. Risks were prioritised based on severity, likelihood and detectability giving them a Risk Priority Number (RPN). The team proposed mitigation strategies, deploying controls to enhance safety and reliability.