Operational Risk Research Articles

Designing comprehensive workforce safety frameworks for high-risk environments: A strategic approach

High-risk industries such as oil and gas, construction, and manufacturing face significant challenges in safeguarding their workforce due to the inherent dangers of their operations. This paper presents a strategic approach to designing comprehensive workforce safety frameworks that address both personal and process safety. Personal safety focuses on the use of personal protective equipment (PPE), behavior-based safety programs, and training, while process safety aims to manage operational risks such as equipment malfunctions and hazardous materials. The interdependency of these two safety pillars is essential in reducing workplace accidents and ensuring a safer operational environment. Challenges in implementing safety frameworks, such as complex operations, human factors, and technological and regulatory barriers, are also discussed. To overcome these challenges, the paper proposes a series of strategic solutions, including risk assessment, technology integration, continuous improvement, and stakeholder collaboration. By adopting these recommendations, industries can develop customized safety frameworks that protect their workforce and enhance operational efficiency. Keywords: High-Risk Industries, Workforce Safety, Personal Safety, Process Safety, Risk Assessment, Safety Frameworks.

Enhancing financial reporting and management efficiency through enterprise resource planning (ERP) systems: A theoretical review for large-scale energy operations

Enterprise Resource Planning (ERP) systems have emerged as essential tools in streamlining financial reporting and improving management efficiency across various industries, including large-scale energy operations. This theoretical review explores how ERP systems enhance financial reporting by integrating real-time data, automating financial processes, and fostering transparency. In energy operations, where financial activities are complex and data-intensive, ERP systems centralize financial data, reducing errors, improving compliance, and supporting decision-making through enhanced visibility and accuracy. By automating tasks such as budgeting, forecasting, and financial statement generation, ERP systems enable energy companies to efficiently manage their financial resources, thus optimizing cost control and profitability. Additionally, ERP systems provide significant benefits for management efficiency, particularly in large-scale operations. The integration of financial, operational, and managerial processes within a single platform allows for better resource allocation, improved communication across departments, and faster response times to market fluctuations. These capabilities are crucial in the energy sector, where companies face volatile market conditions, regulatory pressures, and high operational risks. ERP systems enhance managerial decision-making by providing real-time insights into key performance indicators (KPIs), enabling executives to make data-driven decisions that support long-term strategic goals. However, implementing ERP systems in large-scale energy operations presents challenges, including high costs, lengthy implementation periods, and the need for employee training. This review also examines these barriers and suggests strategies for successful ERP adoption, including phased implementation, stakeholder engagement, and customization to industry-specific needs. In conclusion, ERP systems hold the potential to revolutionize financial reporting and management efficiency in large-scale energy operations. By leveraging ERP's real-time data integration and process automation, energy companies can significantly improve their financial management, enhance decision-making, and adapt more quickly to market changes. Keywords: ERP Systems, Financial Reporting, Management Efficiency, Large-Scale Energy Operations, Process Automation, Real-Time Data Integration, Decision-Making, Cost Control, Market Fluctuations, Energy Sector.

Strategic Transformation and Sustainability: Unveiling the EFQM Model 2025

The EFQM Model (European Foundation for Quality Management), established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The Model’s evolution is driven by the need to adapt to global trends, threats, and technological innovations such as digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI (Artificial Intelligence), and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasizing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environment, including alignment with the Unated Nations Sustainable Development Goals (SDGs). How: The Model introduces an integrated approach to performance management and transformation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensure greater stability and the ability to anticipate future challenges.

Research on the Optimization Method of Bus Travel Safety Considering Drivers’ Risk Characteristics

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses.

Case Study of a State-Owned Company: How does Centralized Procurement affect Operational Risk?

This study aims to understand that the restructuring of the BUMN has a significant impact on the operational activities of the NUMN, along with its risks. The researchers used data saving from the procurement centralization process as an indication of the company's operational activities before and after restructuring. While variabel current risk exposure to indicate the conditions of the company's operational risk. Procurement centralization mechanisms tend to change before and after restructuring. So it gives the contractor his own sentiment about the operational risks and obstacles in the process. The data used refers to the company's monitoring report. Modeling between saving and current risk exposure uses double regression analysis. The results also indicate that changes in procurement centralization mechanisms have a significant impact on the company's operational risk. So in order to meet risk mitigation, the company should specifically regulate the mechanism of centralization of procurement between business groups.

The Critical Role of SREs in Safeguarding Healthcare Data Integrity and Availability

This article explores the critical role of Site Reliability Engineers (SREs) in the rapidly evolving landscape of healthcare technology. As healthcare organizations increasingly adopt digital solutions, SREs have become indispensable in ensuring data integrity, system availability, and regulatory compliance. The article discusses the unique challenges faced by SREs in healthcare environments, including managing vast amounts of sensitive patient data, implementing robust security measures, and maintaining high system reliability. It examines the key responsibilities of SREs, such as proactive monitoring, incident response, data consistency management, and security compliance. Furthermore, the article highlights the significant impact of SRE practices on healthcare delivery, including enhanced patient care, improved operational efficiency, risk mitigation, and cost reduction. Through a combination of statistical data and industry insights, this work underscores the growing importance of SREs in shaping the future of healthcare technology and improving overall patient outcomes.

Innovative Data Visualization for Business Intelligence: Enabling Ad-hoc Querying and Predictive Analysis

This comprehensive analysis explores the transformative impact of innovative Business Intelligence (BI) solutions that combine ad-hoc querying capabilities with advanced predictive analytics. The article examines the limitations of traditional BI tools and highlights the critical need for more flexible and powerful solutions in today's fast-paced business environment. It delves into the key features of modern BI solutions, including interactive visualizations, natural language processing, automated data preparation, multi-source data integration, and collaborative features. The study also investigates the far-reaching effects of these advanced BI tools on various aspects of business operations and strategy, such as operational efficiency, strategic planning, customer-centric approaches, risk mitigation, and innovation. Supported by extensive industry data and research, this analysis demonstrates how these cutting-edge BI solutions are reshaping the way organizations interact with data, make decisions, and compete in an increasingly data-driven marketplace.

Contribution to the assessment of effect distances of atmospheric dispersion: case study

Storage tanks are vital to the oil industry, functioning as essential components in the operation of oil fields. However, their strategic importance is accompanied by significant environmental risks, particularly due to atmospheric dispersion events. These events, characterized by the release and spread of pollutants such as aerosols, gases, and dust into the atmosphere, can stem from both human activities and accidental releases. The consequences are often severe, leading to considerable human, material, and ecological damage. Atmospheric dispersion of pollutants has emerged as a major environmental concern, especially within industries where storage tanks are integral to operations. This concern is magnified by increasingly stringent regulatory frameworks. Industries, particularly those operating within classified facilities subject to environmental protection laws, are now mandated to thoroughly identify, analyze, and assess potential accidental risks associated with their operations. These regulations are designed to mitigate the adverse impacts of such incidents, and this forms the object of this study. In this study, we concentrated on the T-403A/B/C storage spheres at the ALRAR gas complex. Utilizing dynamic consequence modelling with ALOHA software, it was possible to conduct a comprehensive assessment of potential pollutant releases in the processing area. This approach allowed to meticulously map out the hazardous phenomena linked to these scenarios and to develop targeted preventive and protective measures. The findings from this study highlight the critical need for rigorous risk assessments and the implementation of proactive safety strategies. By doing so, the environmental and operational risks associated with storage tanks in the oil industry can be significantly reduced. This research underscores the imperative of integrating advanced modelling techniques and stringent safety protocols to safeguard both the environment and industry operations.

Special audit examination of the coactiva courts of the central bank of Ecuador, period 2013 - 2014

Auditing is a fundamental tool in the management of entities, ensuring transparency and efficiency through adequate controls. This study analyzes the "Special Audit Review of the Coactiva Courts of the Central Bank of Ecuador" conducted by the Superintendency of Banks during the period 2013-2014. The objective is to evaluate the effectiveness of internal controls and administrative processes of the coactiva court. The results reveal significant deficiencies, including a lack of coordination between the areas of the Recovery and Liquidation Department, inconsistencies in the records of insolvency and bankruptcy judgments, and informal management of portfolio recovery. These deficiencies represent a high risk for the bank's operations, affecting approximately 70% of the processes reviewed. A set of practical recommendations are proposed, such as the implementation of an information filing and systematization system, the updating of the lawsuit registry and the creation of an annual operational audit plan. These measures are aligned with international best practices and seek to improve efficiency, effectiveness and transparency in the management of public resources.

Unravelling incipient accidents: a machine learning prediction of incident risks in highway operations

PurposeSafety research has focused on drivers, pedestrians and vehicles, with scarce attention given to highway traffic officers (HTOs). This paper develops a robust prediction model which enables highway safety authorities to predict exclusive incidents occurring on the highway such as incursions and environmental hazards, respond effectively to diverse safety risk incident scenarios and aid in timely safety precautions to minimise HTO incidents.Design/methodology/approachUsing data from a highway incident database, a supervised machine learning method that employs three algorithms [namely Support Vector Machine (SVM), Random Forests (RF) and Naïve Bayes (NB)] was applied, and their performances were comparatively analysed. Three data balancing algorithms were also applied to handle the class imbalance challenge. A five-phase sequential method, which includes (1) data collection, (2) data pre-processing, (3) model selection, (4) data balancing and (5) model evaluation, was implemented.FindingsThe findings indicate that SVM with a polynomial kernel combined with the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is the best model to predict the various incidents, and the Random Under-sampling (RU) algorithm was the most inefficient in improving model accuracy. Weather/visibility, age range and location were the most significant factors in predicting highway incidents.Originality/valueThis is the first study to develop a prediction model for HTOs and utilise an incident database solely dedicated to HTOs to forecast various incident outcomes in highway operations. The prediction model will provide evidence-based information to safety officers to train HTOs on impending risks predicted by the model thereby equipping workers with resilient shocks such as awareness, anticipation and flexibility.

Multi-Criteria Analysis in Circular Economy Principles: Using AHP Model for Risk Assessment in Sustainable Whisky Production

As the whisky industry applies circular economy principles to maximize resource utilization and minimize environmental impact, companies become exposed to several risks, which require complex assessments to ensure reliable outcomes. This study provides an organized framework to identify, prioritize, and rank various risk factors commonly observed in the whisky industry through the development of an analytical hierarchy process (AHP) multi-criteria analysis model. Experts from 18 small European distilleries identified five main risk criteria and nineteen sub-criteria from brainstorming workplace observations and categorized them as: environmental (5), operational (4), technological innovation (3), food safety (3), and economical (4) risks. The analytical hierarchy process (AHP) approach was used to determine the weights and ranks of the main criteria and sub-criteria based on the survey responses received from experts from each distillery. The final judgements are consistent, as indicated by consistency values (CR) of less than 0.1 for all risk criteria. Unlike traditional risk assessment methods, the AHP model effectively integrates qualitative and quantitative data, aiding strategic decision making in the whisky industry by breaking down complex problems into manageable sub-problems. Future research directions may expand the criteria and explore additional sustainable practices.

Evaluation of Operational Safety Risk in Wastewater Treatment Plants Based on WSR and Matter–Element Extension Theory

Wastewater treatment plants are of significant importance in facilitating China’s urbanization process. The fundamental purpose of wastewater treatment plants is to control water pollution and protect the environment. It is therefore essential that they are operated in a safe manner in order to achieve this goal. In order to guarantee a systematic and objective assessment of the operational safety risks inherent to wastewater treatment plants, the Wuli–Shili–Renli (WSR) theory has been introduced. Accordingly, a three-dimensional evaluation index system was developed for the assessment of these risks. A combination of subjective and objective methods utilizing game theory was employed to determine comprehensive risk factor weights. Subsequently, the material–element extension theory model was implemented in a wastewater treatment plant situated in the northern region of the Changchun Development Zone. The assessment classified the plant as Level 1 (safe), although it was determined that improvements in accident prevention are necessary. This study presents a robust framework for operational safety assessment that contributes to enhanced risk management and the long-term reliability of wastewater treatment operations. The results of the sensitivity analyses provide substantial evidence of the reliability and validity of the model.

Evaluation Of Graha Nusantara’s Operational Risk Management Process Using ISO 31000:2018

Graha Nusantara is one of the business units of cooperative x which is engaged in building rental. Since the pandemic in 2020, Graha Nusantara has experienced a decrease in demand for building rentals which has led to a decrease in revenue. In addition, Graha Nusantara has also experienced problems related to its business operations such as canceling events by consumers for various reasons, building maintenance and management, employee management to those related to vendor partners. This causes Graha Nusantara to continue to lose money due to ineffective building operations which can actually be prevented by streamlining Graha Nusantara's operations. This study aims to evaluate operational risk management in the Graha Nusantara business unit, which is a building leasing business, with reference to risk management standards based on ISO 31000. The method used in this research is descriptive qualitative analysis with a case study approach, through interview and documentation data collection techniques. The results showed that risk management has not determined the scope, context and criteria process according to ISO 31000 and nineteen risks with various levels of risk were identified. Therefore, this study recommends handling each risk and it is expected that management establishes a risk management policy, especially operational risk

Dampak Rasio Kecukupan Modal dan Risiko terhadap Profitabilitas pada Bank Konvensional di Indonesia

Purpose: To determine the effect of capital adequacy ratio, market risk, credit risk, liquidity risk, operational risk, bank size, and other variables on banking profitability in Indonesia. Methodology: Panel data regression analysis; purposive sampling; capital adequacy ratio, market risk, credit risk, liquidity risk, operational risk, bank size as instruments; 39 banks listed on Indonesia Stock Exchange; return on assets as dependent variable. Results: Capital adequacy ratio and bank size have a positive and significant influence on profitability. Credit risk and operational risk have a significant negative effect. Market risk and liquidity risk have no significant effect. Limitations: The study is limited to data from banks listed on the Indonesia Stock Exchange between 2018-2024, so it may not reflect conditions outside this period or region. Contribution: This research is useful in the fields of banking management, corporate finance, and investment strategy.

Forecasting disorientation in the tax avoidance map: Tax haven subsidiaries and analyst forecasts

In the context of economic globalization, China has actively encouraged its enterprises to “Go Global” to enhance their core competitiveness. However, in recent years, many companies have been drawn to the unique features of tax havens, establishing subsidiary in these regions to exploit regulatory and institutional arbitrage rather than focusing on genuine production and operations. This study uses panel data from Chinese listed companies from 2007 to 2022 to investigate the impact of tax haven subsidiary layout on analyst forecast accuracy and the underlying mechanisms. Our findings show that tax haven subsidiary layout significantly reduces analyst forecast accuracy. This negative effect is primarily driven by increased information asymmetry and heightened operational risks caused by tax haven subsidiary layout. Moreover, the impact is more pronounced in firms with higher controlling shareholder tunneling, lower media attention, stronger product market competition, and weaker tax enforcement. Our research contributes to a deeper understanding of the adverse effects of tax haven subsidiary layout, offering crucial insights for promoting efficient capital flow and sustainable economic development in emerging markets.

Real-time risk assessment of distribution systems based on Unscented Kalman Filter

The continuous growth of renewable energy and the load level has posed increasingly severe operational risks to distribution systems. In view of this, this paper combines state estimation with risk assessment, and uses the results of distribution system state estimation based on Unscented Kalman Filter as the input of risk assessment. With the combination, the sampling based on probability distributions in traditional risk assessment methods is no longer needed, thus avoiding the difficulty of updating probability distributions timely according to the latest information in real-time operation. By applying the proposed risk assessment method, the real-time assessment of operational risks in perspectives of bus voltage, branch power, and renewable energy utilization is achieved. Meanwhile, the weight of each risk index is properly determined according to both subjective and objective knowledge by using Analytic Hierarchy Process method and entropy weight method. Case studies show that the proposed method achieves effective assessment of comprehensive risks in the operation of distribution system.

Technology-Driven Financial Risk Management: Exploring the Benefits of Machine Learning for Non-Profit Organizations

This study explores how machine learning can optimize financial risk management for non-profit organizations by evaluating various algorithms aimed at mitigating loan default risks. The findings indicate that ensemble learning models, such as random forest and LightGBM, significantly improve prediction accuracy, thereby enabling non-profits to better manage financial risk. In the context of the 2008 subprime mortgage crisis, which underscored the volatility of financial markets, this research assesses a range of risks—credit, operational, liquidity, and market risks—while exploring both traditional machine learning and advanced ensemble techniques, with a particular focus on stacking fusion to enhance model performance. Emphasizing the importance of privacy and adaptive methods, this study advocates for interdisciplinary approaches to overcome limitations such as stress testing, data analysis rule formulation, and regulatory collaboration. The research underscores machine learning’s crucial role in financial risk control and calls on regulatory authorities to reassess existing frameworks to accommodate evolving risks. Additionally, it highlights the need for accurate data type identification and the potential for machine learning to strengthen financial risk management amid uncertainty, promoting interdisciplinary efforts that address broader issues like environmental sustainability and economic development.

Predictive Analytics for Pilot Training in Southern Africa

This paper aims to enhance aviation safety by identifying and addressing pilot performance weaknesses through data-driven techniques, focusing on the strategic adoption of predictive analytics in pilot training across Southern Africa, particularly in South Africa, Namibia, and Botswana. The main objective is to utilize advanced technologies like Natural Language Processing (NLP) and machine learning (ML) to analyze aviation incident reports and identify patterns of pilot errors and operational risks. The study's results highlight vital insights, which pave the way for tailored training programs designed to mitigate risks. The achievements of the study include filling a non-empirical gap by applying the Diffusion of Innovations (DOI) framework to examine the adoption of predictive analytics alongside recommendations for standardized reporting, specialized training modules, and the integration of weather analytics. These outcomes demonstrate the transformative potential of predictive analytics in improving pilot training and enhancing safety in the Southern African aviation sector.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

Risk Management and Software Solutions in Financial Institutions: Move Towards Effective Implementation

Technological advancements and intensified regulatory pressures have led to a new level of the evolution of risk management in financial institutions. The entities have exposure to different categories of risks, such as credit risks, market risks, operational risks, and cybersecurity risks. The growing intensity of these factors makes proper risk management crucial for the stabilization and success of such enterprises. This report analyzes the best practices and software applications adopted by financial institutions for effective risk management. Improved risk identification, assessment, mitigation, and reporting within the organization would be through the integration of advanced analytics, AI, ML, and cloud computing. Based on this, the current paper assesses the part that these technologies play in contributing to compliance, cost savings, and better decision-making skills.