Operational Risk Research Articles

Reliability-aware techno-economic assessment of floating solar power systems

Floating solar photovoltaic systems (FPV) have emerged as a promising technology to harness solar energy on water surfaces. With its numerous benefits, including increased land availability, reduced water evaporation, and improved system cooling, FPV systems hold great potential for sustainable energy generation. However, due to its unique installation and operation in water bodies, the management of aging becomes a critical factor to ensure long-term success. Consequently, reliability analysis plays a pivotal role in predicting and mitigating operational risks and estimating the economic feasibility of FPV projects. In this context, this paper presents a reliability-aware techno-economic assessment approach of FPV systems. The approach is tested with a case study in India, and the results are compared with ground-based photovoltaic (GPV) systems. Here, different failure and repair strategies are taken into account to determine the lifetime performance. Results showed that even though FPV system has higher failure rate, considering standard maintenance, the energy generated by the FPV system is 5.38% higher than similar GPV system. The cost of electricity by the PV system depends on the repair and maintenance. For normal maintenance the levelized cost of electricity (LCOE) for FPV system is calculated as 0.0551 $/kWh which is comparable to the LCOE by GPV systems, while for reduced repair actions, the LCOE of the FPV is higher than the LCOE of the GPV system.

Chloride-Salt Separation Type Nanofiltration Membranes for Efficient Crude Salt Refinement

Application of unrefined crude salt not only leads to a serious equipment scaling and a low product quality, but also increases operational risks and poses health hazards. Therefore, it is essential to refine crude salt prior to its use. However, traditional methods of crude salt refinement are energy-intensive and probably produce additional chemical by-products. In this work, membrane technology was used to accomplish the refinement of crude salt due to its unique characteristics, such as environmentally friendly and lower energy consumption. To improve the separation performance, the membranes were fabricated by simply adjusting the aqueous monomer concentration. For single-salt feed, the membrane (M10) prepared with a high ratio of 10 exhibited excellent salt rejection due to the formation of a thicker and denser PA layer. And it owned a superior MgCl2/NaCl selectivity of ∼22.0, demonstrating that the membrane can achieve outstanding selectivity for mono-/divalent ions. The selectivity of Mg2+/Na+ increased to ∼23.5 for bi-salt feed and further improved to 66.2 for multi-salt feed due to the stronger charge shielding effect. Most importantly, the membrane was also successfully applied in the refinement of crude salt. When using crude salt (NaCl 30 g/L, purity 0.90) as feed, the M10 exhibited both excellent retention of bivalent ions and permeation of univalent ions, resulting in the corresponding Mg2+/Na+ selectivity up to ∼251.6 and the tremendous enhancement in the Na+ purity of ∼0.997. This work offers a feasible strategy for crude salt refinement, and could expand to some other potential applications such as resource utilization in the salinization industry and zero-liquid discharge of industrial wastewater.

Pengaruh Faktor Pengungkapan Corporate Social Responsibility

Research objective: To empirically test the influence of business risk, financial risk and operational risk on corporate social responsibility. The research object is gas, oil and coal sub-sector companies listed on the Indonesian stock exchange. The total sample was 17 companies for 5 years. Hypothesis testing uses the F test and t test. The results of the F test show that simultaneously business risk factors, financial risk factors and operational risk factors influence corporate social responsibility. Meanwhile, the results of the hypothesis using tests show that each factor of business risk, financial risk and operational risk has a partial effect on corporate social responsibility.

Strategies for Mitigating and Managing Risks in Bank Investments

This paper suggests that while studies of banking risk management often emphasize analytics and stress testing to improve risk governance, there is too little emphasis on historical case studies to generate comprehensive risk management strategies. Using numerous case studies, the paper suggests four categories of risk - market risks, credit risks, regulatory risks, and operational risks - that impact overall investment outcomes. By extensively reviewing both historic failures and historic successes, the paper highlights the importance of understanding different types of risks and adopting strategic measures to manage them effectively. Emphasis is placed on the necessity of a diversified investment portfolio to mitigate market volatility and credit exposure. Additionally, the paper provides actionable strategies for navigating regulatory complexities and operational challenges, supported by real-world case studies and practical examples. This research aims to equip investors with valuable insights and tools to optimize investment opportunities while minimizing potential risks in the banking sector.

Analisis Efisiensi Operasional, Manajemen Risiko, dan Pengelolaan Sumber Daya terhadap Keberlanjutan Kinerja Keuangan Perusahaan di Indonesia

This study investigates the effects of Operational Efficiency, Risk Management, and Resource Management on the sustainability of corporate financial performance in Indonesian companies. A quantitative research approach was employed, using data from 170 companies across various industries. The data was analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). The findings reveal that all three variables have a significant positive impact on financial sustainability. Operational Efficiency emerged as the most influential factor, followed by Risk Management and Resource Management. These results highlight the importance of an integrated approach to corporate strategy, focusing on improving operational processes, mitigating risks, and optimizing resource allocation to achieve long-term financial stability. The study offers practical insights for managers and policymakers on how to enhance corporate financial sustainability in Indonesia.

Harnessing Large Language Models in Banking: Banking Innovation with Operational and Security Risks

Harnessing Large Language Models in Banking: Banking Innovation with Operational and Security Risks

Enhancing Organizational Resilience through Digital Innovation in Manufacturing

This study explores the role of digital innovation in strengthening organizational resilience within the manufacturing sector. By analyzing the integration of advanced technologies such as AI, IoT, and automation, the research investigates how these innovations improve operational flexibility, risk management, and adaptability to disruptions. A qualitative approach, including interviews with industry leaders, is employed to assess the impact. Findings demonstrate that digital innovation significantly enhances resilience, allowing manufacturers to respond efficiently to market volatility and operational challenges. The study concludes with recommendations for strategic implementation of digital tools.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

The Impact of Supply Chain Network Centrality on Sustainable Mergers and Acquisitions: Evidence from China

Mergers and acquisitions (M&As) are key drivers for resource integration and the operational efficiency of enterprises. Companies that hold central positions within supply chains may leverage their strategic location to reduce information asymmetry, enhancing their ability to engage in sustainable activities. However, research on the role of supply chain network centrality in M&A decisions remains underexplored. This study aims to empirically examine whether and how centrality in supply chain networks enhances the likelihood and success of M&A activities, contributing to both theory and practice in corporate strategy. In particular, we construct a novel measure of firm centrality within the supply chain and utilize panel data from Chinese A-share listed companies spanning 2008 to 2021. Our findings reveal that the central position of supply chain networks promotes the probability and frequency of mergers and acquisitions. Mechanism analysis reveals that gaining information advantages and relieving financial constraints are two key channels through which the supply chain network promotes mergers and acquisitions. Furthermore, the effects are more pronounced for firms with non-state ownership, with closer proximity to customers or suppliers, with higher operational risk, and with growth and decline phases. A series of robustness tests support these results, including alternative measures, alternative estimating methods, and sub-sample tests. Moreover, central supply chain companies exhibit better long-term financial performance following mergers and acquisitions. This paper enriches our understanding of the roles of supply chain networks in firms’ mergers and acquisitions and holds important practical implications for companies seeking to achieve sustainable and long-term development.

Evaluating the impact of AI and blockchain on credit risk mitigation: A predictive analytic approach using machine learning

In recent years, the integration of artificial intelligence (AI), blockchain technology, and machine learning has transformed credit risk mitigation strategies in the financial industry. This paper explores the practical applications of these technologies in identifying, assessing, and managing credit risk, with a specific focus on predictive analytics and decentralized frameworks. Through a comprehensive literature review and case studies, the research demonstrates how AI-driven algorithms, blockchain's transparent and immutable ledger systems, and machine learning models have enhanced the precision and efficiency of credit risk evaluations. Additionally, the study investigates how these innovations are being adopted by financial institutions to create more accurate credit scoring systems, reduce fraud, and optimize operational risk management. While these technologies hold great promise, challenges such as data privacy, regulatory compliance, and implementation costs remain significant barriers. The paper concludes with recommendations for overcoming these challenges and maximizing the potential of AI, blockchain, and machine learning in credit risk mitigation.

Impact of outward guarantees on labour employment in enterprises

ABSTRACT Outward guarantees serve as a crucial mechanism for enterprises to uphold supply chain stability and foster financial interconnectedness. Amidst endeavours to advance high-quality, comprehensive employment and amidst the frequent occurrence of enterprise guarantee-related risks, this study employs data from Chinese A-share listed companies spanning from 2012 to 2021 to explore the influence of enterprise outward guarantee conduct on labour employment. The investigation reveals that such behaviour inhibits labour employment, a trend robust across various tests and treatments for endogeneity. Mechanism analysis indicates that enterprise outward guarantee conduct primarily curtails labour demand by heightening financing constraints and escalating operational risks. Heterogeneity analysis underscores that the dampening effect of outward guarantees on enterprise labour employment is particularly pronounced in the realms of large-scale enterprises, those reliant on labour, non-state-owned entities, and enterprises situated in central regions with abundant labour supply. These research findings offer valuable policy insights for averting enterprise external guarantee risks, thereby fostering stability in financial and employment domains.

Supply chain risk assessment using best worst method: a case study of agro-industry skipjack tuna in Ambon - Indonesia

One of the main issues in the agro-industry supply chain is identifying and ranking different risk factors to maintain business continuity. This study discusses the agro-food supply chain risk assessment for smoked skipjack tuna in Ambon. Related literature and expert interviews identify risk factors that arise along the supply chain. These risk factors are contained in internal risk, company external operational risk, and macro-level risk. This research aims to evaluate comprehensively the risk factors of the smoked skipjack agro-industry supply chain, which are a priority to be addressed. The decision-making framework uses the BWM (Best Worst Method) to determine each risk factor's relative weight, followed by a sensitivity analysis to determine how robust the outcome is. Experts pick the risk factor assessment, and then an optimization model is modelled to obtain the weight of each risk factor, which is calculated with the help of Lingo software. The findings show that three risk factors will be prioritized to be addressed out of the eleven risk factors assessed, namely "Quality of the final product," "Financial instability," and "degradation of fish populations." Sensitivity analysis was also carried out to see the overall robustness of the results achieved. The weight of the selected risk factor ("final product quality (R1c)") has its weight value changed from 0.1 to 0.9 with an increase of 0.1. These findings are expected to help smoked skipjack tuna agro-industry managers make decisions to reduce supply chain risks and better administration management to maintain the sustainability of their business processes.

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.

Analysis of TEF Operational Risk Management for Trains and Underground Stations of MRT Jakarta

Analysis of TEF Operational Risk Management for Trains and Underground Stations of MRT Jakarta

Optimizing sustainable multiple biomass-to-biofuel conversion network with integrated water resource management utilizing data-driven robust planning

Until now, the biofuel supply chain has lacked an integrated approach to address its fundamental challenges in real-world implementation. The first challenge is maintaining a continuous supply flow in the biofuel network, which can significantly impact economic outcomes and performance measures. The second challenge involves managing water resources for biofuel production to preserve freshwater reserves. The third challenge is managing operational risks, such as uncertainty in biofuel demand, which leads to conservative planning and underscores the need for realistic uncertain programming approaches. To address these challenges, the proposed study designs a biofuel conversion network that utilizes third-generation feedstocks and non-edible seed sources, sourcing necessary water from wastewater resources. This practical solution addresses water-energy relationship concerns, reducing dependency on limited freshwater supplies. To achieve a continuous and cost-effective feed supply, this study incorporates a pre-processing phase using a hybrid Multi-Criteria Decision-Making model. This approach ensures the feed supply remains consistent, economical, and sustainable. To manage the uncertainty in biofuel demand, we developed a robust optimization model with an intersected uncertainty set using a data-driven approach. This approach aims to reduce conservatism in the obtained solutions, making the model more practical for real-world implementation. The numerical results reveal some practical implementation for policymakers. The proposed robust optimization using the configured uncertainty set offers a tool to manage the conservatism of renewable energy demand uncertainty and operational risks more effectively than traditional approaches (0.914%). This formulation can help policymakers to maintain greater robustness against fluctuations in confidence levels. Additionally, the simultaneous use of third-generation and non-edible feedstocks creates a multi-biomass-to-biofuel conversion network, offering practitioners a practical solution that significantly reduces reliance on freshwater resources. Furthermore, policymakers can leverage the proposed pre-processing phase to promote economic and continuous supply feed based on geographical, resource accessibility, and infrastructure criteria.

Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.

The Impact of ESG Criteria on Firm Value: A Strategic Analysis of the Airline Industry

Environmental, social, and governance (ESG) factors are crucial in evaluating a company’s value. High ESG scores reflect ethical practices, social responsibility, and effective governance. This paper examines the impact of ESG criteria on firm value within the airline industry, focusing on their influence on operational efficiency, risk reduction, and financial performance. Using panel data analysis, the study evaluates ESG scores from 32 airline companies over the period of 2018–2023, with an explanatory power of 36.5%. The research explores how integrating environmental, social, and governance factors into strategic management can foster sustainable competitive advantage. It focuses on utilizing internal resources, meeting the needs of various interested parties, and balancing financial, social, and environmental performance. The findings indicate that while ESG practices enhance firm value through improved efficiency and risk management, they do not always lead to higher short-term firm value. Moreover, the study underscores the significance of governance in the airline industry, where robust governance structures can mitigate risks but may also increase costs. This research contributes to the literature by providing empirical evidence of the link between ESG performance and firm value in the airline industry, emphasizing the importance of integrating ESG principles into strategic management for long-term sustainability and financial success.

Impact of climate change risks on equity capital: Evidence-based on Chinese markets

The economic consequences of climate change at the enterprise level have received considerable attention. This study examines the positive effects of climate change risk on equity capital costs, which are realized through firms' operational risk, financial constraints, and the government's service environment. A higher corporate ESG rating enhances the positive effect of climate change risk on equity capital costs. We also find that the Paris Agreement has a benign policy effect on equity capital costs. Finally, expanding upon this groundwork, a thorough heterogeneity analysis has been conducted, encompassing three distinct dimensions—enterprise risk system, industry environmental attributes, and operational ecosystem.

Risk Management and Performance of Deposit Money Banks in Nigeria

This study examined the relationship between risk management and performance of deposit money banks in Nigeria as moderated by environmental turbulence. The study adopted the cross-sectional research survey design. Primary data was generated through structured questionnaire while the population of study was the 24 deposit money banks in Nigeria. The entire population of 24 deposit money banks were adopted as a census. However, the number of respondents/ participants in the study was one hundred and twenty senior level managers of the 24 Deposit Money Banks in Nigeria. The reliability of the instrument was achieved by the use of the Cronbach Alpha coefficient with all the items scoring above 0.70. The hypotheses were tested using the Spearman’s Rank Order Correlation Coefficient. The tests were carried out at a 0.05 significance level. Findings revealed that there is a significant relationship between risk management and performance of deposit money banks in Nigeria. Therefore, the study concludes that the adoption of risk management practice positively enhances the performance of deposit money banks in Nigeria. This implies that by adopting a proactive approach to risk management, deposit money banks can identify, assess, and mitigate potential risks that could adversely impact their operations. Hence, the study recommends that deposit money banks should conduct regular and thorough risk assessments to identify potential risks that could impact the bank's operations and performance. This includes assessing credit, market, liquidity, operational, and other relevant risks. Understanding the risk landscape is crucial for developing effective risk management strategies.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.