Risk Management: A New Project Management Perspective

The current economy has placed pressure on private and public organizations to become more efficient. Increasing requirements and expectations and decreasing resources are making it difficult for organizations to deliver their services on-time, on-budget, with high customer satisfaction. This has caused both professionals and academics to search for ways to minimize waste throughout the entire supply chain. Buyer/Supplier interaction has been one of the major areas of focus to improve the performance and efficiency of service delivery. Buyer/Supplier interaction includes such activities as: selection of suppliers, development of a contracts, and management of services. In 2008, the International Council for Building (CIB), a professional construction industry group performed (Egbu et al. 2008) a worldwide study identifying innovative construction methods that utilized performance metrics to increase project performance and efficiency. The study filtered through more than 15 million articles and reviewed more than 4,500 articles. In the end, the study identified only one system that had documentation showing it could consistently improve project performance. The system was a buyer/supplier interaction model called: Performance Information Procurement System / Performance Information Risk Management System (PIPS/PIRMS). Since 2008, the PIPS/PIRMS system has gained worldwide attention, due to its ability improve construction performance. The purpose of this research was to take an in-depth look into the PIPS/PIRMS system and identify factors that allow this model to improve performance of construction services that traditional methods do not use. Research was also performed to identify if the factors could be implemented into any Buyer/Supplier interaction model to improve service efficiency and performance. Through an in-depth literature research on buyer/supplier interaction models a comparison was made and eight factors were discovered that made the PIPS/PIRMS model unique from other buyer/supplier interaction models. Practitioners were surveyed to validate the uniqueness of the eight factors and predict the potential impact the factors could have on the performance and efficiency of delivered services. The final stage of the research involved conducting case study research with five clients implementing the eight factors on thirty-one different services, ranging from dining services to mental health services. The research results discovered the factors were able to decrease cost, increase value, and improve customer satisfaction of the services. The research indicated that improving performance and efficiency was a resource alignment issue rather than a technical issue.

Effective risk management is crucial for the success of oil projects, particularly in volatile markets like Pakistan. This paper examines the key organizational capabilities and external factors that influence risk management practices and project performance in the Pakistani oil industry. Through a comprehensive literature review and analysis of industry reports, the study identifies critical risk factors, effective risk management strategies and the role of organizational competencies in navigating these challenges. The findings suggest that a proactive, data-driven approach to risk management combined with strong project management capabilities and stakeholder engagement can significantly improve project outcomes. The research provides valuable insights for oil companies operating in Pakistan offering a framework to enhance their risk management practices and increase the likelihood of successful project delivery. The oil industry is inherently complex and fraught with various risks ranging from political and economic instability to technological constraints and regulatory hurdles. Specific to the Pakistani context, the sector faces a unique set of challenges that can significantly impact project performance. Analyzing the interplay between organizational capabilities and external factors is crucial for developing effective risk management strategies to navigate these turbulent waters. This research aims to comprehensively examine the elements influencing project performance within the Pakistani oil sector. The study will explore how various risk management practices including risk identification, assessment, mitigation, and monitoring, might influence project results. The research will also examine how organizational competencies such as skilled personnel, risk management resources, and governance frameworks, can contribute to the successful delivery of oil projects in Pakistan. Pakistan's oil industry is subject to a range of context-specific challenges that can profoundly impact project implementation and performance. Effectively managing these risks is vital for oil companies operating in the country as inadequate risk management can compromise project safety, environmental protection and overall profitability.

A Primer in Prognostic and Predictive Models: Development and Validation of Neutropenia Risk Models

Risk management is one of the eternal themes of the commercial banks. Face unprecedented and increasingly complex financial risks, bankers are increasingly aware that the tools based data which analyze number of traditional risk management is not comprehensive and flexible. To effectively prevent and resolve commercial bank operating risks, enhance the core competitiveness of commercial banks and achieve sustainable development, it is necessary to develop financial engineering as an opportunity for the traditional risk management tools to conduct a holistic deep integration.

The global Internet is rich with resources that provide in-depth information about all aspects of the risk management process. Many theories and practices are shared that can be helpful in analyzing and resolving both pure and speculative uncertainties. Lessons learned can be tailored to both businesses and public sector organizations. In addition to convenience, cybersurfing the Web delivers many viewpoints about risk evaluation criteria and risk management program strategies. Postings include examples of case studies and risk management policy statements. Some sites focus on risk model development, risk audit methodology, risk financing strategies, and risk vulnerability assessment. Techniques are given for integrating risk control into project plans. The author provides an annotated list of Web sites selected for review because they serve as examples of targeted portals and scholarly routes to best practices for business-related risk management.

The general objective of this research was to assess the risk management process and project performance. The descriptive research design with a mixed qualitative and quantitative approach was used to a sample of 118 respondents selected from 168 target population using stratified sampling technique and Sloven’s formula. SPSS Statistical software was used in the analysis of the collected data into descriptive statistics by the help of mean and standard deviation. The inferential statistics were also analyzed as regression and correlation analysis. The results of this research have shown that project risk identification and performance of Mpazi channel construction project has a highly positive and significant relationship, with a coefficient of correlation of 0.970 and sig=.000, which is less than 0.05 level significance. The correlation results have also revealed r=0.979 and sig=0.00 which is less than 0.05 level of significance, hence proving a highly positive and significant relationship between project risk management strategy and performance of Mpazi channel construction project. The correlation results have also shown an r =0.985 and sig=0.00 which is less than 0.05 proving significant and positive relationship between project risk plan response and Mpazi Channel construction project performance. The research also found that a combination of project risk identification, risk management plan and risk plan response contributed to 97.5 percent (R2= 0.975) of the Mpazi Channel construction project success. Thus, the researcher concluded that the project risk management procedure has an impact on the performance of the Mpazi Channel construction project. Basing on the findings of the study, researcher recommends that a formal and planned project risk management procedure should be implemented throughout project planning to key construction personnel, support staff, and laborers. Keywords: Project Risk Management Process, Project Performance, Construction, Rwanda

Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.

Introduction/BackgroundThe European guideline for endometrial cancer (EC) advises lymph node staging for patients with high-intermediate-risk/high-risk disease. The ENDORISK-model can estimate risk of lymph node metastases (LNM) more accurately by combining...

Background: Stomach adenocarcinoma (STAD) is the fourth highest cause of cancer mortality worldwide. Alterations in copper metabolism are closely linked to cancer genesis and progression. We aim to identify the prognostic value of copper metabolism-related genes (CMRGs) in STAD and the characteristic of the tumor immune microenvironment (TIME) of the CMRG risk model. Methods: CMRGs were investigated in the STAD cohort from The Cancer Genome Atlas (TCGA) database. Then, the hub CMRGs were screened out with LASSO Cox regression, followed by the establishment of a risk model and validated by GSE84437 from the Expression Omnibus (GEO) database. The hub CMRGs were then utilized to create a nomogram. TMB (tumor mutation burden) and immune cell infiltration were investigated. To validate CMRGs in immunotherapy response prediction, immunophenoscore (IPS) and IMvigor210 cohort were employed. Finally, data from single-cell RNA sequencing (scRNA-seq) was utilized to depict the properties of the hub CMRGs. Results: There were 75 differentially expressed CMRGs identified, 6 of which were linked with OS. 5 hub CMRGs were selected by LASSO regression, followed by construction of the CMRG risk model. High-risk patients had a shorter life expectancy than those low-risk. The risk score independently predicted STAD survival through univariate and multivariate Cox regression analyses, with ROC calculation generating the highest results. This risk model was linked to immunocyte infiltration and showed a good prediction performance for STAD patients' survival. Furthermore, the high-risk group had lower TMB and somatic mutation counters and higher TIDE scores, but the low-risk group had greater IPS-PD-1 and IPS-CTLA4 immunotherapy prediction, indicating a higher immune checkpoint inhibitors (ICIs) response, which was corroborated by the IMvigor210 cohort. Furthermore, those with low and high risk showed differential susceptibility to anticancer drugs. Based on CMRGs, two subclusters were identified. Cluster 2 patients had superior clinical results. Finally, the copper metabolism-related TIME of STAD was concentrated in endothelium, fibroblasts, and macrophages. Conclusion: CMRG is a promising biomarker of prognosis for patients with STAD and can be used as a guide for immunotherapy.

Risk management aims at managing all the uncertainties that may interfere with the objectives and missions of the organization. Resilience engineering aims at building its capacity to get over disturbances or stress while keeping the functionalities needed to survive, and possibly thrive. A recently open debate on an Internet blog launched by the risk managers of the Scottish Widows Bank seems to arise from what some professionals see as two competing branches of the management sciences. Whereas through the development of ERM – Enterprise-wide Risk Management – risk management is emerging at last to become a science, as well as an art and a practice, the mentioned above centered on the role of a newly forged name “resilience management”. This opens a new front of the many debates that could derail the path to maturity of Risk Management as a science and reopen new silos much as Business Impact Analysis, BIA, or continuity management, might do if a clear distinction is not made between science, objectives and tools. However, because organizations are so interconnected today in the supply cloud that it is inevitable that they will face catastrophic risk and this is why resilience needs to be a core objective of any risk management plan? Whereas traditional risk management techniques alone may not be adequate to deal with such pervasive and insipient risk scenarios, resilience is ingrained into ERM.

In general, individuals will be interested in consumption of goods with “original” prices denominated in various currencies. Traditional risk management is nominally oriented and typically neglects this differentiated consumption preferences of investors. We outline the relevance of a consumption oriented “real” risk management and sketch resulting problems connected with such an approach.

In marine and coastal environments, commercial shipping ballast water is considered to be the most relevant transfer vector of invasions world wide. There are many uncertainties in the management of invasive species. Consideration of the uncertainties is crucial for the determination of the most appropriate management strategies to address this highly variable problem. The traditional risk management has many limitations by administrative complexness and important uncertainties. In order to make risk management more effective, this paper discuss that how to apply the precautionary principle in the risk management of invasive species.

Financial institutions face dynamic threats that range from credit defaults to money laundering. Traditional risk management models struggle with large data and shifting market conditions. Artificial intelligence (AI) offers flexible, data-driven methods that enhance credit scoring, fraud detection, market analytics, and compliance. By leveraging supervised, unsupervised, and reinforcement learning, AI-based solutions identify patterns, adapt to emerging threats, and automate complex processes. However, these approaches require robust governance to maintain fairness, interpretability, and security. This paper surveys AI's applications in financial risk, discusses challenges in deployment, and highlights future directions, including explainable AI, federated learning, and integrated RegTech solutions, requiring oversight.

Several models have been developed to predict the risk of atrial fibrillation (AF) recurrence after radiofrequency catheter ablation (RFCA). However, these models are of poor quality from the start. We, therefore, aimed to develop and validate a predictive model for post-operative recurrence of AF. In a study including 433 patients undergoing the first circumferential pulmonary vein isolation (CPVI) procedure, independent predictors of AF recurrence were retrospectively identified. Using the Cox regression of designated variables, a risk model was developed in a random sample of 70% of the patients (development cohort) and validated in the remaining (validation cohort) 30%. The accuracy and discriminative power of the predictive models were evaluated in both cohorts. During the established 12 months follow-up, 134 patients (31%) recurred. Six variables were identified in the model including age, coronary artery disease (CAD), heart failure (HF), hypertension, transient ischemic attack (TIA) or cerebrovascular accident (CVA), and left atrial diameter (LAD). The model showed good discriminative power in the development cohort, with an AUC of 0.77 (95% confidence interval [CI], 0.69-0.86). Furthermore, the model shows good agreement between actual and predicted probabilities in the calibration curve. The above results were confirmed in the validation cohort. Meanwhile, decision curve analysis (DCA) for this model also demonstrates the advantages of clinical application. A simple risk model to predict AF recurrence after ablation was developed and validated, showing good discriminative power and calibration.

This study examines the application of artificial intelligence (AI) in enhancing risk management within financial services. Through comprehensive analysis, the research reveals that AI technologies, particularly machine learning, and deep learning models, significantly improve the accuracy and efficiency of risk assessment and management processes. AI-powered credit risk models demonstrate a 20% increase in predictive accuracy compared to traditional methods, while market risk management sees a 30% improvement in anomaly detection speed and precision. The study also highlights a 60% reduction in false positives for fraud detection and a 40% increase in accurate favorable rates. Despite these advancements, challenges persist, primarily in data quality and model interpretability. The research projects that by 2028, AI will be integral to risk management in over 80% of large financial institutions, potentially reducing risk-related losses by 25% and improving operational efficiency by 35%. The study concludes by emphasizing the need for strategic implementation and responsible AI use, outlining future research directions, including the long-term impact on systemic risk, ethical implications, and the potential of quantum machine learning in risk modeling. Keywords: Artificial Intelligence, Financial Risk Management, Machine Learning, Regulatory Compliance.