Risks in Public Private Procurement mass housing project (PPP-MHP) initiatives are emerging and this requires early risk identification and allocation to achieve the goal and sustenance of the scheme. The study, being a follow-up of a Delphi survey, elicits the opinion of respondents on the probability of occurrence and severity of identified risks in PPP-MHPs in Nigeria. The study adopts a quantitative research design approach by administering structure questionnaire survey on identified PPP-MHPs partners in Abuja, Nigeria. Data analysis was performed using descriptive and inferential statistical tools such as Mean item score (MIS), standard deviation, and Kruskal Wallis analytical techniques with the aid of SPSS software packages. The findings show that all the listed risk factors were found to be extremely high, very high, high, or moderate in terms of occurrence while all the listed risk factors recorded a very high level of severity on the delivery of PPP-MHPs. The top ten (10) risk factors frequently associated with PPP-MHPs are non-availability of finance, high finance cost, non-involvement of the host community, poor execution of housing policies, corruption and lack of respect for law, wrong perception of housing need by low-income earners, Illegal title to land, land acquisition and site availability, level of demand for the mass housing projects and unstable value of local currency. The respondents differs significantly on 29 risk factors in terms of occurrence and 40 risk factors in term of severity. The study, therefore, recommends that risk management culture should be highly encouraged among the PPP Partners in the sector. The study intends to enumerate the rate of occurrence of some itemized risk factors and their severities on the delivery of PPP – procured mass housing projects in Nigeria and the need to bookmark these risk factors in ensuring the sustainability of the PPP mass housing scheme.

This review aimed to analyze some problems and challenges that emerged from the COVID-19 pandemic since it has affected all global business sectors. During the pandemic, remote work was increased in response to social distance requirements to decrease the transmission of disease. In addition, increased delegation, teamwork, supply chains, sales activities, and business operations have all been disrupted. Many challenges have emerged in the management of organizations due to the pandemic including the lack of direct contact as the lockdown made many people work from home and placed restrictions on movement and travel and uncertainty about the future. The healthcare system was also affected by the COVID-19 pandemic and faced several difficulties including increased demand for medical supplies and personal protective equipment, a greater need for nurses and other skilled healthcare professionals, and increased pressure on healthcare facilities, emergency services, and critical care departments. These challenges have created some opportunities in the management systems of healthcare organizations and other sectors. It is recommended for leaders prepare and continuously work proactively to be ready for unexpected future crises. Risk management and planning for any unexpected situation are among the very important aspects of organizational management.

Background: Building Information Modelling (BIM) improves safety planning in construction by enabling visualization and simulation to identify and reduce risks. However, its adoption in Nigeria is limited. This study examines the application of BIM in enhancing safety and environmental performance on construction sites in Nigeria. Methodology: A quantitative cross-sectional survey was conducted using a structured online questionnaire distributed to professionals in Nigeria’s construction industry. A purposive sampling method was employed to target respondents with relevant BIM experience. Data were analysed using SPSS version 28, applying descriptive statistics, chi-square tests, and logistic regression at a 5% significance level. Result: Findings show that BIM was fully adopted by 7.0% of organizations, with only 19.8% of respondents using it to identify safety hazards during planning. While 76.8% reported no notable safety benefit, 19.5% identified improved risk management as the key benefit. Most respondents (80.2%) reported no noticeable environmental benefits. Among those who did, improved energy efficiency was the most cited benefit (16.4%). Respondents with 10 or more years of experience were significantly more likely to report enhanced safety and environmental outcomes (AOR = 4.555; p = 0.003) and adequate BIM utilization (AOR = 3.255; p = 0.023). Those with intermediate BIM experience were also more likely to report high enhancement (AOR = 2.857; p = 0.039) and effective tool use (AOR = 2.881; p = 0.050). Conclusion: This study revealed that BIM has the potential to improve construction outcomes in Nigeria if supported by training, experience, and structured implementation.

Pharmaceutical supply chains are critical to ensuring the availability of safe and effective medications, yet they face numerous challenges that can jeopardize public health. This article provides a comprehensive analysis of the key issues impacting pharmaceutical supply chains, including regulatory compliance, demand forecasting, supply chain visibility, quality assurance, and geopolitical risks. Regulatory compliance remains a significant concern due to the stringent guidelines imposed by authorities such as the FDA and EMA, which can lead to increased operational costs and time delays. Additionally, traditional demand forecasting methods often fail to accurately predict fluctuations in drug demand, resulting in stockouts or excess inventory. Limited supply chain visibility further complicates these challenges, hindering timely decision-making and operational efficiency. Quality assurance is paramount, as maintaining the integrity of pharmaceutical products throughout the supply chain is crucial to preventing costly recalls and ensuring patient safety. Moreover, the globalization of supply chains introduces vulnerabilities to geopolitical risks, trade disputes, and natural disasters. In response to these issues, this article outlines strategic recommendations for optimizing pharmaceutical supply chains. These include leveraging advanced analytics and IoT technologies to enhance demand forecasting and visibility, strengthening compliance through automated systems and training, fostering collaboration among stakeholders, implementing robust risk management frameworks, and investing in quality management systems. By adopting these strategies, pharmaceutical companies can enhance the efficiency and resilience of their supply chains, ultimately ensuring the continuous availability of essential medications for patients worldwide. This analysis serves as a critical resource for industry professionals seeking to navigate the complexities of pharmaceutical supply chains in an increasingly dynamic global environment.

The management of supply chains is increasingly complex. This study provides a comparative analysis of the cost-benefit analysis for managing various risks. It identifies the financial implications of leveraging artificial intelligence in supply chains to better address risk. Empirical results show a business case for managing some sources of risk more proactively facilitated through predictive modeling techniques offered by AI. Across investigation streams, the use of AI results in an average total cost saving ranging from 41,254 to 4,099,617. Findings from our research can be used to inform managers and theorists about the implications of integrating AI technologies to manage risks in the supply chain. Our work also highlights areas for future research. Given the growing interest in studying sub-second forecasting, our research could be a point of departure for future investigations aimed at considering the impact of forecasting horizons such as an intra-day basis. We formulate a conceptual framework that considers how and to what extent performance evaluation metrics vary according to differences in the fidelity of predictive models and factor importance for identifying risks. We also utilize a mixed-method approach to demonstrate the applicability of our ideas in practice. Our results illustrate the financial implications of integrating AI predictive tools with business processes. Results suggest that real-world companies can circumvent inefficiencies associated with trying to manage many classes of risk via the use of AI-enhanced predictive analytics. As managers need to justify investment to top management, our work supports decision-making by providing a means of conducting a trade-off analysis at the tactical level.

The pharmaceutical industry is fundamental to global healthcare, providing essential medicines that improve health outcomes and quality of life. However, the demand and supply dynamics within this sector are highly complex, shaped by various factors including demographic changes, evolving disease burdens, technological advancements, regulatory challenges, and economic pressures. This manuscript explores the intricate relationship between pharmaceutical medicine demand and supply, focusing on key strategies that can help companies effectively navigate these challenges. The demand for pharmaceutical products is driven by several factors, such as population growth, the aging population, the rise of chronic diseases, and the emergence of new health threats. Additionally, healthcare accessibility, affordability, and policy changes significantly impact the consumption of medicines, while innovations in medical technologies and therapies create new treatment needs. On the supply side, pharmaceutical companies face challenges related to manufacturing capacity, raw material availability, distribution logistics, and compliance with ever-evolving global regulatory frameworks. To address these challenges, the manuscript discusses strategic approaches to managing both demand and supply in the pharmaceutical sector. Key strategies include advanced demand forecasting through data analytics, optimizing supply chains for efficiency and resilience, implementing just-in-time inventory models, and investing in flexible manufacturing systems. Furthermore, global collaboration and partnerships, as well as effective risk management practices, are highlighted as essential to ensuring the availability of medicines, particularly in times of crisis or global health emergencies. This manuscript also delves into the role of policy advocacy and regulatory harmonization in stabilizing the pharmaceutical market, ensuring that medicines are accessible to all populations. In conclusion, the pharmaceutical industry must continually adapt to meet the evolving challenges of demand and supply, embracing innovation and collaboration while maintaining a focus on patient access and global healthcare equity. Through strategic planning and adaptive solutions, the pharmaceutical sector can ensure the continuous availability of critical medicines worldwide, meeting both current and future health needs.

M&A is a strategic concept of business growth through consolidation, gaining market access, increasing strategic positions, and increasing operational efficiency. To understand the dynamics of M&A, this paper looks at aspects such as targeted firm identification, evaluation, bidding for the target firm, and post-acquisition integration. All forms of M&A, including horizontal, vertical, conglomerate, and acquisitions, are discussed in terms of goals and values, including synergy, cost reduction, competitive advantages, and access to better technology. However, issues such as cultural assimilation, adhesion to regulations, and calculating an inaccurate value are also resolved. The paper then goes deeper to provide insight into how predictive analytics applies to M&A, using ML to improve decision-making with forecasting benefits. Including healthcare, education, and construction industries, the presented predictive models using regression analysis, neural networks, and ensemble techniques help to make decisions. Through time series and real-time data, PDA enables sound M&A strategies, effective risk management and smooth integration.

Retirement planning can be a complex endeavor. One consideration is whether or not to invest in an Individual Retirement Account (IRA). The present study compares the effect of several contributions to a traditional, Roth, and rollover IRA. The returns generated for each model are derived from the historic growth rates of the S&P 500 over 40 years. Results are presented in terms of employer match, taxes due, and the number of shares utilized in the long-term investment strategy for each withdrawal method. Results show traditional IRA contributions or Roth IRA contributions are equally matched until employment termination. Taking an active role in managing the investment strategy, possibly by working with a financial representative, suggests a more favorable positioning upon employment termination [1]. Traditional and other pre-tax plans usually do not have an employer match, are usually paired with decreased taxes paid, and the number of shares available to the long-term investment strategy is somewhat reduced. In all cases, risk is increased. Rollover IRAs enjoy a match, lower taxes, and decrease the amount of calculated risk involved. A certified financial planner should be the resource of choice to determine how corporate retirement planning programs fit into the overall investment strategy.

This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without considering external regulatory frameworks. This research draws on the cognitive model of regulation that looks at regulatory compliance as a social construct. It uses a triangulation research method comprising literature review, interview of trade compliance experts, and questionnaire survey of compliance practitioners to understand how regulation affects compliance and what role ICTs play in implementing compliance. The findings of this study present a regulatory compliance framework comprising four cognitive stages and a conceptual regulatory compliance system that presents how BDA and AI automation are applied to mitigate regulatory complexity and enhance regulatory compliance. The conceptual regulatory compliance system shows how BDA and AI enable institutions to dynamically assess regulatory risk, automatically monitor compliance, and intelligently predict risk violations mitigating regulatory complexity and preventing producing unnecessary documents. It provides theoretical contributions to understanding regulatory evolution and compliance and practical implications for understanding how regulation evolves to be more complicated and elements of a regulatory compliance system mitigate proliferating regulations. Additionally, it provides avenues for future research into the relationship between competing regulatory mandates and how institutions cope with that. Regulations are important for ensuring compliance and governance in finance and to curb systemic risk. Complying with regulations is difficult due to their growing volume, complexity, and fragmentation. Institutions use large-scale Information and Communication Technologies (ICTs), such as Big Data Analytics (BDA) and Artificial Intelligence (AI) automation, to monitor compliance and mitigate regulatory complexity. However, less is known about how firms comply with regulation. Most literature does not thoroughly investigate regulatory elements nor explicitly relate them to compliance.