Predictive Analytics Research Articles

The Impact of Big Data and Predictive Analytics on U.S. Healthcare Delivery: Opportunities, Challenges, and Future Directions

Healthcare, like many other industries, has been significantly influenced by big data and predictive analytics. The vast volume, velocity, and variety of information within big data sets have transformed the way we approach patient care and medical innovation. From predicting disease outbreaks to delivering personalized treatment plans, big data analytics offers immense potential for revolutionizing healthcare. This review explores the significant impact of big data and predictive analytics on the U.S. healthcare system. It delves into hospitals' ability to effectively leverage complex information and assess the potential benefits they may reap from successful implementation. The introduction defines big data and predictive analytics in the context of healthcare and outlines the paper's objectives. It further discusses the evolution of big data and analytics in healthcare, its key applications, and the challenges it presents. Additionally, we explore potential solutions and ethical considerations surrounding big data analytics in healthcare. Overall, this paper underscores the transformative power of big data and predictive analytics in revolutionizing U.S. healthcare delivery and improving patient outcomes.

Revolutionizing inventory management: A comprehensive automated data-driven model using power BI incorporating industry 4.0

This is a study on advanced inventory management approaches in the mechanical industry using data-driven models, accompanied by Power BI analytics. Drawing insights from broad collaboration with relevant industrial expertise at different organizational scales, our research work addresses the intricacies and challenges in maintaining optimal inventory levels concerning growing industries and operational risks. In this global mechanical firm, we analyzed historical data using descriptive and predictive analytics to understand the trends of the past and to forecast future inventory needs. Descriptive analytics provided base insights about the present status of inventory, while predictive analytics helped proactive management of stock levels classified into on-hand and critical level categories for greater efficiency in strategies related to planning at the coordination end with its suppliers and mitigating risks. Core to our methodology will be the implementation of an automated data-driven machine-learning approach that brings minimal intervention from humans to solve some of the most critical problems in inventory management. One major contribution this study adds is a major product: a Power BI dashboard that visualizes critical part numbers falling below threshold values and recognizes suppliers with historical shipment shortages. Armed with this intuitive dashboard, supplier-coordinating engineers are better placed to take proactive action on inventory shortages, greatly improving operational efficiency in supply chain resilience.

AI-driven monitoring systems for bioremediation: real-time data analysis and predictive modelling

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of bioremediation by enabling real-time monitoring and optimization of environmental cleanup processes. This paper explores the integration of AI-driven monitoring systems with bioremediation techniques, focusing on how real-time data analysis and predictive modelling can enhance the effectiveness of pollutant degradation. These AI systems continuously collect data from contaminated sites, such as soil and water, and analyse variables like microbial activity, pollutant concentration, and environmental conditions. By processing this data, AI models can optimize the bioremediation environment, adjusting factors such as pH, temperature, and nutrient levels to maximize microbial efficiency. Predictive modelling plays a crucial role in forecasting remediation outcomes, allowing for proactive adjustments to improve the speed and success of the process. The study also highlights the potential of AI in reducing operational costs by automating data collection and analysis, minimizing the need for manual intervention. Furthermore, it discusses challenges related to data quality, system integration, and the scalability of AI applications in real-world bioremediation projects. By leveraging AI's capability to provide real-time insights and predictive analytics, this research demonstrates its potential to significantly enhance the precision and sustainability of bioremediation efforts, paving the way for smarter environmental management.

Robotics and artificial intelligence in unconventional reservoirs: Enhancing efficiency and reducing environmental impact.

Shale, tight gas, and coal bed methane reservoirs are unconventional reservoirs that have geometrically complicated geological formations with low permeability, rendering their extraction operations difficult, expensive, and environmentally sensitive. On the other hand, recent developments in Robotics and Artificial Intelligence continue to transform how exploration, production, and maintenance activities are carried out in these reservoirs. This review paper encompasses how Robotics and AI technologies are applied to these unconventional reservoirs, improving operation efficiency, increasing the recovery rate and minimizing environmental damage. It updates on the latest status of autonomous drilling, AI-driven reservoir characterization and robotic fracturing. The paper also discusses future opportunities: emerging technologies, integration of AI with predictive analytics, and innovations toward sustainability. These are the challenges that come with possible solutions discussed in the paper: high costs, technical integration, and data quality. The review thereby presents the thought that robotics and AI will take a transformative role in the unconventional reservoir sector during the next few years, both in economic performance and environmental sustainability.

Analyzing financial analysts' role in business optimization and advanced data analytics

This paper explores the evolving role of financial analysts in business optimization, focusing on their increasing reliance on advanced data analytics to drive strategic decision-making. It examines the historical shift from traditional financial analysis to more data-centric responsibilities, highlighting how modern financial analysts contribute to operational efficiency, cost reduction, and revenue growth through data-driven insights. Integrating advanced tools such as artificial intelligence, machine learning, and predictive analytics has enabled financial analysts to interpret complex datasets, optimize business processes, and improve market positioning. However, adopting these technologies comes with challenges, including a steep learning curve, data integration difficulties, and ethical concerns related to data privacy and bias in AI algorithms. This paper also discusses future trends in financial analysis, emphasizing the growing importance of real-time data analysis and automation in business optimization. The paper concludes by stressing the need for financial analysts to continually adapt to the rapidly evolving technological landscape to effectively shape corporate strategy.

Implementing AI-driven Customer Relationship Management (CRM) systems: Enhancing customer experience in the retail industry of Thailand

This research explores the impact of AI-driven Customer Relationship Management (CRM) systems on enhancing customer experience in Thailand's retail industry. However, quantitative data analysis was conducted to focus on the 'how' of AI technologies, such as chatbots, predictive analytics, and personalized marketing, to improve customer satisfaction, retention, and sales conversion rates. It also denotes from the analysis of customer transaction data, CRM interaction logs, and sales performance metrics that there has been a very significant improvement in operational efficiency and customer loyalty. The findings have shown that chatbots reduce response time and increase first-contact resolution rates; predictive analytics improve customer retention with accurate identification of at-risk customers; and a 30% increase in sales conversion through personalized marketing. These findings present a significant positive relationship between AI-CRM implementation and improved business performance, showing just how important AI can be in driving customer engagement and operational success for retailers. This empirical research supports the idea that AI-powered CRM systems have become imperative for maintaining competitive advantage by retailers within an increasingly digital marketplace.

AI and blockchain technology for secure and transparent financial transactions

The integration of Artificial Intelligence (AI) and blockchain technology presents a transformative approach to enhancing the security and transparency of financial transactions. Blockchain’s decentralized and immutable ledger system ensures that transaction records are tamper-proof and transparent, significantly reducing the risk of fraud and errors. AI, with its advanced data analytics and machine learning capabilities, further augments this ecosystem by providing real-time monitoring, predictive analytics, and automated decision-making processes. Together, these technologies enable more efficient and secure financial operations, from cross-border payments to trade finance and asset management. This paper explores the synergistic potential of AI and blockchain in revolutionizing the financial sector, highlighting key applications, benefits, and challenges. By leveraging the strengths of both technologies, financial institutions can achieve unprecedented levels of security, efficiency, and trust in their operations.

The Role of Artificial Intelligence (AI) and machine learning in social work practice

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into social work practice is transforming the landscape of service delivery and decision-making. This paper explores how these technologies enhance case management, predictive analytics, and resource allocation in critical areas such as child welfare, mental health, and substance abuse treatment. Key trends highlighted include the use of AI-based predictive analytics to identify at-risk populations and facilitate early interventions, as well as the deployment of chatbots and virtual assistants for providing accessible mental health counselling and social support. Furthermore, the paper addresses ethical considerations and challenges associated with AI implementation, particularly the potential biases in algorithms that may affect the assessment of social needs. Additionally, the integration of AI tools into social work education and training is examined to prepare future professionals for a technology-driven environment. By analysing the current applications of Natural Language Processing (NLP) for client data analysis, AI-powered software for predictive risk assessments, and automated case management systems, this paper advocates for a balanced approach to AI adoption, ensuring that the core values of social work, such as equity and social justice, remain central to practice. Ultimately, this exploration underscores the potential of AI and ML to enhance social work outcomes while also emphasizing the necessity of ethical frameworks and ongoing training for practitioners.

Leveraging big data analytics to combat emerging financial fraud schemes in the USA: A literature review and practical implications

The rise of emerging financial fraud schemes in the USA has presented significant challenges for financial institutions, regulators, and law enforcement agencies. As fraud tactics become increasingly sophisticated, traditional methods of detection and prevention are proving insufficient. This literature review explores the role of big data analytics as a critical tool in combating financial fraud, focusing on its practical applications and effectiveness. By leveraging machine learning, predictive analytics, and artificial intelligence, big data analytics enables real-time monitoring and detection of fraudulent activities, offering a more dynamic and adaptive approach to fraud prevention. The review synthesizes key studies, case reports, and industry practices, highlighting the implementation of big data in analyzing large volumes of transactional and behavioral data to identify suspicious patterns. It further discusses the challenges associated with integrating big data technologies into existing systems, including issues related to data privacy, regulatory compliance, and the high cost of implementation. Additionally, the paper assesses the impact of big data solutions on fraud prevention, comparing their effectiveness to traditional methods and identifying best practices for institutions looking to adopt these technologies. Finally, the review proposes future directions, emphasizing the potential of emerging technologies such as artificial intelligence and blockchain to enhance fraud detection capabilities. It offers recommendations for financial institutions, regulators, and policymakers to collaborate in developing more robust strategies to safeguard the financial system from evolving fraud threats. This paper provides a comprehensive analysis of the current state of big data analytics in combating financial fraud and its practical implications for the financial services industry in the USA.

Application of Internet of Things (IoT) in Energy Infrastructure: Lessons for the Future of Operations and Maintenance

The application of the Internet of Things (IoT) in energy infrastructure is revolutionizing operations and maintenance practices, driving efficiencies, and enhancing sustainability across the sector. This paper explores the transformative impact of IoT technologies on energy infrastructure, focusing on how they facilitate real-time monitoring, predictive maintenance, and data-driven decision-making. By leveraging IoT devices and systems, energy operators can optimize performance, reduce operational costs, and minimize downtime, ultimately contributing to more resilient and sustainable energy systems. The discussion highlights key IoT applications within energy infrastructure, including smart grids, renewable energy systems, and conventional power plants. Smart sensors and devices enable continuous data collection, providing insights into equipment performance, energy consumption, and environmental conditions. Predictive analytics, powered by IoT data, allows operators to anticipate equipment failures and conduct maintenance proactively, thereby extending asset life and improving reliability. Additionally, the abstract addresses the challenges associated with IoT integration in energy infrastructure, such as cybersecurity risks, data management issues, and the need for interoperability among diverse systems. It emphasizes the importance of establishing robust security protocols to protect sensitive data and ensure the integrity of IoT networks. Furthermore, the role of regulatory frameworks and industry standards in promoting the safe and effective deployment of IoT technologies is discussed. The paper also examines case studies of successful IoT implementations in energy infrastructure, showcasing the lessons learned from these initiatives. These examples illustrate the potential for IoT to enhance operational efficiency, reduce greenhouse gas emissions, and support the transition to cleaner energy sources. In conclusion, the abstract underscores that the adoption of IoT technologies in energy infrastructure is not just a trend but a necessity for the future of operations and maintenance. As the energy sector continues to evolve, embracing IoT will be crucial for achieving sustainability goals and meeting the growing demands for reliable energy supply.

AI-driven decarbonization of buildings: Leveraging predictive analytics and automation for sustainable energy management

The decarbonization of buildings is a pivotal strategy in addressing global climate change, and artificial intelligence (AI) is increasingly recognized as a powerful tool in this process. This paper explores the application of AI in optimizing building energy consumption, focusing on predictive analytics, real-time energy monitoring, and smart energy systems. By leveraging machine learning algorithms, AI enhances energy efficiency through precise automation, particularly in heating, ventilation, and air conditioning (HVAC) systems. AI-powered demand response solutions dynamically adjust energy use based on factors like occupancy, weather patterns, and energy prices, further minimizing carbon footprints. Additionally, the integration of renewable energy sources such as solar and wind with AI-driven energy management systems enables smarter utilization of clean energy. The paper examines both new construction projects and the retrofitting of existing infrastructures, offering insights into AI’s role in reducing carbon emissions and improving energy sustainability. Case studies highlight successful implementations, underscoring AI’s ability to drive significant energy savings and carbon reduction. The study also discusses challenges such as data privacy, the high cost of AI technology, and the regulatory framework required for widespread adoption. The findings present AI as an essential component of the future of sustainable, carbon-neutral buildings.

Comprehensive framework for data fusion in distributed ledger technologies to enhance supply chain sustainability

This review proposes a comprehensive framework that integrates data fusion with Distributed Ledger Technologies (DLT) to enhance sustainability in supply chain management. In today’s global supply chains, ensuring transparency, efficiency, and environmental responsibility is critical, yet the lack of real-time visibility and data fragmentation presents significant challenges. The framework addresses these issues by merging data from multiple sources, including IoT devices, operational databases, and external environmental factors, using advanced data fusion algorithms. DLT, with its decentralized, immutable, and transparent nature, ensures the integrity and security of the data, allowing all stakeholders to access accurate and tamper-proof information. The fusion of data within a DLT infrastructure not only improves traceability and accountability but also enables the automation of sustainability checks via smart contracts. These contracts can trigger actions based on predefined sustainability metrics such as carbon emissions, energy consumption, and resource efficiency. Furthermore, predictive analytics and machine learning algorithms integrated into the system provide real-time monitoring and optimization of sustainability performance throughout the supply chain. The proposed review offers numerous benefits, including enhanced transparency, reduced operational costs, improved sustainability outcomes, and risk mitigation. It also addresses challenges such as scalability, data privacy, and regulatory compliance, offering solutions to overcome these hurdles. By exploring case studies of successful implementations, this review demonstrates the practical applications and future potential of combining DLT and data fusion for sustainable supply chain management, positioning it as a critical tool for organizations aiming to meet environmental and regulatory demands in an increasingly digital and eco-conscious world.

AI-driven business analytics for SMES: Unlocking value through predictive and prescriptive analytic

Introduction: With an emphasis on predictive and prescriptive analytics, this study examines the revolutionary implications of AI-driven analytics on small and medium-sized organizations (SMEs). SMEs play a crucial role in the global economy and require advanced solutions to improve decision-making and operational efficiency. The research aims to explore how AI-powered analytics, particularly in predictive and prescriptive forms, can add value to SMEs by enhancing demand forecasting, customer behavior insights, and financial planning. To determine how AI-driven analytics might affect SMEs, a thorough assessment of the literature was undertaken. The study reveals that SMEs implementing predictive analytics experience notable improvements in areas such as inventory management, revenue generation, and overall operational efficiency. Furthermore, businesses that leverage prescriptive analytics benefit from optimized resource allocation, enhanced marketing strategies, and better risk management practices. These findings highlight the potential for AI to overcome key challenges faced by SMEs, including budget constraints and limited data availability. AI-driven analytics can provide valuable insights that allow SMEs to streamline operations and foster growth. With future trends pointing to greater accessibility and developments in machine learning, natural language processing, and the integration of AI with other cutting-edge technologies, like blockchain, AI-powered analytics offers substantial prospects for small and medium-sized enterprises.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

AI in customer feedback integration: A data-driven framework for enhancing business strategy

The integration of artificial intelligence (AI) into customer feedback systems has emerged as a transformative approach for businesses seeking to enhance their strategies and maintain a competitive edge. This review presents a data-driven framework that leverages AI to analyze, interpret, and act upon customer feedback, providing actionable insights for business decision-making. AI techniques such as natural language processing (NLP), machine learning (ML), and sentiment analysis allow companies to automate the feedback collection process and analyze vast amounts of data from diverse sources, including surveys, reviews, social media, and customer support interactions. The proposed framework facilitates real-time feedback analysis, enabling businesses to identify trends, customer preferences, and potential pain points more efficiently. By integrating AI with existing customer relationship management (CRM) systems, businesses can automate the categorization and prioritization of feedback, allowing for timely responses and more effective problem-solving. Furthermore, predictive analytics tools within the framework can forecast customer needs, allowing businesses to tailor products and services to meet evolving expectations. This framework also supports continuous improvement by enabling businesses to track the impact of changes implemented based on customer feedback. Additionally, AI’s ability to personalize the customer experience by recognizing patterns and individual preferences plays a crucial role in increasing customer satisfaction and loyalty. The data-driven insights generated through AI integration can guide businesses in refining their marketing, product development, and customer service strategies, leading to improved operational efficiency and better alignment with customer expectations. In conclusion, the integration of AI into customer feedback mechanisms represents a significant advancement for data-driven business strategy development. This framework not only enhances feedback accuracy and speed but also empowers businesses to deliver more personalized and customer-centric solutions.

Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns

This study explores innovative approaches to risk management in clinical research, focusing on the intricate balance between ethical standards, regulatory compliance, and intellectual property (IP) concerns. As clinical trials become increasingly complex and globalized, managing risks effectively while maintaining participant safety and upholding legal and ethical principles has become paramount. The study adopts a comprehensive review of emerging technologies, such as artificial intelligence (AI), machine learning (ML), blockchain, and big data analytics, to assess their role in transforming risk management strategies. Key findings reveal that AI and predictive analytics significantly enhance real-time monitoring and the early identification of risks, while adaptive trial designs offer flexibility in managing unforeseen challenges during trials. Blockchain technology improves transparency and data integrity, ensuring secure and verifiable data transactions. However, the intersection of IP protections and equitable access to treatments presents ongoing ethical challenges, particularly in low-income regions. The study concludes that while technological innovations provide robust tools for mitigating risks, a balanced approach to regulatory frameworks and IP protections is essential. The study recommends increased collaboration between regulatory bodies, research institutions, and technology providers to harmonize global regulatory standards, ensuring both innovation and accessibility. Furthermore, it advocates for more flexible IP frameworks that address the tension between protecting innovations and ensuring equitable access to life-saving treatments.

Integrating AI into DevOps pipelines: Continuous integration, continuous delivery, and automation in infrastructural management: Projections for future

This paper explores the transformative role of Artificial Intelligence (AI) in enhancing DevOps pipelines, focusing on Continuous Integration (CI), Continuous Delivery (CD), and automation in infrastructural management. As organizations increasingly adopt DevOps methodologies to streamline software development and delivery processes, the integration of AI technologies offers significant potential for improving efficiency, quality, and responsiveness. This study reviews current practices in CI/CD and examines how AI-driven tools can automate repetitive tasks, optimize resource allocation, and facilitate predictive analytics for proactive decision-making. Additionally, the paper discusses the challenges and considerations associated with AI integration in DevOps, including cultural shifts, data governance, and the need for skilled personnel. Projections for future developments highlight the potential for AI to create smarter, more adaptive DevOps environments that align with evolving industry demands. By identifying key trends and innovations, this research provides a comprehensive overview of the future landscape of DevOps, positioning AI as a critical enabler of agility and performance.

Enhancing supply chain security and transparency with ai and blockchain integration for future-proof solutions

The objective of this review paper is to examine the integration of Artificial Intelligence (AI) and blockchain technology to enhance supply chain security and transparency, providing future-proof solutions for global industries. As the digital landscape evolves, traditional supply chain management methods face increasing vulnerabilities and inefficiencies. This paper reviews existing literature to assess the current challenges and potential benefits of integrating AI and blockchain in supply chains. The review highlights how AI enhances predictive analytics, demand forecasting, and operational efficiency, while blockchain ensures immutable, decentralized ledgers for real-time tracking and verification of goods. The synergy between these technologies offers unprecedented levels of transparency, enabling stakeholders to detect and respond to anomalies swiftly. Case studies across various industries demonstrate significant improvements in reducing fraud, counterfeiting, and human errors, alongside enhanced compliance with regulatory standards. Conclusions drawn from this review underscore the transformative potential of AI and blockchain in creating resilient, transparent, and secure supply chains. The integration of these technologies not only addresses current vulnerabilities but also lays the foundation for adaptive, intelligent supply chain networks capable of withstanding future disruptions. This paper provides actionable insights and practical frameworks for businesses and policymakers aiming to implement AI and blockchain solutions, emphasizing the importance of strategic planning, cross-industry collaboration, and continuous innovation in achieving long-term supply chain sustainability and security.

AI-driven personalization framework for SMES: Revolutionizing customer engagement and retention

In today's competitive business landscape, Small and Medium Enterprises (SMEs) face unique challenges in building and maintaining strong customer relationships. An AI-driven personalization framework offers a transformative solution by enabling SMEs to deliver highly targeted and individualized customer experiences, improving both engagement and retention rates. This review outlines how artificial intelligence (AI) can empower SMEs by integrating data-driven insights with customer interaction processes to revolutionize business practices. AI-driven personalization leverages machine learning algorithms, natural language processing (NLP), and predictive analytics to analyze customer behaviors, preferences, and feedback. By aggregating data from various sources—such as online interactions, purchase histories, and social media activity—AI can generate personalized recommendations, offers, and communication strategies that resonate with individual customers. The framework facilitates dynamic customer segmentation, allowing SMEs to tailor marketing efforts and enhance service delivery. The personalization process also extends beyond marketing by optimizing customer support through AI-powered chatbots and recommendation systems, which provide real-time solutions and advice. This level of personalization fosters stronger emotional connections, increasing customer satisfaction and brand loyalty. Additionally, AI-driven insights enable SMEs to anticipate customer needs, predict churn rates, and proactively address potential issues, thereby boosting retention rates. For SMEs, the implementation of an AI-driven personalization framework is not only cost-effective but scalable, making it accessible even to businesses with limited resources. As SMEs increasingly adopt digital tools, the integration of AI-based personalization becomes essential for staying competitive in a rapidly evolving market. This review highlights the potential of AI in transforming customer engagement strategies for SMEs by offering a tailored, efficient, and sustainable approach to enhancing customer experiences.

Data-driven digital marketing and battery supply chain optimization in the battery-powered aircraft industry through case studies of Rolls-Royce’s ACCEL and Airbus's E-Fan X Projects

This paper investigates the interplay between data-driven digital marketing strategies and battery supply chain optimization in the battery-powered aircraft industry, with a focus on Rolls-Royce's ACCEL and Airbus's E-Fan X projects. As the electric aircraft market emerges, these companies face the dual challenge of promoting technological advancements to a skeptical audience while navigating complex supply chain requirements. The study explores how data analytics and targeted content marketing are employed to enhance stakeholder engagement, build consumer trust, and communicate the benefits of electric aviation. By leveraging digital platforms, Rolls-Royce and Airbus effectively highlight key aspects such as battery performance, sustainability, and the safety of electric propulsion, addressing both technical and consumer concerns. Concurrently, the research delves into the intricacies of battery supply chain optimization, examining the processes involved in sourcing high-energy-density batteries, ensuring quality control, and adhering to regulatory standards. Advanced supply chain management techniques, including predictive analytics and real-time monitoring, are analyzed for their role in streamlining procurement and manufacturing processes. The integration of these strategies within digital marketing campaigns demonstrates how each company positions itself as a leader in the industry, emphasizing technological innovation and reliability. This study aims to provide a comprehensive understanding of how digital marketing and supply chain optimization are not only critical for promoting the adoption of electric aircraft but also for overcoming market skepticism and ensuring operational efficiency. By presenting case studies of two pioneering projects, ACCEL and E-Fan X, the research highlights best practices and strategic approaches that can guide other companies in the industry. The findings underscore the importance of a synergistic approach where digital marketing is informed by supply chain realities, creating a transparent and trustworthy narrative for consumers and stakeholders. Ultimately, this paper contributes to the discourse on how emerging technologies in aviation can be successfully marketed and operationalized, offering insights into the effective use of digital marketing and supply chain management in the rapidly evolving electric aircraft sector.