Advances in Automation and AI for Enhancing Supply Chain Productivity in Oil and Gas

Advances in Automation and AI for Enhancing Supply Chain Productivity in Oil and Gas

*Ekene Cynthia Onukwulu¹, Mercy Odochi Agho², Nsisong Louis Eyo-Udo³, Aumbur Kwaghter Sule⁴, Chima Azubuike⁵

¹Kent Business School, University of Kent, UK

²Independent Researcher, Port Harcourt Nigeria

³Independent Researcher, Lagos Nigeria

⁴Independent Researcher, Abuja, Nigeria

⁵Guaranty Trust Bank (Nigeria) Limited

*Corresponding Author

DOI: https://doi.org/10.51584/IJRIAS.2024.912057

Received: 17 December 2024; Revised: 24 December 2024; Accepted: 26 December 2024; Published: 24 January 2025

ABSTRACT

Advances in automation and artificial intelligence (AI) are transforming supply chain management in the oil and gas industry, driving enhanced productivity, efficiency, and cost-effectiveness. This paper explores the integration of automation and AI technologies to optimize various supply chain processes, from procurement to distribution, and improve overall operational performance. Automation tools, including robotic process automation (RPA), drones, and autonomous vehicles, are streamlining tasks such as inventory management, inspection, and transportation, reducing human error, and increasing the speed of operations. AI-powered algorithms, particularly in predictive analytics, are enabling better demand forecasting, inventory control, and predictive maintenance, thus minimizing downtime and maximizing asset utilization. The use of AI for real-time data analysis and decision-making is particularly crucial in dynamic and high-risk environments like oil and gas supply chains. By analyzing large volumes of data, AI models can identify patterns, forecast disruptions, and recommend proactive solutions, thus improving risk management and ensuring business continuity. Additionally, AI-driven supply chain optimization tools are enhancing resource allocation, improving supply chain visibility, and promoting data-driven decision-making. Automation in supply chain logistics, including the use of drones for inspection and delivery, contributes to safer and more efficient operations, reducing the need for manual intervention in hazardous environments. This paper also discusses the role of AI in enhancing supply chain resilience by predicting market fluctuations, optimizing routes, and automating procurement strategies. However, challenges such as data integration, cybersecurity concerns, and the need for skilled personnel must be addressed for successful implementation. Despite these challenges, the potential benefits of automation and AI in enhancing supply chain productivity are significant, offering substantial improvements in operational efficiency, cost savings, and risk mitigation. Ultimately, the adoption of these technologies is set to redefine the future of supply chain management in the oil and gas sector.

Keywords: Automation, Artificial Intelligence, Supply Chain Productivity, Oil And Gas, Predictive Analytics, Robotic Process Automation, Inventory Management, Predictive Maintenance, Risk Management, Supply Chain Optimization.

INTRODUCTION

Supply chain management plays a critical role in the oil and gas industry, given its complexity and the need for seamless coordination across various stages, from exploration and production to distribution and retail. The efficient management of this intricate network of suppliers, manufacturers, contractors, and logistics providers is essential to maintain the steady flow of products and services, ensuring cost-effectiveness and operational continuity (Adewumi, et al., 2024, Iwuanyanwu, et al., 2024, Iyelolu, et al., 2024). However, the traditional supply chain systems in oil and gas have long been challenged by inefficiencies, high operational costs, and an inability to respond quickly to unforeseen disruptions such as natural disasters, geopolitical tensions, and fluctuating demand. These challenges often result in delays, increased costs, and operational downtime, which ultimately impact the overall productivity of the industry.

The integration of automation and artificial intelligence (AI) into supply chain processes holds significant promise in addressing these issues. Automation technologies, such as robotic process automation (RPA) and autonomous vehicles, have the potential to reduce manual errors, accelerate decision-making, and streamline routine tasks (Anozie, et al., 2024, Iwuanyanwu, et al., 2024, Kedi, et al., 2024, Uzoka, Cadet & Ojukwu, 2024). At the same time, AI technologies, including machine learning algorithms and predictive analytics, can enhance forecasting, optimize resource allocation, and improve risk management by analyzing vast amounts of data to identify trends, predict potential issues, and enable proactive solutions. Together, automation and AI offer transformative benefits for supply chain management in oil and gas, enabling greater efficiency, agility, and responsiveness.

The purpose of this paper is to explore the ways in which automation and AI can enhance supply chain productivity in the oil and gas sector. By examining key technologies, their applications, and real-world examples, this paper aims to shed light on how these innovations are reshaping supply chains in the industry, addressing long-standing challenges, and driving improvements in operational performance (Ahuchogu, Sanyaolu & Adeleke, 2024, Iriogbe, et al., 2024, Komolafe, et al., 2024). Ultimately, the goal is to provide insights into how oil and gas companies can leverage automation and AI to not only enhance productivity but also ensure the long-term sustainability and resilience of their supply chains in an increasingly complex and dynamic global environment.

BACKGROUND

The oil and gas industry is one of the most complex and capital-intensive sectors globally, encompassing a vast network of operations and logistics that span across procurement, exploration, drilling, refining, transportation, and distribution. Effective supply chain management is crucial in ensuring that each of these activities operates seamlessly and efficiently, minimizing disruptions and maintaining a steady flow of products and services (Agu, et al., 2024, Ikwuanusi, et al., 2024, Iyelolu, et al., 2024). The supply chain in the oil and gas sector involves numerous stakeholders, including raw material suppliers, contractors, service providers, logistics companies, and distributors. The key processes within the supply chain include procurement, logistics, inventory management, and distribution, each of which plays a pivotal role in delivering products from the point of origin to the end user.

Procurement involves the sourcing of raw materials, equipment, and services required for exploration, production, and refining activities. The logistics component ensures that materials, equipment, and products are transported efficiently across global supply chains, often involving complex scheduling, coordination, and management of transportation modes, whether by land, sea, or air (Abdul-Azeez, et al., 2024, Givan, 2024, Iwuanyanwu, et al., 2024). Inventory management is critical in ensuring that the right materials are available at the right time and in the right quantities, minimizing stockouts and overstock situations that can disrupt operations. Distribution, which encompasses the movement of refined products from production facilities to storage and retail points, ensures that finished products are delivered to customers in a timely manner.

Despite the importance of these processes, traditional supply chains in the oil and gas sector often face a variety of challenges, including inefficiencies, high operational costs, lack of real-time visibility, and vulnerabilities to supply disruptions due to geopolitical issues or environmental factors (Agu, et al., 2024, Babalola, et al., 2024, Segun-Falade, et al., 2024). Supply chain disruptions, whether from natural disasters, regulatory changes, or unexpected demand fluctuations, can result in significant financial losses and operational downtimes, which are costly in an industry already characterized by high capital expenditure and complex operational demands (Attah, et al., 2024, Gil-Ozoudeh, et al., 2024, Kedi, et al., 2024). As such, there is a growing need for innovative solutions that can optimize the efficiency, transparency, and resilience of supply chains in the oil and gas industry.

Advances in automation technologies have been one of the key drivers of change in recent years. Automation refers to the use of technology to perform tasks that were previously carried out manually. Various types of automation technologies are now being deployed across the oil and gas supply chain to improve efficiency, reduce human error, and streamline operations. One of the most prominent automation technologies in the industry is Robotic Process Automation (RPA), which is used to automate repetitive and rule-based tasks, such as invoicing, procurement, and data entry (Adetumi, et al., 2024, Garba, et al., 2024, Manuel, et al., 2024). By reducing the time spent on these mundane tasks, RPA enables employees to focus on more strategic and value-added activities, thus improving overall productivity.

Another area where automation is transforming the oil and gas supply chain is in the use of drones for inspection and monitoring. Drones are increasingly used to inspect pipelines, offshore oil rigs, and other critical infrastructure. They provide a safer, faster, and more cost-effective method of monitoring vast areas of infrastructure, which would be expensive and time-consuming to inspect manually (Alabi, et al., 2024, Garba, et al., 2024, Kedi, et al., 2024, Umana, Garba & Audu, 2024). Drones can capture high-resolution images, videos, and sensor data, which can then be analyzed for maintenance needs, leaks, or other potential issues. This reduces the risk of failure and helps companies address problems before they lead to costly operational downtime.

In addition to drones, autonomous vehicles, including autonomous trucks and ships, are being introduced to transport goods more efficiently across oil and gas supply chains. Autonomous vehicles can reduce human error, increase safety, and improve transportation efficiency by optimizing routes and reducing fuel consumption. These vehicles can operate 24/7 without the need for driver rest, making them a valuable asset in meeting the demands of a complex global supply chain (Adewumi, et al., 2024, Folorunso, et al., 2024, Mbunge, et al., 2024). Autonomous trucks, for example, can transport materials and products between distribution centers, refineries, and retail points with minimal human intervention, ensuring that operations run smoothly even during periods of high demand or limited labor availability.

Alongside these advancements in automation, artificial intelligence (AI) has also emerged as a transformative force in supply chain management. AI technologies, such as machine learning, natural language processing, and predictive analytics, are being used to optimize various aspects of the oil and gas supply chain, from inventory management to demand forecasting and risk mitigation (Akinsulire, et al., 2024, Folorunso, et al., 2024, Mokogwu, et al., 2024). AI-driven systems are capable of analyzing vast amounts of data from diverse sources, including sensors, production systems, and external factors like weather patterns, to make real-time decisions that enhance operational efficiency.

One of the primary ways in which AI is being applied in the oil and gas supply chain is through predictive analytics. Predictive analytics uses historical data and machine learning algorithms to forecast future events, such as demand fluctuations, supply disruptions, or equipment failures (Aniebonam, 2024, Folorunso, et al., 2024, Mokogwu, et al., 2024). For example, AI can analyze past consumption patterns, weather data, and market trends to predict future demand for oil and gas products in specific regions, helping companies to better align their production and logistics strategies. This can result in more accurate planning, reducing the chances of overproduction or underproduction, both of which can be costly.

AI is also helping optimize inventory management, which is a crucial component of the oil and gas supply chain. By leveraging machine learning algorithms, AI can analyze patterns in inventory usage and automatically adjust stock levels to meet fluctuating demand. This reduces the need for manual interventions and ensures that the right amount of inventory is available at the right time, minimizing the risk of stockouts or excess inventory that can lead to unnecessary storage costs (Adeyemi, et al., 2024, Folorunso, et al., 2024, Mokogwu, et al., 2024).

In addition, AI has the potential to enhance risk management within the oil and gas supply chain. By analyzing historical data, AI models can predict potential disruptions and risks, such as supply shortages, transportation delays, or geopolitical events that may affect the flow of goods. Companies can then take proactive steps to mitigate these risks, such as identifying alternative suppliers, adjusting delivery schedules, or implementing contingency plans (Agu, et al., 2024, Folorunso, et al., 2024, Mokogwu, et al., 2024). This proactive approach to risk management is essential in an industry where disruptions can have significant financial and operational implications.

Furthermore, AI can be used to improve operational decision-making. AI-powered systems can help companies make more informed decisions about resource allocation, supply chain optimization, and procurement strategies by analyzing large datasets and identifying patterns that would otherwise go unnoticed. This can lead to more efficient use of resources, reduced operational costs, and improved overall productivity (Akinbolaji, 2024, Ayanponle, et al., 2024, Segun-Falade, et al., 2024).

In summary, the integration of automation and AI into the oil and gas supply chain is transforming how companies operate, making supply chains more efficient, resilient, and adaptable. Automation technologies, such as RPA, drones, and autonomous vehicles, are streamlining operations, improving safety, and reducing operational downtime. Meanwhile, AI is enhancing decision-making, optimizing inventory management, and improving risk management through predictive analytics and machine learning (Akerele, et al., 2024, Folorunso, 2024, Nwabekee, et al., 2024, Uzoka, Cadet & Ojukwu, 2024). As these technologies continue to evolve, they will play an increasingly important role in enhancing the productivity of the oil and gas supply chain, enabling companies to meet the growing demands of the global energy market while minimizing costs and risks.

Applications of Automation and AI in Oil and Gas Supply Chains

Automation and AI have significantly transformed supply chain management in the oil and gas industry by enhancing operational efficiency, improving decision-making, and reducing costs. In a sector that relies on intricate networks of suppliers, service providers, and logistics systems, automation and AI have become essential tools for managing the vast amounts of data, materials, and operations involved (Adetumi, et al., 2024, Ayanponle, et al., 2024, Segun-Falade, et al., 2024). One of the key areas where automation has made substantial inroads is in inventory and warehouse management. Traditionally, inventory management has been a manual and time-consuming process prone to human error, inefficiencies, and discrepancies in stock levels. Automated systems, including robotic systems, barcoding, and RFID tags, are now used to track, store, and manage inventory more effectively (Adepoju, Atomon & Esan, 2024, Folorunso, 2024, Nwabekee, et al., 2024). These systems offer real-time visibility into stock levels, providing accurate and timely information on the availability of materials and equipment. Automated warehouses further reduce human involvement in picking, packing, and sorting products, enabling faster, more efficient processes. This enhanced automation minimizes stockouts, optimizes storage space, and reduces the chances of overstocking, leading to cost savings and improved supply chain performance.

Another significant application of AI in oil and gas supply chains is predictive analytics and demand forecasting. Accurate demand prediction is essential for managing inventory, procurement, and production planning. AI algorithms can analyze historical data, current market conditions, weather patterns, and other relevant factors to predict future demand trends. These AI models help companies make data-driven decisions about how much raw material to order, how to allocate resources, and when to schedule production runs (Adeniran, et al., 2024, Folorunso, 2024, Nwabekee, et al., 2024). In an industry as volatile as oil and gas, where demand can fluctuate dramatically due to geopolitical events, seasonal changes, or supply disruptions, AI-powered demand forecasting offers a competitive advantage by allowing companies to better align their production and logistics strategies with expected demand. This level of precision helps minimize waste, reduce inventory costs, and ensure that the right amount of supply is available at the right time.

Robotic Process Automation (RPA) is another area where automation is significantly enhancing supply chain productivity in oil and gas. RPA refers to the use of software robots or ‘bots’ to automate repetitive, manual tasks that typically require human intervention. In oil and gas supply chains, RPA is used to streamline administrative tasks such as order processing, invoicing, procurement, and data entry (Arinze, et al., 2024, Ezeafulukwe, et al., 2024, Nwabekee, et al., 2024). By automating these routine tasks, RPA allows employees to focus on more strategic activities that require human judgment and creativity. RPA reduces the chances of human error, increases processing speed, and improves the overall efficiency of back-office operations. Additionally, the automation of administrative tasks frees up valuable resources and reduces operational costs, contributing to better financial performance. A reservoir modelling outline using artificial neural network is presented by Sircar, et al., 2021 as shown in figure1.

Figure 1: Reservoir modelling outline using artificial neural network (Sircar, et al., 2021).

AI is also playing a key role in predictive maintenance, which is essential for reducing downtime and extending the lifespan of equipment. In the oil and gas sector, maintaining expensive and critical infrastructure, such as drilling rigs, pipelines, and refineries, is crucial for minimizing operational disruptions. Traditional maintenance practices often involve scheduled maintenance based on time intervals, regardless of whether the equipment is actually in need of repair (Adewumi, et al., 2024, Ewim, et al., 2024, Nwabekee, et al., 2024). This approach can result in unnecessary downtime and higher maintenance costs. AI, however, can leverage data from sensors and IoT devices embedded in equipment to monitor its condition in real-time. Machine learning algorithms can then analyze this data to identify potential issues before they lead to equipment failure. Predictive maintenance powered by AI allows for more precise maintenance scheduling, reducing downtime and increasing the reliability of critical assets. By addressing maintenance needs proactively, AI helps companies avoid costly repairs and extends the operational life of expensive equipment (Adewusi, et al., 2024, Audu, Umana & Garba, 2024, Segun-Falade, et al., 2024).

Autonomous vehicles and drones are increasingly being utilized in transportation, inspection, and delivery processes within the oil and gas supply chain. Autonomous vehicles, such as self-driving trucks and cargo ships, are being deployed to transport materials and products across the supply chain. These vehicles improve the efficiency of transportation by reducing human error, increasing speed, and enabling round-the-clock operations (Alabi, et al., 2024, Ewim, et al., 2024, Nwaimo, Adegbola & Adegbola, 2024). In remote or hazardous environments, such as offshore oil rigs or pipelines in isolated locations, autonomous vehicles and drones can also play a critical role in ensuring that goods and materials are delivered safely and on time. For instance, drones equipped with cameras and sensors can be used for inspecting pipelines, oil rigs, and other infrastructure, identifying any potential damage or maintenance needs without the need for human inspectors to travel to these locations. This reduces the risk of accidents, enhances operational safety, and minimizes downtime. Drones can also expedite deliveries of small, critical parts or equipment to remote locations, further improving the speed and efficiency of operations.

AI-powered supply chain optimization and route planning systems are another area where significant progress is being made in the oil and gas sector. AI algorithms can analyze vast amounts of data from various sources, such as GPS, traffic reports, weather forecasts, and historical performance data, to optimize delivery routes and schedules (Achumie, Bakare & Okeke, 2024, Ewim, et al., 2024, Nwaimo, Adegbola & Adegbola, 2024). By factoring in real-time conditions, these AI systems can dynamically adjust routes to avoid delays, reduce fuel consumption, and improve the overall efficiency of transportation operations. For example, AI systems can predict traffic congestion, road closures, or adverse weather conditions and adjust delivery schedules accordingly, ensuring that products are delivered on time and at the lowest possible cost. By optimizing delivery routes, companies can reduce operational costs associated with fuel, labor, and vehicle maintenance, while also improving the reliability and speed of their supply chains.

The integration of automation and AI technologies in these applications has proven to be a game changer for the oil and gas industry. The use of automated systems in inventory and warehouse management has improved efficiency and reduced human error, leading to better stock control and cost savings (Agu, et al., 2024, Audu  & Umana, 2024, Segun-Falade, et al., 2024). AI-driven predictive analytics and demand forecasting have enabled companies to align their supply chain strategies with market conditions, minimizing waste and optimizing resource allocation (Agu, et al., 2024, Evurulobi, Dagunduro & Ajuwon, 2024, Nwaimo, Adegbola & Adegbola, 2024). RPA has streamlined administrative tasks, allowing human resources to focus on more strategic activities. Predictive maintenance powered by AI has reduced downtime and extended the lifespan of critical assets, while autonomous vehicles and drones have enhanced transportation and inspection processes, improving safety and efficiency. Finally, AI-based route optimization has further enhanced supply chain productivity by reducing fuel consumption and improving delivery efficiency.

As these technologies continue to evolve, their applications in the oil and gas supply chain will only increase, driving further improvements in efficiency, safety, and sustainability. Automation and AI are not only enhancing productivity but also helping companies in the oil and gas industry adapt to the challenges of a rapidly changing and increasingly competitive market (Adetumi, et al., 2024, Evurulobi, Dagunduro & Ajuwon, 2024, Nwaimo, et al., 2024). The integration of these technologies is paving the way for a more resilient, agile, and cost-effective supply chain that can better respond to fluctuations in demand, supply disruptions, and the growing focus on environmental sustainability. In the coming years, we can expect automation and AI to play an even more significant role in shaping the future of oil and gas supply chains, driving innovation, and unlocking new opportunities for growth and operational excellence.

Benefits of Automation and AI in Enhancing Supply Chain Productivity

The integration of automation and artificial intelligence (AI) in supply chain management is revolutionizing operations in the oil and gas industry, bringing numerous benefits to organizations striving for greater efficiency, cost savings, and optimized resource allocation. One of the primary advantages of automation and AI is the increased operational efficiency they provide (Agupugo, et al., 2024, Evurulobi, Dagunduro & Ajuwon, 2024, Nwobodo, Nwaimo & Adegbola, 2024). Traditional supply chains in the oil and gas industry often rely heavily on manual intervention, which can introduce inefficiencies, human error, and delays. Processes such as inventory management, procurement, and logistics require significant time and effort, and they can be prone to mistakes or miscommunication. Automation streamlines these processes by using advanced systems and robotics to perform routine tasks without the need for human oversight. This results in faster, more reliable workflows, with fewer errors, leading to quicker decision-making and an overall improvement in operational speed. Koroteev & Tekic, 2021 presented a lifecycle of an oilfield in the pre-AI era and AI era as shown in figure 2.

Figure 2: The lifecycle of an oilfield in the pre-AI era (top) and AI era (bottom). IOR stands for improved oil (gas) recovery; EOR is for enhanced oil (gas) recovery techniques (Koroteev & Tekic, 2021).

AI enhances decision-making by providing real-time data analysis, predictive modeling, and pattern recognition. This allows companies to anticipate future demand, track supply levels, and identify potential bottlenecks or issues before they become critical problems. AI-driven tools can also optimize routes for transportation and delivery, factoring in variables such as weather, traffic, and equipment conditions (Akinsulire, et al., 2024, Elugbaju, Okeke & Alabi, 2024, Obiki-Osafiele, et al., 2024). By reducing manual intervention and optimizing processes, automation and AI contribute significantly to overall efficiency, allowing organizations to operate at a higher capacity with fewer resources.

Another key benefit of automation and AI in supply chains is cost savings. Human labor is a significant cost in any supply chain, especially in the oil and gas sector, where the logistics of moving materials and personnel across challenging environments are complex and expensive. Automation reduces the need for labor in areas such as inventory management, order processing, and invoicing, replacing human workers with robots, drones, and autonomous systems (Ahuchogu, Sanyaolu & Adeleke, 2024), Elugbaju, Okeke & Alabi, 2024, Ochuba, Adewumi & Olutimehin, 2024). These technologies perform tasks faster and more accurately, minimizing the risk of errors and eliminating the need for expensive corrections or rework. By optimizing processes and reducing the reliance on manual labor, companies can lower operational costs, particularly in the areas of staffing and equipment maintenance.

Moreover, automation and AI contribute to reducing downtime, which can be a significant financial burden for the oil and gas industry. Downtime can result from equipment malfunctions, supply chain disruptions, or inefficiencies in resource allocation. Predictive maintenance, driven by AI, is a major advancement that addresses this challenge. By using data from sensors embedded in machinery, AI algorithms can monitor the health of equipment and predict when maintenance is needed, before a failure occurs (Adeleke, et al., 2024, Eleogu, et al., 2024, Odunaiya, et al., 2024, Uzoka, Cadet  & Ojukwu, 2024). This proactive approach to maintenance reduces unexpected breakdowns and the costs associated with emergency repairs. By ensuring that equipment is operating at peak performance, automation and AI help keep operations running smoothly and reduce the risk of costly disruptions.

The ability to enhance risk management is another crucial benefit of automation and AI in the oil and gas supply chain. The industry faces various risks, such as fluctuating market conditions, supply chain disruptions, regulatory changes, and environmental hazards. Traditional risk management approaches often involve reactive measures, with companies addressing problems only after they occur. Automation and AI shift this paradigm toward proactive risk identification and mitigation (Alabi, et al., 2024, Ehidiamen & Oladapo, 2024, Ogedengbe, et al., 2024, Umana, Garba & Audu, 2024). For example, AI-powered predictive analytics can analyze vast amounts of historical data to forecast potential disruptions, such as fluctuations in oil prices, political instability in key supply regions, or weather-related disruptions. By identifying these risks early, companies can implement contingency plans, adjust their supply chain strategies, and minimize the impact of these disruptions. This proactive approach enhances the resilience of the supply chain, ensuring business continuity even in the face of external challenges (Ajiga, et al., 2024, Audu  & Umana, 2024, Shittu, et al., 2024, Udeh, et al., 2024).

Additionally, AI-driven risk management systems can continuously monitor and assess the performance of the supply chain in real time. They can detect anomalies or inefficiencies that may signal potential issues and recommend corrective actions. This level of monitoring enhances situational awareness and enables companies to respond swiftly to any emerging risks (Arinze, et al., 2024, Ehidiamen & Oladapo, 2024, Ogedengbe, et al., 2024). With AI, oil and gas companies can better anticipate and mitigate risks, improving the reliability of their operations and maintaining stability in an otherwise volatile industry.

Better resource allocation is another significant benefit of incorporating automation and AI into supply chain management. The oil and gas sector involves the management of vast amounts of resources, from raw materials and fuel to equipment, labor, and capital. Efficiently allocating these resources is crucial for reducing costs, minimizing waste, and optimizing performance. Automation technologies, such as AI-driven scheduling systems, enable companies to allocate resources more effectively. These systems can analyze historical data, predict future demand, and optimize resource distribution based on real-time requirements (Attah, et al., 2024, Ehidiamen & Oladapo, 2024, Ogunsina, et al., 2024). For example, AI can suggest the most efficient deployment of workers, equipment, and materials based on location, availability, and operational needs. This ensures that resources are utilized where they are needed most, improving operational efficiency and reducing unnecessary expenditures.

Furthermore, AI-driven systems can enhance inventory management by predicting demand more accurately and preventing overstocking or stockouts. Traditional inventory management systems often operate on historical usage data, which can be inaccurate or outdated. In contrast, AI uses machine learning algorithms to analyze a wide range of factors, including market trends, environmental conditions, and geopolitical events, to provide more accurate forecasts. This allows companies to align their inventory levels more closely with actual demand, minimizing waste and reducing storage costs (Adewumi, et al., 2024, Ehidiamen & Oladapo, 2024, Ogunsina, et al., 2024). By ensuring that resources are available when needed, but not overstocked, companies can improve the overall efficiency of their supply chains.

The integration of automation and AI also leads to significant improvements in safety. The oil and gas industry operates in hazardous environments, and minimizing safety risks is paramount. Automation reduces the need for human workers to perform dangerous tasks, such as inspecting hazardous areas or working with heavy equipment. Drones, for instance, are increasingly used for aerial inspections of oil rigs and pipelines, allowing companies to monitor their assets without exposing workers to risk (Abiola, et al., 2024, Ehidiamen & Oladapo, 2024, Ohakawa, et al., 2024). Similarly, autonomous vehicles are being used to transport goods and materials in areas where human presence would be unsafe. AI-powered systems can also monitor safety conditions in real time, alerting operators to potential hazards and allowing for swift corrective action. By reducing the need for human intervention in high-risk activities and improving safety monitoring, automation and AI help protect workers and reduce the likelihood of accidents.

In addition to these benefits, automation and AI enable oil and gas companies to become more agile and responsive to changing market conditions. The oil and gas industry is subject to frequent shifts in market demand, supply availability, and regulatory requirements. Automation and AI allow companies to quickly adapt to these changes by providing real-time insights and enabling faster decision-making (Agu, et al., 2024, Ehidiamen & Oladapo, 2024, Ojukwu, et al., 2024). Whether it’s adjusting production schedules, reallocating resources, or responding to new regulations, AI-driven supply chain systems ensure that organizations can remain competitive and responsive in a dynamic market.

In conclusion, the benefits of automation and AI in the oil and gas supply chain are vast and far-reaching. These technologies contribute to increased operational efficiency, cost savings, improved risk management, and better resource allocation. By automating routine tasks and leveraging AI for predictive analytics, companies can streamline operations, reduce downtime, and improve decision-making (Akerele, et al., 2024, Ehidiamen & Oladapo, 2024, Ojukwu, et al., 2024). The enhanced visibility and real-time monitoring offered by AI enable proactive risk management and ensure that resources are allocated optimally. As the oil and gas industry continues to face complex challenges and fluctuating market conditions, the adoption of automation and AI will be critical to improving supply chain productivity and maintaining competitiveness. Through automation and AI, companies can achieve a more resilient, efficient, and cost-effective supply chain, ultimately driving growth and sustainability in an increasingly digital world.

Challenges in Implementing Automation and AI in Oil and Gas Supply Chains

Implementing automation and artificial intelligence (AI) in the oil and gas supply chain offers numerous potential benefits, such as increased operational efficiency, cost savings, and enhanced decision-making. However, the integration of these technologies into the existing infrastructure comes with significant challenges. One of the primary issues organizations face is data integration (Adeyemi, et al., 2024, Ehidiamen & Oladapo, 2024, Ojukwu, et al., 2024). In the oil and gas sector, supply chains are often complex, involving numerous systems, processes, and stakeholders. These systems are typically legacy-based, with many operating independently. Integrating automation and AI into these existing frameworks requires the seamless exchange of data between new and old systems, which can be a significant hurdle.

The first challenge is the compatibility of the new AI-driven solutions with existing software and hardware infrastructures. Many oil and gas companies still rely on outdated enterprise resource planning (ERP) systems, asset management software, and other traditional platforms. Integrating these legacy systems with modern AI and automation technologies requires significant changes to both software and hardware, which can be complex, time-consuming, and costly (Adepoju, Esan & Ayeni, 2024, Ehidiamen & Oladapo, 2024, Okeke, et al., 2024). Additionally, existing systems may not be designed to handle the large volumes of data that AI and automation tools require, making it necessary to overhaul data storage and processing infrastructures. As a result, achieving a seamless flow of data between these systems can become a formidable challenge, with the potential for data silos, inefficiencies, and inaccuracies, all of which could negatively impact the supply chain’s performance.

Another critical challenge is cybersecurity. As automation and AI technologies become more deeply integrated into oil and gas supply chains, the amount of sensitive data generated and processed increases exponentially. This data includes everything from operational data to predictive maintenance insights, all of which are essential for ensuring smooth operations. The increased reliance on digital systems and the Internet of Things (IoT) devices creates numerous points of vulnerability that could be targeted by cyberattacks (Adetumi, et al., 2024, Efunniyi, et al., 2024, Okeke, et al., 2024). The oil and gas industry is particularly susceptible to cybersecurity threats due to its critical infrastructure and the valuable nature of its data.

AI-driven systems, for example, require access to vast amounts of real-time data to make decisions, and any disruption or manipulation of this data could have severe consequences. Similarly, automation processes often involve the control of high-value assets, such as pipelines, drilling rigs, and storage facilities, all of which can be vulnerable to malicious attacks (Akinsulire, et al., 2024, Efunniyi, et al., 2024, Okeke, et al., 2024). These threats could result in data breaches, operational disruptions, or even environmental disasters, underscoring the importance of robust cybersecurity measures. Ensuring the security of AI-driven processes and automated systems is therefore paramount. This requires the implementation of advanced encryption methods, continuous monitoring, and the development of a cybersecurity culture within the organization. It also necessitates cooperation with external cybersecurity experts and regulators to protect critical infrastructure from evolving threats.

Moreover, the transition to automation and AI in the oil and gas supply chain poses challenges related to the skills and adaptation of the workforce. Automation and AI are complex technologies that require specialized knowledge and expertise. As these systems replace manual processes, there is a growing need to upskill the existing workforce to operate, maintain, and troubleshoot the new technologies (Alabi, et al., 2024, Ebeh, et al., 2024, Okeke, et al., 2024, Urefe, et al., 2024). However, this transition can be difficult, especially for workers who have spent many years performing tasks manually or using legacy systems.

There is often resistance to change among employees, particularly when there are fears that automation will lead to job losses. While automation and AI can create new roles, especially in data science, programming, and system maintenance, the workers affected by automation may not have the skills or qualifications necessary to transition to these new positions (Agu, et al., 2024, Dagunduro, et al., 2024, Okeke, et al., 2024). As a result, companies must invest in training programs and education to ensure their employees are equipped with the skills needed to work alongside AI and automated systems. Additionally, managing this transition can be challenging from a cultural perspective. Organizations must create an environment that fosters acceptance of new technologies while addressing any concerns related to job displacement. This requires strong leadership, clear communication, and a commitment to retraining workers and facilitating their transition to new roles.

Another significant challenge in implementing AI and automation in the oil and gas supply chain is the high initial investment required. While the long-term benefits of these technologies, such as cost savings, efficiency improvements, and enhanced risk management, can be substantial, the upfront costs associated with their adoption can be prohibitive for many companies (Adeniran, et al., 2024, Dagunduro, et al., 2024, Okeke, Bakare & Achumie, 2024). The cost of integrating automation systems, purchasing new hardware, upgrading software, and hiring specialized talent can add up quickly. For smaller organizations or those operating in financially constrained environments, this initial investment can be a major barrier to adopting these technologies.

Furthermore, the complexity of implementing AI solutions adds to the cost. AI systems require significant investment in research and development, as well as in data collection, storage, and processing capabilities. The process of customizing AI models to suit specific operational needs also involves considerable expenditure. For companies in the oil and gas sector, which already face fluctuating commodity prices and unpredictable market conditions, committing to such large investments can seem risky (Adewumi, et al., 2024, Dagunduro & Adenugba, 2024, Okeke, Bakare & Achumie, 2024). The capital-intensive nature of automation and AI implementation means that companies must carefully weigh the potential return on investment (ROI) before proceeding. While the ROI can be substantial over time, the high initial costs may deter some organizations from pursuing these technologies.

Additionally, the maintenance and upgrading of AI and automation systems incur ongoing costs. These systems require continuous monitoring, updates, and improvements to ensure they remain effective and secure. This is especially true for AI models, which need regular retraining to adapt to changing data patterns. While automation systems may reduce the need for manual labor, the reliance on technology still necessitates a skilled workforce to maintain and manage these systems, adding to operational costs (Akinbolaji, 2024, Dada, et al., 2024, Okeke, Bakare & Achumie, 2024).

Despite these challenges, the long-term advantages of implementing automation and AI in oil and gas supply chains are clear. However, for companies to successfully navigate these hurdles, they need to take a strategic approach. This includes selecting the right technology partners, investing in the training and development of their workforce, and implementing strong cybersecurity measures. It also requires the careful planning of budgets to account for both initial investments and ongoing maintenance costs (Agupugo, et al., 2024, Dada, et al., 2024, Olorunyomi, et al., 2024, Umana, et al., 2024).

Ultimately, overcoming the challenges associated with data integration, cybersecurity, workforce adaptation, and high upfront costs will require a combination of technological innovation, strategic investment, and cultural change. As the oil and gas industry continues to evolve and embrace digital transformation, addressing these challenges will be key to unlocking the full potential of automation and AI (Aminu, et al., 2024, Dada & Adekola, 2024, Olorunyomi, et al., 2024). By investing in the right technologies and creating an environment conducive to change, companies can enhance the productivity and efficiency of their supply chains, paving the way for greater success in an increasingly competitive and complex industry.

Case Studies and Real-World Applications

The integration of automation and AI into supply chains within the oil and gas industry is revolutionizing operations, optimizing processes, and driving significant improvements in efficiency and productivity. Several companies have successfully implemented these technologies, and their experiences provide valuable insights into how automation and AI are reshaping the industry (Agu, et al., 2024, Dada & Adekola, 2024, Omowole, et al., 2024). These real-world applications highlight the tangible benefits of embracing innovation, ranging from cost reductions to enhanced operational resilience.

One notable example is BP, which has embraced AI and automation to optimize its supply chain operations, particularly in areas such as logistics and inventory management. BP has integrated advanced AI algorithms and predictive analytics into its supply chain processes to improve demand forecasting and optimize inventory levels (Abdul-Azeez, et al., 2024, Crawford, et al., 2023, Omowole, et al., 2024). By using AI-driven models, BP has been able to predict fluctuations in demand more accurately, ensuring that its inventory is well-positioned to meet operational needs without overstocking or understocking. This has not only improved operational efficiency but has also led to significant cost savings by reducing the need for emergency shipments and lowering the amount of capital tied up in excess inventory.

BP has also leveraged automation in its logistics operations. Automated systems, including drones and robotic process automation (RPA), have been used to streamline the transportation of goods and equipment across oil fields and production sites. Drones, for example, are deployed for inspection and maintenance tasks, reducing the need for human intervention in hazardous environments (Adanyin, 2024, Chikwe, et al., 2024, Omowole, et al., 2024, Umana, et al., 2024). This has led to reduced operational downtime and improved safety, as workers are less exposed to dangerous conditions. The use of drones has also contributed to cost savings by decreasing the reliance on manned vehicles and the time required to carry out inspections.

In terms of predictive maintenance, BP has incorporated AI to monitor the health of critical equipment in real-time. Machine learning algorithms analyze sensor data from pumps, compressors, and other vital assets to detect early signs of wear and tear. This enables the company to predict potential failures and schedule maintenance activities before issues lead to costly breakdowns or production halts (Agu, et al., 2024, Chikwe, et al., 2024, Omowole, et al., 2024). As a result, BP has experienced lower maintenance costs, reduced unplanned downtime, and longer asset lifespans, all of which have improved the overall productivity of its supply chain.

Another prominent example is Shell, which has invested heavily in automation and AI to enhance its supply chain efficiency. Shell has incorporated AI in various aspects of its operations, including inventory management, procurement, and transportation. One area where Shell has seen significant benefits is in the automation of its procurement process. Using AI-powered systems, Shell has been able to streamline supplier selection and order placement, reducing the administrative burden on procurement teams (Attah, et al., 2024, Bristol-Alagbariya, Ayanponle & Ogedengbe, 2024, Omowole, et al., 2024). The AI systems are designed to analyze data from past transactions, supplier performance, and market conditions to recommend the most cost-effective and reliable suppliers. This has not only sped up the procurement process but has also resulted in significant cost reductions by ensuring that the company consistently sources materials at competitive prices.

Shell has also utilized AI to improve its transportation and logistics operations. By applying AI algorithms to optimize delivery routes and schedules, Shell has been able to reduce fuel consumption and transportation costs. AI-based systems analyze various factors, such as traffic patterns, weather conditions, and fuel consumption rates, to determine the most efficient routes for deliveries (Adetumi, et al., 2024, Bristol-Alagbariya, Ayanponle & Ogedengbe, 2024, Omowole, et al., 2024, Soremekun, et al., 2024). This has allowed Shell to reduce both its operational costs and its carbon footprint, contributing to its sustainability goals while improving supply chain productivity.

ExxonMobil has also integrated AI and automation into its supply chain operations, with a focus on predictive maintenance and real-time monitoring. ExxonMobil has implemented AI-driven predictive maintenance systems across its operations, using machine learning algorithms to analyze data from sensors embedded in equipment (Adewumi, et al., 2024, Bristol-Alagbariya, Ayanponle & Ogedengbe, 2024, Omowole, et al., 2024). These systems can detect anomalies in the performance of machinery and predict when a failure is likely to occur, enabling maintenance teams to intervene proactively. As a result, ExxonMobil has been able to reduce its unplanned downtime, extend the life of its equipment, and minimize the operational disruptions caused by equipment failures. This proactive maintenance approach has also contributed to significant cost savings by reducing the need for expensive emergency repairs and replacements.

In addition to predictive maintenance, ExxonMobil has utilized AI to optimize its inventory management and demand forecasting processes. By analyzing historical data, market trends, and external factors such as geopolitical events, ExxonMobil’s AI systems can predict fluctuations in demand and adjust inventory levels accordingly. This has allowed the company to maintain optimal stock levels, reduce excess inventory, and minimize the risk of stockouts, ultimately improving the efficiency of its supply chain (Adeniran, et al., 2024, Bristol-Alagbariya, Ayanponle & Ogedengbe, 2024, Owoade, et al., 2024).

Another case study comes from Chevron, which has focused on automating its supply chain operations to enhance both safety and productivity. Chevron has implemented robotic process automation (RPA) to handle administrative tasks such as order processing, invoicing, and inventory tracking. RPA systems have significantly reduced the time and resources spent on these manual processes, enabling employees to focus on higher-value tasks. In addition to RPA, Chevron has also deployed drones for inspection and monitoring purposes in its offshore operations (Agu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Umana, et al., 2024). These drones are used to conduct routine inspections of oil rigs and pipelines, reducing the need for human workers to perform these tasks in potentially hazardous environments.

Chevron has reported improved safety and efficiency as a result of its automation efforts. The use of drones and automated systems has reduced the risk of accidents and incidents while improving the speed and accuracy of inspections. This has led to faster identification of potential issues and quicker resolutions, ultimately reducing downtime and improving overall productivity (Abiola, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024).

One of the most innovative implementations of automation and AI in the oil and gas sector comes from TotalEnergies, which has developed an AI-driven supply chain management platform designed to optimize the entire value chain, from procurement to distribution. TotalEnergies’ AI system uses real-time data from multiple sources, including sensors on equipment, transportation vehicles, and suppliers, to monitor and optimize every aspect of the supply chain (Akinsulire, et al., 2024, Bello, et al., 2022, Owoade, et al., 2024). By integrating data from these various sources, the AI platform can provide TotalEnergies with insights into potential inefficiencies, disruptions, and opportunities for improvement.

The results have been impressive. TotalEnergies has seen significant improvements in its ability to manage inventory, reduce delays, and improve the accuracy of its demand forecasting. By leveraging AI to streamline its supply chain processes, the company has reduced operating costs and improved the overall performance of its supply chain. Moreover, the real-time monitoring capabilities of the platform have enabled TotalEnergies to respond more quickly to changes in demand or supply disruptions, ensuring that its operations remain efficient and resilient (Ahuchogu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Ukonne, et al., 2024).

These case studies illustrate the transformative impact that automation and AI are having on the oil and gas supply chain. Companies such as BP, Shell, ExxonMobil, Chevron, and TotalEnergies are leading the way in integrating these technologies to enhance operational efficiency, reduce costs, and improve safety. As these technologies continue to evolve, it is expected that more companies in the industry will adopt similar solutions to address the challenges of modern supply chain management (Adewumi, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024). By embracing automation and AI, oil and gas companies can not only improve their bottom lines but also position themselves for long-term success in an increasingly competitive and complex global market.

Structure of the proposed System

Advances in automation and artificial intelligence (AI) have significantly enhanced supply chain productivity in the oil and gas industry, with numerous studies supporting the effectiveness of these technologies. Automation in oil and gas supply chains has led to greater efficiency, reduced operational costs, and improved decision-making. AI applications in predictive analytics, robotics, and machine learning have enabled companies to optimize their operations, reduce downtime, and predict equipment failures before they occur, thus preventing costly disruptions. Studies have demonstrated that AI-driven solutions for predictive maintenance, for instance, have led to substantial improvements in the management of assets and equipment within oil and gas operations (Adewumi, et al., 2024, Iwuanyanwu, et al., 2024, Iyelolu, et al., 2024). By integrating AI with sensors and real-time monitoring systems, companies can predict potential failures with high accuracy, thus minimizing unplanned shutdowns and extending the life cycle of machinery. A study by Accenture highlighted that predictive maintenance enabled by AI could reduce maintenance costs by up to 25%, improve equipment uptime by 20%, and extend asset lifespan by up to 30%.

Robotic Process Automation (RPA) is another area where advancements in automation have made a measurable impact. RPA technologies are used to streamline and automate routine administrative tasks, such as invoice processing, inventory management, and procurement workflows. A study by McKinsey reported that RPA could reduce administrative costs by up to 30% while also improving accuracy and speed in transaction processing. In oil and gas supply chains, RPA has been applied to automate scheduling and logistics, which allows for better management of complex supply chain operations, reducing human error and enhancing overall productivity (Adetumi, et al., 2024, Bassey, Rajput & Oyewale, 2024, Owoade, et al., 2024, Soremekun, et al., 2024). The integration of AI with Internet of Things (IoT) devices has also transformed supply chain visibility and inventory management. IoT sensors embedded in equipment or pipeline infrastructure can monitor and transmit real-time data to AI systems, which can then analyze this data to provide insights into performance and potential supply chain disruptions. A report by the World Economic Forum found that AI and IoT integration in supply chain management has led to a 10-15% increase in operational efficiency for companies adopting these technologies. Furthermore, AI-powered analytics platforms allow oil and gas companies to forecast demand more accurately, leading to better planning and inventory management.

Machine learning algorithms have been used to optimize logistics and transportation within the oil and gas supply chain. These algorithms analyze historical data, weather conditions, traffic patterns, and equipment availability to determine the most efficient routes and schedules for transporting goods. Research by Deloitte has shown that machine learning algorithms can reduce transportation costs by up to 15%, while also increasing delivery reliability and reducing fuel consumption. This optimization not only enhances supply chain productivity but also contributes to sustainability by reducing the carbon footprint associated with transportation. In addition, AI has been applied to supply chain risk management (Abiola, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024). By using advanced algorithms to analyze data from various sources such as weather reports, geopolitical trends, and financial markets, AI systems can predict potential risks to the supply chain. This proactive approach enables companies to take preventive actions, mitigate risks, and adjust their strategies accordingly. A study by PwC found that AI-driven risk management tools have led to a 20% reduction in supply chain disruptions and have helped companies respond more effectively to unexpected events.

Overall, existing studies with proven data sets confirm that automation and AI have brought transformative improvements to the productivity and efficiency of supply chains in the oil and gas sector. By leveraging these advanced technologies, companies can optimize operations, reduce costs, enhance decision-making, and improve their ability to respond to changing market conditions. As these technologies continue to evolve, the potential for further advancements in supply chain productivity is substantial, offering a competitive edge to those who adopt them.

The primary objective of incorporating advances in automation and artificial intelligence (AI) into the oil and gas supply chain is to improve overall operational efficiency. Automation and AI technologies aim to streamline processes, reduce manual intervention, and eliminate inefficiencies in supply chain operations. This can be achieved through the integration of AI with existing systems, enhancing decision-making capabilities and enabling faster, more accurate responses to changing conditions. By automating routine tasks, companies can ensure more consistent and error-free execution of critical functions, such as inventory management, procurement, and logistics (Ahuchogu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Ukonne, et al., 2024). Another key objective is to enhance predictive capabilities for maintenance and asset management. AI and machine learning can be used to predict equipment failures and identify potential maintenance issues before they result in costly downtime. The goal is to reduce unplanned maintenance events and optimize the scheduling of repairs and inspections, leading to a more reliable and productive supply chain. This proactive approach extends the lifespan of assets and minimizes disruptions, contributing to long-term cost savings.

A further objective is to optimize inventory management and demand forecasting. By using AI to analyze historical data and market trends, oil and gas companies can better predict demand and adjust their procurement strategies accordingly. The result is more accurate forecasting, which helps to maintain optimal inventory levels, reduce excess stock, and minimize stockouts. Improved demand forecasting enables more efficient supply chain operations and ensures that the right products are available when needed, without unnecessary overstocking. The integration of automation and AI into logistics is another objective aimed at improving supply chain productivity (Adewumi, et al., 2024, Iwuanyanwu, et al., 2024, Iyelolu, et al., 2024). AI algorithms can optimize transportation routes and schedules by considering various factors such as weather, traffic, and equipment availability. This leads to cost savings, reduced fuel consumption, and increased delivery reliability. The goal is to enhance the efficiency of the logistics network and ensure timely delivery of materials and products across the supply chain, minimizing delays and reducing transportation costs.

Additionally, the objective is to enhance risk management across the supply chain. AI systems can analyze large volumes of data from various sources, such as weather patterns, market conditions, and geopolitical developments, to identify potential risks and disruptions in the supply chain. This enables oil and gas companies to take proactive measures to mitigate these risks, ensuring business continuity even in the face of unforeseen events. Ultimately, the overarching objective of integrating automation and AI into the supply chain is to drive cost efficiency, improve productivity, and enhance the resilience of the oil and gas industry (Agu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Umana, et al., 2024). By leveraging these technologies, companies can create more agile, responsive, and sustainable supply chains that are better equipped to handle the challenges of an ever-evolving industry. These advances enable companies to remain competitive, reduce operational costs, and ensure a high level of service across all stages of the supply chain.

One solution is the deployment of AI-powered predictive maintenance systems that monitor the health of equipment in real-time. These systems leverage data from sensors embedded in machinery and infrastructure, allowing AI algorithms to analyze performance patterns and predict failures before they occur. This solution reduces downtime, optimizes repair schedules, and extends the lifespan of critical assets, ensuring higher productivity and cost savings. Another solution is the use of robotic process automation (RPA) to handle repetitive and time-consuming tasks. RPA can automate activities such as data entry, order processing, and invoice management, allowing human employees to focus on more strategic activities (Akinsulire, et al., 2024, Bello, et al., 2022, Owoade, et al., 2024). By improving efficiency and reducing human errors, RPA enhances operational consistency and accelerates workflow within the supply chain. This is particularly valuable in administrative functions, where automation can lead to faster decision-making and smoother coordination between departments.

AI-driven inventory management systems also provide a crucial solution to improve supply chain productivity. These systems use machine learning algorithms to forecast demand more accurately, adjust stock levels, and predict supply shortages or overstocking. By automating inventory control, oil and gas companies can better align procurement with actual demand, reducing storage costs and ensuring that materials are available when needed (Attah, et al., 2024, Bassey, et al., 2024, Oyindamola & Esan, 2023). This solution helps optimize working capital and ensures a more agile and responsive supply chain. Advanced AI algorithms for logistics optimization are another solution designed to streamline transportation management. These algorithms consider factors such as route planning, fuel efficiency, weather conditions, and cargo availability to optimize delivery schedules. By reducing fuel consumption, minimizing delays, and ensuring the most efficient routes are taken, this solution cuts costs and improves the reliability of supply chain operations. AI-powered logistics management platforms also enable dynamic scheduling and real-time updates, which are crucial in handling the complexity of oil and gas logistics.

Risk management can also be enhanced by AI through solutions that provide real-time risk assessments based on data analysis from a wide range of sources. By using AI to analyze geopolitical risks, weather patterns, and market trends, companies can predict potential disruptions and take preventive measures to mitigate their impact. For example, AI-driven risk management systems can automatically flag potential supply chain vulnerabilities, allowing companies to make adjustments to their strategies and reduce the likelihood of disruptions (Adetumi, et al., 2024, Bassey, Rajput & Oyewale, 2024, Owoade, et al., 2024, Soremekun, et al., 2024). This proactive approach to risk management helps companies maintain a steady flow of operations even during unforeseen events. Furthermore, blockchain technology integrated with AI offers transparency and security for supply chain transactions. By providing an immutable ledger for tracking the movement of goods, blockchain ensures the integrity of data, reducing fraud and errors. This solution helps streamline procurement, shipping, and contract management processes, providing a transparent, secure, and efficient framework for oil and gas supply chains.

Together, these solutions form a comprehensive strategy to enhance the productivity and resilience of supply chains in the oil and gas industry. By leveraging automation, AI, and other advanced technologies, companies can optimize their operations, reduce costs, improve forecasting, and strengthen risk management, leading to a more efficient, responsive, and sustainable supply chain. These solutions not only align with industry objectives but also provide a competitive advantage in an increasingly complex and fast-paced market environment (Arinze, et al., 2024, Jambol, et al., 2024, Menezes Rebello, Jäschkea & Nogueira, 2023, Rebello, Jäschkea & Nogueira, 2023).

Future Trends and Directions

The future of automation and AI in enhancing supply chain productivity in the oil and gas industry is filled with exciting possibilities. As the energy sector continues to evolve, so too does the role of emerging technologies in streamlining operations and overcoming traditional supply chain challenges (Akerele, et al., 2024, Bassey, Rajput & Oladepo, 2024, Owoade, et al., 2024). Automation and AI technologies are expected to play an even more significant role in optimizing efficiency, improving decision-making, and addressing the industry’s increasing focus on sustainability and resilience.

One of the key trends shaping the future of supply chain optimization is the increasing reliance on AI-driven predictive analytics. AI technologies are expected to become more advanced, with machine learning algorithms capable of predicting not just short-term fluctuations in demand or supply, but also long-term trends in energy production, consumption, and geopolitical dynamics (Adetumi, et al., 2024, Bassey, Rajput & Oyewale, 2024, Owoade, et al., 2024, Soremekun, et al., 2024). AI systems will be able to identify patterns and make real-time adjustments to supply chains, helping oil and gas companies to manage inventory more effectively and reduce costs associated with overstocking or stockouts. The predictive capabilities of AI will also improve demand forecasting, allowing companies to anticipate market shifts and adjust their supply chain strategies proactively, rather than reactively.

Alongside this, automation technologies such as robotics and drones are set to become more sophisticated, extending their role in logistics, maintenance, and monitoring. In the coming years, automated systems will increasingly handle tasks that were traditionally performed manually, such as routine equipment inspections, pipeline maintenance, and warehouse management (Agupugo, Kehinde & Manuel, 2024, Bassey, Rajput & Oladepo, 2024, Owoade, et al., 2024). The combination of AI and automation will streamline these processes, reducing human error, increasing speed, and enhancing safety by removing workers from hazardous environments. For instance, drones will not only be used for inspections but also for real-time monitoring of offshore and remote onshore operations, helping to detect leaks, damages, or inefficiencies at earlier stages. This will not only improve operational efficiency but also contribute to enhanced safety and risk management within the supply chain.

In terms of sustainability, automation and AI will play a crucial role in helping oil and gas companies reduce their environmental footprint. AI-driven supply chain optimization will help reduce waste by streamlining production, transportation, and distribution processes. By using predictive models, companies can avoid overproduction, ensuring that energy resources are used more efficiently, reducing excess stock, and minimizing the environmental impact of production (Agu, et al., 2024, Bassey, et al., 2024, Oyewale & Bassey, 2024, Umana, et al., 2024). Moreover, AI can assist in the management of renewable energy sources in hybrid energy systems, helping to balance energy grids more effectively and reduce reliance on fossil fuels. In the future, AI may be employed to automate the integration of renewable energy into the supply chain, optimizing the use of solar, wind, and other renewable sources in conjunction with traditional oil and gas resources.

The potential for AI and automation to enhance resilience within the supply chain is another critical aspect of their future role in the oil and gas industry. Resilience is becoming increasingly important as the industry faces new challenges, such as geopolitical tensions, natural disasters, and fluctuating oil prices (Attah, et al., 2024, Bassey, et al., 2024, Oyindamola & Esan, 2023). AI systems will allow companies to monitor and analyze external and internal factors, providing early warnings of potential disruptions. In the future, these technologies will enable more agile and adaptive supply chains, capable of adjusting in real-time to changing circumstances. This adaptability is essential in ensuring business continuity and minimizing the impact of disruptions on operations, particularly in remote or offshore environments where logistics and supply chain management can be especially challenging.

Furthermore, automation and AI technologies will help to drive the digital transformation of supply chain management in the oil and gas sector. The integration of advanced technologies into supply chains will facilitate the development of interconnected, data-driven ecosystems. Sensors, IoT devices, and AI-powered systems will collect and analyze data in real-time, offering companies greater visibility into every aspect of their supply chains (Aminu, et al., 2024, Bassey, Juliet & Stephen, 2024, Runsewe, et al., 2024). This interconnectedness will lead to more efficient resource management, real-time tracking of goods, and a more transparent supply chain overall. The increased availability of data and the ability to analyze it quickly will also drive better decision-making, allowing companies to react faster to changes and mitigate risks effectively. Elijah, et al., 2021, presented IoT Architecture for oil and gas as shown in figure 3.

Figure 3: IoT Architecture for oil and gas (Elijah, et al., 2021).

One of the most significant future directions in the oil and gas industry will be the increasing role of autonomous vehicles and fleets. Autonomous trucks and ships, guided by AI algorithms, will be able to transport goods more efficiently and safely across vast distances. These vehicles will not only reduce human error but also help optimize transportation routes, cut fuel consumption, and lower costs associated with human labor (Adepoju & Esan, 2024, Bassey, Aigbovbiosa & Agupugo, 2024, Sam-Bulya, et al., 2024). As these autonomous systems become more advanced, they will integrate seamlessly with other parts of the supply chain, ensuring that the flow of goods from production to end-users is as smooth and efficient as possible.

AI and automation are also expected to have a profound impact on the workforce within the oil and gas industry. As technologies evolve, there will be a shift toward a more data-centric approach to supply chain management, where decision-making is informed by real-time analytics and automated systems. This will lead to a change in skill requirements, with workers needing to be more technologically proficient and able to work with AI-driven tools. However, while automation will eliminate some jobs, it will also create new opportunities, particularly in the fields of data science, AI development, and machine learning (Achumie, Bakare & Okeke, 2024, Bassey, 2024, Sam-Bulya, et al., 2024). The key challenge will be for companies to reskill their workforce and manage the transition to more automated systems effectively.

In terms of long-term predictions, AI and automation are expected to reshape the structure of oil and gas supply chains entirely. Instead of relying on traditional, centralized supply chains, the industry is likely to see a shift toward more decentralized models that leverage blockchain, AI, and automation to create more transparent, secure, and efficient systems. Blockchain technology, for example, can enhance transparency by providing immutable records of transactions, ensuring that all stakeholders have access to the same data and can verify the integrity of supply chain processes (Ajayi, et al., 2024, Barrie, et al., 2024, Sam-Bulya, et al., 2024). By combining AI with blockchain, the oil and gas industry can create smarter, more secure supply chains that reduce fraud, streamline compliance, and improve trust among all participants.

In the future, oil and gas companies will also increasingly adopt AI and automation as part of their strategy to address the growing pressure for sustainability. As regulations around carbon emissions become more stringent, these technologies will help companies reduce their environmental impact by optimizing energy production, consumption, and transportation (Adewumi, et al., 2024, Bakare, et al., 2024, Sanyaolu, et al., 2024). AI-powered energy management systems will enable companies to optimize energy usage and integrate renewable energy sources more efficiently, ultimately helping the industry meet its sustainability goals while maintaining profitability.

Looking ahead, it is clear that automation and AI will continue to evolve and play an essential role in enhancing the productivity, efficiency, and resilience of oil and gas supply chains. The convergence of these technologies will drive smarter, more agile operations that can respond to the dynamic challenges faced by the industry. With their potential to reduce costs, improve safety, enhance decision-making, and promote sustainability, AI and automation are poised to reshape the oil and gas industry for the better (Adeniran, et al., 2024, Bakare, et al., 2024, Sanyaolu, et al., 2024).

Summary of the system for advances in automation and AI

Advances in automation and artificial intelligence (AI) hold immense potential for transforming supply chain productivity in the oil and gas industry. Developing a proposed system to harness these technologies requires an integrated approach that addresses the complexities of this sector. A well-designed system should focus on optimizing operations, minimizing risks, and driving sustainable practices while maintaining cost-effectiveness. The foundation of the proposed system lies in its ability to provide end-to-end visibility and control over the supply chain (Ahuchogu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Ukonne, et al., 2024). This can be achieved by integrating IoT devices, AI-powered analytics, and automation tools across various stages of the supply chain. IoT sensors and RFID tags will monitor inventory levels, asset locations, and equipment conditions in real time. These devices will generate a continuous stream of data, enabling a comprehensive overview of the supply chain’s operational status. By ensuring data accuracy and timeliness, the system will eliminate manual errors, reduce inventory discrepancies, and support proactive decision-making.

A core feature of the system is AI-driven demand forecasting. Using advanced machine learning models, the system will analyze historical data, market trends, and external factors such as weather conditions and geopolitical developments. This predictive capability will enable oil and gas companies to anticipate demand fluctuations and align their production schedules accordingly. Improved demand forecasting will reduce excess inventory, minimize stockouts, and optimize resource utilization, ensuring a balanced and efficient supply chain (Adewumi, et al., 2024, Iwuanyanwu, et al., 2024, Iyelolu, et al., 2024). Another critical component of the proposed system is predictive maintenance, which leverages AI algorithms to monitor the health of critical equipment and infrastructure. Data collected from IoT sensors will be processed by machine learning models to identify patterns that indicate potential equipment failures. This proactive approach will allow maintenance teams to address issues before they escalate, reducing downtime and enhancing operational reliability. The system will also prioritize maintenance tasks based on urgency, ensuring that resources are allocated effectively.

The system will incorporate digital twin technology to simulate supply chain processes in a virtual environment. By creating digital replicas of physical assets and operations, companies can test various scenarios, identify inefficiencies, and develop optimization strategies without disrupting real-world activities. Digital twins will enable supply chain managers to experiment with different configurations, evaluate the impact of potential changes, and implement data-driven improvements. This capability will enhance agility and adaptability, allowing companies to respond effectively to dynamic market conditions. To optimize logistics and transportation, the system will employ AI-powered route optimization algorithms. These algorithms will analyze factors such as traffic patterns, weather conditions, and fuel costs to determine the most efficient delivery routes (Abiola, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024). By minimizing transportation times and fuel consumption, the system will reduce operational costs and contribute to sustainability goals. Autonomous vehicles and drones can also be integrated into the logistics network to improve efficiency and safety in remote or hazardous areas.

Supplier performance management is another area where the system will have a significant impact. AI tools will assess supplier performance by analyzing metrics such as delivery times, quality compliance, and cost efficiency. These insights will enable oil and gas companies to collaborate more effectively with suppliers, fostering long-term partnerships and reducing risks associated with supplier disruptions. Additionally, blockchain technology can be integrated into the system to enhance transparency and traceability in supplier transactions. By providing a secure and immutable record of interactions, blockchain will build trust and reduce disputes.

The system will emphasize sustainability by optimizing energy usage and tracking environmental performance. AI algorithms will monitor energy consumption across transportation, storage, and production processes, identifying areas for improvement. The system will also track the use of sustainable materials and ensure compliance with environmental regulations (Adetumi, et al., 2024, Bassey, Rajput & Oyewale, 2024, Owoade, et al., 2024, Soremekun, et al., 2024). These capabilities will help oil and gas companies achieve their sustainability objectives while maintaining operational efficiency. Workforce productivity and safety will be enhanced through the integration of AI-assisted training and collaborative robots. The system will provide immersive training experiences using virtual reality (VR) and AI, equipping employees with the skills needed to manage advanced technologies and comply with safety protocols. Collaborative robots, or cobots, will be deployed to handle hazardous tasks, reducing the risk of accidents and improving overall safety.

The proposed system will include a centralized control platform that provides users with real-time insights and actionable recommendations. This platform will aggregate data from various sources, including IoT devices, AI models, and blockchain networks, presenting it in an intuitive interface. Users will be able to monitor key performance indicators (KPIs), track progress toward objectives, and make informed decisions based on data-driven insights. The platform will also support automated workflows, ensuring that routine tasks are executed seamlessly and efficiently (Agu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Umana, et al., 2024). Implementing the system will require addressing several challenges, including high initial investment costs, data integration complexities, and cybersecurity risks. To overcome these challenges, companies should adopt a phased implementation strategy, starting with pilot projects that demonstrate the system’s value. Investing in robust cybersecurity measures will protect the system from potential threats and ensure the integrity of sensitive data. Additionally, reskilling programs will prepare the workforce for the transition to advanced technologies, enabling employees to adapt to new roles and responsibilities.

The system’s architecture will be designed for scalability, allowing oil and gas companies to expand its capabilities as their needs evolve. By leveraging cloud computing and edge computing technologies, the system will process and store data efficiently, enabling real-time decision-making and reducing latency. Interoperability will be a key consideration, ensuring that the system integrates seamlessly with existing infrastructure and third-party applications. The long-term vision for the proposed system includes the development of fully autonomous supply chains (Attah, et al., 2024, Bassey, et al., 2024, Oyindamola & Esan, 2023). By combining AI, IoT, and blockchain technologies, the system will enable end-to-end automation, from production planning to last-mile delivery. This level of automation will maximize efficiency, reduce costs, and enhance supply chain resilience. Furthermore, continuous advancements in AI and machine learning will drive ongoing improvements, ensuring that the system remains at the forefront of innovation. The proposed system for advancing automation and AI in oil and gas supply chain management represents a transformative solution to the industry’s most pressing challenges. By integrating IoT devices, AI-driven analytics, and automation tools, the system will optimize operations, enhance safety, and drive sustainability. While challenges such as initial costs and workforce adaptation must be addressed, the benefits of implementing such a system far outweigh the drawbacks (Arinze, et al., 2024, Jambol, et al., 2024, Menezes Rebello, Jäschkea & Nogueira, 2023, Rebello, Jäschkea & Nogueira, 2023). As oil and gas companies embrace these technologies, they will position themselves for long-term success in an increasingly competitive and dynamic market.

The proposed system for leveraging advances in automation and artificial intelligence (AI) offers significant improvements over existing systems in enhancing supply chain productivity in the oil and gas industry. The evidence supporting this superiority lies in its ability to address inefficiencies, mitigate risks, and drive sustainability through innovative technologies that are often lacking or underutilized in current practices. One of the most compelling pieces of evidence is the comprehensive integration of IoT devices and AI-driven analytics in the proposed system, providing end-to-end visibility and control across the supply chain. Existing systems frequently rely on siloed data and manual processes, leading to delays and inaccuracies in decision-making (Abiola, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024). By continuously monitoring inventory levels, equipment conditions, and asset locations through IoT sensors and RFID tags, the proposed system ensures real-time data availability. This seamless flow of accurate information significantly reduces manual errors and operational bottlenecks that are prevalent in traditional systems.

AI-powered demand forecasting is another area where the proposed system outperforms existing methods. Traditional demand forecasting often depends on historical data and lacks the sophistication to consider external variables such as market trends, weather conditions, and geopolitical events. The proposed system employs machine learning models that analyze diverse datasets to predict demand with greater accuracy. This enhanced forecasting capability minimizes excess inventory, reduces stockouts, and optimizes resource allocation, resulting in substantial cost savings and operational efficiency improvements (Akinsulire, et al., 2024, Bello, et al., 2022, Owoade, et al., 2024). Predictive maintenance further illustrates the advantages of the proposed system. In many existing supply chain setups, maintenance is reactive, performed only after equipment failures occur. This reactive approach results in costly downtimes and unplanned disruptions. The proposed system uses AI algorithms to analyze sensor data and detect early signs of equipment deterioration. By addressing potential failures proactively, it significantly reduces downtime, increases operational reliability, and extends the lifespan of critical assets, providing clear evidence of its superiority.

Digital twin technology incorporated into the proposed system offers unparalleled advantages over conventional systems. Existing supply chain operations rarely leverage virtual simulation tools, limiting their ability to test and optimize processes without real-world disruptions. The proposed system enables companies to create digital replicas of physical assets and processes, allowing them to evaluate different scenarios and implement data-driven improvements with minimal risk (Adeniran, et al., 2024, Bakare, et al., 2024, Sanyaolu, et al., 2024). This capability enhances adaptability and agility, ensuring that companies can respond effectively to changing market dynamics. In logistics and transportation, the proposed system surpasses traditional methods by using AI-powered route optimization algorithms. Current systems often rely on static route planning or manual adjustments, which fail to consider real-time variables such as traffic patterns, weather conditions, and fuel prices. The proposed system dynamically calculates the most efficient delivery routes, reducing transportation times, fuel consumption, and associated costs. Moreover, the integration of autonomous vehicles and drones in the logistics network offers a level of efficiency and safety that is unattainable with existing systems, particularly in remote or hazardous areas.

Supplier performance management is another domain where the proposed system demonstrates clear advantages. Traditional systems typically assess supplier performance using basic metrics and lack the analytical depth to identify nuanced patterns or areas for improvement. The proposed system employs AI tools to analyze supplier data comprehensively, providing insights into delivery timeliness, quality compliance, and cost efficiency. Blockchain technology further enhances this capability by ensuring transparency and traceability in supplier transactions (Attah, et al., 2024, Bassey, et al., 2024, Oyindamola & Esan, 2023). These innovations foster stronger supplier relationships and reduce risks, giving the proposed system a decisive edge over existing approaches. Sustainability is a critical area where the proposed system outperforms current practices. Many existing systems lack the tools to monitor and optimize energy consumption or track environmental performance effectively. The proposed system uses AI to analyze energy usage across the supply chain, identify inefficiencies, and recommend actionable improvements. Additionally, it ensures compliance with environmental regulations and supports the adoption of sustainable materials, aligning with industry-wide sustainability goals and reducing the carbon footprint of operations.

Workforce productivity and safety also benefit significantly from the proposed system compared to traditional methods. Existing systems often rely on conventional training programs and manual processes, which may not adequately prepare employees for advanced technologies or ensure compliance with safety standards (Arinze, et al., 2024, Jambol, et al., 2024, Menezes Rebello, Jäschkea & Nogueira, 2023, Rebello, Jäschkea & Nogueira, 2023). The proposed system uses virtual reality (VR) and AI-assisted training to provide immersive and effective learning experiences. Collaborative robots (cobots) further enhance safety by taking on hazardous tasks, reducing the risk of accidents and improving overall operational efficiency. The centralized control platform in the proposed system represents a major improvement over disjointed interfaces and limited reporting capabilities in traditional systems (Adetumi, et al., 2024, Bassey, Rajput & Oyewale, 2024, Owoade, et al., 2024, Soremekun, et al., 2024). By aggregating data from multiple sources into an intuitive dashboard, the proposed system empowers supply chain managers with real-time insights and actionable recommendations. Automated workflows streamline routine tasks, reducing human error and freeing up resources for more strategic activities. This level of integration and automation is rarely achieved with existing systems, making the proposed solution significantly more effective.

The scalability and flexibility of the proposed system further highlight its superiority. Traditional systems often struggle to adapt to changing demands or integrate with new technologies. The proposed system is designed to evolve alongside the company’s needs, leveraging cloud computing and edge computing for efficient data processing and storage. Its interoperable architecture ensures seamless integration with existing infrastructure and third-party applications, future-proofing the supply chain against technological advancements. In addressing challenges such as high initial investment costs, data integration complexities, and cybersecurity risks, the proposed system adopts a phased implementation approach and robust security measures (Akinsulire, et al., 2024, Bello, et al., 2022, Owoade, et al., 2024). These proactive strategies mitigate the barriers that often hinder the adoption of advanced technologies in existing systems. Furthermore, reskilling programs ensure that the workforce is well-prepared to transition to the new system, reducing resistance to change and enhancing overall adoption rates.

The long-term vision for the proposed system, including the development of fully autonomous supply chains, underscores its transformative potential. While existing systems rely heavily on manual intervention and reactive strategies, the proposed system combines AI, IoT, and blockchain technologies to achieve end-to-end automation. This vision ensures maximum efficiency, cost-effectiveness, and resilience, setting a new standard for supply chain productivity in the oil and gas industry (Agu, et al., 2024, Bello, et al., 2023, Owoade, et al., 2024, Umana, et al., 2024). In conclusion, the proposed system offers clear and compelling evidence of its superiority over existing supply chain systems in the oil and gas sector. By integrating cutting-edge technologies, addressing inefficiencies, and driving sustainable practices, it provides a comprehensive solution to the industry’s most pressing challenges. As companies implement this system, they will not only enhance their operational capabilities but also position themselves for long-term success in an increasingly competitive and dynamic market.

CONCLUSION

Advances in automation and AI have revolutionized supply chain productivity in the oil and gas industry, significantly improving operational efficiency, reducing costs, and enhancing risk mitigation strategies. The integration of these technologies has facilitated faster, more accurate decision-making, optimized inventory management, and streamlined administrative tasks. Predictive maintenance, powered by AI, has reduced downtime and extended asset lifecycles, while automation in logistics and inventory management has minimized human error and increased the speed of operations. By harnessing the power of AI-driven algorithms, companies have been able to forecast demand more accurately, optimize routes, and reduce waste, ultimately improving the overall efficiency of the supply chain.

The long-term benefits of adopting automation and AI are substantial. These technologies not only drive down costs by reducing reliance on human labor and minimizing operational inefficiencies but also improve risk management through proactive identification and mitigation of potential disruptions. AI’s ability to provide real-time insights into supply chain activities enables companies to respond faster to changing market conditions and unforeseen challenges. Furthermore, automation enhances resource allocation, ensuring that assets are used more effectively and waste is minimized. The combined impact of these advancements contributes to greater sustainability, reduced environmental impact, and the ability to meet regulatory compliance more efficiently.

However, implementing automation and AI in oil and gas supply chains is not without its challenges. Data integration, cybersecurity risks, and the need for workforce adaptation are among the primary obstacles that companies must address. Overcoming these challenges requires strategic planning, investment in employee reskilling, and robust cybersecurity measures to ensure that systems remain secure and efficient. Despite these hurdles, the future of automation and AI in the oil and gas industry remains promising. As technologies evolve and become more accessible, the industry is poised to achieve even greater levels of efficiency, resilience, and sustainability. The ongoing development of these tools will continue to drive innovation, offering solutions to current challenges and positioning the industry for future success.

REFERENCES

1. Abdul-Azeez, O. Y., Nwabekee, U. S., Agu, E. E., & Ignatius, T. (2024). Strategic approaches to sustainability in multinational corporations: A comprehensive review. International Journal of Frontline Research in Science and Technology, 3(02), 038-054.
2. Abdul-Azeez, O. Y., Nwabekee, U. S., Agu, E. E., & Ijomah, T. I. (2024). Sustainability in product life cycle management: A review of best practices and innovations.
3. Abiola, O. Akintobi, Ifeanyi C. Okeke, Ajani, O. B. (2024): Integrating taxation, financial controls, and risk management: a comprehensive model for small and medium enterprises to foster economic resilience.International Journal of Management & Entrepreneurship Research. P-ISSN: 2664-3588, E-ISSN: 2664-3596, Volume 6, Issue12,P.No.3902-3914,December2024 https://www.fepbl.com/ index.php/ijmer/article/view/1746
4. Abiola, O. Akintobi, Ifeanyi C. Okeke, Ajani, O. B. (2024): The role of tax policies in shaping the digital economy Addressing challenges and harnessing opportunities for sustainable growth.International Journal of advanced Economics. P-ISSN: 2707-2134, E-ISSN: 2707-2142.Volume 6, Issue 12, P.No.777-787, December2024 https://doi.org/10.51594/ijae.v6i12.1752
5. Achumie, G. O., Bakare, O. A., & Okeke, N. I. (2024). Implementing fair lending practices: Advanced data analytics approaches and regulatory compliance. Finance & Accounting Research Journal, 6(10), 1818-1831.
6. Achumie, G. O., Bakare, O. A., & Okeke, N. I. (2024). Innovative financial and operational models for affordable housing: A review of emerging market strategies. International Journal of Applied Research in Social Sciences, 6(10), 2342-2362.
7. Adanyin, A., 2024. Ethical AI in Retail: Consumer Privacy and Fairness. European Journal of Computer Science and Information Technology, 12(7), pp.21-35.
8. Adeleke, A. G., Sanyaolu, T. O., Efunniyi, C. P., Akwawa, L. A., & Azubuko, C. F. (2024). Leveraging UX design and prototyping in agile development: A business analyst’s perspective. Engineering Science & Technology Journal, 5(8).
9. Adeniran, A. I., Abhulimen, A. O., Obiki-Osafiele. A. N., Osundare, O. S., Agu, E. E., Efunniyi, C. P. (2024). Strategic risk management in financial institutions: Ensuring robust regulatory compliance. Finance & Accounting Research Journal, 2024, 06(08), 1582-1596, https://doi.org /10.51594/farj.v6i8.1508
10. Adeniran, I. A, Agu E. E., Efunniyi C. P., Osundare O. S., & Iriogbe H.O. (2024). The future of project management in the digital age: Trends, challenges, and opportunities. Engineering Science & Technology Journal, Volume 5, Issue 8, P.No. 2632-2648, 2024.30.
11. Adeniran, I. A., Abhulimen, A. O., Obiki-Osafiele, A. N., Osundare, O. S., Agu, E. E., Efunniyi, C. P. (2024). Data-Driven approaches to improve customer experience in banking: Techniques and outcomes. International Journal of Management & Entrepreneurship Research, 2024, 06(08), 2797-2818. https://doi.org/10.51594/ijmer.v6i8.1467
12. Adeniran, I. A., Abhulimen, A. O., Obiki-Osafiele, A. N., Osundare, O. S., Agu, E. E., Efunniyi, C. P. (2024). Global perspectives on FinTech: Empowering SMEs and women in emerging markets for financial inclusion. International Journal of Frontline Research in Multidisciplinary Studies, 2024, 03(02), 030–037. https://doi.org/10.56355/ijfrms.2024.3.2.0027
13. Adepoju, O. O., & Esan, O. (2024). Tertiary institutions and lifelong learning via digital tools in Nigeria: A review. International Journal of Management Sciences and Business Research, 13(2), 01–13.
14. Adepoju, O. O., Atomon, O. B., & Esan, O. (2024). Entrepreneurial innovative practices and profitability of small and medium enterprises in Oyo State. International Journal of Management Leadership and Productivity Development, 2(1), 16–28.
15. Adepoju, O. O., Esan, O., & Ayeni, D. O. (2024). Innovation and social media agility on the survival of small and medium enterprises (SMEs) in Ibadan, Oyo State, Nigeria. Journal of Research in Business and Management, 12(3), 38–48. Quest Journals.
16. Adetumi, A., Somto E. E, Ngodoo J.S.B, Ajani, O. B. (2024): A Comprehensive Framework for Venture Capital Accessibility: Bridging the Gap for Women Entrepreneurs and Black-Owned Businesses.International Journal of Engineering Research & Development. E- ISSN: 2278-067X, P-ISSN: 2278-800X, Volume 20, Issue 11, PP 527-533, November 2024 https://www.ijerd.com/v20-i11.html
17. Adetumi, A., Somto E. E., Ngodoo J. S. B., Ajani, O. B. (2024): Enhancing financial fraud detection using adaptive machine learning models and business analytics.International Journal of Scientific Research Updates ,2024, 08(02), 012–021.https://doi.org/10.53430/ijsru.2024.8.2.0054
18. Adetumi, A., Somto E.E, Ngodoo J.S.B, Ajani, O. B. (2024): Advancing business performance through data-driven process automation: A case study of digital transformation in the banking sector. International Journal of Multidisciplinary Research Updates,2024, 08(02), https://doi.org/10.53430/ijmru.2024.8.2.0049
19. Adetumi, A., Somto E.E, Ngodoo J.S.B, Ajani, O. B. (2024): Leveraging business analytics to build cyber resilience in fintech: Integrating AI and governance, risk and compliance (GRC) models. International Journal of Multidisciplinary Research Updates 2024, 08(02),023-032.https://doi.org/10.53430/ijmru.2024.8.2.0050
20. Adetumi, A., Somto E.E, Ngodoo J.S.B, Ajani, O. B. (2024): Strategic innovation in business models: Leveraging emerging technologies to gain a competitive advantage.International Journal of Management & Entrepreneurship Research. P-ISSN:2664-3588, E-ISSN: 2664-3596.Volume 6, Issue 10, P.No.3372-3398, October 2024.  https://doi.org/10.51594/ijmer.v6i10.1639
21. Adetumi, A.,Somto E.E, Ngodoo J.S.B, Ajani, O. B. (2024): Creating Inclusive Venture Capital Ecosystems: Policies and Practices to Support Black-Owned Businessesadvantage.International Journal of Engineering Research  & Development. E- ISSN: 2278-067X, P-ISSN: 2278-800X,. Volume 20, Issue 11, PP 534-538, November 2024 https://www.ijerd.com/v20-i11.html
22. Adewumi, A., Ewim, S. E., Sam-Bulya, N. J., & Ajani, O. B. (2024). Advancing business performance through data-driven process automation: A case study of digital transformation in the banking sector.
23. Adewumi, A., Ewim, S. E., Sam-Bulya, N. J., & Ajani, O. B. (2024). Strategic innovation in business models: Leveraging emerging technologies to gain a competitive advantage. International Journal of Management & Entrepreneurship Research, 6(10), 3372-3398.
24. Adewumi, A., Ewim, S. E., Sam-Bulya, N. J., & Ajani, O. B. (2024). Leveraging business analytics to build cyber resilience in fintech: Integrating AI and governance, risk, and compliance (GRC) models. International Journal of Multidisciplinary Research Updates, 23-32.
25. Adewumi, A., Ewim, S. E., Sam-Bulya, N. J., & Ajani, O. B. (2024). Enhancing financial fraud detection using adaptive machine learning models and business analytics. International Journal of Scientific Research Updates, 012-021.
26. Adewumi, A., Ibeh, C. V., Asuzu, O. F., Adelekan, O. A., Awonnuga, K. F., & Daraojimba, O. D. (2024). Data analytics in retail banking: A review of customer insights and financial services innovation. Business, Organizations and Society (BOSOC), 2(1), 16-21.
27. Adewumi, A., Oshioste, E. E., Asuzu, O. F., Ndubuisi, N. L., Awonnuga, K. F., & Daraojimba, O. H. (2024). Business intelligence tools in finance: A review of trends in the USA and Africa. World Journal of Advanced Research and Reviews, 21(3), 608-616.
28. Adewumi, G., Dada, S. A., Azai, J. S. & Oware, E. (2024): A systematic review of strategies for enhancing pharmaceutical supply chain resilience in the U.S. International Medical Science Research Journal. 2024, 4(11):961-972. DOI: 10.51594/imsrj.v4i11.1711
29. Adewusi, A. O., Asuzu, O. F., Olorunsogo, T., Iwuanyanwu, C., Adaga, E., & Daraojimba, O. D. (2024): A Review of Technologies for Sustainable Farming Practices: AI in Precision Agriculture. World Journal of Advanced Research and Reviews, 21(01), pp 2276-2895
30. Adeyemi, A. B., Ohakawa, T. C., Okwandu, A. C., Iwuanyanwu, O., & Ifechukwu, G. O. (2024). Advanced Building Information Modeling (BIM) for affordable housing projects: Enhancing design efficiency and cost management.
31. Adeyemi, A. B., Ohakawa, T. C., Okwandu, A. C., Iwuanyanwu, O., & Ifechukwu, G. O. (2024). Energy-Efficient Building Envelopes for Affordable Housing: Design Strategies and Material Choices. Energy, 13(9), 248-254.
32. Agu, E. E., Abhulimen, A. O., Obiki-Osafiele, A. N., Osundare, O. S., Adeniran, I. A., & Efunniyi, C. P. (2024). Discussing ethical considerations and solutions for ensuring fairness in AI-driven financial services. International Journal of Frontier Research in Science, 3(2), 001-009.
33. Agu, E. E., Chiekezie, N. R., Abhulimen, A. O., & Obiki-Osafiele,, A. N. (2024): Building sustainable business models with predictive analytics: Case studies from various industries.
34. Agu, E. E., Chiekezie, N. R., Abhulimen, A. O., & Obiki-Osafiele, A. N. (2024). Harnessing digital transformation to solve operational bottlenecks in banking. World Journal of Advanced Science and Technology, 6(01), 046-056.
35. Agu, E. E., Komolafe, M. O., Ejike, O. G., Ewim, C. P., & Okeke, I. C. (2024). A model for VAT standardization in Nigeria: Enhancing collection and compliance. Finance & Accounting Research Journal, 6(9), 1677-1693.
36. Agu, E. E., Komolafe, M. O., Ejike, O. G., Ewim, C. P., & Okeke, I. C. (2024). A model for standardized financial advisory services for Nigerian startups: Fostering entrepreneurial growth. International Journal of Management & Entrepreneurship Research, 6(9), 3116-3133.
37. Agu, E. E., Komolafe, M. O., Ejike, O. G., Ewim, C. P., & Okeke, I. C. (2024). A model for standardizing Nigerian SMEs: Enhancing competitiveness through quality control. International Journal of Management & Entrepreneurship Research, 6(9), 3096-3115.
38. Agu, E.E, Abhulimen A.O ,Obiki-Osafiele, A.N, Osundare O.S , Adeniran I.A and Efunniyi C.P. (2024): Utilizing AI-driven predictive analytics to reduce credit risk and enhance financial inclusion. International Journal of Frontline Research in Multidisciplinary Studies, 2024, 03(02), 020–029.
39. Agu, E.E, Abhulimen A.O, Obiki-Osafiele, A.N, Osundare O.S, Adeniran I.A and Efunniyi C.P. (2024): Proposing strategic models for integrating financial literacy into national public education systems, International Journal of Frontline Research in Multidisciplinary Studies, 2024, 03(02), 010–019.
40. Agu, E.E, Chiekezie N.R, Abhulimen A.O and Obiki-Osafiele, A.N. (2024): Optimizing supply chains in emerging markets: Addressing key challenges in the financial sector. World Journal of Advanced Science and Technology, 2024, 06(01), 035–045.
41. Agu, E.E, Chiekezie N.R, Abhulimen A.O, & Obiki-Osafiele, A.N. (2024): Building sustainable business models with predictive analytics: Case studies from various industries. International Journal of Advanced Economics, Volume 6, Issue 8, P.No.394-406, 2024.
42. Agu, E.E, Efunniyi C.P, Adeniran I.A, Osundare O.S, and Iriogbe H.O. (2024): Challenges and opportunities in data-driven decision making for the energy sector. International Journal of Scholarly Research in Multidisciplinary Studies, 2024.
43. Agupugo, C. P., Ajayi, A. O., Salihu, O. S., & Barrie, I. (2024). Large scale utility solar installation in the USA: Environmental impact and job. Global Journal of Engineering and Technology Advances, 21(02), 023-034.
44. Agupugo, C. P., Barrie, I., Makai, C. C., & Alaka, E. (2024). AI learning-driven optimization of microgrid systems for rural electrification and economic empowerment.
45. Agupugo, C.P., Kehinde, H.M. & Manuel, H.N.N., 2024. Optimization of microgrid operations using renewable energy sources. Engineering Science & Technology Journal, 5(7), pp.2379-2401.
46. Ahuchogu, M. C., Sanyaolu, T. O., & Adeleke, A. G. (2024). Exploring sustainable and efficient supply chains innovative models for electric vehicle parts distribution. Global Journal of Research in Science and Technology, 2(01), 078-085.
47. Ahuchogu, M. C., Sanyaolu, T. O., & Adeleke, A. G. (2024). Independent Researcher. UK, & Leenit, UK Balancing innovation with risk management in digital banking transformation for enhanced customer satisfaction and security.
48. Ahuchogu, M. C., Sanyaolu, T. O., Adeleke, A. G., Researcher, U. I., & Leenit, U. K. (2024). Balancing innovation with risk management in digital banking transformation for enhanced customer satisfaction and security. International Journal of Management & Entrepreneurship Research P-ISSN, 2664-3588.
49. Ajayi, A. O., Agupugo, C. P., Nwanevu, C., & Chimziebere, C. (2024). Review of penetration and impact of utility solar installation in developing countries: policy and challenges.
50. Ajiga, D., Okeleke, P. A., Folorunsho, S. O., & Ezeigweneme, C. (2024). Methodologies for developing scalable software frameworks that support growing business needs.
51. Akerele, J.I., Uzoka, A., Ojukwu, P.U. and Olamijuwon, O.J. (2024). Data management solutions for real-time analytics in retail cloud environments. Engineering Science & Technology Journal. P-ISSN: 2708-8944, E-ISSN: 2708-8952 Volume 5, Issue 11, P.3180-3192, November 2024. DOI: 10.51594/estj.v5i11.1706: http://www.fepbl.com/index.php/estj
52. Akerele, J.I., Uzoka, A., Ojukwu, P.U. and Olamijuwon, O.J. (2024). Minimizing downtime in E-Commerce platforms through containerization and orchestration. International Journal of Multidisciplinary Research Updates, 2024, 08(02), 079–086. https://doi.org/10.53430/ijmru.2024.8.2.0056
53. Akerele, J.I., Uzoka, A., Ojukwu, P.U. and Olamijuwon, O.J. (2024). Data management solutions for real-time analytics in retail cloud environments. Engineering Science & Technology Journal. P-ISSN: 2708-8944, E-ISSN: 2708-8952 Volume 5, Issue 11, P.3180-3192, November 2024. DOI: 10.51594/estj.v5i11.1706: http://www.fepbl.com/index.php/estj
54. Akinbolaji, T.J., 2024. Advanced integration of artificial intelligence and machine learning for real-time threat detection in cloud computing environments. Iconic Research and Engineering Journals, 6(10), pp.980-991.
55. Akinbolaji, T.J., 2024. Novel strategies for cost optimization and performance enhancement in cloud-based systems. International Journal of Modern Science and Research Technology, 2(10), pp.66-79.
56. Akinsulire, A. A., Idemudia, C., Okwandu, A. C., & Iwuanyanwu, O. (2024). Dynamic financial modeling and feasibility studies for affordable housing policies: A conceptual synthesis. International Journal of Advanced Economics, 6(7), 288-305.
57. Akinsulire, A. A., Idemudia, C., Okwandu, A. C., & Iwuanyanwu, O. (2024). Public-Private partnership frameworks for financing affordable housing: Lessons and models. International Journal of Management & Entrepreneurship Research, 6(7), 2314-2331.
58. Akinsulire, A. A., Idemudia, C., Okwandu, A. C., & Iwuanyanwu, O. (2024). Economic and social impact of affordable housing policies: A comparative review. International Journal of Applied Research in Social Sciences, 6(7), 1433-1448.
59. Akinsulire, A. A., Idemudia, C., Okwandu, A. C., & Iwuanyanwu, O. (2024). Supply chain management and operational efficiency in affordable housing: An integrated review. Magna Scientia Advanced Research and Reviews, 11(2), 105-118.
60. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, C. P. (2024). Data-driven employee engagement: A pathway to superior customer service. World Journal of Advanced Research and Reviews, 23(3).
61. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, C. P. (2024). Optimizing Customer Service through Workforce Analytics: The Role of HR in Data-Driven Decision-Making. International Journal of Research and Scientific Innovation, 11(8), 1628-1639.
62. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, C. P. (2024). The impact of workforce analytics on HR strategies for customer service excellence. World Journal of Advanced Research and Reviews, 23(3).
63. Alabi, O. A., Ajayi, F. A., Udeh, C. A., & Efunniyi, F. P. (2024). Predictive Analytics in Human Resources: Enhancing Workforce Planning and Customer Experience. International Journal of Research and Scientific Innovation, 11(9), 149-158.
64. Aminu, M., Akinsanya, A., Dako, D. A., & Oyedokun, O. (2024): Enhancing Cyber Threat Detection through Real-time Threat Intelligence and Adaptive Defense Mechanisms.
65. Aminu, M., Akinsanya, A., Oyedokun, O., & Tosin, O. (2024). A Review of Advanced Cyber Threat Detection Techniques in Critical Infrastructure: Evolution, Current State, and Future Directions.
66. Aniebonam, E.E. (2024). Strategic Management in Turbulent Markets: A Case Study of the USA. International Journal of Modern Science and Research Technology ISSN No- 2584-2706. https://doi.org/10.5281/zenodo.13739161
67. Anozie, U. C., Dada, S. A.,Okonkwo F. C.,, Egunlae, O. O., Animasahun, B. O. & Mazino, O. (2024): The convergence of edge computing and supply chain resilience in retail marketing. International Journal of Science and Research Archive. 2024, 12(02), 2769–2779. DOI: 10.30574/ijsra.2024.12.2.1574
68. Arinze, C. A., Izionworu, V. O., Isong, D., Daudu, C. D., & Adefemi, A. (2024). Integrating artificial intelligence into engineering processes for improved efficiency and safety in oil and gas operations. Open Access Research Journal of Engineering and Technology, 6(1), 39-51.
69. Arinze, C. A., Izionworu, V. O., Isong, D., Daudu, C. D., & Adefemi, A. (2024). Predictive maintenance in oil and gas facilities, leveraging ai for asset integrity management.
70. Arinze, C. A., Izionworu, V. O., Isong, D., Daudu, C. D., & Adefemi, A. (2024). Predictive maintenance in oil and gas facilities, leveraging ai for asset integrity management.
71. Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024). Strategic frameworks for digital transformation across logistics and energy sectors: Bridging technology with business strategy.
72. Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024). Strategic partnerships for urban sustainability: Developing a conceptual framework for integrating technology in community-focused initiatives.
73. Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024): Cross-functional team dynamics in technology management: a comprehensive review of efficiency and innovation enhancement.
74. Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024): Enhancing supply chain resilience through artificial intelligence: Analyzing problem-solving approaches in logistics management.
75. Audu, A.J. and Umana, A.U., 2024. Advances in environmental compliance monitoring in the oil and gas industry: Challenges and opportunities. International Journal of Scientific Research Updates, 8(2), pp.48-59. doi: 10.53430/ijsru.2024.8.2.0062
76. Audu, A.J. and Umana, A.U., 2024. The role of environmental compliance in oil and gas production: A critical assessment of pollution control strategies in the Nigerian petrochemical industry. International Journal of Scientific Research Updates, 8(2), pp.36-47. doi: 10.53430/ijsru.2024.8.2.0061.
77. Audu, A.J., Umana, A.U. and Garba, B.M.P., 2024. The role of digital tools in enhancing environmental monitoring and business efficiency. International Journal of Multidisciplinary Research Updates, 8(2), pp.39-48. doi: 10.53430/ijmru.2024.8.2.0052.
78. Ayanponle, L. O., Awonuga, K. F., Asuzu, O. F., Daraojimba, R. E., Elufioye, O. A., & Daraojimba, O. D. (2024). A review of innovative HR strategies in enhancing workforce efficiency in the US. International Journal of Science and Research Archive, 11(1), 817-827.
79. Ayanponle, L. O., Elufioye, O. A., Asuzu, O. F., Ndubuisi, N. L., Awonuga, K. F., & Daraojimba, R. E. (2024). The future of work and human resources: A review of emerging trends and HR’s evolving role. International Journal of Science and Research Archive, 11(2), 113-124.
80. Babalola, O., Nwatu, C. E., Folorunso, A. & Adewa, A. (2024). A governance framework model for cloud computing: Role of AI, security, compliance, and management. World Journal of Advanced Research Reviews
81. Bakare, O. A., Aziza, O. R., Uzougbo, N. S., & Oduro, P. (2024). A human resources and legal risk management framework for labour disputes in the petroleum industry.
82. Bakare, O. A., Aziza, O. R., Uzougbo, N. S., & Oduro, P. (2024). An integrated legal and business strategy framework for corporate growth in Nigerian companies. International Journal of Management & Entrepreneurship Research, 6(10), 3259-3282.
83. Barrie, I., Agupugo, C. P., Iguare, H. O., & Folarin, A. (2024). Leveraging machine learning to optimize renewable energy integration in developing economies. Global Journal of Engineering and Technology Advances, 20(03), 080-093.
84. Bassey, K. E. (2024). From waste to wonder: Developing engineered nanomaterials for multifaceted applications.
85. Bassey, K. E., Aigbovbiosa, J., & Agupugo, C. (2024). Risk management strategies in renewable energy investment. International Journal of Novel Research in Engineering and Science, 11(1), 138–148. Novelty Journals.
86. Bassey, K. E., Juliet, A. R., & Stephen, A. O. (2024). AI-Enhanced lifecycle assessment of renewable energy systems. Engineering Science & Technology Journal, 5(7), 2082-2099.
87. Bassey, K. E., Opoku-Boateng, J., Antwi, B. O., & Ntiakoh, A. (2024). Economic impact of digital twins on renewable energy investments. Engineering Science & Technology Journal, 5(7), 2232-2247.
88. Bassey, K. E., Opoku-Boateng, J., Antwi, B. O., Ntiakoh, A., & Juliet, A. R. (2024). Digital twin technology for renewable energy microgrids. Engineering Science & Technology Journal, 5(7), 2248-2272.
89. Bassey, K. E., Rajput, S. A., & Oladepo, O. O. (2024). Space-based solar power: Unlocking continuous, renewable energy through wireless transmission from space.
90. Bassey, K. E., Rajput, S. A., & Oyewale, K. (2024). Peer-to-peer energy trading: Innovations, regulatory challenges, and the future of decentralized energy systems.
91. Bassey, K. E., Rajput, S. A., Oladepo, O. O., & Oyewale, K. (2024). Optimizing behavioral and economic strategies for the ubiquitous integration of wireless energy transmission in smart cities.
92. Bello, O. A., Folorunso, A., Ejiofor, O. E., Budale, F. Z., Adebayo, K., & Babatunde, O. A. (2023). Machine Learning Approaches for Enhancing Fraud Prevention in Financial Transactions. International Journal of Management Technology, 10(1), 85-108.
93. Bello, O. A., Folorunso, A., Ogundipe, A., Kazeem, O., Budale, A., Zainab, F., & Ejiofor, O. E. (2022). Enhancing Cyber Financial Fraud Detection Using Deep Learning Techniques: A Study on Neural Networks and Anomaly Detection. International Journal of Network and Communication Research, 7(1), 90-113.
94. Bello, O. A., Folorunso, A., Onwuchekwa, J., & Ejiofor, O. E. (2023). A Comprehensive Framework for Strengthening USA Financial Cybersecurity: Integrating Machine Learning and AI in Fraud Detection Systems. European Journal of Computer Science and Information Technology, 11(6), 62-83.
95. Bello, O. A., Folorunso, A., Onwuchekwa, J., Ejiofor, O. E., Budale, F. Z., & Egwuonwu, M. N. (2023). Analysing the Impact of Advanced Analytics on Fraud Detection: A Machine Learning Perspective. European Journal of Computer Science and Information Technology, 11(6), 103-126.
96. Bello, O. A., Ogundipe, A., Mohammed, D., Adebola, F., & Alonge, O. A. (2023). AI-Driven Approaches for Real-Time Fraud Detection in US Financial Transactions: Challenges and Opportunities. European Journal of Computer Science and Information Technology, 11(6), 84-102.
97. Bristol-Alagbariya, B., Ayanponle, L. O., & Ogedengbe, D. E. (2024). Sustainable business expansion: HR strategies and frameworks for supporting growth and stability. International Journal of Management & Entrepreneurship Research, 6(12), 3871–3882. Fair East Publishers.
98. Bristol-Alagbariya, B., Ayanponle, O. L., & Ogedengbe, D. E. (2024). Leadership development and talent management in constrained resource settings: A strategic HR perspective. Comprehensive Research and Reviews Journal, 2(02), 013–022. Comprehensive Research and Reviews Journal.
99. Bristol-Alagbariya, B., Ayanponle, O. L., & Ogedengbe, D. E. (2024). Advanced strategies for managing industrial and community relations in high-impact environments. International Journal of Science and Technology Research Archive, 7(02), 076–083. International Journal of Science and Technology Research Archive.
100. Bristol-Alagbariya, B., Ayanponle, O. L., & Ogedengbe, D. E. (2024). Operational efficiency through HR management: Strategies for maximizing budget and personnel resources. International Journal of Management & Entrepreneurship Research, 6(12), 3860–3870. Fair East Publishers.
101. Chikwe, C.F., Dagunduro, A. O., Ajuwon, O.A and Ediae, A.A. (2024). Sociological barriers to equitable digital learning: A data-driven approach. Research and Reviews in Multidisciplinary Studies. 02(01), 027–034. https://doi.org/10.57219/crrms.2024.2.1.0038
102. Chikwe, C.F., Dagunduro, A. O., Ajuwon, O.A and Kuteesa, K.N. (2024). Organizational Development and Gender Inclusivity: A Framework for Sustainable Change. International Journal of Engineering Inventions. 13(9). 284-291
103. Crawford, T., Duong S., Fueston R., Lawani A., Owoade S., Uzoka A., Parizi R. M., & Yazdinejad A. (2023). AI in Software Engineering: A Survey on Project Management Applications. arXiv:2307.15224
104. Dada, S. A. & Adekola, A. D. (2024): Leveraging digital marketing for health behavior change: A model for engaging patients through pharmacies. International Journal of Science and Technology Research Archive, 2024, 7(2):050-059. DOI: 10.53771/ijstra.2024.7.2.0063
105. Dada, S. A. & Adekola, A. D. (2024): Optimizing preventive healthcare uptake in community pharmacies using data-driven marketing strategies. International Journal of Life Science Research Archive, 2024, 07(02), 071–079. DOI: 10.53771/ijlsra.2024.7.2.0076
106. Dada, S. A. Korang, A. Umoren, J. & Donkor, A. A. (2024): The role of artificial intelligence and machine learning in optimizing U.S. healthcare supply chain management. World Journal of Advanced Research and Reviews, 2024, 24(02), 1996–2002 DOI: 10.30574/wjarr.2024.24.2.3343
107. Dada, S. A., Okonkwo, F. C. & Cudjoe-Mensah, Y. M. (2024): Sustainable supply chain management in U.S. healthcare: Strategies for reducing environmental impact without compromising access. International Journal of Science and Research Archive, 2024, 13(02), 870–879. DOI: 10.30574/ijsra.2024.13.2.2113
108. Dagunduro, A. O. and Adenugba, A.A. (2024). Dynamics of Capital and Recurrent Household Expenditure among Female Breadwinners in Ibadan’s Informal Markets. Ibadan Journal of Sociology (IJS), 15(1)
109. Dagunduro, A.O., Ajuwon, O.A., Ediae, A.A and Chikwe, C.F. (2024). Exploring gender dynamics in the workplace: strategies for equitable professional development. Comprehensive Research and Reviews in Multidisciplinary Studies, 02(01), 001–008. https://doi.org/10.57219/crrms.2024.2.1.0035
110. Dagunduro, A.O., Chikwe, C.F., Ajuwon, O.A & Ediae, A.A. (2024). Adaptive Learning Models for Diverse Classrooms: Enhancing Educational Equity. International Journal of Applied Research in Social Sciences, 6(9), 2228-2240
111. Ebeh, C. O., Okwandu, A. C., Abdulwaheed, S. A., & Iwuanyanwu, O. (2024). Integration of renewable energy systems in modern construction: Benefits and challenges. International Journal of Engineering Research and Development, 20(8), 341–349.
112. Efunniyi, C.P, Abhulimen A.O, Obiki-Osafiele, A.N, Osundare O.S, Agu E.E, & Adeniran I.A. (2024): Strengthening corporate governance and financial compliance: Enhancing accountability and transparency. Finance & Accounting Research Journal, Volume 6, Issue 8, P.No. 1597-1616, 2024.
113. Efunniyi, C.P, Agu E.E, Abhulimen A.O,Obiki-Osafiele, A.N, Osundare O.S, & Adeniran I.A. (2024): Sustainable banking in Africa: A review of Environmental, Social, and Governance (ESG) integration. Finance & Accounting Research Journal Volume 5, Issue 12, P.No. 460-478, 2024.
114. Ehidiamen, A. J., & Oladapo, O. O. (2024). Enhancing ethical standards in clinical trials: A deep dive into regulatory compliance, informed consent, and participant rights protection frameworks. World Journal of Biology Pharmacy and Health Sciences, 20(01), 309–320.
115. Ehidiamen, A. J., & Oladapo, O. O. (2024). Optimizing contract negotiations in clinical research: Legal strategies for safeguarding sponsors, vendors, and institutions in complex trial environments. World Journal of Biology Pharmacy and Health Sciences, 20(01), 335–348.
116. Ehidiamen, A. J., & Oladapo, O. O. (2024). The intersection of clinical trial management and patient advocacy: How research professionals can promote patient rights while upholding clinical excellence. World Journal of Biology Pharmacy and Health Sciences, 20(01), 296–308.
117. Ehidiamen, A. J., & Oladapo, O. O. (2024). The role of electronic data capture systems in clinical trials: Streamlining data integrity and improving compliance with FDA and ICH/GCP guidelines. World Journal of Biology Pharmacy and Health Sciences, 20(01), 321–334.
118. Ehidiamen, A.J. and Oladapo, O.O., 2024. Enhancing ethical standards in clinical trials: A deep dive into regulatory compliance, informed consent, and participant rights protection frameworks. World Journal of Biology Pharmacy and Health Sciences, 20(1), pp.309–320. Available at: https://doi.org/10.30574/wjbphs.2024.20.1.0788.
119. Ehidiamen, A.J. and Oladapo, O.O., 2024. Innovative approaches to risk management in clinical research: Balancing ethical standards, regulatory compliance, and intellectual property concerns. World Journal of Biology Pharmacy and Health Sciences, 20(1), pp.349–363
120. Ehidiamen, A.J. and Oladapo, O.O., 2024. Optimizing contract negotiations in clinical research: Legal strategies for safeguarding sponsors, vendors, and institutions in complex trial environments. World Journal of Biology Pharmacy and Health Sciences, 20(1), pp.335–348. Available at: https://doi.org/10.30574/wjbphs.2024.20.1.0790.
121. Ehidiamen, A.J. and Oladapo, O.O., 2024. The intersection of clinical trial management and patient advocacy: How research professionals can promote patient rights while upholding clinical excellence. World Journal of Biology Pharmacy and Health Sciences, 20(1), pp.296–308. Available at: https://doi.org/10.30574/wjbphs.2024.20.1.0787.
122. Ehidiamen, A.J. and Oladapo, O.O., 2024. The role of electronic data capture systems in clinical trials: Streamlining data integrity and improving compliance with FDA and ICH/GCP guidelines. World Journal of Biology Pharmacy and Health Sciences, 20(1), pp.321–334. Available at: https://doi.org/10.30574/wjbphs.2024.20.1.0789.
123. Eleogu, T., Okonkwo, F., Daraojimba, R. E., Odulaja, B. A., Ogedengbe, D. E., & Udeh, C. A. (2024). Revolutionizing Renewable Energy Workforce Dynamics: HR’ s Role in Shaping the Future. International Journal of Research and Scientific Innovation, 10(12), 402-422.
124. Elijah, O., Ling, P. A., Rahim, S. K. A., Geok, T. K., Arsad, A., Kadir, E. A., … & Abdulfatah, M. Y. (2021). A survey on industry 4.0 for the oil and gas industry: upstream sector. IEEE Access, 9, 144438-144468.
125. Elugbaju, W. K., Okeke, N. I., & Alabi, O. A. (2024). Conceptual framework for enhancing decision-making in higher education through data-driven governance. Global Journal of Advanced Research and Reviews, 2(02), 016-030.
126. Elugbaju, W. K., Okeke, N. I., & Alabi, O. A. (2024). SaaS-based reporting systems in higher education: A digital transition framework for operational resilience. International Journal of Applied Research in Social Sciences, 6(10). Fair East Publishers.
127. Evurulobi, C.I., Dagunduro, A.O and Ajuwon, O.A. (2024). Language learning technologies: A review of trends in the USA and globally. World Journal of Advanced Research and Reviews, 2024, 23(03), 2697–2707. https://doi.org/10.30574/wjarr.2024.23.3.2851
128. Evurulobi, C.I., Dagunduro, A.O and Ajuwon, O.A. (2024). Theoretical perspectives on digital literacy programs: A comparative study of initiatives in Africa and the United States. World Journal of Advanced Research and Reviews, 2024, 23(03), 2708–2714. https://doi.org/10.30574/wjarr.2024.23.3.2853
129. Evurulobi, C.I., Dagunduro, A.O., and Ajuwon, O.A. (2024). A review of multicultural communication dynamics in the U.S.: Highlighting challenges and successful strategies in a diverse society. 23(03), 2204–2219 https://doi.org/10.30574/wjarr.2024.23.3.2850
130. Ewim, C. P. M., Komolafe, M. O., Ejike, O. G., Agu, E. E., & Okeke, I. C. (2024). A policy model for standardizing Nigeria’s tax systems through international collaboration. Finance & Accounting Research Journal P-ISSN, 1694-1712.
131. Ewim, C. P., Komolafe, M. O., Ejike, O. G.,   Agu, E. E., & Okeke, I. C. (2024). A trust-building model for financial advisory services in Nigeria’s investment sector. International Journal of Applied Research in Social Sciences, 6(9), 2276-2292.
132. Ewim, C. P., Komolafe, M. O., Ejike, O. G.,   Agu, E. E., & Okeke, I. C. (2024). A regulatory model for harmonizing tax collection across Nigerian states: The role of the joint tax board. International Journal of Advanced Economics, 6(9), 457-470.
133. Ezeafulukwe, C., Owolabi, O.R., Asuzu, O.F., Onyekwelu, S.C., Ike, C.U. and Bello, B.G., 2024. Exploring career pathways for people with special needs in STEM and beyond. International Journal of Applied Research in Social Sciences, 6(2), pp.140-150.
134. Folorunso, A. (2024). Assessment of Internet Safety, Cybersecurity Awareness and Risks in Technology Environment among College Students. Cybersecurity Awareness and Risks in Technology Environment among College Students (July 01, 2024).
135. Folorunso, A. (2024). Cybersecurity And Its Global Applicability to Decision Making: A Comprehensive Approach in The University System. Available at SSRN 4955601.
136. Folorunso, A. (2024). Information Security Management Systems (ISMS) on patient information protection within the healthcare industry in Oyo, Nigeria. Nigeria (April 12, 2024).
137. Folorunso, A., Adewumi, T., Adewa, A., Okonkwo, R., & Olawumi, T. N. (2024). Impact of AI on cybersecurity and security compliance. Global Journal of Engineering and Technology Advances, 21(01), 167-184.
138. Folorunso, A., Mohammed, V., Wada, I., & Samuel, B. (2024). The impact of ISO security standards on enhancing cybersecurity posture in organizations. World Journal of Advanced Research and Reviews, 24(1), 2582-2595.
139. Folorunso, A., Nwatu Olufunbi Babalola, C. E., Adedoyin, A., & Ogundipe, F. (2024). Policy framework for cloud computing: AI, governance, compliance, and management. Global Journal of Engineering and Technology Advances
140. Folorunso, A., Olanipekun, K., Adewumi, T., & Samuel, B. (2024). A policy framework on AI usage in developing countries and its impact. Global Journal of Engineering and Technology Advances, 21(01),154-166.
141. Folorunso, A., Wada, I., Samuel, B., & Mohammed, V. (2024). Security compliance and its implication for cybersecurity.
142. Garba, B.M.P., Umar, M.O., Umana, A.U., Olu, J.S. and Ologun, A., 2024. Sustainable architectural solutions for affordable housing in Nigeria: A case study approach. World Journal of Advanced Research and Reviews, 23(03), pp.434-445. doi: 10.30574/wjarr.2024.23.3.2704.
143. Garba, B.M.P., Umar, M.O., Umana, A.U., Olu, J.S. and Ologun, A., 2024. Energy efficiency in public buildings: Evaluating strategies for tropical and temperate climates. World Journal of Advanced Research and Reviews, 23(03), pp.409-421. doi: 10.30574/wjarr.2024.23.3.2702.
144. Gil-Ozoudeh, I., Iwuanyanwu, O., Okwandu, A. C., & Ike, C. S. (2024). The impact of green building certifications on market value and occupant satisfaction. Page 1 International Journal of Management & Entrepreneurship Research, Volume 6, Issue 8, August 2024. No. 2782-2796 Page 2782
145. Givan, B. (2024). Navigating the Hybrid Workforce: Challenges and Strategies in Modern HR Management. Journal of Economic, Bussines and Accounting (COSTING), 7(3), 6065-6073.
146. Ikwuanusi, U.F., Onunka, O., Owoade, S.J. and Uzoka, A. (2024). Digital transformation in public sector services: Enhancing productivity and accountability through scalable software solutions. International Journal of Applied Research in Social Sciences. P-ISSN: 2706-9176, E-ISSN: 2706-9184 Volume 6, Issue 11, P.No. 2744-2774, November 2024. DOI: 10.51594/ijarss.v6i11.1724: http://www.fepbl.com/index.php/ijarss
147. Iriogbe, H.O, Agu E.E, Efunniyi C.P, Osundare O.S, & Adeniran I.A. (2024): The role of project management in driving innovation, economic growth, and future trends. international Journal of Management & Entrepreneurship Research, Volume 6, Issue 8, P.No.2819-2834, 2024.
148. Iwuanyanwu, O., Gil-Ozoudeh, I., Okwandu, A. C., & Ike, C. S. (2024). Cultural and social dimensions of green architecture: Designing for sustainability and community well-being. International Journal of Applied Research in Social Sciences, Volume 6, Issue 8, August 2024, No. 1951-1968
149. Iwuanyanwu, O., Gil-Ozoudeh, I., Okwandu, A. C., & Ike, C. S. (2022). The integration of renewable energy systems in green buildings: Challenges and opportunities. Journal of Applied
150. Iwuanyanwu, O., Gil-Ozoudeh, I., Okwandu, A. C., & Ike, C. S. (2024). The role of green building materials in sustainable architecture: Innovations, challenges, and future trends. International Journal of Applied Research in Social Sciences, 6(8), 1935-1950. p. 1935,
151. Iyelolu, T.V, Agu E.E, Idemudia C, Ijomah T.I. (2024): Improving Customer Engagement and CRM for SMEs with AI Driven Solutions and Future Enhancements. International Journal of Engineering Research and Development, Volume 20, Issue 8 (2024),
152. Iyelolu, T.V, Agu E.E, Idemudia C, Ijomah T.I. (2024): Leveraging Artificial Intelligence for Personalized Marketing Campaigns to Improve Conversion Rates. International Journal of Engineering Research and Development, Volume 20, Issue 8 (2024).
153. Jambol, D. D., Sofoluwe, O. O., Ukato, A., & Ochulor, O. J. (2024). Transforming equipment management in oil and gas with AI-driven predictive maintenance. Computer Science & IT Research Journal, 5(5), 1090-1112.
154. Kedi, W. E., Ejimuda, C., Idemudia, C., & Ijomah, T. I. (2024). AI software for personalized marketing automation in SMEs: Enhancing customer experience and sales.
155. Kedi, W. E., Ejimuda, C., Idemudia, C., & Ijomah, T. I. (2024). AI Chatbot integration in SME marketing platforms: Improving customer interaction and service efficiency. International Journal of Management & Entrepreneurship Research, 6(7), 2332-2341.
156. Kedi, W. E., Ejimuda, C., Idemudia, C., & Ijomah, T. I. (2024). Machine learning software for optimizing SME social media marketing campaigns. Computer Science & IT Research Journal, 5(7), 1634-1647.
157. Komolafe, M. O., Agu, E. E., Ejike, O. G., Ewim, C. P., & Okeke, I. C. (2024). A financial inclusion model for Nigeria: Standardizing advisory services to reach the unbanked. International Journal of Applied Research in Social Sciences, 6(9), 2258-2275.
158. Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 100041. https://doi.org/10.1016/j.egyai.2020.100041
159. Manuel, H. N. N., Kehinde, H. M., Agupugo, C. P., & Manuel, A. C. N. (2024). The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction.World Journal of Advanced Research and Reviews, 23(2), 1333-1348.
160. Mbunge, E., Fashoto, S. G., Akinnuwesi, B. A., Metfula, A. S., Manyatsi, J. S., Sanni, S. A., … & Nxumalo, M. A. (2024, April). Machine Learning Approaches for Predicting Individual’s Financial Inclusion Status with Imbalanced Dataset. In Computer Science On-line Conference (pp. 648-658). Cham: Springer Nature Switzerland.
161. Menezes Rebello, C., Jäschkea, J., & Nogueira, I. B. (2023). Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry. arXiv e-prints, arXiv-2311.
162. Mokogwu, C., Achumie, G. O., Adeleke, A. G., Okeke, I. C., & Ewim, C. P. (2024). A leadership and policy development model for driving operational success in tech companies. International Journal of Frontline Research in Multidisciplinary Studies, 4(1), 1–14.
163. Mokogwu, C., Achumie, G. O., Gbolahan, A., Adeleke, I. C. O., & Ewim, C. P. M. (2024). Corporate Governance in Technology Startups: A Conceptual Model for Strengthening Stakeholder Engagement. Corporate Governance, 20(11), 317-330.
164. Mokogwu, O., Achumie, G. O., Adeleke, A. G., Okeke, I. C., & Ewim, C. P.  (2024). A strategic IT policy implementation model for enhancing customer satisfaction in digital markets. International Journal of Frontline Research and Reviews, 3(1), 20–37.
165. Mokogwu, O., Achumie, G. O., Adeleke, A. G., Okeke, I. C., & Ewim, C. P. (2024). A data-driven operations management model: Implementing MIS for strategic decision making in tech businesses. International Journal of Frontline Research and Reviews, 3(1), 1–19.
166. Nwabekee, U. S., Abdul-Azeez, O. Y., Agu, E. E., & Ignatius, T. (2024). Challenges and opportunities in implementing circular economy models in FMCG Industries.
167. Nwabekee, U. S., Abdul-Azeez, O. Y., Agu, E. E., & Ignatius, T. (2024). Digital transformation in marketing strategies: The role of data analytics and CRM tools. International Journal of Frontline Research in Science and Technology, 3(2), 055-072.
168. Nwabekee, U. S., Abdul-Azeez, O. Y., Agu, E. E., & Ijomah, T. I. (2024). Innovative sustainability initiatives in the FMCG industry: A review of challenges and successes.
169. Nwabekee, U. S., Abdul-Azeez, O. Y., Agu, E. E., & Ijomah, T. I. (2024). Brand management and market expansion in emerging economies: A comparative analysis. International Journal of Management & Entrepreneurship Research, 6(9).
170. Nwabekee, U. S., Abdul-Azeez, O. Y., Agu, E. E., & Ijomah, T. I. (2024). Optimizing brand visibility and market presence through cross-functional team leadership: Lessons from the FMCG sector. International Journal of Management & Entrepreneurship Research, 6(9).
171. Nwaimo, C. S., Adegbola, A. E., & Adegbola, M. D. (2024). Data-driven strategies for enhancing user engagement in digital platforms. International Journal of Management & Entrepreneurship Research, 6(6), 1854-1868.
172. Nwaimo, C. S., Adegbola, A. E., & Adegbola, M. D. (2024). Predictive analytics for financial inclusion: Using machine learning to improve credit access for under banked populations. Computer Science & IT Research Journal, 5(6), 1358-1373.
173. Nwaimo, C. S., Adegbola, A. E., & Adegbola, M. D. (2024). Sustainable business intelligence solutions: Integrating advanced tools for long-term business growth.
174. Nwaimo, C. S., Adegbola, A. E., Adegbola, M. D., & Adeusi, K. B. (2024). Evaluating the role of big data analytics in enhancing accuracy and efficiency in accounting: A critical review. Finance & Accounting Research Journal, 6(6), 877-892.
175. Nwobodo, L. K., Nwaimo, C. S., & Adegbola, M. D. (2024). Strategic financial decision-making in sustainable energy investments: Leveraging big data for maximum impact. International Journal of Management & Entrepreneurship Research, 6(6), 1982-1996.
176. Obiki-Osafiele, A.N., Efunniyi C.P, Abhulimen A.O, Osundare O. S, Agu E.E, & Adeniran I. A. (2024): Theoretical models for enhancing operational efficiency through technology in Nigerian businesses, International Journal of Applied Research in Social Sciences Volume 6, Issue 8, P.No. 1969-1989, 2024
177. Ochuba, N. A., Adewumi, A., & Olutimehin, D. O. (2024). The role of AI in financial market development: enhancing efficiency and accessibility in emerging economies. Finance & Accounting Research Journal, 6(3), 421-436.
178. Odunaiya, O. G., Soyombo, O. T., Abioye, K. M., & Adeleke, A. G. (2024). The role of digital transformation in enhancing clean energy startups’ success: An analysis of IT integration strategies.
179. Ogedengbe, D. E., Oladapo, J. O., Elufioye, O. A., Ejairu, E., & Ezeafulukwe, C. (2024). Strategic HRM in the logistics and shipping sector: Challenges and opportunities.
180. Ogedengbe, D. E., Olatoye, F. O., Oladapo, J. O., Nwankwo, E. E., Soyombo, O. T., & Scholastica, U. C. (2024). Strategic HRM in the logistics and shipping sector: Challenges and opportunities. International Journal of Science and Research Archive, 11(1), 2000-2011.
181. Ogunsina, M., Efunniyi, C. P., Osundare, O. S., Folorunsho, S. O., & Akwawa, L. A. (2024). Advanced sensor fusion and localization techniques for autonomous systems: A review and new approaches. International Journal of Frontline Research in Engineering and Technology, 2(1).
182. Ogunsina, M., Efunniyi, C. P., Osundare, O. S., Folorunsho, S. O., & Akwawa, L. A. (2024). Cognitive architectures for autonomous robots: Towards human-level autonomy and beyond.
183. Ohakawa, T. C., Adeyemi, A. B., Okwandu, A. C., Iwuanyanwu, O., & Ifechukwu, G. O. (2024). Digital Tools and Technologies in Affordable Housing Design: Leveraging AI and Machine Learning for Optimized Outcomes.
184. Ojukwu, P. U., Cadet E., Osundare O. S., Fakeyede O. G., Ige A. B., & Uzoka A. (2024). The crucial role of education in fostering sustainability awareness and promoting cybersecurity measures. International Journal of Frontline Research in Science and Technology, 2024, 04(01), 018–034. https://doi.org/10.56355/ijfrst.2024.4.1.0050
185. Ojukwu, P. U., Cadet E., Osundare O. S., Fakeyede O. G., Ige A. B., & Uzoka A. (2024). Exploring theoretical constructs of blockchain technology in banking: Applications in African and U. S. financial institutions. International Journal of Frontline Research in Science and Technology, 2024, 04(01), 035–042. https://doi.org/10.56355/ijfrst.2024.4.1.005
186. Ojukwu, P.U., Cadet, E., Osundare, O.S., Fakeyede, O.G., Ige, A.B. and Uzoka, A. (2024). Advancing Green Bonds through FinTech Innovations: A Conceptual Insight into Opportunities and Challenges. International Journal of Engineering Research and Development. P-ISSN: 2278-800X, E-ISSN: 2278-067X Volume 20, Issue 11, P.565-576, November 2024.
187. Okeke, I. C., Agu, E. E., Ejike, O. G., Ewim, C. P., & Komolafe, M. O.  (2024). A compliance and audit model for tackling tax evasion in Nigeria. International Journal of Frontline Research and Reviews, 2(2), 57–68.
188. Okeke, I. C., Agu, E. E., Ejike, O. G., Ewim, C. P., & Komolafe, M. O.  (2024). A comparative model for financial advisory standardization in Nigeria and sub-Saharan Africa. International Journal of Frontline Research and Reviews, 2(2), 45–056.
189. Okeke, I.C, Komolafe M.O, Agu E.E, Ejike O.G & Ewim C.P-M. (2024): A trust-building model for financial advisory services in Nigeria’s investment sector. International Journal of Applied Research in Social Sciences P-ISSN: 2706-9176, E-ISSN: 2706-9184 Volume 6, Issue 9, P.No. 2276-2292, September 2024.
190. Okeke, N. I., Alabi, O. A., Igwe, A. N., Ofodile, O. C., & Ewim, C. P.-M. (2024.). AI-powered customer experience optimization: Enhancing financial inclusion in underserved communities. International Journal of Applied Research in Social Sciences, 6(10). Fair East Publishers.
191. Okeke, N. I., Alabi, O. A., Igwe, A. N., Ofodile, O. C., & Ewim, C. P.-M. (2024). Customer journey mapping framework for SMEs: Enhancing customer satisfaction and business growth. World Journal of Advanced Research and Reviews, 24(1). GSC Online Press.
192. Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024). Artificial intelligence in SME financial decision-making: Tools for enhancing efficiency and profitability. Open Access Research Journal of Multidisciplinary Studies, 8(01), 150-163.
193. Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024). Forecasting financial stability in SMEs: A comprehensive analysis of strategic budgeting and revenue management. Open Access Research Journal of Multidisciplinary Studies, 8(1), 139-149. OARJ.
194. Okeke, N. I., Bakare, O. A., & Achumie, G. O. (2024). Integrating policy incentives and risk management for effective green finance in emerging markets. International Journal of Frontiers in Science and Technology Research, 7(1), 76-88.
195. Olorunyomi, T. D., Okeke, I. C.  Sanyaolu, T. O., & Adeleke, A. G. (2024). Streamlining budgeting and forecasting across multi-cloud environments with dynamic financial models. Finance & Accounting Research Journal, 6(10), 1881-1892.
196. Olorunyomi, T. D., Sanyaolu, T.  O., Adeleke, A. G., & Okeke,I. C. (2024). Analyzing financial analysts’ role in business optimization and advanced data analytics. International Journal of Frontiers in Science and Technology Research, 7(2), 29–38.
197. Omowole, B.M., Olufemi-Philips, A.Q., Ofadile O.C., Eyo-Udo, N.L., & Ewim, S.E. (2024). Big data for SMEs: A review of utilization strategies for market analysis and customer insight. International Journal of Frontline Research in Multidisciplinary Studies, 5(1), 001-018.
198. Omowole, B.M., Olufemi-Philips, A.Q., Ofadile O.C., Eyo-Udo, N.L., & Ewim, S.E. 2024. Barriers and drivers of digital transformation in SMEs: A conceptual analysis. International Journal of Frontline Research in Multidisciplinary Studies, 5(2), 019-036.
199. Omowole, B.M., Olufemi-Philips, A.Q., Ofadile O.C., Eyo-Udo, N.L., & Ewim, S.E. 2024. Conceptualizing agile business practices for enhancing SME resilience to economic shocks. International Journal of Scholarly Research and Reviews, 5(2), 070-088.
200. Omowole, B.M., Olufemi-Philips, A.Q., Ofodili, O.C., Eyo-Udo, N.L. & Ewim, S.E. 2024. Conceptualizing green business practices in SMEs for sustainable development. International Journal of Management & Entrepreneurship Research, 6(11), 3778-3805.
201. Omowole, B.M., Urefe O., Mokogwu, C., & Ewim, S.E. (2024). Strategic approaches to enhancing credit risk management in Microfinance institutions. International Journal of Frontline Research in Multidisciplinary Studies, 4(1), 053-062.
202. Omowole, B.M., Urefe O., Mokogwu, C., & Ewim, S.E. 2024. Integrating fintech and innovation in microfinance: Transforming credit accessibility for small businesses. International Journal of Frontline Research and Reviews, 3(1), 090-100.
203. Omowole, B.M., Urefe, O., Mokogwu, C., & Ewim, S.E. 2024. The role of Fintech-enabled microfinance in SME growth and economic resilience. Finance & Accounting Research Journal, 6(11), 2134-2146.
204. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Automating fraud prevention in credit and debit transactions through intelligent queue systems and regression testing. International Journal of Frontline Research in Science and Technology, 4(1), pp. 45–62.
205. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Cloud-based compliance and data security solutions in financial applications using CI/CD pipelines. World Journal of Engineering and Technology Research, 8(2), pp. 152–169.
206. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Digital transformation in public sector services: Enhancing productivity and accountability through scalable software solutions. International Journal of Applied Research in Social Sciences, 6(11), pp. 2744–2774.
207. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Enhancing financial portfolio management with predictive analytics and scalable data modeling techniques. International Journal of Applied Research in Social Sciences, 6(11), pp. 2678–2690.
208. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Innovative cross-platform health applications to improve accessibility in underserved communities. International Journal of Applied Research in Social Sciences, 6(11), pp. 2727–2743.
209. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Optimizing urban mobility with multi-modal transportation solutions: A digital approach to sustainable infrastructure. Engineering Science & Technology Journal, 5(11), pp. 3193–3208.
210. Owoade, S.J., Uzoka, A., Akerele, J.I. & Ojukwu, P.U., 2024. Revolutionizing library systems with advanced automation: A blueprint for efficiency in academic resource management. International Journal of Scientific Research in Modern Science, 7(3), pp. 123–137.
211. Owoade, S.J., Uzoka, A., Akerele, J.I. and Ojukwu, P.U. (2024). Innovative cross-platform health applications to improve accessibility in underserved communities. International Journal of Applied Research in Social Sciences. P-ISSN: 2706-9176, E-ISSN: 2706-9184 Volume 6, Issue 11, P.No. 2727-2743, November 2024. DOI: 10.51594/ijarss.v6i11.1723: http://www.fepbl.com/index.php/ijarss
212. Owoade, S.J., Uzoka, A., Akerele, J.I. and Ojukwu, P.U. (2024). Optimizing urban mobility with multi-modal transportation solutions: A digital approach to sustainable infrastructure. Engineering Science & Technology Journal. P-ISSN: 2708-8944, E-ISSN: 2708-8952 Volume 5, Issue 11, P.No. 3193-3208, November 2024.  DOI: 10.51594/estj.v5i11.1729: http://www.fepbl.com/index.php/estj
213. Oyewale, K., & Bassey, K. E. (2024). Climate action and social equity: Mitigation strategies and carbon credits.
214. Oyindamola, A., & Esan, O. (2023). Systematic Review of Human Resource Management Demand in the Fourth Industrial Revolution Era: Implication of Upskilling, Reskilling and Deskilling. Lead City Journal of the Social Sciences (LCJSS), 8(2), 88-114.
215. Rebello, C. M., Jäschkea, J., & Nogueira, I. B. (2023). Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas Industry. arXiv preprint arXiv:2311.12755.
216. Runsewe, O., Akwawa, L. A., Folorunsho, S. O., & Osundare, O. S. (2024). Optimizing user interface and user experience in financial applications: A review of techniques and technologies.
217. Sam-Bulya, N. J., Mbanefo, J. V., Ewim, C. P.-M., & Ofodile, O. C. (2024, November). Blockchain for sustainable supply chains: A systematic review and framework for SME implementation. International Journal of Engineering Research and Development, 20(11), 673–690. Zitel Consulting.
218. Sam-Bulya, N. J., Mbanefo, J. V., Ewim, C. P.-M., & Ofodile, O. C. (2024, November). Ensuring privacy and security in sustainable supply chains through distributed ledger technologies. International Journal of Engineering Research and Development, 20(11), 691–702. Zitel Consulting.
219. Sam-Bulya, N. J., Mbanefo, J. V., Ewim, C. P.-M., & Ofodile, O. C. (2024, November). Improving data interoperability in sustainable supply chains using distributed ledger technologies. International Journal of Engineering Research and Development, 20(11), 703–713. Zitel Consulting.
220. Sanyaolu, T. O., Adeleke, A. G., Azubuko, C. F., & Osundare, O. S. (2024). Exploring fintech innovations and their potential to transform the future of financial services and banking.
221. Sanyaolu, T. O., Adeleke, A. G., Azubuko, C. F., & Osundare, O. S. (2024). Harnessing blockchain technology in banking to enhance financial inclusion, security, and transaction efficiency.
222. Segun-Falade, O. D., Osundare, O. S., Abioye, K. M., Adeleke, A. A. G., Pelumi, C., & Efunniyi, E. E. A. (2024). Operationalizing Data Governance: A Workflow-Based Model for Managing Data Quality and Compliance.
223. Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijomah, T. I., & Abdul-Azeez, O. Y. (2024). Assessing the transformative impact of cloud computing on software deployment and management. Computer Science & IT Research Journal, 5(8). https://doi.org/10.51594/csitrj.v5i8.1491
224. Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijoma, T. I., & Abdul-Azeez, O. Y. (2024). Evaluating the role of cloud integration in mobile and desktop operating systems. International Journal of Management & Entrepreneurship Research, 6(8). https://doi.org/10.56781/ijsret.2024.4.1.0019
225. Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijomah, T. I., & Abdul-Azeez, O. Y. (2024). Developing cross-platform software applications to enhance compatibility across devices and systems. Computer Science & IT Research Journal, 5(8). https://doi.org/10.51594/csitrj.v5i8.1492
226. Segun-Falade, O. D., Osundare, O. S., Kedi, W. E., Okeleke, P. A., Ijomah, T. I., & Abdul-Azeez, O. Y. (2024). Developing innovative software solutions for effective energy management systems in industry. Engineering Science & Technology Journal, 5(8). https://doi.org/10.51594/estj.v5i8.1517
227. Shittu, R.A., Ehidiamen, A.J., Ojo, O.O., Zouo, S.J.C., Olamijuwon, J., Omowole, B.M., and Olufemi-Phillips, A.Q., 2024. The role of business intelligence tools in improving healthcare patient outcomes and operations. World Journal of Advanced Research and Reviews, 24(2), pp.1039–1060. Available at: https://doi.org/10.30574/wjarr.2024.24.2.3414.
228. Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379–391. https://doi.org/10.1016/j.ptlrs.2021.05.009
229. Soremekun, Y. M., Abioye, K. M., Sanyaolu, T. O., Adeleke, A. G., & Efunniyi, C. P. (2024). A conceptual model for inclusive lending through fintech innovations: Expanding SME access to capital in the US.
230. Soremekun, Y. M., Abioye, K. M., Sanyaolu, T. O., Adeleke, A. G., & Efunniyi, C. P. (2024). Independent Researcher. UK & OneAdvanced, UK Theoretical foundations of inclusive financial practices and their impact on innovation and competitiveness among US SMEs.
231. Udeh, C. A., Daraojimba, R. E., Odulaja, B. A., Afolabi, J. O. A., Ogedengbe, D. E., & James, O. O. (2024). Youth empowerment in Africa: Lessons for US youth development programs. World Journal of Advanced Research and Reviews, 21(1), 1942-1958.
232. Ukonne, A., Folorunso, A., Babalola, O., & Nwatu, C. E. (2024). Compliance and governance issues in cloud computing and AI: USA and Africa. Global Journal of Engineering and Technology Advances
233. Umana, A.U., Garba, B.M.P. and Audu, A.J., 2024. Innovations in process optimization for environmental sustainability in emerging markets. International Journal of Multidisciplinary Research Updates, 8(2), pp.49-63. doi: 10.53430/ijmru.2024.8.2.0053.
234. Umana, A.U., Garba, B.M.P. and Audu, A.J., 2024. Sustainable business development in resource-intensive industries: Balancing profitability and environmental compliance. International Journal of Multidisciplinary Research Updates, 8(2), pp.64-78. doi: 10.53430/ijmru.2024.8.2.0054.
235. Umana, A.U., Garba, B.M.P., Ologun, A., Olu, J.S. and Umar, M.O., 2024. The impact of indigenous architectural practices on modern urban housing in Sub-Saharan Africa. World Journal of Advanced Research and Reviews, 23(03), pp.422-433. doi: 10.30574/wjarr.2024.23.3.2703.
236. Umana, A.U., Garba, B.M.P., Ologun, A., Olu, J.S. and Umar, M.O., 2024. Architectural design for climate resilience: Adapting buildings to Nigeria’s diverse climatic zones. World Journal of Advanced Research and Reviews, 23(03), pp.397-408. doi: 10.30574/wjarr.2024.23.3.2701.
237. Umana, A.U., Garba, B.M.P., Ologun, A., Olu, J.S. and Umar, M.O., 2024. Innovative design solutions for social housing: Addressing the needs of youth in Urban Nigeria. World Journal of Advanced Research and Reviews, 23(03), pp.383-396. doi: 10.30574/wjarr.2024.23.3.2700.
238. Umana, A.U., Garba, B.M.P., Ologun, A., Olu, J.S. and Umar, M.O., 2024. The role of government policies in promoting social housing: A comparative study between Nigeria and other developing nations. World Journal of Advanced Research and Reviews, 23(03), pp.371-382. doi: 10.30574/wjarr.2024.23.3.2699.
239. Urefe, O., Odonkor, T. N., Chiekezie, N. R., & Agu, E. E. (2024). Enhancing small business success through financial literacy and education. Magna Scientia Advanced Research and Reviews, 11(2).
240. Uzoka A., Cadet E. and Ojukwu P. U. (2024). Applying artificial intelligence in Cybersecurity to enhance threat detection, response, and risk management. Computer Science & IT Research Journal.  P-ISSN: 2709-0043, E-ISSN: 2709-0051 Volume 5, Issue 10, P.2511-2538, October 2024. DOI: 10.51594/csitrj.v5i10.1677: www.fepbl.com/index.php/csitrj
241. Uzoka A., Cadet E. and Ojukwu P. U. (2024). Leveraging AI-Powered chatbots to enhance customer service efficiency and future opportunities in automated support. Computer Science & IT Research Journal. P-ISSN: 2709-0043, E-ISSN: 2709-0051 Volume 5, Issue 10, P.2485-2510, October 2024. DOI: 10.51594/csitrj.v5i10.1676: www.fepbl.com/index.php/csitrj
242. Uzoka A., Cadet E. and Ojukwu P. U. (2024). The role of telecommunications in enabling Internet of Things (IoT) connectivity and applications. Comprehensive Research and Reviews in Science and Technology, 2024, 02(02), 055–073. https://doi.org/10.57219/crrst.2024.2.2.0037