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Settibathini, Venkata Surendra Kumar. "Future of ERP: AI-Driven Transformation for Business Success." International Journal of Professional Studies 19, no. 1 (2025): 212–25. https://doi.org/10.37648/ijps.v19i01.017.
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Integration of artificial intelligence (AI) into Enterprise Resource Planning (ERP) systems is fundamentally changing corporate operations and competitiveness. Historically used to unify corporate operations like finance, supply chain, and human resources, ERP systems are now on the brink of becoming intelligent, flexible platforms able to react rapidly to evolving corporate needs. Apart from improving present operations, artificial intelligence (AI) technologies include robotic process automation, machine learning, and natural language processing are also projecting trends, avoiding disruptions, and customising user experiences. Artificial intelligence (AI) helps ERP systems go from reactive data processors to proactive decision-makers by spotting inefficiencies, demand prediction, and real-time market reaction facilitation. AI-driven ERP systems also enable hyper automation, the simplification of repetitive operations, hence releasing human resources for strategic projects. Edge computing, voice-activated commands, and self-healing software redefining user interfaces and system responsiveness. This change offers previously unheard-of operational agility, higher customer satisfaction, and better decision-making among other advantages. It also offers challenges such data quality management, cybersecurity risks, and the need for qualified workers. As businesses go over this paradigm change, strategic integration of artificial intelligence into ERP systems will be crucial for their security of competitive advantages and future-proof operations. This study investigates the development, benefits, challenges, and strategic orientations of AI-driven ERP systems in an environment going more and more digital, so establishing them as indispensable tools for long-term company success. ERP, or systems for resource planning, have long been indispensable for the seamless running of businesses in many different fields. Artificial intelligence (AI) is causing notable changes in ERP systems. This paper investigates how artificial intelligence affects ERP systems, corporate processes, decision-making, and success. By means of thorough investigation, we examine the main artificial intelligence technologies influencing ERP, successful implementation case studies, benefits and drawbacks, and developing trends influencing the upcoming wave of intelligent ERP systems.
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Bekmirzaev, Obidjon, Kumushbibi Gulomova, and Sanjar Mukhamadiev. "Research on New Trends and Development Prospects of Enterprise Resource Planning (ERP) Systems." European International Journal of Multidisciplinary Research and Management Studies 5, no. 3 (2025): 50–54. https://doi.org/10.55640/eijmrms-05-03-12.
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This article analyzes modern trends and development prospects of ERP systems. The importance of ERP systems in automating business processes and increasing efficiency is highlighted, and their integration with cloud technologies, artificial intelligence, IoT and mobile applications is discussed. Also, the development prospects of ERP systems are considered as artificial intelligence-based automation, increased cybersecurity measures, flexibility and the creation of user-friendly interfaces. Continuous improvement of ERP systems serves to increase business efficiency and ensure competitiveness.
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Antonova, I. I., V. A. Smirnov, and M. G. Efimov. "Integrating artificial intelligence into ERP systems: advantages, disadvantages and prospects." Russian Journal of Economics and Law 18, no. 3 (2024): 619–40. http://dx.doi.org/10.21202/2782-2923.2024.3.619-640.
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Objective: to identify the key benefits and potential risks associated with the use of artificial intelligence in ERP systems to improve decision-making processes, management efficiency and operational performance of various sectors, including commercial and non-profit organizations. Methods: systematic literature review, empirical data analysis, analytical and experimental research methods. Results: the key directions of artificial intelligence implementation in ERP-systems are reflected, providing improvement of operational efficiency, customer relations, as well as optimization of business processes, data management, supply chain and personnel management, automation of operations related to finance, optimization of customer relations; implementation of artificial intelligence in ERP-systems reduces inventory management costs, improves the accuracy of forecasting andinventory optimization, accelerates financial analysis and increases the accuracy of budgeting, resulting in reduced budget planning time; it also increases productivity by optimizing necessary production processes and reducing equipment downtime. However, there are also risks of confidential data leakage, unauthorized access to data; job losses due to automation of tasks; and vulnerability to cyberattacks. Scientific novelty: the little-studied directions of artificial intelligence integration in ERP-systems are analyzed; an integrative approach to the application of artificial intelligence in ERP-systems is proposed, which combines methods of machine learning, natural language processing and predictive analytics and provides a comprehensive assessment of the complex impact on the business processes’ efficiency. Practical significance: the formulated directions for solving the identified problems of artificial intelligence integration in ERP-systems can be implemented in practice, as they will enable to better take into account local requirements and laws.
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Vinay, Singh. "AI and ERP Integration for Adaptive Dynamic Costing Based on Consumer Demand Fluctuations in Manufacturing." European Journal of Advances in Engineering and Technology 12, no. 3 (2025): 1–7. https://doi.org/10.5281/zenodo.15165976.
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This research aims to automate product costing based on consumer demand fluctuation using artificial intelligence (AI) integration with the Oracle ERP system. From raw material procurement and factory scheduling to real-time product cost estimates and financial forecasts, AI can simplify and improve many activities as manufacturing processes get more complicated. Automating product costing based on consumer demand allows artificial intelligence to help producers reach higher accuracy, efficiency, and cost optimization, thus enhancing organizations profitability. The paper examines how artificial intelligence changes real-time data analysis, predictive analytics, machine learning, and conventional product costing techniques. Furthermore, the advantages of AI-driven solutions are underlined—cost control, quicker decision-making, enhanced forecasts, and best use of resources. Future developments of artificial intelligence and ERP integration—including autonomous production systems, dynamic pricing models, and improved sustainability practices—predicted to change the manufacturing landscape significantly—are also covered in the paper. Using case studies from top companies such as Siemens, Coca-Cola, and Nestle, the paper illustrates artificial intelligence's practical use and quantifiable advantages in manufacturing. The study concludes that manufacturers who provide a clear road toward operational excellence and long-term profitability depend on the junction of artificial intelligence and ERP.
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Researcher. "ARTIFICIAL INTELLIGENCE IN ENTERPRISE RESOURCE PLANNING: A SYSTEMATIC REVIEW OF INNOVATIONS, APPLICATIONS, AND FUTURE DIRECTIONS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1276–89. https://doi.org/10.5281/zenodo.14170247.
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The revolutionary significance of artificial intelligence (AI) in contemporary enterprise resource planning (ERP) systems is examined in this article systematic review, which synthesizes recent findings and advancements from a variety of fields. The article highlights thirteen major areas where artificial intelligence (AI) is transforming ERP functionality through an examination of recent technological developments. These include cognitive computing for decision support, natural language processing for improved user interfaces, and machine learning-driven predictive analytics. With a focus on cutting-edge technologies like edge computing, blockchain integration, and quantum computing applications, the essay covers both theoretical frameworks and real-world applications. With average processing time savings of 35–45% and decision accuracy increases of up to 60% across a range of business activities, the results show that AI-enhanced ERP systems exhibit notable benefits in operational efficiency. System integration, data quality management, and regulatory compliance still face difficulties, nevertheless. The paper also identifies important research needs in industry-specific AI applications and cross-platform standards. In addition to describing future research paths centered on scalability, security, and enterprise-wide integration techniques, this thorough article analysis offers insightful information for scholars, practitioners, and businesses looking to utilize AI capabilities in ERP systems. In order to further theoretical knowledge and real-world application in the sector, the essay ends by suggesting a methodology for assessing and integrating AI advancements in ERP systems.
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Vidushi, Sharma. "The Role of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in Scaling Enterprise Resource Planning (ERP) Systems." International Journal of Leading Research Publication 4, no. 6 (2023): 1–6. https://doi.org/10.5281/zenodo.14769567.
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Enterprise Resource Planning (ERP) systems have long been the backbone of organizational efficiency, integrating key business functions such as finance, HR, and supply chain management. However, the complexity and scale of modern enterprises demand greater agility, automation, and intelligent decision-making capabilities. This research explores the role of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in scaling ERP systems. The study investigates how RPA streamlines repetitive, rule-based processes, while AI enhances ERP systems with predictive analytics and decision support. A mixed-methods approach, combining qualitative case studies with quantitative analysis of ERP performance metrics, was used to assess the impact of RPA and AI on ERP scalability. The findings indicate that the integration of RPA and AI significantly improves operational efficiency, reduces human error, and enables better decision-making in large-scale ERP environments. Moreover, organizations that adopted these technologies experienced enhanced scalability, making it easier to adapt to business growth. The study concludes that RPA and AI are key enablers of ERP systems, providing the necessary capabilities for enterprises to scale effectively in the digital era. This research contributes to the growing body of knowledge by highlighting practical applications and providing a roadmap for future ERP enhancements.
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Ravi Sankar Korapati. "Leveraging AI-Driven Predictive Analytics in Modern ERP Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1639–51. https://doi.org/10.32628/cseit251112193.
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This comprehensive article explores the transformative impact of AI-driven predictive analytics in modern Enterprise Resource Planning (ERP) systems. The article examines how the integration of artificial intelligence and machine learning capabilities has revolutionized organizational decision-making processes, operational efficiency, and strategic planning. The article investigates key application areas including financial forecasting, inventory optimization, and customer behavior analysis, while also addressing technical implementation considerations and system architecture requirements. The article demonstrates how AI-enhanced ERP systems have enabled organizations to achieve significant improvements in operational performance, risk management, and market competitiveness through advanced data processing and predictive modeling capabilities.
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Nathany, Deepika. "Connected ERP: Integrating Enterprise Systems for Enhanced Business Performance." Journal of Research in Business and Management 7, no. 2 (2019): 73–79. https://doi.org/10.35629/3002-07027379.
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Modern business operations rely heavily on Enterprise Resource Planning (ERP) systems because these systems deliver a centralized platform to oversee different organizational procedures. The evolution of businesses along with their expanding complexities requires more advanced and networked ERP systems now more than ever. This study examines "Connected ERP" as the future stage of enterprise systems integration. Connected ERP surpasses conventional ERP systems by establishing seamless connections between various business systems, departments, and external partners to develop an agile and responsive business environment. This research examines both advantages and obstacles of Connected ERP systems and outlines methods for their implementation. A thorough examination of existing literature and industry case studies reveals how Connected ERP systems boost data precision and decision-making while enhancing operational effectiveness in various organizations. The study examines how cloud computing along with artificial intelligence and the Internet of Things (IoT) serve as technological foundations for Connected ERP systems. Our findings demonstrate that Connected ERP systems surpass traditional ERP implementations by providing real-time data synchronization along with improved collaboration capabilities and better supply chain visibility. The research paper recognizes several potential obstacles to adoption including concerns about data security and complexities related to system integration alongside organizational resistance to change. The research finalizes with a framework proposal for successful Connected ERP deployment which highlights strategic planning alongside change management and continuous improvement as essential components. The research enhances our understanding of enterprise systems integration while offering practical advice to companies evaluating Connected ERP solutions.
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Nasirzada, Nigar. "ERP System Integration to Optimize Financial Reporting in Real Estate Management." Universal Library of Business and Economics 02, no. 01 (2025): 22–26. https://doi.org/10.70315/uloap.ulbec.2025.0201004.
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This article explores the integration of Enterprise Resource Planning (ERP) systems for optimizing financial reporting within the real estate sector. Drawing on case studies from established vendors (e.g., SAP, Oracle, MRI Software) and incorporating recent advances in AI, Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI), the study highlights the critical role of financial modules in ensuring transparency, mitigating regulatory risks, and promoting strategic decision-making. A comparative classification of ERP implementations underscores the importance of scalability and real estate-specific functionalities, while the integration of AI-driven tools demonstrates the potential for predictive maintenance, fraud detection, and enhanced cost-effectiveness. The findings suggest that an ethically and technologically sound ERP framework, coupled with robust governance models for AI, can deliver sustainable competitive advantages and inform the next generation of data-driven real estate management practices.
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Keresztesi, Albert Attila, and Moreno-Doru Reş. "Elements of Artificial Intelligence in Integrated Information Systems." Acta Marisiensis. Seria Oeconomica 16, no. 1 (2022): 81–90. http://dx.doi.org/10.2478/amso-2022-0008.
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Abstract The importance of the chosen theme is given by the lack of implementation of these technologies on the Romanian market, respectively to companies in the SME category, because this niche at the moment is one in full development and expansion. In the first part, entitled ‘Artificial intelligence – definitions, classifications’, the theoretical aspects of artificial intelligence systems in general, the direction of development in general, are presented, the types and categories of artificial intelligence and not least the facilities and effects of the use of artificial intelligence in general and in the fields of medicine, energy, production, education, finance and the transport industry respectively. In the second part entitled “Current trends in the development of integrated ERP systems” it analyzes the procedures for the integration of artificial intelligence elements as well as market analysis through the analysis of ERP solutions equipped with existing AI, the modules of the IT solutions that have been improved with artificial intelligence and other key elements with an impact on the implementations.
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VENKATA KALYAN CHAKRAVARTHY MANDAVILLI. "How AI is Transforming SAP/ERP Systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1625–30. https://doi.org/10.30574/wjaets.2025.15.2.0708.
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This article examines how Artificial Intelligence (AI) is fundamentally transforming SAP and Enterprise Resource Planning (ERP) systems across multiple dimensions. The integration of AI capabilities into enterprise systems is creating unprecedented opportunities for businesses to enhance operational efficiency, improve decision-making processes, and deliver superior user experiences. The paper explores key transformations including the automation of repetitive tasks like invoice processing and compliance monitoring, the evolution of predictive analytics for forecasting and risk assessment, and enhanced decision-making capabilities through real-time data analysis. Additionally, the article investigates how AI is revolutionizing user interaction through conversational interfaces, personalized experiences, and generative AI applications for content creation and code generation. The research draws on multiple studies to demonstrate how these technologies are not merely adding new features but fundamentally reshaping how businesses operate and interact with their core systems, with implications for future developments in autonomous operations, predictive maintenance, and cross-system intelligence.
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Vinay, Singh. "AI-Driven ERP Evolution: Enhancing Supply Chain Resilience with Neural Networks and Predictive LSTM Models." European Journal of Advances in Engineering and Technology 12, no. 2 (2025): 47–52. https://doi.org/10.5281/zenodo.15044216.
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Major advances in enterprise resource planning (ERP) systems have come from the fast development of artificial intelligence (AI) technology, therefore enabling businesses to streamline their operations, enhance decision-making, and increase supply chain efficiency. This article looks at how integrating artificial intelligence with ERP systems can enable companies to improve procurement procedures and enhance supply chain efficiency. Using artificial intelligence technologies—including machine learning, natural language processing, robotic process automation (RPA), and predictive analytics—one may automate repetitive operations, streamline processes, and provide insightful analysis for proactive decision-making. Emphasizing demand forecasting, inventory management, Sourcing, supplier management, and process automation—among other AI-driven capabilities—the study shows how these developments enable companies to reach smarter, data-driven decisions The article also looks at the benefits and difficulties of using artificial intelligence in ERP including data privacy issues and complicated integration. Emphasizing the transforming power of artificial intelligence to revolutionize organizational efficiency in the digital age, the study ends with recommendations and real-world examples for companies trying to use AI for improved ERP functionality.
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Sokunbi, Ayodeji Julius. "The Role of Artificial Intelligence in Enhancing Financial Decision-Making: A Case for AI-Integrated ERP Systems." International Journal of Novel Research in Marketing Management and Economics 12, no. 1 (2025): 24–33. https://doi.org/10.5281/zenodo.14937792.
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<strong>Abstract:</strong> The evolution of Enterprise Resource Planning (ERP) systems in accounting has profoundly impacted how businesses manage financial data, streamline operations, and achieve strategic goals. From their inception as standalone financial tools to becoming integrated platforms that support a range of business processes, ERP systems have continually adapted to meet the changing needs of organizations. This paper explores the historical development of ERP systems, emphasizing their role in digitalizing accounting functions and integrating with other business modules. It also examines the challenges faced in adopting ERP solutions, such as high costs, complexity, and resistance to change, while highlighting emerging technologies like blockchain, IoT, and AI that are shaping their future. The study concludes with insights into the potential of ERP systems to redefine accounting practices through innovation and enhanced connectivity, aligning with broader digital transformation initiatives. <strong>Keywords:</strong> ERP systems, Accounting, Digitalisation, Integration, Blockchain, Future directions. <strong>Title:</strong> The Role of Artificial Intelligence in Enhancing Financial Decision-Making: A Case for AI-Integrated ERP Systems <strong>Author:</strong> Sokunbi Ayodeji Julius <strong>International Journal of Novel Research in Marketing Management and Economics</strong> <strong>ISSN 2394-7322</strong> <strong>Vol. 12, Issue 1, January 2025 - April 2025</strong> <strong>Page No: 24-33</strong> <strong>Novelty Journals</strong> <strong>Website: www.noveltyjournals.com</strong> <strong>Published Date: 27-</strong><strong>February-2025</strong> <strong>DOI: https://doi.org/10.5281/zenodo.14937792</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.noveltyjournals.com/upload/paper/The%20Role%20of%20Artificial%20Intelligence-27022025-3.pdf</strong>
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Mucherla, Sampath, and Sachin More. "Artificial Intelligence in ERP: Unlocking New Horizons in Supply Chain Forecasting and Resource Optimization." International Journal of Supply Chain Management 10, no. 1 (2025): 1–10. https://doi.org/10.47604/ijscm.3234.
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Purpose: This article aims to evaluate the potential of Artificial intelligence (Ai) in ERP systems to enhance Forecasting in supply chain and resource optimization. It explores how AI can improve forecast accuracy, automate routine tasks, and optimize resource allocation within ERP systems. By analyzing the benefits and challenges of AI integration, the document provides insights for organizations seeking to leverage AI for enhanced supply chain management. Methodology: The methodology employed in the document involves both qualitative and quantitative research techniques. Quantitative data, such as forecasting accuracy, inventory turnover, and cost, is gathered from real-time ERP system data logs to measure ERP performance before and after AI implementation. Qualitative data is collected through interviews with ERP system administrators and supply chain managers to gain insights on user experience and challenges faced in Supply chain function and during AI integration. The research also discusses the selection of suitable machine learning models and their implementation methodology, including data preprocessing, training, and testing phases. Performance metrics, such as Mean Absolute Percentage Error (MAPE), are used to assess the improvements achieved through AI integration. Findings: The study found that AI integration in ERP systems significantly improved forecasting accuracy by 20%. This was attributed to AI's ability to analyze vast amounts of data and identify patterns that traditional ERP systems cannot do without significant work. Inventory turnover ratio increased by 33%, indicating faster movement of stock and reduced holding costs. This was due to AI's improved demand forecasting and real-time inventory adjustments. Operational costs were reduced by 15% due to automation of routine tasks, optimized resource allocation, and minimized waste in production and logistics. Unique Contribution to Theory, Practice and Policy: The research supports existing literature and case studies, confirming AI's potential to revolutionize ERP systems and supply chain management. The findings support existing literature on the potential of AI in supply chain management, specifically in forecasting and resource optimization. The research demonstrates the tangible benefits of AI integration, such as improved forecasting accuracy, optimized resource allocation, and reduced operational costs. The discussion on potential challenges, such as data security and algorithmic bias, helps organizations anticipate and address these issues proactively. The findings can inform government policies and industry regulations related to AI adoption in ERP systems and supply chain management. The emphasis on addressing algorithmic bias and data security concerns encourages responsible and ethical AI implementation. The research highlights the transformative potential of AI, encouraging businesses and policymakers to invest in AI-driven solutions for enhanced supply chain resilience and competitiveness.
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Pavan Kumar Bollineni. "Leveraging generative AI for predictive analytics in ERP Cloud systems." World Journal of Advanced Engineering Technology and Sciences 15, no. 1 (2025): 965–72. https://doi.org/10.30574/wjaets.2025.15.1.0248.
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This article explores the transformative potential of generative artificial intelligence in enhancing predictive analytics capabilities within Enterprise Resource Planning cloud systems. We examine how advanced machine learning models, particularly Generative Adversarial Networks, can be integrated with existing ERP infrastructures to revolutionize forecasting accuracy across supply chain management, financial planning, and inventory optimization. The technical foundations required for successful implementation are analyzed alongside practical integration strategies for various ERP modules. Through examination of cross-industry case studies, we demonstrate tangible business value while addressing critical challenges in data quality, system architecture, and model maintenance. This article concludes with an assessment of emerging technologies and implementation frameworks, providing organizations with a strategic roadmap for leveraging generative AI to achieve competitive advantage through enhanced operational efficiency and data-driven decision-making in their ERP ecosystems.
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Sathyananda Kumar Pamarthy. "Implementing Robust Data Privacy and Security in Modern ERP Systems: A Technical Deep Dive." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2360–65. https://doi.org/10.32628/cseit251112258.
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Enterprise Resource Planning (ERP) systems have become crucial components in modern business operations, demanding robust security measures to protect sensitive data and maintain operational integrity. This comprehensive article explores the multifaceted aspects of ERP security, focusing on advanced access control mechanisms, data protection strategies, and compliance requirements. The implementation of Role-Based Access Control (RBAC), multi-factor authentication, and biometric verification has significantly enhanced security protocols while improving operational efficiency. Data protection measures, including end-to-end encryption and dynamic data masking, have strengthened the defense against cyber threats across various industry sectors. The evolution of regulatory compliance has necessitated substantial investments in security infrastructure, leading to enhanced audit capabilities and improved risk management. Customer confidence and business partner integration have been reinforced through transparent security practices and secure collaboration frameworks. Looking ahead, emerging technologies such as artificial intelligence, blockchain, and zero-trust architecture are reshaping ERP security landscapes, promising more robust protection against evolving cyber threats while maintaining operational efficiency and user convenience.
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Giridhar Raj Singh Chowhan. "AI-enabled autonomous ERP: redefining business operations and decision-making." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 260–67. https://doi.org/10.30574/wjaets.2025.15.2.0555.
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This article examines the transformative impact of artificial intelligence on Enterprise Resource Planning (ERP) systems, marking a paradigm shift from traditional manual approaches to autonomous operations. It consolidates findings from multiple studies across various sectors, including educational institutions, manufacturing organizations, and public agencies. AI-enabled autonomous ERP systems demonstrate considerable improvements in operational efficiency through machine learning, predictive analytics, robotic process automation, and natural language processing technologies. These intelligent systems deliver significant benefits in continuous learning and adaptation, anomaly detection, risk mitigation, and predictive decision support. However, implementation challenges persist in data quality and integration, security and compliance concerns, and establishing appropriate ethical frameworks and human oversight mechanisms. Despite these challenges, organizations adopting autonomous ERP systems report enhanced business agility, cost efficiency through intelligent automation, and competitive advantages through predictive capabilities. It suggests that as implementation methodologies mature and AI capabilities advance, autonomous ERP adoption will accelerate across industries, fundamentally redefining business operations and decision-making processes.
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Srikanth G. "An AI-driven framework for modernizing oracle ERP systems in the public sector." International Journal of Science and Research Archive 14, no. 2 (2025): 903–13. https://doi.org/10.30574/ijsra.2025.14.2.0477.
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Public sector organizations face serious need to upgrade their Enterprise Resource Planning systems because this upgrade helps them to work more efficiently and serve customers effectively. This paper suggests an innovative plan to include Artificial Intelligence into Oracle ERP systems, which helps improve work processes and makes better decisions while handling automatic repetitive tasks. This framework enables easy modifications to ERP systems through AI while also working with less human action to give improved data insights. The research analyzes ERP system challenges before explaining how AI tools help systems to execute processes faster and more securely plus offering better adaptability. The analysis uses actual practice evidence and numerical data to explain how AI supports government organizations when they transform their Oracle ERPs. Research data proves that digital ERP systems that employ Artificial Intelligence technologies assist companies to work better and make smart transformational digital decisions.
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Srinivasan Pakkirisamy. "AI-driven cloud ERP: The next frontier in predictive financial management." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4160–69. https://doi.org/10.30574/wjarr.2025.26.1.1516.
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This article explores groundbreaking advancements in AI-driven Cloud Enterprise Resource Planning (ERP) systems, focusing on Oracle Cloud ERP implementation. The integration of artificial intelligence with cloud-based ERP platforms represents a transformative evolution in financial management capabilities. Through the implementation of hybrid AI agents combining deep learning with Bayesian networks, a sophisticated fraud detection framework utilizing graph neural networks, and automated payment reconciliation through reinforcement learning, organizations can achieve enhanced financial precision while maintaining robust security protocols. These innovations establish a new paradigm for predictive financial management that increases operational agility, strengthens decision-making processes, and maintains data integrity across complex enterprise environments while ensuring regulatory compliance through explainable AI frameworks.
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Rushabh Mehta. "Strategic Integration of ERP and Manufacturing Information Systems: Overcoming Implementation Challenges and Driving Digital Transformation." Journal of Information Systems Engineering and Management 10, no. 45s (2025): 835–45. https://doi.org/10.52783/jisem.v10i45s.9034.
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The integration of Enterprise Resource Planning (ERP) systems with Manufacturing Information Scanning Systems is essential for establishing a robust infrastructure capable of supporting high-quality big data flows. This foundational integration enables more sophisticated analytical modeling, leading to enhanced decision-making capabilities and effective incorporation of Artificial Intelligence supply chain operations, ultimately driving cost optimization. Key findings of this research highlight that successful implementation of integrated ERP solutions extends beyond technical complexities; it critically depends on management's strategic decision-making at each implementation stage. Effective integration contributes significantly to improved operational efficiency, stronger customer relationship management, and more accurate accounting processes. However, substantial challenges persist, particularly related to the complexities of migrating historical data from legacy systems such as MS Access to modern ERP systems Additionally, organizations face ongoing data management issues and significant organizational resistance toward developing and sustaining a data-driven culture This paper explores these challenges in-depth, presenting strategic insights and practical methodologies for organizations to successfully integrate ERP and Manufacturing Information Systems. By overcoming the highlighted barriers, organizations can fully leverage their integrated ERP systems, unlock comprehensive analytical capabilities, and achieve substantial cost optimization within supply chain management.
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Siva Prasad Sunkara. "AI-powered CRM and ERP systems: Transforming business operations through smart technology." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 4199–209. https://doi.org/10.30574/wjarr.2025.26.1.1499.
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This comprehensive article examines the transformative impact of Artificial Intelligence on Customer Relationship Management (CRM) and Enterprise Resource Planning (ERP) systems in modern business environments. It demonstrates how AI technologies serve as intelligent assistants that enhance traditional business management platforms, enabling organizations to transition from reactive to predictive operational models. The article provides a thorough analysis of AI's distinct applications in CRM for customer behavior prediction and personalization, alongside its role in ERP for operational efficiency and resource optimization. Through detailed demonstration of implementation strategies, technological foundations, and real-world applications, this article offers valuable insights for business leaders seeking to leverage AI-enhanced systems to drive sustainable competitive advantage and operational excellence in an increasingly data-driven business landscape.
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Kamalendar Reddy Kotha, Sai Charan Tokachichu, and Sudheer Chennuri. "AI and Machine Learning in Enhancing Scalability and Efficiency of Integrated E-commerce and ERP Systems." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 254–64. http://dx.doi.org/10.32628/cseit24105108.
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This article explores the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the integration of E-commerce platforms with Enterprise Resource Planning (ERP) systems. As E-commerce experiences explosive growth and ERP systems become increasingly complex, businesses face significant challenges in maintaining scalability and efficiency. We examine how AI and ML can optimize various aspects of these integrated systems, from intelligent automation and predictive analytics to anomaly detection and decision support. Through case studies and analysis of current trends, we demonstrate the tangible benefits of AI/ML implementation, including reduced costs, improved accuracy, and enhanced customer experiences. The article also addresses key challenges such as data quality, scalability, ethical considerations, and the skills gap. Finally, we explore future research directions in explainable AI, edge computing, blockchain integration, and natural language processing, highlighting their potential impacts on the E-commerce and ERP landscape.
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Researcher. "THE INTEGRATION AND IMPACT OF ARTIFICIAL INTELLIGENCE IN MODERN ENTERPRISE RESOURCE PLANNING SYSTEMS: A COMPREHENSIVE REVIEW." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 79–88. https://doi.org/10.5281/zenodo.14050064.
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This comprehensive article examines the transformative integration of Artificial Intelligence (AI) within Enterprise Resource Planning (ERP) systems, analyzing its impact across various organizational domains and functional areas. The article investigates how AI technologies revolutionize traditional ERP frameworks through advanced process automation, intelligent analytics, and adaptive learning capabilities, fundamentally enhancing organizational efficiency and decision-making processes. Through detailed analysis of core applications, including automated workflow management, predictive analytics, and supply chain optimization, this paper demonstrates the substantial benefits of AI-ERP integration while addressing implementation challenges and future opportunities. The research explores critical aspects of system architecture, risk management, and compliance automation, providing insights into how organizations can effectively leverage AI capabilities within their enterprise systems. Furthermore, the article examines the evolution of functional applications across financial management, human resources, and customer relationship management, highlighting how AI enhances these core business functions through intelligent automation and predictive capabilities. The findings indicate that while organizations face significant implementation challenges, the integration of AI in ERP systems offers unprecedented opportunities for operational excellence, strategic decision-making, and competitive advantage in the modern business landscape, setting the stage for future innovations in enterprise management and digital transformation.
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Siva Reddy Pulluru. "Emerging Technologies Integration with Enterprise Systems: A Technical Deep Dive." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 56–64. https://doi.org/10.32996/jcsts.2025.7.5.8.
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Integrating emerging technologies with enterprise resource planning systems represents a transformative shift in modern business operations. The convergence of Internet of Things, Artificial Intelligence, and Blockchain has revolutionized traditional ERP frameworks, enabling enhanced operational intelligence, cognitive process automation, and secure transaction networks. Organizations implementing these integrated solutions have experienced substantial improvements in operational efficiency, data processing capabilities, and decision-making processes. The implementation of IoT has enabled real-time monitoring and predictive maintenance across manufacturing facilities, while AI integration has enhanced fraud detection, procurement automation, and customer service response times. Blockchain technology has strengthened transaction security and transparency, particularly in supply chain and financial operations. These advancements have created new professional opportunities, requiring specialized skills in emerging technologies. The evolving landscape demands continuous adaptation of technical expertise and professional certifications, reshaping the future of enterprise technology integration and workforce development.
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Anica-Popa, Liana-Elena, Marinela Vrîncianu, Irina-Bogdana Pugna, and Dana-Maria Boldeanu. "Addressing Cybersecurity Issues in ERP Systems – Emerging Trends." Proceedings of the International Conference on Business Excellence 18, no. 1 (2024): 1306–23. http://dx.doi.org/10.2478/picbe-2024-0108.
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Abstract The integration of emerging technologies in Enterprise Resource Planning systems has the potential to enhance security, automation, decision-making, and predictive capabilities. However, this also introduces new cybersecurity challenges, as the systems may become targets for malicious attacks or data breaches. Understanding the nexus between organizational systems, artificial intelligence (AI), and cyber-security requirements, offers new insights for the modern business environment. Our study begins with an exploration of recent cases of AI-enhanced cybersecurity tools implemented within organizational information systems, as these currently stand. This research landscape is our starting point for an analysis of the impact of these tools on different types of systems, of the cyber risks reported in recent literature, and the configuration of cyber-security solutions tailored after current vulnerabilities of the business environment. We also identify trends and gaps in the existing research that suggest possible new topics for further investigation.
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Bajaj, Deepak. "The Evolution of Oracle Fusion Cloud: Exploring AI-Driven Enhancements for ERP Systems." International Journal of Advances in Engineering and Management 7, no. 2 (2025): 332–41. https://doi.org/10.35629/5252-0702332341.
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This article explores the transformative evolution of Oracle Fusion Cloud's AI-driven enhancements in Enterprise Resource Planning systems. The article examines how artificial intelligence has revolutionized decisionmaking processes, automated operations, and enhanced predictive analytics capabilities across various business functions. Through a comprehensive analysis of implementation data spanning multiple organizations, the article investigates the impact of adaptive intelligence applications, machine learning frameworks, neural network implementations, and natural language processing capabilities. The article further delves into AI-powered financial management, supply chain optimization, and operational efficiency improvements, providing insights into their effectiveness and practical implications. Additionally, the article examines emerging trends, potential enhancements, and future research directions in AI-ERP integration, offering valuable insights for practitioners and researchers in the field.
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Bokil, Jagdish K. "Leveraging ERP for Digital Transformation in ITIs." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47720.
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Abstract— This paper examines the implementation of Enterprise Resource Planning (ERP) systems in Industrial Training Institutes (ITIs) to enhance their administrative and operational efficiency. By integrating modern technologies such as cloud computing, IoT, and artificial intelligence, ERP systems streamline key functions like student data management, inventory control, and financial oversight. The study highlights the advantages of ERP adoption in ITIs, the challenges faced during implementation, and the future trends shaping ERP solutions. This research aims to provide insights into how ITIs can modernize their management processes and improve decision-making using technology-driven ERP systems. Keywords— ERP Systems, ITI, Cloud Computing, IoT, AI, Inventory Management, Educational Technology, Automation
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Neetu, Bansla, Rajneesh, and Bansla Archana. "ERP: Emerging Trends, Business Intelligence and Future Insight." Journal of Information Technology and Sciences 5, no. 1 (2019): 23–29. https://doi.org/10.5281/zenodo.2597973.
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<em>An ERP stands for enterprise resource planning and it is used to perform daily operations carried out in an organization on daily basis.An ERP does all the jobs, an organization needs i.e. storing information,processing information & implementing constraints on data accessed.Moreover an ERP takes care of the data security up to some extent by denying unauthorized access. Main idea is to gather information at one place & provide ease for access on a wider scale. In comparison to scattered database stored in the form of excel files, ERP maintains a standard platform for storing information in an in organized and meaningfulway. An organization can rely on the accuracy, security&correctness of data being used by the employees over a certain period. Also the day to day data remains in a consistent state by maintaining the integrity.Impact of ERP systems cannot be ignored in today’s time as small or big business organizations rely heavily on these systems.Business owners want their business to go systematic and organized & hence they prefer a platform where they can store,retrieve and process their information in a secured way.Unlike spreadsheets ERP systems are fully functional,interactive,time saving and logical.Further these systems are used for making future strategies by analyzing the historical data that is stored in the ERP databases.Therefore this paper is an insight into the ERP as a wholesome system for the new generation business,emerging trends in ERP like artificial intelligence & ERP,IOT & ERP,ERP future analysis & impact.</em>
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Sanjiv Kumar Bhagat. "Enterprise Architecture in the Age of Generative AI: Adapting ERP Systems for Next-Generation Automation." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 370–80. https://doi.org/10.32628/cseit25112368.
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Enterprise architecture is experiencing a profound transformation through the integration of generative artificial intelligence into Enterprise Resource Planning (ERP) systems, fundamentally reshaping how organizations approach automation, decision-making, and strategic planning. This article examines the architectural implications of incorporating AI capabilities into ERP frameworks, focusing on three key dimensions: predictive analytics for enhanced forecasting and risk management, intelligent process automation for operational efficiency, and strategic decision support through natural language processing. Drawing from industry implementations and architectural patterns, this article explores the challenges and opportunities in designing resilient AI-enabled ERP systems that balance innovation with enterprise constraints. The discussion encompasses critical considerations for enterprise architects, including data privacy, integration complexity, and governance frameworks, while providing actionable insights for organizations transitioning to next-generation ERP architectures. This article suggests that successful AI integration in ERP systems requires a holistic architectural approach that aligns technological capabilities with organizational objectives, supported by robust governance mechanisms and clear implementation strategies. This article contributes to the growing body of knowledge on enterprise architecture evolution in the context of emerging AI technologies, offering practical guidance for architects and decision-makers navigating this transformative landscape.
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Adarsh Vaid and Chetan Sharma. "Leveraging SAP and Artificial Intelligence for optimized enterprise resource planning: enhancing efficiency, automation, and decision-making." World Journal of Advanced Research and Reviews 14, no. 3 (2022): 762–69. https://doi.org/10.30574/wjarr.2022.14.3.0276.
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In today’s fast-paced business environment, enterprises are increasingly relying on advanced technologies to optimize operations, streamline processes, and make data-driven decisions. Enterprise Resource Planning (ERP) systems, particularly SAP (Systems, Applications, and Products in Data Processing), have long been pivotal in managing core business functions. However, with the advent of Artificial Intelligence (AI), the potential of ERP systems, especially SAP, has expanded significantly. This paper explores the integration of AI into SAP ERP systems, demonstrating how AI enhances business performance through automation, predictive analytic s, and real-time decision-making capabilities. The study presents a literature review, a case study of an AI-powered SAP implementation, a comparative analysis of traditional and AI-integrated SAP systems, and a discussion of the benefits and challenges of such integration. The paper concludes by emphasizing the future of AI in ERP systems and its transformational potential for organizations.
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BARNA, Laura Eugenia Lavinia, and Corina Cătălina HURDUCACI (GOREA). "THE IMPACT OF USING ARTIFICIAL INTELLIGENCE AND ERP SYSTEMS IN THE WORK OF ACCOUNTING PROFESSIONALS AND AUDITORS." Annals of the University of Oradea. Economic Sciences 33, no. 1 (2024): 246–58. http://dx.doi.org/10.47535/1991auoes33(1)028.
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Recent developments in IT have changed the way accounting professionals and auditors do business. The research conducted in this article aims to explore how artificial intelligence and ERP systems offer opportunities to increase efficiency, accuracy and improve decision making in companies operating in the accounting and auditing industry. One of the results obtained from the bibliometric analysis indicates that artificial intelligence enables the automation of repetitive tasks, allowing the analysis of a large set of data to support strategic decision making. In addition, the integration of ERP systems streamlines financial processes, improves data management and ensures compliance with regulatory requirements. The digitalization of the accounting profession has transformed traditional practices and revolutionized the way accounting professionals operate in today's digital age. By embracing digital tools and platforms, accounting professionals can enhance efficiency, accuracy, and collaboration, ultimately improving the quality of financial reporting and analysis. The role of these technologies (artificial intelligence and ERP systems) is to streamline workflows, increase productivity and adapt to evolving industry requirements. The research in this article was based on a bibliometric analysis that aimed to observe research trends in this field, through which to observe or identify uncovered areas and future research directions in this field. Following a comprehensive analysis of the benefits and challenges associated with the adoption of artificial intelligence and ERP systems in accounting and auditing practices, this study aims to provide valuable insights to these professionals as a result of the upward trend of the digitalization phenomenon. As a result of the digitisation of business, the article provides valuable information needed by accounting professionals and auditors to help them remain competitive in a rapidly changing landscape.
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Sungatullin, Rais G. "AUTOMATED ENTERPRISE MANAGEMENT SYSTEMS (ERP): ECONOMIC EFFICIENCY ANALYSIS." EKONOMIKA I UPRAVLENIE: PROBLEMY, RESHENIYA 3/2, no. 156 (2025): 40–49. https://doi.org/10.36871/ek.up.p.r.2025.03.02.005.
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The development of digital technologies increases the need for enterprises to integrate automated management systems (ERP) aimed at optimizing business processes and reducing operating costs. The implementation of ERP systems provides centralized control of resources, automation of accounting, management of financial flows and demand forecasting. The economic efficiency of these solutions is expressed in increased productivity, reduced transaction costs and improved supply chain management. The article analyzes investment costs for ERP implementation, sources of financing, key business benefits and possible risks. The prospects for the development of ERP systems are considered taking into account technological trends, including cloud computing, artificial intelligence and integration with the Internet of Things.
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Tulli, Sai krishna Chaitanya. "Enhancing Supply Chain Efficiency in the Oil and Gas Industry: The Role of Digital Transformation in ERP Systems for Real-Time Analytics and Decision-Making." Research and Analysis Journal 7, no. 03 (2024): 01–27. https://doi.org/10.18535/raj.v7i03.394.
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The oil and gas industry faces increasingly complex supply chain challenges, including operational inefficiencies, demand volatility, and the need for enhanced decision-making capabilities. Enterprise Resource Planning (ERP) systems have long been pivotal in managing supply chain processes, but traditional systems struggle to meet the demands of a dynamic and data-driven environment. This study explores the transformative role of digital technologies—such as IoT, artificial intelligence, blockchain, and real-time analytics—in modernizing ERP systems to enhance supply chain efficiency. Using a mixed-methods approach, the research analyzes primary data from industry stakeholders and secondary data from case studies to evaluate the impacts of digital transformation. Key findings reveal significant improvements in supply chain performance metrics, including reduced operational costs, better inventory management, and improved demand forecasting accuracy. This study also identifies the challenges of implementing digital ERP systems, such as cost barriers and technological integration issues, and proposes a framework for effective adoption. The insights from this research provide actionable recommendations for stakeholders aiming to optimize supply chain operations in the oil and gas sector.
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Egbuhuzor, Nnaemeka Stanley, Ajibola Joshua Ajayi, Experience Efeosa Akhigbe, and Oluwole Oluwadamilola Agbede. "AI in Enterprise Resource Planning: Strategies for Seamless SaaS Implementation in High-Stakes Industries." International Journal of Social Science Exceptional Research 1, no. 1 (2022): 81–95. https://doi.org/10.54660/ijsser.2022.1.1.81-95.
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Enterprise Resource Planning (ERP) systems play a pivotal role in streamlining business operations and improving decision-making in high-stakes industries such as healthcare, finance, and manufacturing. With the rise of Artificial Intelligence (AI), ERP solutions have undergone a paradigm shift, offering enhanced capabilities for real-time data analysis, predictive analytics, and process automation. This paper explores AI's transformative impact on ERP, with a specific focus on strategies for seamless Software-as-a-Service (SaaS) implementation in industries with critical operational demands. The integration of AI into ERP systems not only optimizes resource utilization but also mitigates risks associated with manual data handling and fragmented workflows. Key challenges in implementing SaaS-based AI-driven ERP solutions include data security, interoperability, scalability, and organizational resistance to change. This study presents a comprehensive framework to address these challenges, emphasizing AI-enabled data migration, adaptive learning algorithms, and robust cybersecurity measures tailored for high-stakes environments. Additionally, it highlights the importance of stakeholder engagement, training programs, and iterative implementation strategies to ensure smooth adoption and maximize ROI. Case studies from healthcare and manufacturing sectors illustrate successful AI-SaaS ERP adoption, showcasing significant improvements in supply chain optimization, financial forecasting, and regulatory compliance. The role of predictive analytics in anticipating operational bottlenecks and machine learning in automating repetitive tasks is emphasized, demonstrating tangible outcomes such as cost reduction, enhanced decision-making, and operational efficiency. This paper concludes by outlining future trends, including the integration of generative AI for custom ERP module development, AI-driven self-healing systems for real-time troubleshooting, and the use of natural language processing (NLP) for intuitive user interfaces. These advancements are poised to redefine ERP systems, empowering enterprises to navigate complex challenges in high-stakes industries with greater agility and precision.
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Vukman, Karla, Kristina Klarić, Krešimir Greger, and Ivana Perić. "Driving Efficiency and Competitiveness: Trends and Innovations in ERP Systems for the Wood Industry." Forests 15, no. 2 (2024): 230. http://dx.doi.org/10.3390/f15020230.
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Enterprise Resource Planning (ERP) systems offer various functionalities to support an organization’s core functions. However, many anticipated benefits often need to materialize due to business context changes and users’ high expectations. Continuous adaptation and improvement are necessary to address user disappointments. This research focuses on ERP systems, exploring key factors influencing the success of their implementation. Recognizing challenges in ERP system implementation, this study provides a comprehensive literature review, identifying essential and contemporary Critical Success Factors (CSFs) influenced by technological advancements. Addressing challenges specific to the wood industry, this research introduces additional industry-adapted CSFs, including industry adaptability, integration with production machinery, effective warehouse management, and supply chain tracking. Furthermore, this paper emphasizes the need for continuous adaptation and improvement of ERP systems, especially in light of current trends and technological achievements. This study recommends a holistic approach, considering traditional or essential CSFs while adapting to new trends. Critical success factors in ERP implementation in the next decade involve considering cloud technology, artificial intelligence and machine learning, data security, mobile access, IoT integration, user experience, and training. The main objective of this paper is to identify the latest CSFs in ERP implementation. This research highlights essential success factors in ERP implementation, and contemporary trends in ERP implementation with a particular focus on the specifics of wood industry. While organizations should aim to maximize the potential of ERP systems, they should also acknowledge the crucial role played by human intervention in the effective and responsible implementation of artificial intelligence.
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Manoj, Gudala. "Adaptive ERP Systems: A Comprehensive Framework for Dynamic Business Environments." European Journal of Advances in Engineering and Technology 9, no. 1 (2022): 54–61. https://doi.org/10.5281/zenodo.13626834.
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This paper presents a fully detailed framework of A-ERP systems concerning dynamic business environments. It provides insights into the integration of artificial intelligence, machine learning, and real-time data analytics into the ERP architectures for creating autonomous-adapting systems in response to the dynamic business environment. It presents in detail the theoretical bases, methodologies for implementation, and possible impacts of A-ERP on the performance of organizations and their agility. We also address challenges and ethical considerations linked with their implementation and future research directions in this field.
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Jaiswal, Chandra. "AI-Enhanced SAP S/4HANA Implementations for Distribution and Retail." International Journal of Computer Science and Mobile Computing 13, no. 1 (2024): 106–23. https://doi.org/10.47760/ijcsmc.2024.v13i01.009.
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The integration of Artificial Intelligence (AI) into SAP S/4HANA has revolutionized distribution and retail industries by enabling predictive analytics, real-time decision-making, and automated workflows. This paper explores architectural frameworks, methodologies, and applications of AI in SAP S/4HANA, emphasizing demand forecasting, supply chain optimization, and hyper-personalization. Quantitative analysis reveals a 20–35% reduction in stockouts and a 15–25% improvement in ROI for AI-enhanced workflows. Challenges such as ethical AI governance and scalability in multi-tenant environments are critically examined. The study concludes with strategic recommendations for enterprises adopting AI-driven ERP systems.
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Dachepalli, Veeresh. "AI-Driven Decision Support Systems in ERP." International Journal of Computer Science and Data Engineering 2, no. 2 (2025): 1–7. https://doi.org/10.55124/csdb.v2i2.248.
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The integration of artificial intelligence (AI) into enterprise resource planning (ERP) systems has fundamentally transformed organizational decision-making by providing more precise, data-driven insights. This paper examines how AI has been incorporated into ERP systems, focusing on improving strategic decision-making through the use of advanced data visualization techniques and algorithmic decision support algorithms. By leveraging the power of machine learning (ML) and business intelligence (BI) tools, a robust decision support algorithm is proposed that facilitates real-time data analysis, predictive forecasting, and actionable insights. The integration of ML models allows ERP systems to analyze a wide range of historical and real-time data, identify trends, and make predictions, thereby improving forecast accuracy. Meanwhile, BI tools provide intuitive dashboards and visualizations that help decision-makers interpret complex data and effectively monitor key performance indicators (KPIs). This combination significantly improves operational efficiency, streamlines decision-making processes, and reduces time spent on manual tasks. The proposed decision support system enhances the adaptability of ERP systems, helping organizations respond proactively to changing business environments. These findings demonstrate considerable advancements in predictive analytics, operational effectiveness, and the overall adaptability of ERP systems, enabling businesses to remain proactive in market trends and make well-informed strategic choices.
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Tran Duy, Dr Thuc. "Management Accounting's Performance Dominated by ERP Systems and Artificial Intelligence: An Evidence from Vietnam." International Journal of Advanced Multidisciplinary Research and Studies 5, no. 2 (2025): 1303–16. https://doi.org/10.62225/2583049x.2025.5.2.3981.
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In contemporary Vietnamese society, the utilization of management accounting has achieved considerable popularity. To optimize sustainable business performance and mitigate future risks, companies should promptly leverage the microeconomy to regain market share and reveal their true capabilities. This is essential to optimize their business performance. The emergence of artificial intelligence (AI) and the progressive evolution of enterprise resource planning (ERP) have both instigated substantial transformations in contemporary civilization. Nevertheless, contemporary civilization has also been profoundly transformed by these two advancements. The fundamental question we face is how to efficiently deploy the new management accounting tool in contemporary industry. This is our paramount focus indicates the potential for combining enterprise resource planning (ERP) with artificial intelligence (AI) to enhance efficiency and evaluate the research trend in Vietnam for the fiscal year 2025. This will be conducted to assess the research trend. To evaluate the Vietnamese market, now integrating AI with ERP to supplant traditional management accounting functions, a self-administered online survey employing quantitative methods is developed. Furthermore, it utilizes a five-point Likert scale to assess the market. Finally, this study's findings indicate that the Vietnamese market's response is advantageous for its adaption in both the present and future. This applies to both the present and the future. For AT to remain aligned with the advancements made thus far, it must be regarded as an essential element of the specialized ERP system designed to facilitate effective management accounting in practice.
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Kiran Kumar Lekkala. "Technical deep dive: SAP's evolution in enterprise resource planning." World Journal of Advanced Research and Reviews 26, no. 1 (2025): 2848–54. https://doi.org/10.30574/wjarr.2025.26.1.1386.
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The transformation of enterprise resource planning systems through SAP's technological innovations marks a significant advancement in cloud computing, artificial intelligence, and sustainable business practices. SAP's evolution from traditional ERP to cloud-based solutions demonstrates the integration of cutting-edge technologies while maintaining robust security and performance standards. The introduction of SAP S/4HANA Cloud and Business Technology Platform has revolutionized enterprise computing through in-memory processing, microservices architecture, and advanced analytics capabilities. These developments, combined with sustainability initiatives and enhanced partner ecosystem integration, position SAP at the forefront of digital transformation in enterprise software solutions.
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Yathiraju, Nikhitha. "Investigating the use of an Artificial Intelligence Model in an ERP Cloud-Based System." International Journal of Electrical, Electronics and Computers 7, no. 2 (2022): 01–26. http://dx.doi.org/10.22161/eec.72.1.
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Enterprise Resource Planning (ERP) systems are necessary to improve an enterprise's management performance. However, the perception of information technology (IT) professionals about the integration of artificial intelligence (AI) and machine learning with ERP cloud service platforms is unknown. Few studies have examined how leaders can implement AI for strategic management, but no study has qualitatively explored AIs integration in the cloud ERP system. This qualitative phenomenological study explored IT professionals’ perceptions regarding the integration of AI and Supervised-machine (S-machine) learning into cloud service platforms in the enhancement of the cloud ERP system. Two research questions were developed for this study: 1) What are the perceptions of IT professionals regarding the use of an AI model to integrate SaaS and ERP? and 2) What are the perceptions of IT professionals regarding how AI can be integrated in order to enhance the security of using an ERP cloud-based system? Through a hermeneutical lens and a focus on integrating the Application Programming Interface (API), purposive sampling was used to interview five AI experts, three Machine Learning (ML) experts, five Cybersecurity experts, and two Cloud Service Providers provided their lived experiences with AI and S-machine learning. Five main themes emerged, including 1) use of an AI model to integrate SaaS and ERP helped perform work efficiently, 2) challenges for integrating AI into cloud service ERP and SaaS, 3) resources needed to fully implement an AI into cloud-service ERP or SaaS, 4) the best practices for developing and implementing an AI model for ERP and SaaS, and 5) how security of an ERP clouds-based system is optimized by integrating AI. The culmination of these findings has positive implications for individuals and organizations to improve management performance. While this study does not proposal a new theory, this study extends current literature on the application of theories related to technology integration.
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Venkata Siva Prasad Maddala. "Optimizing the oil and natural gas industry: The role of ai and data analytics in ERP integration." International Journal of Science and Research Archive 14, no. 1 (2025): 1808–18. https://doi.org/10.30574/ijsra.2025.14.1.2351.
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Global energy requirements depend on the oil and natural gas industry which deals with efficiency issues and must meet both regulatory standards and environmental protection needs. Our research shows how adding artificial intelligence and data analysis tools into ERP systems solves industry problems. Enterprise resource planning systems powered by artificial intelligence make better supply chain decisions while helping businesses predict equipment maintenance needs leading to lower costs and more efficient operations. Research findings from case studies and industry data show that businesses reducing maintenance costs by 25% and improving logistics savings by 15% through AI-ERP implementation. While legacy systems, high costs and data security risks pose challenges the positive outcomes of merging AI with ERP systems prove greater in value. The research shows how these systems help energy companies operate more sustainably and emit less carbon in our modern energy environment. The sector will progress further as new developments like generative AI plus blockchain and edge computing enter the market. Companies need to train their teams, work with experts in the field and use clean technology solutions to stay ahead in today's changing market.
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Gbenga Omoegun, Joyce Efekpogua Fiemotongha, Julius Olatunde Omisola, Odira Kingsley Okenwa, and Osazee Onaghinor. "Advances in ERP-Integrated Logistics Management for Reducing Delivery Delays and Enhancing Project Delivery." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 547–79. https://doi.org/10.32628/ijsrset241488.
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The increasing complexity and globalization of supply chains have necessitated more efficient logistics operations to mitigate delivery delays and enhance overall project performance. This paper presents a systematic review of advances in Enterprise Resource Planning (ERP)-integrated logistics management systems and their role in reducing delivery delays and improving project delivery outcomes. ERP systems serve as centralized platforms that streamline data flow and enable real-time decision-making across various business functions. When integrated with logistics management, ERP solutions offer end-to-end visibility, automation of procurement and inventory processes, improved demand forecasting, and enhanced coordination among stakeholders. Recent technological developments, including cloud computing, Internet of Things (IoT), artificial intelligence (AI), and blockchain, have further augmented ERP capabilities in logistics. These technologies enable predictive analytics, automated tracking, and enhanced transparency, which are critical in identifying potential disruptions early and taking preemptive actions. The review identifies key ERP modules such as materials management, warehouse management, transportation planning, and supplier relationship management as pivotal in synchronizing logistics activities. Empirical studies analyzed in this review report significant reductions in lead times, improved adherence to delivery schedules, cost savings, and increased customer satisfaction as outcomes of ERP-integrated logistics implementations. Furthermore, the research underscores the role of ERP systems in facilitating agile responses to dynamic market conditions and disruptions, such as those experienced during the COVID-19 pandemic. Challenges such as high implementation costs, user resistance, and data integration complexities are acknowledged; however, best practices and success factors, including stakeholder engagement, phased implementation, and continuous training, are emphasized to mitigate these obstacles. In conclusion, ERP-integrated logistics management emerges as a transformative approach for organizations aiming to optimize their supply chains, reduce delivery delays, and enhance the timeliness and reliability of project delivery. Future research is encouraged to explore industry-specific ERP customizations and the long-term impact of emerging digital technologies on logistics performance. This review contributes to the growing body of knowledge supporting digital transformation in logistics and project management disciplines.
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Omoegun, Gbenga, Joyce Efekpogua Fiemotongha, Julius Olatunde Omisola, Odira Kingsley Okenwa, and Osazee Onaghinor. "Advances in ERP-Integrated Logistics Management for Reducing Delivery Delays and Enhancing Project Delivery." International Journal of Advanced Multidisciplinary Research and Studies 4, no. 6 (2024): 2374–92. https://doi.org/10.62225/2583049x.2024.4.6.4355.
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The increasing complexity and globalization of supply chains have necessitated more efficient logistics operations to mitigate delivery delays and enhance overall project performance. This paper presents a systematic review of advances in Enterprise Resource Planning (ERP)-integrated logistics management systems and their role in reducing delivery delays and improving project delivery outcomes. ERP systems serve as centralized platforms that streamline data flow and enable real-time decision-making across various business functions. When integrated with logistics management, ERP solutions offer end-to-end visibility, automation of procurement and inventory processes, improved demand forecasting, and enhanced coordination among stakeholders. Recent technological developments, including cloud computing, Internet of Things (IoT), artificial intelligence (AI), and blockchain, have further augmented ERP capabilities in logistics. These technologies enable predictive analytics, automated tracking, and enhanced transparency, which are critical in identifying potential disruptions early and taking preemptive actions. The review identifies key ERP modules such as materials management, warehouse management, transportation planning, and supplier relationship management as pivotal in synchronizing logistics activities. Empirical studies analyzed in this review report significant reductions in lead times, improved adherence to delivery schedules, cost savings, and increased customer satisfaction as outcomes of ERP-integrated logistics implementations. Furthermore, the research underscores the role of ERP systems in facilitating agile responses to dynamic market conditions and disruptions, such as those experienced during the COVID-19 pandemic. Challenges such as high implementation costs, user resistance, and data integration complexities are acknowledged; however, best practices and success factors, including stakeholder engagement, phased implementation, and continuous training, are emphasized to mitigate these obstacles. In conclusion, ERP-integrated logistics management emerges as a transformative approach for organizations aiming to optimize their supply chains, reduce delivery delays, and enhance the timeliness and reliability of project delivery. Future research is encouraged to explore industry-specific ERP customizations and the long-term impact of emerging digital technologies on logistics performance. This review contributes to the growing body of knowledge supporting digital transformation in logistics and project management disciplines.
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Barna, Laura-Eugenia-Lavinia, Bogdan-Ștefan Ionescu, and Corina-Cătălina Hurducaci Gorea. "Professional Skills of Future Accountants Working in a Digitized Environment Dominated by ERP Systems or Artificial Intelligence." Proceedings of the International Conference on Business Excellence 18, no. 1 (2024): 1290–305. http://dx.doi.org/10.2478/picbe-2024-0107.
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Abstract The massive evolution of the digitization concept in recent years has also gained momentum among accounting professionals, as a result of the massive use of IT systems called ERP (Enterprise Resource Planning) systems. Thus, their entire activity is focused on the processing of financial-accounting data with the help of ERP (Enterprise Resource Planning) systems. The modular structure of ERP (Enterprise Resource Planning) systems can be integrated for other departments within an organization, but in this article the financial-accounting module of the ERP (Enterprise Resource Planning) system will be discussed, in order to identify the main skills of future accountants. Artificial Intelligence, and learning algorithms in particular, offer exciting opportunities to help professionals such as accountants improve their skills, the way they deliver services and the way they create value. The research method used for this article is quantitative, based on the bibliometric analysis of the digitization of the accounting profession, focusing primarily on what skills future accountants should develop. The sample of articles used for the analysis was selected from the Web of Science platform, then to be analyzed using the VOS viewer application. The results obtained indicate a significant increase in the analysis capacity of the future accountants, greatly reducing the part of manual data processing that they did manually in the past. The conclusion of the article demonstrates how much the activities of professional accountants have evolved as a result of the digitization of their activities, using ERP (Enterprise Resource Planning) systems or Artificial Intelligence.
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KOROL, Svitlana, and Olha ROMASHKO. "Artificial intelligence in accounting." Scientia fructuosa 154, no. 2 (2024): 145–57. http://dx.doi.org/10.31617/1.2024(154)08.
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Artificial Intelligence (AI) technologies open up broad horizons for enhancing business efficiency and advancing various professional domains, boosting their productivity and competitiveness. There is an active exploration of approaches to incorporating AI technologies in the accounting sphere, promising a seamless transition from human to machine involvement. The aim of this article is to summarize the acquired experience, identify perspectives, constraints, and risks associated with the use of AI technologies in the professional activities of accountants. The research is based on the hypothesis that widespread use of AI in the professional activity of an accountant with an insufficient level of professional skepticism and caution carries significant threats and risks for both the accountant and the business as a whole. Scientific search methods, comparative and critical analysis, theoretical generalization, and synthesis were used. A prerequisite for implementing AI technologies in accounting is expert information systems and ERP systems. The analysis of AI technology implementation experience in various industries demonstrates their relevance in the accounting field for performing routine tasks (automated recognition of primary documents, processing incoming signals, and other standard operations with a simultaneous reduction in the probability of errors), analyzing large datasets, and providing information support for decision-making (processing business data and regulatory documents), training professionals, and organizing internal and external communication (particularly between humans and machines). Identified potential risks include breaches of privacy and data security, misinterpretation of output data, and the disregard of activity context, external and internal environments, especially due to the absence of emotional intelligence, which influences the trust level in integrated information systems. The requirement for the application of professional assessments and judgments, mandated by regulatory documents, limits the scope of AI technology utilization in accounting. Future research should focus on exploring the possibilities of widespread integration of AI technologies in information systems for accounting and improving legislation based on the principle of risk assessment.
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Țîrlea, Anca Victoria, Claudiu Vasile Kifor, and Florin Cristian Țîrlea. "Integrated Neuronal Network in ERP for Management Decision Making." Balkan Region Conference on Engineering and Business Education 1, no. 1 (2019): 206–12. http://dx.doi.org/10.2478/cplbu-2020-0024.
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AbstractEnterprise Resource Planning systems have proven to be efficient and have become a de facto standard for coordinating vital business components. However, the obvious question has arisen: if each company uses the same ERP system, what happens to the competitive aspect of the business after the implementation of the IT systems?While for some organizations, ERPs have only become a necessity for running and organizing business, others want to exploit it to exceed the performance of competitors.Consequently, ERP systems are often a combined solution between the legacies of the systems they have replaced and the model proposed by the ERP provider, resulting in systems with unique, customized features. Keeping this idea, we aim to add to the present paper elements of artificial intelligence within a module for managing car sales within an ERP.
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Chandra, T. Bharath. "Bridging the Visual Gap: Integrating Vision Language Models (VLM) and Artificial Intelligence (AI) with Enterprise Resource Planning (ERP) Software System." International Research Journal of Innovations in Engineering and Technology 09, Special Issue ICCIS (2025): 200–205. https://doi.org/10.47001/irjiet/2025.iccis-202532.
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Abstract - Businesses generate vast visual data (e.g., quality check photos, warehouse snapshots, invoices, customer images), but traditional Enterprise Resource Planning (ERP) systems, built for structured data, cannot process it. This study explores integrating Vision Language Models (VLMs), AI combining computer vision and language processing, with ERPs to automate tasks like quality control, inventory monitoring, and document processing. We assess integration feasibility with Microsoft Dynamics 365 Business Central, Salesforce, and SAP S/4HANA, proposing an API-driven system architecture. VLMs face precision challenges, and ERP readiness varies: Microsoft Dynamics needs custom development, Salesforce offers flexible APIs, and SAP S/4HANA is robust but complex. Strategic planning and leveraging VLM strengths enable AI-enhanced enterprise systems.
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Varun Sridharan. "Ethical AI Integration in Enterprise Resource Planning Systems: A Framework for Balancing Innovation and Responsibility in B2B Environments." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 489–504. https://doi.org/10.32996/jcsts.2025.7.5.56.
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This article examines the ethical dimensions of artificial intelligence integration within Enterprise Resource Planning (ERP) systems, with particular focus on manufacturing, distribution, and food & beverage sectors. The article proposes a comprehensive framework for balancing innovation imperatives with responsible AI practices in business-to-business environments where trust and regulatory compliance are paramount. The article identifies key challenges and best practices across three critical domains: ethical governance of decision-making algorithms, data privacy and security frameworks, and accessibility measures that address the digital divide between large and small enterprises. The article reveals that organizations implementing structured approaches to algorithmic transparency, bias mitigation, and inclusive design not only reduce ethical risks but also gain significant competitive advantages through enhanced trust, improved partner relationships, and more resilient business ecosystems. The proposed Ethical AI Governance Framework for ERP offers a practical roadmap for organizations at various stages of AI maturity, emphasizing that ethical implementation should be viewed not as a compliance exercise but as a strategic business imperative creating sustainable value across supply chains. This article contributes both theoretical insights and actionable guidance for technology providers, implementing organizations and regulatory bodies navigating the complex ethical landscape of AI-enhanced enterprise systems.
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Sirse, Shreyas, Sushank Khanzode, and Atharva Bhide. "ERP: SAP S/4HANA Cloud RISE Enterprise Solution Automation with Artificial Intelligence Features." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (2023): 2055–62. http://dx.doi.org/10.22214/ijraset.2023.56384.
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Abstract: Cloud computing has experienced significant growth in recent years for business transformation, with an increasing number of applications migrating to the cloud, where they can centrally manage a wide range of resources, including hardware and software. SAP S/4HANA Cloud is a next-generation ERP system that is built on the SAP Cloud Platform. It offers a number of advantages over traditional on-premises ERP systems. SAP S/4HANA Cloud is a strategic move for organizations aiming to enhance their ERP capabilities and embrace the digital future. SAP RISE is a cloud-based enterprise resource planning (ERP) solution that combines the power of SAP S/4HANA Cloud with artificial intelligence (AI) and machine learning (ML) capabilities. S/4HANA Cloud migration and AI integration is a powerful combination that can help businesses of all sizes to achieve their transformation goals and stay ahead of the competition. By migrating to S/4HANA Cloud and integrating AI features, businesses can improve their operational efficiency, increase their agility, accelerate their innovation, and reduce their costs. In this paper, author has explained the requirement of SAP S/4HANA Cloud RISE Enterprise Solution Automation with Artificial Intelligence features – a current business transformation needs to achieve business goal.