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Enhancing ERP System Efficiency through Integration of Cloud Technologies
Subject area: Science,Engineering and Technology · Area of research: Cloud Technologies
Abstract
In the rapidly evolving business landscape, organizations increasingly rely on Enterprise Resource Planning (ERP) systems to streamline operations and improve decision-making processes. However, traditional ERP systems often face challenges related to scalability, flexibility, and real-time data access. This paper explores the integration of cloud technologies into ERP systems as a means to enhance their efficiency and overall performance. By leveraging cloud computing, organizations can benefit from scalable infrastructure, reduced operational costs, and improved collaboration among stakeholders. This study examines various cloud models?public, private, and hybrid?and their respective impacts on ERP functionalities such as data management, resource allocation, and reporting. Additionally, the research highlights the significance of adopting advanced cloud-based tools, including Machine Learning (ML) and Artificial Intelligence (AI), to further optimize ERP processes. Key performance indicators (KPIs) are identified to measure the success of cloud integration, emphasizing improvements in operational efficiency, user satisfaction, and data accuracy. The findings suggest that organizations that embrace cloud technologies in their ERP systems not only achieve enhanced agility and responsiveness but also foster innovation through real-time data insights. Ultimately, this paper advocates for a strategic approach to cloud integration, positioning it as a critical component in modernizing ERP systems and driving sustainable growth in competitive markets.
Keywords
ERP systems, cloud technologies, integration, scalability, flexibility, operational efficiency, data management, machine learning, artificial intelligence, real-time insights, performance indicators, innovation
How to cite this paper
@article{1706304,
author = {Ramya Ramachandran, Balaji Govindarajan, Imran Khan, Om Goel, Prof. (Dr.) Arpit Jain; Dr. Lalit Kumar},
title = {Enhancing ERP System Efficiency through Integration of Cloud Technologies},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {3},
pages = {748-764},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706304.pdf},
abstract = {In the rapidly evolving business landscape, organizations increasingly rely on Enterprise Resource Planning (ERP) systems to streamline operations and improve decision-making processes. However, traditional ERP systems often face challenges related to scalability, flexibility, and real-time data access. This paper explores the integration of cloud technologies into ERP systems as a means to enhance their efficiency and overall performance. By leveraging cloud computing, organizations can benefit from scalable infrastructure, reduced operational costs, and improved collaboration among stakeholders. This study examines various cloud models?public, private, and hybrid?and their respective impacts on ERP functionalities such as data management, resource allocation, and reporting. Additionally, the research highlights the significance of adopting advanced cloud-based tools, including Machine Learning (ML) and Artificial Intelligence (AI), to further optimize ERP processes. Key performance indicators (KPIs) are identified to measure the success of cloud integration, emphasizing improvements in operational efficiency, user satisfaction, and data accuracy. The findings suggest that organizations that embrace cloud technologies in their ERP systems not only achieve enhanced agility and responsiveness but also foster innovation through real-time data insights. Ultimately, this paper advocates for a strategic approach to cloud integration, positioning it as a critical component in modernizing ERP systems and driving sustainable growth in competitive markets.},
keywords = {ERP systems, cloud technologies, integration, scalability, flexibility, operational efficiency, data management, machine learning, artificial intelligence, real-time insights, performance indicators, innovation},
month = {September},
}