Enterprise Resource Planning (ERP) systems have truly transformed how businesses operate. By bringing various functions together into a single platform, they enhance efficiency and provide valuable insights for strategic decision-making. One notable example is Oracle NetSuite, a cloud-based ERP solution that has the potential to significantly improve both business performance and sustainability. In this study, we delve into how Oracle NetSuite ERP boosts operational performance in several ways. It increases efficiency, enables real-time data analytics, and streamlines processes. Additionally, it plays a vital role in promoting sustainability through better resource management, adherence to environmental regulations, and a commitment to corporate social responsibility (CSR). To provide a comprehensive view, we used a mixed-methods approach, incorporating case studies, surveys, and secondary data to evaluate the dual benefits of this ERP system. Our findings indicate that Oracle NetSuite not only enhances financial and operational outcomes but also helps businesses align with modern sustainability objectives. To wrap up, we offer strategic recommendations for organizations looking to harness the power of ERP systems as they pursue a more competitive edge while contributing to a sustainable future.

Over time, a number of people have brought in their input with the intention of enriching the lives of others positively.

The contributions of the study are aimed at enhancing the existing debate on the application of AI and data engineering through demonstration of the effective blend. For practitioners, the results act as a guide on how to adopt data engineering practices that improve PA systems’ performance reliability. For researchers, the study creates possibilities for other research to explore the relationship of emergent technologies and their efficiency in revolutionizing data flows.

Call to Action

Those organisations which are implementing AI solutions or have plans to do so should be aware of the central importance of data engineering to this effort. This way, they are able to develop more accurate and efficient as well as cheaper predictive analytic systems enabled by scalable, automated, and real-time data workflows. Stakeholders are urged to extend the findings of this research by examining more diverse sectors and forthcoming technologies to add new ideas and approaches to the field.

The rise of edge computing has transformed the ways in which the data is consumed, analyzed and utilized with the possibilities of decision making at the point when they come into the creation. However, the traditional machine learning approaches are not suitable in the modern smart applications like smart cities, health care, and IoT due to the dynamic, distributed and resource limited nature of the edges. As a result, existing machine learning techniques fall short of handling these issues, and Graph Neural Networks (GNNs), are the solution to these challenges due to their ability to model and learn from graph-structured data.

The present article offers a detailed examination of how GNNs can be incorporated into edge computing to enhance the development of prediction techniques. It describes the architecture of GNNs and focuses on the benefits offered by these networks in learning and analyzing connected data and deeply embedded patterns that escape other architectures. We expand the discussion on factors influencing GNNs deployment on edge devices to include computational capabilities, latency, and security threats. To solve these problems, several solutions are put forward including the model optimization, lightweight algorithms, and federated learning.

Moreover, to validate the novelty of edges-GNN integration and its capabilities, real-world cases are also discussed. Applications are, for example, in intelligent transportation systems in smart cities, in force IoT and in diagnostics in medicine. However, the integration of GNNs and edge computing is a way of opening up more opportunities for the development of efficient, scalable and privacy preserving analytic prediction systems.

Finally, the authors present the outlook for further research, in which they consider the potential application of self-supervised learning, the interaction with new technologies like quantum computing or the creation of collaborative platforms for the definition of a clear edge AI system. This work also established the significance a of narrowing the divide between advanced AI techniques and edge computing in opening the possibility for revolutionary advancements in predictive analytics across fields.