Big Data and Analytical Strategies for Cloud ERP Systems with Business Simulation

Abstract

In the changing landscape of the business world, the rapid pace of change has led to an increasing demand for innovative approaches to problem-solving and optimization. With the ongoing advancement of ERP (Enterprise Resource Planning) systems, which now incorporate predictive analytics, cloud computing, and business analysis through simulations, businesses have new opportunities to enhance their operations. This paper explores a business analysis framework within the context of cloud ERP systems using test data of one company as a case study. Such a business-oriented approach, driven by the adoption of innovative practices, empowers companies to adapt swiftly to the changing dynamics of the market. This paper primarily offers insights into the possibilities unlocked by ERP systems using predictive analytics and business analysis via simulations. These insights encompass a combination of various factors that impact business operations, such as pricing strategies, market segmentation, resource allocation, and other critical components. Furthermore, leveraging a myriad of today's tools, this research demonstrates that after constructing a data model, operations like classification and time series forecasting can be executed on the dataset, all of which significantly influence the final business decisions. This research seeks to provide a broader perspective on problem-solving within the context of ERP systems and how they enable data-driven decision-making for enhanced business performance.

Keywords

• Big data
• Business intelligence
• Cloud ERP systems

References

1. Masnec, L., & Picek, R. (2024). Big data and analytical strategies for cloud ERP systems with business simulation. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 28, 234- 240.