AI-Powered Cloud-Based ERP Solutions Transforming Product Lifecycle Management in the Manufacturing Sector
The manufacturing sector is experiencing a transformative shift through the adoption of AI-powered cloud-based Enterprise Resource Planning (ERP) solutions, which are redefining Product Lifecycle Management (PLM). These advanced systems integrate artificial intelligence and cloud technologies to streamline processes, improve decision-making, and enhance collaboration across all stages of the product lifecycle, from design to decommissioning. Traditional challenges in PLM, such as fragmented data, inefficient workflows, and limited predictive capabilities, are effectively addressed by leveraging AI-driven analytics and real-time data synchronization. This paper explores the functionalities of AI-powered cloud-based ERP solutions, focusing on their ability to optimize manufacturing operations. Features such as intelligent resource allocation, predictive maintenance, and seamless IoT integration are highlighted for their role in reducing operational inefficiencies, improving product quality, and accelerating time-to-market. Real-world case studies demonstrate significant productivity gains, cost savings, and enhanced customer satisfaction achieved through the implementation of these solutions. Additionally, the paper discusses the scalability and flexibility of cloud infrastructure, enabling global manufacturing enterprises to adapt to dynamic market demands. While acknowledging potential challenges such as cybersecurity and data privacy, the study emphasizes the strategic advantages of these technologies in fostering a digital-first manufacturing ecosystem. By identifying emerging trends like digital twins and advanced machine learning models, this research underscores the potential of AI-powered cloud-based ERP systems to revolutionize PLM. This study provides actionable insights for industry professionals, researchers, and policymakers, contributing to the evolution of smarter, more agile manufacturing practices.
Authors
- Suneel Kumar Mogali
Publication Details
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1109/ginotech63460.2025.11076971
- Citations
- 1
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 1.24