Authors: Ravindu Dissanayake
Abstract: This review article investigates the integration of SAP Digital Manufacturing with IoT and cloud-based machine learning to achieve intelligent, self-optimizing production environments. As the manufacturing sector transitions toward mass customization and Industry 4.0, the synergy between the S/4HANA digital core and edge computing becomes critical for maintaining real-time operational agility. The research evaluates architectural frameworks that enable a seamless digital thread from the enterprise planning layer to the shop floor, focusing on the role of SAP Business Technology Platform in orchestrating high-frequency IoT data. Key methodologies examined include the application of Time-Series analysis for predictive maintenance and the use of Deep Learning architectures, such as Convolutional Neural Networks, for automated computer vision-based quality inspection. Furthermore, the article analyzes the strategic implementation of Digital Twins to simulate production scenarios and optimize resource utilization. The study addresses technical constraints related to legacy equipment integration, data quality at the edge, and the necessity for zero-trust cybersecurity in connected factories. The review concludes that the shift toward agentic manufacturing workflows and quantum-enhanced scheduling is essential for global enterprises seeking to achieve the dual goals of high-efficiency production and ESG-compliant sustainability in 2026.
DOI: https://doi.org/10.5281/zenodo.19427850
