Core Insights - Many small and medium-sized enterprises (SMEs) face challenges in realizing the return on investment (ROI) from their digital transformation efforts, despite having implemented various systems and automation equipment [3] - Approximately 90% of manufacturing enterprise data remains "asleep," particularly in SMEs, due to a lack of unified data and business process standards, leading to data silos and inefficient business collaboration [3][4] - The digitalization of supply chains is evolving from merely moving procurement processes online to achieving end-to-end collaboration and optimization through data integration [3] Group 1: Challenges in Digital Transformation - Enterprises often have multiple systems (e.g., SAP, PLM, MES) but struggle with data integration, resulting in data silos that hinder effective decision-making [4] - The absence of standardized business and data processes is a fundamental issue, as many companies jump into system implementation without proper design [4] - The "sleeping data" problem is exacerbated by the lack of a centralized data management system and effective edge data processing capabilities [5] Group 2: Solutions and Innovations - Companies are leveraging AI technologies to break down data barriers and enhance data sharing and value realization [4][5] - AI is being applied to improve supply chain transparency, responsiveness, and risk management, with successful case studies demonstrating proactive measures against price increases and stock shortages [6] - The development of platforms like "Procurement Butler" aims to streamline non-standard procurement processes, making them as simple and controllable as online shopping [6] Group 3: Future of AI in Manufacturing - 2025 is anticipated to be a pivotal year for AI applications, particularly in generative AI and large model technologies, although the manufacturing sector's approach differs from that of the internet industry [7] - The focus in manufacturing AI is on "small data" and "scenario closure," rather than the pursuit of large models, emphasizing practical applications over theoretical advancements [7] - Ultimately, the effectiveness of systems and AI in manufacturing will be measured by improvements in supply chain stability, speed, and intelligence [7]