賴宥呈
賴宥呈
智能製造執行系統之數據分析架構設計
Data Analytics Framework for Smart Manufacturing Execution Systems
工業4.0、數據科學、製造執行系統、智能化
Industry 4.0、Data Science、MES、Smart
製造執行系統(Manufacturing Execution System, MES)為製造的應用系統之一，能將即時的生產資訊和其他資訊系統（如企業資源規劃、生產規劃與排程系統等）整合，使得營業、工廠或流程控制系統得以連結，以提高企業營運與生產績效。此外，MES系統也收集製程中人、機、料、法、環以及生產相關之資料，進行生產績效影響因子或重要管制點監控，以確保生產之效率與產品品質。
大數據之產生、資訊科技之進步與運算能力之提高，使人工智能(Artificial Intelligence, AI)再度興起，並成功地應用在許多領域，也使產業進入「工業4.0」時代。在工業4.0環境下，資料科學(Data Science)之方法與技術被廣泛應用，冀從「資料」萃取出「有價值之資訊」以改善決策，MES系統也必須進步為Smart-MES來適應智能生產的需求。
本研究針對工業4.0智能化生產之需求，運用Data Science之方法與人工智慧技術，規劃與設計Smart MES模式與資料分析架構，並開發其生產績效影響因子分析技術以及生產績效預測技術。本研究成果將有助生產智能化之實現，進而提昇產業競爭力。
Manufacturing Execution System (MES) is one of the manufacturing systems that integrates real-time production information with other information systems (such as production planning and scheduling systems) to make business, plant or process control systems to be linked, and improve business operations and production performance. In addition, the MES system collects data from people, machines, materials, processes, environmental, and production-related data from the manufacturing process to monitor impact factors on production performance or critical control points for guaranteeing efficiency and quality of production.
The emergence of big data, the advancement of information technology and the improvement of computing power have led to the re-emergence of artificial intelligence (AI) and its successful application in many fields, and the industry has entered the era of Industry 4.0. In the industry 4.0 environment, Data Science's methods and technologies are widely used. From the data to extract valuable information to improve decision-making, MES systems must also evolve into Smart-MES to adapt to intelligent production. Demand.
This study focuses on the requirement of industrial 4.0 intelligent production, using Data Science's method and artificial intelligence technology, planning and designing Smart MES model and data analysis architecture, and developing its analysis technology of impact factors on production performance and forecasting technology of production. The results of this study will help to realize intelligent production, and thus enhance the competitiveness of the industry.