Process Modeling for CPFR System

• Main Authors: Chih-Wei Lai, 賴志偉
• Other Authors: Yuan-Ping Luh
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/7eya76

碩士 === 國立臺北科技大學 === 機電整合研究所 === 95 === In the competitive environment of global supply chain at this present, in order to satisfy more and more demands and expectations from customers, enterprises face many pressures such as cost reduction, short lead-time, quality promotion, and global operation management…etc. But enterprises can’t resolve this pressure by their own; instead, it shall be achieved by the collaboration and integration of overall business entities of supply chain based on the same targets. The VICS committee has published the standards for Collaborative Planning, Forecasting and Replenishment (CPFR), and CPFR has the revolutionary influence on supply chain. However, according to the relevant research and investigation, the applications of CPFR in Taiwan are not prevalent yet currently. Most of enterprises still have the issue of forecast and replenishment with their business entities, and can not reduce cost and shorten lead time effectively. The major cause is enterprises haven’t implemented a CPFR system to link and coordinate overall business entities of supply chain. According to this, this research are based on the enterprise’s demands on CPFR system and literatures review, and use seven-stages development methodology which are including define objectives, process modeling, data modeling, apply business rules, UI interface design, coding and testing. Then focus on the key stages of define objectives and process modeling, and provide a collaborative process modeling that is suitable for overall business entities of supply chain (including four organizations: global operation, subsidiary, factory and buyer). Hopefully, the process modeling will help who are planning to implement a CPFR system. Besides, in order to promote the service capability of orders with multiple deliveries, the process modeling in this research integrate the technology of real-time CTP to shorten lead time and reduce cost based on the well operation of capacity and inventory.