世界環境不斷變化，企業的營運環境也隨之變動。在這樣的環境下，訂單交付流程成為企業營運中不可或缺的流程。快速滿足客戶需求能夠顯示公司的競爭力；如期如質地將貨物交付到客戶手中能夠展現公司的信用程度。因此，隨著時勢變化，企業必須不斷調整經營方向，持續改善才能奠定永續經營的基礎。 限制理論的基礎來自於思維的改變。慣性思維是一般人的通病，也是限制公司成長的原因。本研究基於限制理論的思維方法，針對個案公司在訂單交付流程上所面臨的問題及挑戰進行研究。選定系統限制，確認問題所在，找到核心問題，並建構執行方案。同時考量整體流程，運用管理機制解決問題、突破瓶頸限制。同時以透明化之資訊為基礎，資源最佳配置為原則，建構營運模擬系統。 同時透過資訊工具的協助，能夠動態調整訂單交付計劃與生產排程，管控瓶頸節點，縮短各單位作業時間，降低人工查詢作業，有效資源分配極大化，流程中各資訊節點的埋設，也為往後的數據分析奠定基礎。 ;The world environment is constantly changing, and the operational environment of businesses is also changing accordingly. In such an environment, the order delivery process has become an indispensable process in business operations. Quickly meeting customer needs can demonstrate a company′s competitiveness; delivering goods to customers on time and with quality can demonstrate a company′s credibility. Therefore, as the times change, businesses must constantly adjust their business direction and continue to improve in order to establish a foundation for sustainable operations. The foundation of the Theory of Constraints is based on a shift in thinking. Inertia thinking is a common problem among people and is also a limiting factor for a company′s growth. This study is based on the thinking method of the Theory of Constraints, focusing on the problems and challenges faced by a case company in the order delivery process. The system constraint was identified, the problem was confirmed, the core problem was found, and an execution plan was constructed. At the same time, the overall process was considered, and management mechanisms were used to solve problems and overcome bottlenecks. Based on transparent information and the principle of optimal resource allocation, an operational simulation system was constructed. With the assistance of information tools, the order delivery plan and production schedule can be dynamically adjusted, bottleneck nodes can be controlled, operating times for each unit can be shortened, and manual query operations can be reduced. This maximizes the allocation of resources effectively. The embedding of information nodes in the process also lays the foundation for future data analysis.
