半導體產品之需求型態於限制理論需求拉動補貨策略之影響

Abstract

限制理論建議使用簡單的需求拉動補貨方式配合緩衝管理來管理存貨，其 主要運作方式是以顧客的需求作為補貨的量，並以適當的方式管理存貨（即緩衝） 應有的目標水準。此種方式可以適當地反應變動的需求，在使用時也不需要預測 顧客未來的需求，已被實際應用於許多產業中。但高科技產品具有生命週期短而 前置時間長的特性，此種需求型態是否適用此種補貨方式是值得研究的議題。除 此之外，在實際應用此方法時，一般均以經驗法則決定其管理參數(如調整緩衝的 時機與應調整的幅度)，或僅以一兩組參數經試驗後視情況選用，並未對真正適用 的管理參數進行深入探討，其原因之一在於缺乏有效的研究工具。 本計畫以晶圓製造廠的產品為研究對象，研究目的在於探討半導體產品的 需求型態與需求拉動補貨策略間的關係。本計畫將先建立一套決策支援系統，協 助晶圓製造廠的決策者在使用需求拉動補貨結合緩衝管理的方式管理其成品庫 存時，能以有效的方式找尋適當的管理參數。本計畫並將利用此決策支援系統分 析實際的半導體產品歷史需求資料，以評估需求拉動補貨策略於管理此種產品庫 存的適用性，並將產品以統計方法與決策樹將各產品依策略的適用性與管理參數 的設定值分成數個族群，找出影響產品分群之關鍵需求型態因子並訂定分群規則， 進而為晶圓製造廠發展適合不同半導體產品群之補貨策略。

Theory of Constraints (TOC) suggests the application of demand-pull approach combined with buffer management to effectively manage inventory. The strategy of demand-pull caters customers’ demand to drive inventory replenishment while buffer management is designed to adjust target inventory level (buffer size). It is a very simple way of managing inventory and is attracted to many industry executives. However, there exist few studies that discuss the determination of parameters of buffer management in depth. Most applications follow the rule-of-thumb principles to determine the target buffer size and the amount of buffer adjusted. Although it is possible to find appropriate parameters suitable for different applications, the process is often time-consuming due to lack of useful tools. On the other hand, very few research explores the applicable timing or product types such that demand-pull approach can be effectively applied. This study aims at probing into how demand characteristics for semiconductor products affect the effectiveness of demand-pull replenishment policy. In order to achieve that, a decision support system (DSS) based on simulation will be constructed to provide a tool for users to search for applicable parameters when using demand-pull replenishment combined with buffer management. Using this DSS, this study will analyze historical demand data of semiconductor products to determine if demand-pull replenishment policy is suitable for managing the inventory of a product. Statistical analysis and decision tree technique will be applied to further categorize products into severl groups and apporpirate replenishing strategies will be suggested for each product group to conclude this study.