The performance analysis of effects brought by CPFR and VMI strategies under different environments.

[[abstract]]傳統訂單作業模式中，由於夥伴間彼此未能分享資訊，下游需求能見度或整體供應鏈可視度（Supply Chain Visibility）太低，「長鞭效應」（Bullwhip Effect）於焉形成。 供應鏈中缺乏真實需求能見度以及聯合決策協同關係是供應鏈整合的重大障礙，而CPFR則是克服此瓶頸之指導方針。在此同時，為求資訊共享以達到快速回應顧客的能力，國內產業目前仍多以VMI為因應的供應鏈管理策略，對於CPFR引進的可能成效，仍抱持觀望態度。 本研究探討CPFR與VMI在不同作業環境下之績效分析，並輔以傳統模式以驗證對照；首先對於三者作一系統化描述，加以界定清楚；進而找出彼此在策略意含與執行流程之差異，由影響整體供應鏈績效的環境因素，建置不同的實驗情境，運用模擬方法以及統計檢定，分析其整體供應鏈績效並進行比較。根據模擬結果與統計分析檢定結果看來，在需求變異性小或穩定情況下，無論零售商訂貨前置時間長短，CPFR與VMI二者並無顯著差異。但在需求變異性高的環境下，無論零售商訂貨前置時間長短，在相同的服務水準下，CPFR在供應鏈總庫存均有顯著降低，顯示在需求變異性高的環境下，供應鏈協同整合程度越高，績效表現越佳。[[abstract]]Supply chain visibility in a traditional supply chain is limited due to lack of information sharing among supply chain partners. The biggest barrier is lack of demand visibility and collaborative initiative among partners. Collaborative strategies such as VMI (Vendor Managed Inventory) and CPFR (Collaborative Planning, Forecasting, and Replenishment) appear to be candidate solutions. VMI currently is the major choice to facilitate customer response by information sharing,. As for CPFR strategy, it still remains intact because limited successful data can support its validity. The main focus of this research is to investigate the differences of effects brought by CPFR and VMI strategies under different environments. Traditional transaction model is also considered for comparison basis. Strategy philosophy and execution procedures are investigated thoroughly, first, Differences between strategies are pointed out. Environmental elements affecting the performance are then identified. Simulation models for three supply chain strategies are constructed. Experiments are then conducted under combinations of environmental factors. According to the result of simulation and statistic analysis, in the situation of low demand variance there are not different performances between CPFR and VMI. But the total inventory level under CPFR strategy is significantly lower than those of other strategies in the situation of high demand variance with the same service level . This implies that higher integration level will get better performance in supply chain with highly-unstable demand