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題名：SPC與EPC整合系統在製程管制與供應鏈管理上的應用
作者：林宜霓
作者(外文)：Yi-Ni Lin
校院名稱：國立臺灣大學
系所名稱：商學研究所
指導教授：黃崇興
學位類別：博士
出版日期：2003
主題關鍵詞：統計製程管理；工程製程管理；品質管理；供應鏈管理；長鞭效應；SPC；EPC；Quality management；Supply chain management；Bullwhip effect
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生產管理上整合SPC與EPC已被證實可同時獲得消除可歸屬變異與共同變異的好處，許多論文也已經證明整合系統的效果的確比僅採用單一控制方法要來得佳；不過如果SPC未能及時尋得失控且未能及時歸類干擾的類型，在移除可歸屬原因的瞬間反而會導致EPC的回授機制出現過度補償的現象。本論文除了驗證此一過度補償的現象，我們也發現即使將EPC的回授機制還原至干擾介入前的狀態，仍無法完全避免過度補償的發生；基於這個發現，我們將成本概念導入SPC與EPC的整合系統中，藉由成本分析來評估在連續製程中移除可歸屬原因的實質效益，讓製程管理者對於移除可歸屬原因與否能有所依據；最後，我們也運用成本函數的建構來進一步推導出較為合理的SPC管制圖上下限，以使得管制上下限的設定能真正切合實務上的需要。
而在於供應鏈的管理上，最近的論文已開始以回授控制的模型來模擬供應鏈的行為，基於此，過去的研究也發現可以量化供應鏈管理上的重要發現: Bullwhip Effect。而在本論文中，我們試著去探索一個過去研究模型裡的盲點，我們覺得，在著名的啤酒遊戲裡，真正導致Bullwhip Effect的最大原因，在於一些突發性的需求變化，而非過去模型裡所討論的干擾模型，也因此，我們重新驗證了在此模型下，突發性需求變化對供應鏈所造成的影響，並發現突發性的干擾也會造成Bullwhip Effect。再者，過去學者爭論的關於需求資訊分享所形成的效益多寡，我們也試著以數學模型來推導，發現如果上下游供應鏈的預測模型已知的情形下，需求資訊分享的效益並不大，實驗也支持這樣的看法。
而回授模型裡，事實上有許多的理論可以加速預測的收斂速度，例如利用SPC的工具，來偵測較不尋常的變動，然後改變系統參數等等，這樣的做法普遍且直觀。但在供應鏈的管理上我們發現，這樣的SPC與EPC的整合機制雖然快速的滿足了客戶的需求，但卻可能付出了加大Bullwhip Effect的代價。在本文中，我們試著提供一個決策的規則，讓管理者在是否加速回授上有較合乎成本的選擇。此外，根據這個決策模型，我們可以調整SPC的管制上下限，使得統計管制僅僅偵測符合成本需求的需求變異。
基本上，這是一篇對於管理科學的研究，事實上，我們得承認，管理這件事，絕對是複雜且無法以如此簡單的模型來說明的。但這樣的量化，可以給於管理上的直觀更強大的科學基礎，讓管理者考量的涵蓋面可以更廣，風險可以更小。最後我們引System Dynamic理論大師 Jay W. Forrester的一句話:「通常複雜系統的表現是『反直覺』的。」我們論文發現對於干擾的移除或者加速補救所付出的代價，似乎也驗證了這句話。
For manufacturing processes, in order to obtain the advantages of eliminating both assignable causes and common causes, an effective method to integrate SPC (Statistical Process Control) and EPC (Engineering Process Control) under the continuous process is rousing escalated interests. In our study, it is found that EPC feedback compensation mechanism affects SPC out-of-control detection, and degrades the output quality once suddenly assignable causes are removed. Based on this finding, we propose a novel SPC control scheme that can effectively detect and identify the disturbance types. Further, we introduce a cost-oriented decision rule for removing assignable causes in SPC and EPC integration system. Based on this decision rule, a more accurate selection methodology of SPC charts’ control limits is also proposed.
For supply chain management, many significant researches have successfully completed the quantifying the bullwhip effect of the supply chain management. However, the unexpected demand change, as what happens in the beer game scenario, is not taken into consideration. In this research, we quantify the bullwhip effect resulting from the unexpected demand change and also compare the cases with and without demand information sharing. In addition, based on the above findings, we re-evaluate the value of promotion activities when considering the impact of the bullwhip effect. Also, we propose a cost-oriented decision rule to help to decide whether to adopt Run-by-Run control, which integrates SPC control charts, or not. Last, we propose a formula to decide SPC control limits to detect cost-effective unexpected demand change.
Basically, this is a paper regarding management science. Again, it is noticeable that such a simple model cannot completely represent the cases in the real world. However, through the discussion and simulation, we hope to provide the managerial intuitive a more scientific basis. Our goal is to make the managerial decision more comprehensive and less risky. Just as the idea from Jay W. Forrester, the great master in the system dynamic field, that the behavior of the complicated system is often counter-intuitive. It evidences the dilemma we demonstrated in the paper.
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