臺灣博碩士論文加值系統

近年來，供應鏈的活動影響了社會和環境，為了避免在供應鏈下游提出需求後發生缺貨的問題，上游端會建立庫存系統來確保能供應下游的需求變動，因此存補貨管理無疑是供應鏈管理中重要其中一個環節。相較於傳統補貨策略需要較多且複雜的參數運算求出最佳解，本研究利用限制理論(Theory of Constraints, TOC)搭配需求拉動補貨(Demand-Pull)與緩衝管理(Buffer Management)並輔以指數加權移動平均法(Exponentially Weighted Moving Average, EWMA)，只需要參考顧客即時需求資訊與需求趨勢便能做出補貨的策略。
本研究主要考量的產品為具有生命週期短、需求變動大特性之半導體封裝產業，因此利用指數加權移動平均法(Exponentially Weighted Moving Average, EWMA)整合顧客提供之實際需求預測(Forecast)與顧客實際需求量(Demand)掌握顧客需求變動趨勢。並觀察在庫量水位於之緩衝區域搭配EWMA指標正、負向需求趨勢管理緩衝與調整訂購量。藉由EWMA特性能反映時間內曲線小幅度變化，能快速反應改善補貨策略。本研究以國內某封裝廠實際案例分析，並依照不同產品需求模式提供權重，在需求變異大(小)、預測變異大(小)、需求與預測差距大(小)之產品特性中，評估出本研究方法適用性。統整分析後結果顯示本研究確實能適用且加強限制理論補貨策略之應用，達到整體庫存量降低之效果。

關鍵字：供應鏈管理、限制理論、緩衝管理、需求拉動補貨、指數加權移動平均

In recent years, supply chain activities play an important role in society and environment. In order to avoid the supply chain downstream demand running out of stock problems, the upstream end of the inventory system is controlled to monitor the downstream demand changes. Therefore, the replenishment management within the supply chain is very important. Compared with the traditional replenishment strategy, which requires more complex parameter operations to find the optimal solution, this study uses the Theory of Constraints (TOC) and demand-pull (Buffer Management). Supplemented by an Exponentially Weighted Moving Average (EWMA) strategy, one can replicate and refer to the customer''s immediate demand information and demand trends.
In this study, we consider the semiconductor packaging industry which is characterized by short life cycle and large demand fluctuation. Therefore, we use the Exponentially Weighted Moving Average (EWMA) to integrate customer demand forecast with customer actual demand to monitor the trend of changes in customer demand. We then observe in the buffer zone the amount of inventory in the zone. EWMA will indicate positive and negative demand trend; management can then adjust the order quantity. EWMA features can reflect the small changes in the trend and quickly adjust the replenishment strategy. In this study, the actual data of a domestic packaging plant is used. Different weights and some parameter variations are done for different demand characteristics of the products. They are large (small) demand variations, large (small) forecast variations, large (small) demand and forecast variations. The results of our model in this study not only strengthen the theory of behind the replenishment policy, it also reduces the overall inventory cost.

Keywords：Supply chain management, theory of constraints, buffer management, demand-pull strategy, exponentially weighted moving average

中文摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1研究背景 1
1.2研究動機 3
1.3研究目的 4
1.4研究方法與步驟 5
1.5論文章節架構 6
第二章 文獻探討 8
2.1供應鏈管理相關文獻 8
2.1.1供應鏈之意義及其演進 14
2.1.2供應鏈管理的整合 16
2.2限制理論相關文獻 17
2.2.1限制理論補貨策略 19
2.2.1.1需求拉動補貨(Demand-Pull) 19
2.2.1.2緩衝管理 20
2.2.1.3初始目標緩衝與調整參數建立 22
2.3指數加權移動平均相關文獻 26
2.3.1指數加權移動平均方法 26
2.3.2指數加權移動平均EWMA方法之應用 27
第三章 整合預測與需求EWMA之限制理論補貨模式 30
3.1基本假設 31
3.2利用EWMA結合實際需求與預測資訊決定緩衝調整時機 33
3.2.1 TOC緩衝目標訂定 33
3.2.2 EWMA參數與計算公式 34
3.2.3 補貨量與目標緩衝調整管理範例 36
3.2.4 EWMA結合TOC補貨模式圖 38
3.3績效衡量指標 40
3.4案例說明 41
第四章 案例與資料分析 49
4.1基本假設條件 49
第五章 結論 80
5.1方法比較 80
5.2結論與方向 81
5.3未來研究改善方向 83
5.4產學合作心得 84
參考文獻 85

 
圖目錄
圖1-1研究步驟 7
圖2.1供應鏈模式Poirier & Reiter(1996) 9
圖2.1限制理論五個步驟(Goldratt&Cox,1984) 17
圖2.2存貨管理衝突圖(李榮貴&張勝鴻,2005) 18
圖2.3緩衝管理三區(李榮貴&張盛鴻，2005) 20
圖2.4緩衝管理調整圖 21
圖2-5每期分配權重(α=0.2)，引用自李沐謙(2011) 26
圖3.1紅、黃、綠緩衝區建立 33
圖3-3 EWMA動向指標結合TOC補貨模式流程圖 38
圖3-4決定權重流程圖 39
圖3-5數據模擬 43
圖3-6本研究補貨模式之在庫量變化圖(X產品) 44
圖3-7項彥華(2012)toc補貨模式 44
圖3-8傳統TOC補貨模式 45
圖3-9本研究、項彥華、TOC補貨模式在庫量曲線比較 45
圖3.10本研究、項彥華、傳統TOC (X產品)平均庫存長條圖 46
圖4.1 A產品實際需求與需求預測折線圖 51
圖4.2 A產品在庫量比較折線圖 51
圖4.3A產品績效改善長條圖 53
圖4.3 B產品實際需求與需求預測折線圖 55
圖4.4 B產品在庫量比較折線圖 55
圖4.5 B產品績效改善長條圖 57
圖4.5 C產品實際需求與需求預測折線圖 59
圖4.6 C產品在庫量比較折線圖 59
圖4.7C產品績效改善長條圖 61
圖4.7實際需求與需求預測折線圖 64
圖4.8 D產品在庫量折線圖 64
圖4.9 E產品實際需求與需求預測折線圖 69
圖4.10 E產品在庫量折線圖 69
圖4.11 E產品績效改善長條圖 71
圖4.11 F產品實際需求與需求預測折線圖 74
圖4.12 F產品在庫量比較折線圖 74
圖4.13 F產品績效改善長條圖 76


 
表目錄

表2.1供應鏈管理文獻整理表 10
表2.2限制理論存貨管理相關文獻彙整 22
表2.3指數加權移動平均 27
表3.1符號說明 32
表3-2補貨量與緩衝管理規範 37
表3-3顧客13週未來需求預測值 42
表3-4本研究補貨模式績效表現整理表 46
表3-5本研究與項彥華績效指標比較 47
表3-6本研究與傳統TOC績效指標比較 47
表4.1本研究與傳統TOC績效比較 52
表4.2本研究與項彥華(2012)績效比較 52
表4.3本研究與傳統TOC績效比較 56
表4.4本研究與項彥華(2012)績效比較 56
表4.5本研究與傳統TOC績效比較 60
表4.6本研究與項彥華(2012)績效比較 60
表4.7本研究與項彥華(2012)、傳統TOC假設不同缺貨與存貨成本比較表 61
表4.8本研究與傳統TOC績效比較 65
表4.9本研究與項彥華(2012)績效比較 65
表4.10本研究與項彥華(2012)、傳統TOC假設不同缺貨與存貨成本比較表 66
表4.11本研究與傳統TOC績效比較 70
表4.13本研究與項彥華(2012)、傳統TOC假設不同缺貨與存貨成本比較表 71
表4.14本研究與傳統TOC績效比較 75
表4.15本研究與項彥華(2012)績效比較 75
表4.16本研究與項彥華(2012)、傳統TOC假設不同缺貨與存貨成本比較表 76
表4.17顧客產品類型分類 78
表5.1補貨策略比較表 80

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