| dc.contributor.advisor | 林我聰 | zh_TW |
| dc.contributor.advisor | Lin, Woo-Tsong | en_US |
| dc.contributor.author (Authors) | 陳寬茂 | zh_TW |
| dc.contributor.author (Authors) | Chen, Kuan-Mau | en_US |
| dc.creator (作者) | 陳寬茂 | zh_TW |
| dc.creator (作者) | Chen, Kuan-Mau | en_US |
| dc.date (日期) | 2004 | en_US |
| dc.date.accessioned | 8-Dec-2010 16:03:06 (UTC+8) | - |
| dc.date.available | 8-Dec-2010 16:03:06 (UTC+8) | - |
| dc.date.issued (上傳時間) | 8-Dec-2010 16:03:06 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0923560091 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/49658 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊管理研究所 | zh_TW |
| dc.description (描述) | 92356009 | zh_TW |
| dc.description (描述) | 93 | zh_TW |
| dc.description.abstract (摘要) | 協同規劃、預測與補貨（Collaborative Planning, Forecasting and Replenishment; CPFR）是協同商務中一個新發展的應用實務，主要強調供應鏈上買賣雙方協同合作流程的概念，以提升供應鏈上流程的處理效率。企業需要利用協同合作所獲得之即時資訊來進行預測，減少不確定性因素之影響，提高預測之準確性。CPFR流程下協同預測階段分為銷售預測與訂單預測，兩者之預測項目與目的並不相同且所需要之資訊亦有所差異。銷售預測著重在市場需求部份的預測；訂單預測則是依據銷售預測、存貨狀況與生產面因素來做實際訂單之預測。由於訂單預測作為下個階段之實際補貨的參考，其預測準確性的要求就格外重要。然而研究文獻多偏向CPFR流程架構與導入效益等管理議題，雖有少數針對預測模型之研究，但亦以企業內部銷售預測為主，並未有文獻提出跨企業之協同訂單預測模型，故CPFR流程下訂單預測方法之研究探討有其必要性。本研究以CPFR流程中接續銷售預測之訂單預測階段為研究主題，蒐集近年來國內外研究CPFR與訂單預測之相關文獻為基礎，歸納出協同合作下訂單預測所須具備之屬性與影響因素，並作為模型解釋變數，透過時間序列、多元迴歸與演化策略法（Evolution Strategies）的結合，建構一個統整供應鏈上、下游協同資訊與符合CPFR流程下訂單預測特性之預測模型。最後以國內某製造業公司與其顧客（一國際大型零售商）之訂單資料進行模型驗證，與單純使用時間序列方法或統計迴歸分析的預測結果作績效評比，實驗顯示本研究所提出之訂單預測方法較傳統使用單一時間序列或統計回歸方法之預測結果佳。 | zh_TW |
| dc.description.abstract (摘要) | Collaborative Planning, Forecasting and Replenishment (CPFR) is nowadays a practice of collaborative commerce, emphasizing buyers and sellers’ coordination for the efficiency of the process in supply chain. Enterprises utilize instant information obtained from coordinate processes to forecast in order to reduce the influence of the uncertain factor and improve forecasting accuracy. The stage of the collaborative forecasting in CPFR process is divided into sales forecasting and order forecasting which make differences on forecasting objective, subject, and information needed. Sales forecasting focuses on the prediction of the market demand; order forecasting is the prediction of the real orders according to sales forecasting, stock state and productive factor. The accuracy of order forecasting is extremely important because it is regarded as the reference of the replenishment at next stag. The literatures about CPFR mostly probe into manage topics like benefits of implementation or process structures though there are some researches on the forecasting model which mainly discuss sales forecasting inside enterprises. Therefore, it is necessary to investigate into the coordinative order forecasting model under CPFR process. This paper regards order forecasting following sales forecasting in CPFR as the theme. Besides generalizing the necessary parameter of order forecasting based on literatures review, the research presents a hybrid forecasting model which considers coordinative information and order forecasting requirements. It integrates the time series model, regression model, and use evolution strategies to determine its coefficients efficiently. The validity of the forecasting model is verified by experiment on order datum from one manufacturer in Taiwan and its international retailer. The results show that the order forecasting model has better forecasting performance than not only the time series model but also the ordinary regression model. | en_US |
| dc.description.tableofcontents | 中文摘要 I英文摘要 II目錄 III表目錄 V圖目錄 VI第壹章 緒論 11.1 研究背景 11.2 研究動機 21.3 研究目的 31.4 研究範圍 31.5 研究流程 31.6 章節架構 5第貳章 文獻探討 62.1 協同商務 62.2 協同規劃預與補貨（CPFR） 82.3 預測概論 152.4 訂單預測 182.5 演化策略法 21第參章 模型建構 253.1 協同訂單預測模型整體架構 253.2 驗證方法與績效指標 283.3 時間序列模型 303.4 多元迴歸模型 333.5 演化策略模型 38第肆章 實驗分析與模型驗證 474.1 資料蒐集與敘述 474.2 產品A預測實驗與績效 484.3 產品B預測實驗與績效 56第伍章 結論與建議 635.1 結論 635.2 後續研究方向 64參考文獻 65表目錄表2-1 CPFR流程中針對主導角色的四個情境 9表2-2 預測模型特性整理 17表2-3 演化策略法與基因演算法之比較表 24表3-1 CPFR流程下銷售預測與訂單預測影響因素之差異 37表4-1 產品Ａ之階段一時間序列模型平滑係數敏感度分析 49表4-2 產品A之階段二多元迴歸模型係數表 51表4-3 產品A之階段三演化策略模型第一期初始值 52表4-4 產品A之演化策略法突變率績效測試前3名 53表4-5 三階段訂單預測模型產品A之10次實驗績效 53表4-6 產品A之一般迴歸模型係數表 55表4-7 產品A之4週預測績效比較 55表4-8 產品A之8週預測績效比較 56表4-9 產品B之階段一時間序列模型平滑係數敏感度分析 57表4-10 產品B之階段二多元迴歸模型係數表 58表4-11 產品B之階段三演化策略模型第一期初始值 59表4-12 產品B之演化策略法突變率績效測試前3名 60表4-13 三階段訂單預測模型產品B之10次實驗績效 60表4-14 產品B之一般迴歸模型係數表 61表4-15 產品B之4週預測績效比較 61表4-16 產品B之8週預測績效比較 62圗目錄圖1-1 研究流程圖 4圖2-1 CPFR協同合作的八個主要任務 10圖2-2 協同預測關係圖 18圖2-3 產生訂單預測的資料流 20圗2-4 演化策略法之簡化流程圖 22圖3-1 協同訂單預測模型架構圖 26圖3-2 離散型重組運算結果 41圖3-3 中間產物型重組運算結果 42圖3-4 本研究演化策略模型虛擬碼 45圖3-5 本研究演化策略模型流程圖 46圗4-1 本研究模型各階段實驗資料區間示意圖 47圗4-2 產品Ａ之實際訂單量與時間序列訂單預測曲線圖 49圗4-3 演化策略法演化代數之績效收斂趨勢 52圗4-4 產品B之實際訂單量與時間序列訂單預測曲線圖 57 | zh_TW |
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| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0923560091 | en_US |
| dc.subject (關鍵詞) | 協同規劃預測補貨 | zh_TW |
| dc.subject (關鍵詞) | 訂單預測 | zh_TW |
| dc.subject (關鍵詞) | 演化策略 | zh_TW |
| dc.subject (關鍵詞) | Collaborative Planning, Forecasting and Replenishment | en_US |
| dc.subject (關鍵詞) | CPFR | en_US |
| dc.subject (關鍵詞) | Order forecasts | en_US |
| dc.subject (關鍵詞) | Evolution strategies | en_US |
| dc.title (題名) | CPFR流程下之訂單預測方法 | zh_TW |
| dc.type (資料類型) | thesis | en |
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