| dc.contributor.advisor | 林我聰 | zh_TW |
| dc.contributor.author (Authors) | 曾永勝 | zh_TW |
| dc.creator (作者) | 曾永勝 | zh_TW |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 14-Sep-2009 09:18:30 (UTC+8) | - |
| dc.date.available | 14-Sep-2009 09:18:30 (UTC+8) | - |
| dc.date.issued (上傳時間) | 14-Sep-2009 09:18:30 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0923560301 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/31128 | - |
| 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) | 92356030 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 協同規劃、預測與再補貨(Collaborative Planning, Forecasting and Replenishment; CPFR)，是目前供應鏈管理下重要的討論議題；台灣近年來由於加入WTO與製造業外移使競爭壓力加劇，全球運籌需求提升，使廠商間的合作更加密切，且近年來企業資訊環境與基礎建設逐漸成熟，有助於協同商務之發展。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同且績效良好的銷售預測具有關鍵的重要性，是管理決策與協同合作時的重要依據；但是多數的企業並沒有一個結構化、有系統化的預測流程及方法，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。 在CPFR流程下，強調買賣雙方透過完整、即時資訊的交流，進行短期、單一銷售預測，以提供雙方後續訂單預測、訂單補貨等決策的依據。本研究利用演算法（類神經網路和演化策略法）找出更適合混合性預測架構的解釋變數，再以較適合於實數解之演化策略法於修改黃蘭禎（2004）的三階段之預測模型架構，最後採用實驗方法，進行模型績效驗證。 | zh_TW |
| dc.description.abstract (摘要) | Collaborative Planning, forecasting and replenishment (CPFR) is an important issue of supply chain management currently. Because of the severer competition resulted from entrance into WTO and industry integration, cooperation between Taiwanese companies becomes more intensely; enterprises’ information environment and foundation construction attain to maturity also boost the development of collaboration business. In CPRF process and supply chain operation environment, it is critical that a good performance sale forecasting collaborated by both supplier and buyer sides, and it is also the basis of policy decision and collaboration. However, the majority of the companies lack for a structural and systematical forecasting process to proceed with a multi-points forecasting with different methods. This kind of sale forecasting is less of stable quality and is harder to provide the managers a reasonable statistics explanation. Under the CPRF process, both buyers and sellers are able to obtain the short-term and single sale forecasting by real time information communication. Furthermore, the follow-up order forecasting and replenishment strategy decision can be also established through this process. This research finds the variables that are more suitable to the mixed structure by usage of the algorithms, ANN and Evolution Strategy. And this research uses Evolution Strategy that is more suitable to real question to improve the mixed structure of Huang (2004). In the end, experimentation is adopted in order to verify the performance of the model. | en_US |
| dc.description.tableofcontents | 第一章 緒論 1 1.1研究背景 1 1.2研究動機 2 1.3研究目的 4 1.4研究方法 4 1.5研究架構與步驟 5 1.6研究範圍 6 1.7章節架構 6 第二章 文獻探討 8 2.1供應鏈管理 8 2.2協同規劃、預測和再補貨(CPFR) 11 2.3銷售預測與預測方法 14 2.4類神經網路 21 2.5演化策略法 25 第三章 混合預測模型架構 30 3.1研究架構 30 3.2資料蒐集整理與應用 32 3.3時間序列---指數平滑模型時間序列 33 3.4模型變數之訓練與選取 34 3.5多元線性回歸模型 41 3.6演化策略法求最佳化之混合預測模型 41 3.7 驗證方法、工具與績效衡量指標 44 第四章、實驗分析與模型績效驗證 47 4.1資料敘述與分析 47 4.2 產品A 預測實驗與績效 48 4.3 產品B 預測實驗與績效 59 第五章、結論與建議 66 5.1 結論 66 5.2 後續研究方向與建議 67 中文參考文獻 68 英文參考文獻 69 圖 目 錄 圖1- 1、本研究架構與步驟示意圖 5 圖2- 1、供應鏈管理圖示 9 圖2- 2、黃蘭禎(2004)之三階段預測模型架構圖 20 圖2- 3、類神經網路主要架構圖 22 圖2- 4、網路架構分類圖 23 圖3- 1、混合預測模型架構圖 30 圖3- 2、本研究各階段劃分與資料應用示意圖 33 圖3- 3、以時間序列變數為輸入變數的類神經網路的架構圖 38 圖3- 4、以時間序列變數及另一個解釋變數的引進為輸入變數的類神經網路的架構圖 39 圖4- 1、兩產品各週銷售之資料圖 47 圖4- 2、本研究各階段劃分與資料應用示意圖 48 圖4- 3、產品A 時間序列實際銷售量與預測曲線圖 49 圖4- 4、產品B 時間序列實際銷售量與預測曲線圖 59 表 目 錄 表1- 1、協同預測或CPFR 流程相關文獻—著重於管理面者 2 表1- 2、協同預測或CPFR 流程相關文獻—著重於預測技術者 3 表2- 1、供應鏈管理之定義整理 8 表2- 2、CPFR 的三階段與九流程步驟 13 表2- 3、CPFR流程模型中的銷售預測與訂單預測之差異 14 表2- 4、因果銷售預測函數型態 18 表3- 1、本研究模型與欲比較之模型特性對照表 45 表4- 1、產品A 時間序列模型參數敏感度訓練分析(霍特的兩參數線性指數平滑法) 48 表4- 2、演化策略法代數績效測試前10名 50 表4- 3、演化策略法「突變率」績效測試前10名 51 表4- 4、演化策略法「策略參數」績效測試前10名 52 表4- 5、產品A變數選取流程之第一輪結果 53 表4- 6、產品A變數選取流程之第二輪結果 53 表4- 7、產品A變數選取流程之第三輪結果 54 表4- 8、產品A變數選取流程之第四輪結果 55 表4- 9、產品A變數選取流程之第五輪結果 55 表4- 10、產品A 第三階段回歸模型分析與模型參數表 56 表4- 11、產品A 演化策略模型第一期初始值 57 表4- 12、產品A之10次實驗平均績效 57 表4- 13、產品B變數選取流程之第一輪結果 59 表4- 14、產品B變數選取流程之第二輪結果 60 表4- 15、產品B變數選取流程之第三輪結果 60 表4- 16、產品B變數選取流程之第四輪結果 61 表4- 17、產品B變數選取流程之第五輪結果 61 表4- 18、產品B變數選取流程之第六輪結果 62 表4- 19、產品B 第三階段回歸模型分析與模型參數表 63 表4- 20、產品B 演化策略模型第一期初始值 63 表4- 21、產品B之10次實驗平均績效 64 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0923560301 | en_US |
| dc.subject (關鍵詞) | 協同規劃、預測與再補貨 | zh_TW |
| dc.subject (關鍵詞) | 銷售預測 | zh_TW |
| dc.subject (關鍵詞) | 混合預測模型 | zh_TW |
| dc.subject (關鍵詞) | 類神經網路 | zh_TW |
| dc.subject (關鍵詞) | 演化策略法 | zh_TW |
| dc.subject (關鍵詞) | Collaborative Planning, Forecasting and Replenishment | en_US |
| dc.subject (關鍵詞) | Sales Forecasting | en_US |
| dc.subject (關鍵詞) | Mixed Forecasting Structure | en_US |
| dc.subject (關鍵詞) | Artificial Neural Network | en_US |
| dc.subject (關鍵詞) | Evolution Strategy | en_US |
| dc.title (題名) | CPFR銷售預測模式之探討 | zh_TW |
| dc.type (資料類型) | thesis | en |
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