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???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
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dc.contributor.advisor | 黃奎隆 | |
dc.contributor.author | Yu-Min Chiang | en |
dc.contributor.author | 江堉旻 | zh_TW |
dc.date.accessioned | 2021-06-08T02:23:44Z | - |
dc.date.copyright | 2015-08-25 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-08-19 | |
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[2] 彭克仲,「灰色預測應用於臺灣地區鳳梨零售價格預測之研究」,農業經濟半年刊,2001,69期:107-127。 [3] 黃蘭禎,「CPFR流程下之銷售預測方法~混合預測模型」,國立政治大學資訊管理研究所碩士論文,2004。 [4] 丁恬文,「流通業協同規劃預測補貨解決方案」,國立臺灣大學資訊管理研究所碩士論文,2007。 [5] 歐陽嘉盈,「新商品銷售預測解決方案之研究」,國立臺灣大學資訊管理研究所碩士論文,2007。 [6] 歐宗殷,「資料探勘為基礎之零售業銷售預測模式—以連鎖超商鮮食商品為例」,國立清華大學工業工程與工程管理研究所博士論文,2010。 [7] Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, AC-19(6), 716-723. [8] Box, G.E.P., & Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control, 2nd ed. San Francisco: Holden Day. [9] Deng, J. L. (1989). Introduction to Grey System. Journal of Grey System, 1(1), 1-24. [10] Dickey, D. A., Hasza, D. P., & Fuller, W. A. (1984). Testing for unit roots in seasonal time series. Journal of the American Statistical Association, 79(386), 355-367. [11] Ediger, V. S., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), 1701-1708. [12] Hsu, C. C., & Chen, C.Y. (2003). Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management, 44(14), 2241-2249. [13] Hyndman, R. J., & Athana¬sopou¬los, G. (2013). Forecasting: principles and practice, accessed October 14, 2014, available at http://otexts.org/fpp/. [14] Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22, 679-688. [15] Lei, M., & Feng, Z. (2012). A proposed grey model for short-term electricity price forecasting in competitive power markets. Electrical Power and Energy Systems, 43, 531-538. [16] Montgomery, D. C. (2007). Introduction to statistical quality control. John Wiley & Sons. [17] Nishina, K. (1992). A comparison of control charts from the viewpoint of change-point estimation. Quality and reliability engineering international, 8(6), 537-541. [18] Pai, P. F., & Lin, C. S. (2005). A hybrid ARIMA and support vector machines model in stock price forecasting. Omega, 33(6), 497-505. [19] Taylor, B. W. III (2006). Forecasting. In B. W. Taylor III (Ed.), Introduction to Management Science, Ninth Edition. Upper Saddle River, NJ: Prentice Hall, 666-727. [20] Wackerly, D., Mendenhall, W., & Scheaffer, R. (2007). Mathematical statistics with applications. Cengage Learning. [21] Xia, M., & Wong, W.K. (2014). A seasonal discrete grey forecasting model for fashion retailing. Knowledge-Based Systems, 57, 119-126. [22] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19862 | - |
dc.description.abstract | 近年來藥局逐漸成為患者或消費者最重要的取藥途徑,且隨著消費習性的改變,連鎖藥局已是藥品零售業者主要的發展型態,而根據資料顯示民眾對於藥品的需求逐年增加,為了提高利潤業者必須改善存貨管理品質,其中需求預測扮演著重要的角色,然而由於藥品種類繁多且特性複雜,因此準確的預測十分困難,於是本研究提出一套適用於不同特性藥品的需求預測系統,藉由分析藥品需求歷史資料的充足性、隨機性以及季節性將藥品分為四大類:新產品、隨機性長期型產品、無季節性長期型產品以及季節性長期型產品,其中對於新產品以及隨機性長期型產品以灰色預測法進行預測,而無季節性長期型產品以及季節性長期型產品則以時間序列法進行預測,包含移動平均法、指數平滑法以及自我迴歸整合移動平均,並用分解法處理藥品需求的季節性,此外,本研究也探討新資料加入時的再預測,透過預測模型的適用性評估以做為延續或重建模型之依據,同時也進一步評估歷史資料的適用性以去除不適用的資料。最後,透過實際資料對本研究所提出的預測系統進行驗證與分析,結果顯示預測系統能夠適時依據需求資料的特性改變其產品分類並選用適合模型之機制,確實有助於預測準確度的提升並且大幅減少預測所需的時間。 | zh_TW |
dc.description.abstract | Forecasting enhances the efficiency and effectiveness in decision-making. Through demand forecasting, retailers not only handle demand uncertainties but also improve inventory management. However, demand forecasting for drugs is more complicated due to the various types and features. In this paper, we propose a demand forecasting system for regional pharmacy chain stores. Drugs are categorized into four types based on sufficiency, randomness and seasonality, namely, new products, random long-term products, non-seasonal long-term products, and seasonal long-term products. Grey forecasting method is applied to forecast new products and random long-term products. We also apply time-series methods, which include moving average methods, exponential smoothing methods, and ARIMA, to forecast non-seasonal long-term products and seasonal long-term products. Then, the seasonality of historical data is analyzed through decomposition method. Moreover, we discuss whether the model and parameters should be reconstructed with new data. The suitability of data is simultaneously discussed in the system. Finally, we verify our forecasting system with real data. Results indicate that the proposed forecasting system can determine a suitable model for predicting demand accurately. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:23:44Z (GMT). No. of bitstreams: 1 ntu-104-R02546001-1.pdf: 1677694 bytes, checksum: 55d359c630682569b1f7a965f6d153a9 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 致謝 I
摘要 II ABSTRACT III 目錄 IV 圖目錄 VI 表目錄 VIII 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 研究架構 4 第二章 文獻探討 6 2.1 預測方法及相關研究 6 2.2 需求預測於零售業之應用 10 2.3 預測能力評估指標 11 第三章 研究方法論 13 3.1 需求預測系統主架構 13 3.2 藥品需求特性分析與分類 14 3.2.1 隨機性檢定 15 3.2.2 季節性檢定 16 3.3 適配預測模型之單期預測 16 3.3.1 GM(1,1) 預測模型之建構 17 3.3.2 無季節性時間序列預測模型 (NTSFM) 20 3.3.3 季節性時間序列預測模型 (STSFM) 24 3.4 模型與資料之適用性評估 26 3.4.1 預測模型適用性評估 28 3.4.2 歷史資料適用性評估 29 第四章 實例驗證與結果分析 33 4.1 需求預測系統 33 4.1.1 資料敘述與系統參數 33 4.1.2 系統績效評估指標 34 4.2 預測模型及系統參數分析 35 4.2.1 GM(1,1) 模型分析 35 4.2.2 ARIMA模型分析 37 4.2.3 STSFM參數N(P) 之分析 38 4.2.4 模型與資料適用性評估之參數分析 39 4.3 實例驗證預測系統 42 4.3.1 單次性預測 42 4.3.2 多次性預測 48 4.3.3 全部藥品之驗證結果 51 第五章 結論 53 5.1 結論與建議 53 5.2 未來研究方向 54 參考文獻 55 | |
dc.language.iso | zh-TW | |
dc.title | 應用灰色預測法與時間序列法於連鎖藥局需求預測系統之研究 | zh_TW |
dc.title | Applying Grey Forecasting and Time-series Methods in Demand Forecasting System for Pharmacy Chain Stores | en |
dc.type | Thesis | |
dc.date.schoolyear | 103-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 余峻瑜,藍俊宏 | |
dc.subject.keyword | 需求預測,灰色預測法,時間序列法,自我迴歸整合移動平均, | zh_TW |
dc.subject.keyword | demand forecasting,grey forecasting method,time-series methods,autoregressive integrated moving average (ARIMA), | en |
dc.relation.page | 56 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2015-08-19 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
Appears in Collections: | 工業工程學研究所 |
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ntu-104-1.pdf Restricted Access | 1.64 MB | Adobe PDF |
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