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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 蔣明晃 | |
dc.contributor.author | Wei-Ting Lin | en |
dc.contributor.author | 林威廷 | zh_TW |
dc.date.accessioned | 2021-06-08T05:59:06Z | - |
dc.date.copyright | 2007-08-03 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-31 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/24959 | - |
dc.description.abstract | 需求規劃的結果高度影響著供應鏈整體網路規劃決策的品質,顯示出需求規劃在企業營運活動中扮演著舉足輕重的角色。然而,需求資訊往往在經過供應鏈層層傳遞後逐漸被扭曲,這種上下游間不對稱的需求資訊將嚴重傷害供應鏈規劃的品質。隨著產品多樣化及其生命週期愈益縮短,若對每一產品細項皆進行預測,其所需之實質成本與時間成本不僅龐大,也未必能得到較準確的預測結果。然而,聚合的預測雖然較為準確,但其所提供的資訊有限,無法因應企業中更為細部之規劃及作業層級之決策。因此企業為了提升需求預測的效益,應有效運用聚合與分解策略進行預測。本研究將以季節性資料—冷氣機製造商之冷氣月出庫量需求為實證分析對象,利用層級式預測方法,將各層級間資料聚合與分解,建立一套有效之預測模型,進而提高預測準確度。
本研究利用時間序列方法中的季節性ARIMA轉換函數模型,配合產品層級架構分類,先分別預測出產品不同層級之銷量,並利用Top-down、Middle-out及Bottom-up預測方式,由各產品階層向上聚合或依比例向下分解至其他階層,最終獲得產品層級架構中所有分類項目之預測值,建構出一套完整之季節性資料層級式預測模型,接著再進行不同預測方式間預測能力之比較,以找出最適預測階層以及預測方式。 經比較Top-down、Middle-out及Bottom-up三種預測方法之後,本研究發現Bottom-up為冷氣機出庫量之最適預測方法,並將預測結果依不同之層級類別作呈現,可提供不同作業層級之決策依據。如此所建構出之預測模型,不僅改善了廠商原先所利用的預測準則,亦能掌握出庫量時間序列之變動型態,使廠商未來於產品規劃及通路出貨量配置時能更有效掌握相關資訊,做出正確決策考量。 | zh_TW |
dc.description.abstract | The results of demand planning highly influence the quality of supply chain network planning. It reveals that demand planning plays a very important role in business operations. However, demand information is often distorted through supply chain. The phenomenon of asymmetric demand information seriously harms the quality of supply chain planning. As a result of product diversity and shorter product life cycle, forecasting for every single product item will cost too much in physical and time aspect, and also unlikely obtain accurate forecasts. Although forecasting for aggregating demand is more accurate, it has difficulty to handle operations planning and decisions in more details.. In order to enhance the benefits of demand forecasting, managers must take advantage of aggregation and disaggregation strategies in demand forecasting. This research takes seasonal data from monthly sales data of an air conditioner manufacturer in last four years as an example. We apply hierarchical forecasting methodology with seasonal ARIMA transfer function to aggregate and disaggregate data between product levels, and come up a effective forecasting model.
At first, we forecast for sales at different levels respectively, and then aggregate upwards or disaggregate downwards to other levels by using Top-down, Middle-out and Bottom-up approaches. Finally, we obtain forecasts of all items in the hierarchical product structure. After establishing this complete hierarchical forecasting model of seasonal data, we compare forecast predictability generated by different forecasting approaches in order to find the most suitable forecasting approach. After comparing three forecasting approaches, Top-down, Middle-out and Bottom- up, we find that Bottom-up is the most suitable forecasting approach for sales of air conditioner. The forecasting model we build up not only improves the forecasting predictability of the firm but also gets a grip of the pattern of sales series such that the firm can make accurate decision in product planning and shipment allocation. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T05:59:06Z (GMT). No. of bitstreams: 1 ntu-96-R94741045-1.pdf: 2907702 bytes, checksum: 37eabba81e9ccd2382dfe7d6e5ea2c20 (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 目錄
致謝 I 論文摘要 II Abstract III 目錄 V 圖目錄 VIII 表目錄 IX 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 3 1.3 論文架構 4 第二章 文獻探討 6 2.1 預測相關理論 6 2.1.1 預測定義 6 2.1.2 預測分類 8 2.1.2.1 預測之大分類 8 2.1.2.2 依預測期間之分類 9 2.1.3 預測相關理論 9 2.1.3.1 定性分析法 10 2.1.3.2 迴歸模式 10 2.1.3.3 計量經濟模型 11 2.1.3.4 人工智慧法 12 2.1.3.5 時間序列預測 12 2.1.3.6 層級式預測方法 13 2.1.4 預測目的與架構 15 2.2 層級式預測 18 2.2.1 層級式預測說明 18 2.2.2 層級式預測分類 19 2.2.3 比例分配方法 20 2.2.4 影響層級式預測績效之因素 22 2.2.5 層級式預測方式之比較 22 2.3 小結 24 第三章 研究方法 25 3.1 研究架構與基本說明 25 3.2 層級架構之建立 27 3.2.1 市場分類方法 27 3.2.2 Friedman檢定法 27 3.2.3 Hollander-Wolfe之多重比較 29 3.3 外部驅動變數分析 30 3.3.1 總體面外部驅動變數介紹 30 3.3.2 季節性資料外部驅動變數之選擇 33 3.3.2.1 變數型態之考量 33 3.3.2.2 資料型態之考量 33 3.3.2.3 外部驅動變數之選擇 34 3.4 預測模型建構 35 3.4.1 資料型態與統計方法選擇 35 3.4.2 時間序列模式 40 3.4.2.1 趨勢預測 40 3.4.2.2 指數平滑模式 41 3.4.2.3 時間序列分解模式 41 3.4.2.4 ARIMA模式 42 3.4.3 SARIMA與SARIMAT模型介紹與預測流程 44 3.4.3.1 SARIMA模型介紹 45 3.4.3.2 SARIMAT模型介紹 51 3.4.4 各階層預測模型 54 3.4.4.1 Top-level之預測模型 54 3.4.4.2 Middle-level之預測模型 54 3.4.4.3 Bottom-level之預測模型 55 3.5 各階層預測值產生 56 3.5.1 Top-down 56 3.5.2 Middle-out 57 3.5.3 Bottom-up 58 3.6 預測績效評估 59 3.6.1 預測誤差計算方法 59 3.6.2 各階層不同預測方法之比較 61 第四章 實證研究 62 4.1 研究樣本描述 62 4.2 產品層級架構 64 4.3 外部驅動變數之決定與預測模型 67 4.3.1 外部驅動變數之決定 67 4.3.2外部驅動變數與反應變量之預測模型 68 4.3.3月均溫之預測模型 69 4.4 Top level冷氣出庫量預測 72 4.4.1 預測變數說明 72 4.4.2 預測模型建構 72 4.4.2.1 研究流程 72 4.4.2.2 模式及意涵 73 4.4.3 預測結果分析 75 4.5 Middle level冷氣出庫量預測 76 4.5.1 預測變數說明 76 4.5.2 預測模型建構 76 4.5.2.1 研究流程 76 4.5.2.2 模式及意涵 77 4.5.3 預測結果分析 85 4.6 Bottom level冷氣出庫量預測 86 4.6.1 預測變數說明 86 4.6.2 預測模型建構 86 4.6.2.1 研究流程 86 4.6.2.2 模式及意涵 87 4.6.3 預測結果分析 103 4.7 預測方法比較 104 第五章 結論與建議 105 5.1 研究結論與貢獻 105 5.2 管理上之建議與應用 108 5.3 未來研究方向 109 參考文獻 111 | |
dc.language.iso | zh-TW | |
dc.title | 應用層級式預測方法於季節性資料分析之研究─以家電製造商之出貨量為例 | zh_TW |
dc.title | Applying Hierarchical Forecasting Methodology to Seasonal Sales Forecasting in Appliance Industry | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 郭瑞祥 | |
dc.contributor.oralexamcommittee | 王福琨,葉小蓁 | |
dc.subject.keyword | 層級式預測方法,季節性ARIMA轉換函數模型, | zh_TW |
dc.subject.keyword | hierarchical forecasting methodology,seasonal ARIMA transfer function, | en |
dc.relation.page | 117 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2007-08-01 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 商學研究所 | zh_TW |
顯示於系所單位: | 商學研究所 |
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