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論文名稱(中文) 機台維修備品需求預測-以人造纖維自動包裝物流設備為例
論文名稱(英文) Forecasting demand for spare parts: A case study of automatic packaging and logistic machine for artificial fiber
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 103
學期 2
出版年 104
研究生(中文) 張嘉庭
研究生(英文) Jia-Ting Chang
學號 R37021278
學位類別 碩士
語文別 中文
論文頁數 60頁
口試委員 指導教授-王泰裕
口試委員-陳梁軒
口試委員-謝中奇
口試委員-林君維
中文關鍵字 需求預測  時間序列分析  迴歸分析  倒傳遞類神經網路 
英文關鍵字 Demand forecast  Time series  Multiple Regression  Artificial Neural Networks 
學科別分類
中文摘要 人造紡絲製造工業有著高度勞力密集及高技術密集的產業特性,因此必須納入自動化生產的設備及技術。維護單位要維護良好的維修品質、降低停機成本並使生產的稼動率在一定的水準之上時,除了良好的維修技術、快速的問題判別外,更需要有充足的備品數量做為支援後盾。但備品過剩時增加工廠的存貨成本及管理成本,積壓了可用資金;備品不足時則影響生產增加停機成本。因此,本研究計劃針對人造纖維包裝物流設備之備品做需求預測,研究方法使用時間序列法、迴歸分析、倒傳遞類神經網路以及目前產業使用的啟發式法則來建立備品需求預測模型。評估的準則以均方根誤差來比較不同預測模型以及現有預測方法之預測結果進而找出最佳預測模型,盼研究結果能建立備品需求預測模型以及提升預測準確度。
本研究以高單價、使用量大、關鍵性及客製化備品做為後續個案實證分析的選擇依據。蒐集產能資料、定保次數、人員介入損壞次數、控制或機械異常損壞次數、殘絲影響損壞、固定處剪斷、棧板品質不佳損壞這些因子對三種不同的機台備品利用統計軟體進行模型的建立以及驗證。另外因預算審核以及費用呈報皆是以月做為單位,故以月做為收集資料的時間單位。結果發現三種不同的機台備品皆是以類神經網路法在解釋變異的能力上最佳,以多重線性迴歸次之,而其他二種預測方法則不相上下。在預測準確性上同樣以類神經網路法最高,但目前產業使用的啟發式法則最差,最後以這二種法則與實際需求量比較其對經濟效益的影響。
英文摘要 While researchers are paying more attention to production capacity in production management, less attention were paid to facility maintenance and supplement of spare parts. A maintenance department, aiming to maintain a high volume of output, should ensure sufficient quantities of spare parts are available to repair machines when malfunctions occur. This is to minimize any adverse effects on the production process and production capacity. The aim of this study is to investigate the demand forecasts for spare parts for repairing packaging machines in the artificial fiber manufacturing industry. Real data is used to compare differences among the time series methods, regression, an artificial neural network, and the rules of thumb in case company. The accuracy of the model is assessed by the Root Mean Square Error. The results show that the artificial neural networks method provides the best level of accuracy and goodness of fit. On the contrary, a multiple regression method performs poorly as far as accuracy and goodness of fit concerned. The time series method and the heuristic rule provide the worst level of accuracy and goodness of fit.
論文目次 摘要 i
Abstract ii
誌謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 2
第四節 研究範圍與限制 3
第五節 研究流程 3
第六節 論文架構 3
第二章 文獻探討 6
第一節 聚酯纖維發展概述 6
第二節 需求預測 7
第三節 時間序列法 9
第四節 迴歸分析 13
第五節 類神經網路 16
第六節 小結 19
第三章 建立機台維修備品需求預測模型 20
第一節 問題描述 20
第二節 研究架構 20
第三節 變數選取及定義 23
第四節 模型建立 26
第五節 小結 33
第四章 個案分析 34
第一節 資料介紹 34
第二節 預測模型的建立與檢定 36
第三節 預測模型的結果比較 50
第五章 結論與建議 55
第一節 研究結論 55
第二節 研究建議 55
參考文獻 57
中文文獻 57
英文文獻 57
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