請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57577
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 周雍強(Yon-Chun Chou) | |
dc.contributor.author | Hsin-Yang Lu | en |
dc.contributor.author | 呂昕洋 | zh_TW |
dc.date.accessioned | 2021-06-16T06:52:20Z | - |
dc.date.available | 2019-07-29 | |
dc.date.copyright | 2014-07-29 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-22 | |
dc.identifier.citation | [1]Bacchetti, A., Saccani, N., 2012, “Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice,” Omega, Vol. 40, pp. 722-737.
[2]Boylan, J. E., Syntetos, A. A., and Karakostas, G. C., 2008, “Classification for Forecasting and Stock Control: A Case Study,” The Journal of the Operational Research Society, Vol. 59, pp. 473-481. [3]Bradley, T. R. and Guerrero, H. H., 2009, “Lifetime Buy Decision with Multiple Obsolete Parts,” Production And Operations Management, Vol. 18, No. 1, pp. 114-126. [4] Braglia, M., Grassi, A., & Montanari, R., 2004, “Multi-attribute classification method for spare parts inventory management,” Journal of Quality in Maintenance Engineering, Vol. 10, No. 1, pp. 55-65. [5]Cattani, K. D. and Souza, G. C., 2003, “Good buy? Delaying end-of-life purchases,” European Journal of Operational Research, Vol. 146, No. 1, pp. 216-228 [6]Cohen, M. A., Agrawal, N., and Agrawal, V., 2006, “Winning in the aftermarket,” Harvard business review. Vol. 84, No. 5, pp. 129-138. [7]Ebeling, C. E., 1997, “An Introduction to Reliability and Maintainability Engineering,” United States of America: McGraw-Hill Companies, Inc [8]Farnum, N. R., Stanton, L. W., 1989, Quantitative forecasting Methods, PWS-Kent Publishing Company, Boston, MA, pp. 57-63. [9]Flores, B. E., Whybark, D. C., 1987, “Implementing multiple criteria ABC analysis,” Journal of Operations Management, Vol. 7, No. 1, pp.79-85. [10]Fortuin, L., 1980, “The All-Time Requirement of Spare Parts for Service after Sales─Theoretical Analysis and Practical Results,” International Journal of Operations & Production Management, Vol. 1, No. 1, pp.59-70. [11]Fortuin, L., 1981, “Reduction of the All-Time Requirement for Spare Parts,” International Journal of Operations & Production Management, Vol. 2, No. 1, pp. 29-37 [12]Gajpal, P.P., Ganesh, L.S. and Rajendran, C., 1994, “Criticality analysis of spare parts using the analytic hierarchy process”, International Journal of Production Economics, Vol. 35, No. 1-3, pp. 293-297. [13]Gelders, L. F., Van Looy, P. M., 1978, “An inventory policy for slow and fast movers in a petrochemical plant: a case study,” Journal of the Operational Research Society, pp.867-874. [14]Heinecke, G., Syntetos, A. A., and Wang, W., 2011, “Forecasting-based SKU classification,” International Journal of Production Economics. [15]Hong, J. S., Koo, H. Y., Lee, C. S., and Ahn, J., 2008, “Forecasting service parts demand for a discontinued product,” IIE Transactions, Vol. 40, No. 7, pp. 640-649. [16]Kennedy, W. J., Wayne Patterson, J., Fredendall, L. D., 2002, “An overview of recent literature on spare parts inventories,” International Journal of production economics, Vol. 76, No. 2, pp. 201-215. [17]Kobbacy, K. A., Liang, Y., 1999, “Towards the development of an intelligent inventory management system,” Integrated Manufacturing Systems, Vol. 10, No. 6, pp. 354-366. [18]Lewise, C.D., 1970, Scientific Inventory Control, Butterworth-Heineman, Oxford. [19]Minner, S., 2011, “Forecasting and Inventory Management for Spare Parts: An Installed Base Approach,” in Altay, N. and Litteral, L. A. (Eds), “Service Parts Management: Demand Forecasting and Inventory Control,” London: Springer, pp.157-169. [20]Moore, J. R., 1971, “Forecasting and Scheduling for Past-Model Replacement Parts,” Management Science, Vol. 18, No. 4, pp. 200-213. [21]Ng, W. L., 2007, “A simple classifier for multiple criteria ABC analysis,” European Journal of Operational Research, Vol. 177, No. 1, pp.344-353. [22]Pourakbar, M., Frenk, J. B., and Dekker, R., 2012, “End-of-Life Inventory Decisions for Consumer Electronics Service Parts,” Production and Operations Management, Vol. 21, No. 5, pp. 889–906. [23]Rencher, A. C., and Schaaljie, G. B., 2008, “Linear models in statistics,” New York: Wiley-Interscience. [24]Syntetos, A. A., Boylan, J. E., and Croston, J. D., 2005, “On the categorization of demand patterns” Journal of the Operational Research Society, Vol. 56, No. 5, pp. 495-503. [25]Syntetos, A. A., Keyes, M., and Babai, M. Z., 2009, “Demand Categorisation in a European Spare Parts Logistics Network,” International Journal of Operations & Production Management, Vol. 29, No. 3, pp.292-316. [26]Teunter, R. H., and Fortuin, L., 1998, “End-of-life service: A case study,” European Journal of Operational Research, Vol. 107, No. 1, pp. 19-34. [27]Teunter, R. H., and Fortuin, L., 1999, “End-of-life service,” International Journal Production Economics, Vol. 59, No. 1-3, pp. 489-497 [28]Teunter, R. H., and Klein Haneveld, W. K., 1998, “The ‘final order’ problem,” European Journal of Operational Research, Vol. 107, No. 1, pp. 35-44. [29]Teunter, R. H., and Klein Haneveld, W. K., 2002, “Inventory control of service parts in the final phase,” European Journal of Operational Research, Vol. 137, No. 3, pp. 497-511 [30]Van Kampen, T. J., Akkerman, R., and Van Donk, D. P., 2012, “SKU classification: A literature review and conceptual framework,” International Journal of Operations & Production Management. Vol. 32, pp.850 -876. [31]林合志,備用零件最後訂購數量模型,國立台灣大學工業工程研究所碩士論文,2013。 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57577 | - |
dc.description.abstract | 隨著產品市場競爭日趨激烈,售後服務的重要性也日益提高,它不僅能累計對於顧客行為的知識,以建立廠商服務的差異性,更是產品銷售後另外持續創造利潤的來源。一般的售後服務提供顧客一段產品的保固期,在期間內免費提供維修服務,而對於耐久財或是產品單價較高的產品,例如汽車產業,其零組件服務期間比產品的生命週期還長,在產品停止生產後,由於零組件供應商必須在考量成本因素下將不會持續提供零件至服務期間結束,此時汽車代理商須進行零件的最後一次訂購,以滿足零件在衰退期間的剩餘需求。基於僅有一次的訂購機會,為了避免訂購過多導致存貨成本的增加,或是訂購太少而影響服務水準,因此零組件的最後訂購量議題在實務上往往對服務廠商造成管理存貨上的問題。
因此,本研究以汽車產業為個案研究對象,以此提出一個較準確且符合實際的零件需求預測模型。透過Installed base 模型,將零件的需求原因歸納為三種因素,分別是市面上汽車流通數量、顧客回廠維修機率以及零件的失效機率,利用前兩項已知資訊及零件於最後訂購點前的需求量,估計零件在各使用年齡下的失效機率。接著,由於失效機率為一時間序列值並具有趨勢,因此透過趨勢判斷將零件分類,各自採用不同的方程式進行估計,並再次利用Installed base模型逆推求出零件的各期需求量,加總後成為最後訂購數量。 最後,為了使本研究所提出之預測模型更具廣泛性與適用性,因此以不同車型零件與筆記型電腦零件進行模型驗證,並比較本研究所提出的方法與個案公司現行方法之優劣。模型驗證的結果顯示不同種類的零件其失效機率確實存在著不同趨勢,而本研究所提出之預測模型因結合不同趨勢的預測方法,在預測誤差上有較佳之表現。 | zh_TW |
dc.description.abstract | While the competition in the market becomes severe, the importance of after-sales service has been much emphasized. It not only help companies to gain a deep understanding of customer’s behavior which provides a competitive advantage, but also generates a revenue stream after products are sold. Basically, after-sales services provide a warranty period to maintain the product by service parts. Since the periods of maintenance and replacements of spare parts are much longer than the product’s production periods, after the sale of a product is discontinued, there is an installed base to be serviced and there is only one final chance to stock up the part inventory. As a result, solving this end-of-life final-order inventory problem is crucial in practice.
This paper presents an empirical study of an automobile firm on this problem by applying an installed-base forecast method. Installed base model divides parts’ demand into three factors: the population of products in use, replacement probability of the failed parts and the failure rate of parts. For each part type, the failure probabilities over the life time are first estimated and a trend test is applied to the failure probability. A hybrid method is proposed by fusing the trend and end-of-life customer behavior. At last, in order to strengthen the application of the proposed model, data from the automobile and notebook computer industries are used to validate the model which shows significant improvement over an existing method used in practice. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T06:52:20Z (GMT). No. of bitstreams: 1 ntu-103-R01546021-1.pdf: 2860832 bytes, checksum: 674c9496b86c4f6f6d724de6ae66c147 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 研究動機與背景 1 1.2 問題描述 3 1.3 研究目的 5 第二章 文獻探討 7 2.1 最後訂購量議題 7 2.1.1 Service-driven approach 8 2.1.2 Cost-driven approach 10 2.1.3 Forecasting-based approach 13 2.2 備用零件存貨管理 17 2.2.1 備用零件分類 18 2.2.2 分類目的為存貨管理 19 2.2.3 分類目的為需求預測 26 第三章 模型設計 28 3.1 符號定義 29 3.2 現行個案公司計算方法 30 3.3 Installed base 模型建立 31 3.3.1 Installed base 模型 31 3.3.2 回廠維修機率 33 3.3.3 資料選取 34 3.3.4 估計零件失效機率 35 3.4 趨勢判斷 36 3.4.1 Spearman 等級相關係數 36 3.4.2 趨勢判斷結果 38 3.5 迴歸方程式 44 3.6 模型績效指標 47 3.7 預測結果 48 第四章 模型驗證 54 4.1 模型驗證-以汽車產業為例 54 4.1.1 建立Installed base模型 54 4.1.2 趨勢判斷 56 4.1.3 預測結果 58 4.2 模型驗證-以筆記型電腦產業為例 64 4.2.1 建立Installed base模型 67 4.2.2 趨勢判斷 68 4.2.3 預測結果 71 第五章 結論與建議 77 5.1 研究結論 77 5.2 未來研究建議 78 參考文獻 79 Appendix 83 | |
dc.language.iso | zh-TW | |
dc.title | 以機具存量為基礎的售後零組件需求預測 | zh_TW |
dc.title | Installed Base Forecast of Spare Part Demand for After-sales Services | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 楊烽正(Feng-Cheng Yang),楊朝龍(Chao-Lung Yang) | |
dc.subject.keyword | 售後服務零組件,最後訂購數量,Installed base,失效機率,趨勢判斷, | zh_TW |
dc.subject.keyword | After-sales,Last order,Spare part,Installed base,Failure rate,Trend test, | en |
dc.relation.page | 86 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-07-22 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-103-1.pdf 目前未授權公開取用 | 2.79 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。