Skip navigation

DSpace

機構典藏 DSpace 系統致力於保存各式數位資料(如:文字、圖片、PDF)並使其易於取用。

點此認識 DSpace
DSpace logo
English
中文
  • 瀏覽論文
    • 校院系所
    • 出版年
    • 作者
    • 標題
    • 關鍵字
  • 搜尋 TDR
  • 授權 Q&A
  • 幫助
    • 我的頁面
    • 接受 E-mail 通知
    • 編輯個人資料
  1. NTU Theses and Dissertations Repository
  2. 工學院
  3. 工業工程學研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45482
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor蔣明晃(Ming-Huang Chiang)
dc.contributor.authorChi-An Tengen
dc.contributor.author滕喆安zh_TW
dc.date.accessioned2021-06-15T04:22:38Z-
dc.date.available2009-11-07
dc.date.copyright2009-10-28
dc.date.issued2009
dc.date.submitted2009-10-08
dc.identifier.citation中文部分:
1.林姿依,建立適合顧客關係管理之模糊分群模型 - 以汽車維修服務為例,國
立台灣大學商學研究所碩士論文,2007。
2.陳育珊,考慮產品生命週期末端之服務性零組件最後訂購模型–以汽車產業
為例,國立台灣大學商學研究所碩士論文,2009
英文部分
1. Anderberg, M. R., “Cluster Analysis for Applications,” Academic Press, 8, 1, (1973), 2-7
2. Ankerst, M., Breunig, M., Kriegel, H. P. and Sander, J., “OPTICS:Ordering Points to Identify the Clustering Structure”, In Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99), Philadelphia, PA, June (1999), 49-60
3. Bezdek, J. C., “Pattern Recognition with Fuzy Objective Function Algorithms”,
New York:Plenum Press, (1981)
4. Brown R. G., “Advanced Service Parts Inventory Control”, 2nd Edition, Material Managerment Systems, Inc., (1982)
5. Cameron, A. C. and Trivedi, P. K., “Econometric Models Based on Count Data: Comparisons and Application of Some Estimators and Tests”, Journal of Applied Econometrics, 1, (1986), 29-53
6. Cattani, K. D. and Souza, G. C., “Good buy? Delaying end-of-life purchases”, European Journal of Operational Research, 146, 1, (2003), 216–228
7. Chen, F. Y. and Krass, D., “Inventory Models with Minimal Service Level Constraints”, European Journal of Operational Research, 134, (2001), 120-140
8. Cohen, M. A. and Lee, H. L., “Strategic Analysis of Integrated Production-Distribution Systems: Models and Methods”, Operations Research, 36, 2, Operations Research in Manufacturing (1988), 216-228
9. Cohen, M. A., Deshpande, V. and Donohue, K., “An empirical study of service differentiation for weapon system service parts”, Operation Research, 51, 4, (2003), 518-530
10. Dunn, J. C., “Well-Separated Clusters and the Optimal Fuzzy Partitions”, Journal of Cybernetics, 4, (1974), 95-104
11. Ester, M., Kriegel, H. P., Sander, J. and Xu, X., “Density-Based Algorithm for Discovering Clusters in Large Spatial Database with Noise”, In Proc. 1996 Int. Conf. Knowledge Discovery and Data Mining (KDD’96), Portland, OR, Aug. (1996), 226-231
12. Fortuin, L., “The All-Time Requirement of Spare Parts for Service After Sales-Theoretical Analysis and Practical Results”, International Journal of Operations and Production Management, 1, 1, (1980), 59-69
13. Grogger, J. T. and Carson, R. T., “Models for Truncated Counts”, Journal of Applied Econometrics, 6, 3, (1991), 225-238
14. Gurmu, S., “Tests for Detecting Overdispersion in the Positive Poission Regression Model”, Journal of Business and Econimic Statistics, 9, (1991), 215-222
15. Han, J., and Kamber, M., “Data Mining:Concepts and Techniques”, San Francisco:Morgan Kaufmann Publishers, (2000)
16. Hill, R. M., “Production Planning Towards the End of A Product Life Cycle”, IMA Journal of Management Mathematics, 10, 2, (1999), 165-176
17. Hill, R. M., Omar, M. and Smith, D. K., “Stock Replenishment Policies for A Stochastic Exponentially-Declining Demand Process”, European Journal of Operational Research, 116, 2, (1999), 374-388
18. Inderfurth, K. and Mukherjee, K., “Decision Support for Spare Parts Acquisition in
Post Product Life Cycle”, Central European Journal of Operations Research, 16, 1, (2008), 17-42
19. Kaufman, L. and Rousseeuw, P. J., “Finding Groups in Data:An Introduction to Cluster Analysis”, John Wiley&Sons, (1990)
20. Kleber, R. and Inderfurth, K., “A Heuristic Approach for Inventory Control of Spare Parts after End-of-Production”, Springer, (2007)
21. Kleinbaum, D. G., Kupper, L. L., Muller K. E. and Nizham A., “Applied Regression Analysis and Multivariate Methods”, 3rd edition, Duxbury Press, (1998)
22. Kranenburg, A. A. and van Houtum, G. J., “A Multi-Item Spare Parts Inventory Model with Customer Differentiation”, Working Paper (2004)
23. Li, L., Kabadi, S. N. and Nair, K. P. K., “Fuzzy Models for Single-Period Inventory Problem”, Fuzzy Sets and Systems, 132, 3, (2002), 273-289
24. MacQueen, J. B., “Some Methods for Classification and Analysis of Multivariate Observation”, In Proc. 5th Berkeley Symp. Math. Statistics, (1967), 281-297
25. Moore, JR., “Forecasting and Scheduling for Past-Model Replacement Parts”, Management Science, 18, 4, (1971), 200-213
26. Naddor, E., “Sensitivity to Distributions in Inventory Systems”, Management Science, 24, 16, (1978), 1769-1772
27. Ng, R. and Han, J., “Efficient and Effective Clustering Method for Spatial Data Mining”, In Proc. 1994 Int. Conf, Very Large Database (VLDB’94), Santiago, Chile, Sept. (1994), 144-155
28. Petrovic, D., Petrovic, R. and Vujosevic, M., “Fuzzy model for the newsboy problem”, International Journal of Production Economics, 45, 1-3, (1996), 435-441
29. Schneider, H., “Effect of service-levels on order-points or order-levels in inventory models”, International Journal of Production Research, 19, 6, (1981), 615-631
30. Schneider, H. and Rinquest, J. L., “Power Approximation for Computing (s, S) Policies Using Service Level” Management Science, 36, 7, (1990), 822-834
31. Sheikholeslami, G., Chatterjee, S. and Zhang, A., “WaveCluster:A Multi-Resolution Clustering Approach for Very Large Spatial Databases”, In Proc. 1998 Int. Conf. Very Large Databases (VLDB’98), New York, Aug. (1998), 428-439
32. Smith, S. A. and Agrawal, N., “Management of Multi-Item Retail Inventory Systems with Demand Substitution, Operation Research, 48, 1, (2000), 50-64
33. Sven A., “Inventory Control”, 2nd edition, Springer, (2006)
34. Tempelmeier, H., “Inventory Service-Levels in the Customer Supply Chain”, OR Spektrum, 22, (2000), 361–380
35. Teunter, R. H. and Fortuin, L., “End-of-life service”, International Journal of Production Economics, 59, 1-3, (1999), 487-497
36. Teunter, R. H. and Fortuin, L., “End-of-life service: A case study”, European Journal of Operational Research, 107, 1, (1998), 19-34
37. Teunter, R. H. and Haneveld, W. K. K., “Inventory control of service parts in the final phase”, European Journal of Operational Research, 137, 3, (2002), 497-511
38. van Kooten, J. P. J. and Tan, T., “The Final Order Problem for Repairable Spare Parts under Condemnation”, Journal of the Operational Research Society, (2008), 1-13
39. Walker, J., “The Single-Period Inventory Problem with Triangular Demand Distribution”, The Journal of the Operational Research Society, 44, 7, (1993), 725-731
40. Wang, S. C. and Huang, P. H., “STING:A Statistical Information Grid Approach to Spatial Data Mining”, In Proc. 1997 Int. Conf. Very Large Data Bases (VLDB’ 97), Athens, Greece, Aug. (1997), 186-195
41. Weiss, S. M. and Indurkhya, N., “Predictive Data Mining:A Practical Guide”, CA:Morgan Kaufmann, (1998)
42. Xie, X. L. and Beni, G., “A validity measure for fuzzy clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 8, (1991), 814-847
43. Xu, R. and Zhai, X., “Optimal models for single-period supply chain problems with fuzzy demand”, Information Sciences, 178, 17, (2008), 3374-3381
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45482-
dc.description.abstract對許多產品公司而言,包括汽車製造商,在車輛產出後,由於無法直接在世界各地提供製造後服務,因此通常會透過當地代理商利用其原先所建立的通路來取得售後服務的先機。由於用以維修及更換的服務性零組件服務期間往往比車輛生產期間還長,在車輛停止生產後,零組件所需持續服務的期間通常還需持續一段時間。在此時零組件生命週期多數已步入衰退期,零組件供應商為考量經濟生產批量的成本因素下,並不會持續生產至服務期間結束。因此,這時代理商面臨上游供應商即將停產某些服務性零組件的情況,得做最後一次訂購以滿足剩餘服務年限內所有的需求。代理商必須在成本控管與服務水準之間取得平衡,決定最適的最後訂購量。
然而,若代理商的最後一次訂購太多將產生滯銷存貨,訂購太少則會有顧客抱怨與銷貨損失。現行汽車產業為方便起見,通常以歷史資料的平均需求做為預測起始參考依據,假定服務期間結束時需求為零,剩餘年限需求以一定數量隨時間遞減,如此不僅過於直覺且可能造成多餘存貨。
因此,本研究希望利用卜瓦松迴歸模型,建立一套更有效的衰退期零組件需求預測模式,但是零組件的種類太多,因此接著根據不同零組件中需求資料不確定的特性以FCM法進行分群,制定各群的服務水準優先順序,以提出有效的存貨管理機制。最後在報童模型極小化呆料與缺貨成本的情況下,滿足整體服務水準,求得各群最適訂購量,各群內之零組件皆以該群最適訂購量向上游廠商訂購,如此可減低訂單複雜度。並以實際需求資料比較個案T公司和本研究提出最後訂購方法之優劣,使在面臨最後訂購問題的廠商,不僅能以更具成本效益的訂貨方式進行訂貨決策,而且可以有效的滿足顧客對各種類別所有零組件的需求。
zh_TW
dc.description.abstractFor many car manufacturers, to sell products in the global market usually need local agents to provide after-sales service through their channels. Because the periods of maintenance and replacement of the service parts have much longer than vehicle’s production periods, the service parts requirement will last for a certain period of time even the vehicle manufacturer stops producing the car. The period after the vehicle stops production is called the end-of-life service period.
At that period, the majority of parts are within the declining phase of part life cycle. The supplier of service parts may no longer manufactures the parts after the car stop producing for considering economic production scale. Before the suppliers stop supplying service parts, the agents must place a final order to meet future demands at a certain service level to lower the customers’ complaints at the minimum cost. If the final order size is larger than actual demand, the salvage parts become obsolete inventory. On the other hand, if the final order size is smaller than actual demand, it will cause customer complaints and results in lost sales.
Therefore, this study firstly establishes a Poisson regression model for service parts in the period of declining demands. Secondly, a FCM method is applied to cluster service parts into different groups based on their costs and essentialness such that service level priorities can be determined in order to provide effective inventory management. Next, the newsboy model considering dead-stock cost and shortage cost under the overall service level constraint is developed to determine the optimal order quantities for each group. Parts within each group are ordered the same optimal quantity to reduce complexity. Finally, we use the real data from company T to compare our model with current practice of company T. The result shows our model reduces cost effectively and meets customer needs for various categories of parts.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T04:22:38Z (GMT). No. of bitstreams: 1
ntu-98-R95546002-1.pdf: 666637 bytes, checksum: 8d14b373be6b04f19d4d9869120c1af8 (MD5)
Previous issue date: 2009
en
dc.description.tableofcontents謝辭......................................................Ⅰ
中文摘要..................................................Ⅱ
英文摘要..................................................Ⅲ
目錄......................................................Ⅳ
圖目錄....................................................Ⅶ
表目錄....................................................Ⅷ
第一章 緒論..............................................1
1.1 研究背景.........................................1
1.2 研究動機.........................................1
1.3 問題描述.........................................2
1.4 研究目的及架構...................................3
1.5 論文架構.........................................5
第二章 文獻回顧..........................................6
2.1 最後訂購相關文獻.................................6
2.2 服務水準.........................................8
2.2.1 服務水準衡量指標........................8
2.2.2 服務水準限制及差異化...................10
2.3 報童模型相關文獻................................11
2.4 需求預測........................................12
2.4.1 零組件衰退期需求預測...................13
2.4.2 卜瓦松迴歸模型.........................14
2.5 分群演算法......................................16
2.5.1 分群之定義與應用.......................16
2.5.2 分群演算法介紹.........................16
2.6 小結............................................18
第三章 模型設計與研究方法...............................20
3.1 汽車零組件最後訂購現況概述—以T公司為例.........20
3.1.1 零組件供應商停產準則...................20
3.1.2 最後一次訂購量.........................21
3.2 模型假設........................................22
3.3 模型分析架構....................................22
3.4 以卜瓦松迴歸模型建立需求預測模式................23
3.4.1 卜瓦松迴歸模型符號說明.................24
3.4.2 卜瓦松迴歸模型建立.....................24
3.4.3 卜瓦松迴歸模型參數估計方法.............25
3.4.4 卜瓦松迴歸模型之檢定...................25
3.4.5 各零組件剩餘供應年限需求預測模式.......26
3.5 服務水準差異化下之分群模式.........................27
3.5.1 分群指標選擇..............................28
3.5.2 各群服務水準差異化........................28
3.5.3 FCM分群演算法流程.........................29
3.5.4 判定最佳分群數.........................33
3.6 報童模型結合分群結果及需求預測決定最適訂購......35
3.6.1 報童模型符號定義.......................35
3.6.2 分群後各群剩餘年限需求分配.............36
3.6.3 各群缺貨與呆料成本.....................36
3.6.4 各群最適訂購量與服務水準...............36
3.6.5 服務水準限制下最適訂購量...............38
第四章 T公司個案研究與實證分析..........................39
4.1 汽車零組件最後訂購實務-以T公司為例................41
4.2 卜瓦松迴歸模型預測T公司停產零組件需求............43
4.3 T公司停產服務性零組件分群.......................46
4.3.1 資料整理與標準化.......................46
4.3.2 FCM分群演算法..........................46
4.3.3 最適分群數決定.........................48
4.3.4 最適分群下之服務水準差異化.............49
4.4 報童模型結合分群結果與需求預測決定T公司最適訂購.50
4.4.1 各群剩餘年限需求分配...................50
4.4.2 各群最適訂購量及服務水準數值運算.......51
4.4.3 T公司現行策略與整體服務水準限制下
本研究方法之比較.......................55
第五章 總結與建議.......................................58
5.1 研究結論........................................58
5.2 研究貢獻........................................59
5.3 研究限制........................................60
5.4 未來研究方向....................................60
參考文獻..................................................62
dc.language.isozh-TW
dc.title服務性零組件最後訂購情況下最適服務水準之研究
-以汽車產業為例
zh_TW
dc.titleThe Optimal Service-Levels of Final Order for Auto Service Partsen
dc.typeThesis
dc.date.schoolyear98-1
dc.description.degree碩士
dc.contributor.coadvisor郭瑞祥(Ruey-Shan Guo)
dc.contributor.oralexamcommittee吳政鴻,郭佳瑋
dc.subject.keyword最後訂購,服務水準,卜瓦松迴歸,Fuzzy C-Means法,報童模型,zh_TW
dc.subject.keywordFinal Order,Service Level,Poisson Regression,Fuzzy C-Means,Newsboy model,en
dc.relation.page66
dc.rights.note有償授權
dc.date.accepted2009-10-09
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-98-1.pdf
  目前未授權公開取用
651.01 kBAdobe PDF
顯示文件簡單紀錄


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。

社群連結
聯絡資訊
10617臺北市大安區羅斯福路四段1號
No.1 Sec.4, Roosevelt Rd., Taipei, Taiwan, R.O.C. 106
Tel: (02)33662353
Email: ntuetds@ntu.edu.tw
意見箱
相關連結
館藏目錄
國內圖書館整合查詢 MetaCat
臺大學術典藏 NTU Scholars
臺大圖書館數位典藏館
本站聲明
© NTU Library All Rights Reserved