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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳家麟 | |
dc.contributor.author | Yi-Chun Wu | en |
dc.contributor.author | 吳依純 | zh_TW |
dc.date.accessioned | 2021-06-17T04:50:58Z | - |
dc.date.available | 2023-08-22 | |
dc.date.copyright | 2018-08-22 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2018-07-31 | |
dc.identifier.citation | Brauner P., Runge S., Groten M., Schuh G., and Ziefle M. (2013) human factors in supply chain management. In: Yamamoto S. (ed.) Human Interface and the Management of Information. Information and Interaction for Learning, Culture, Collaboration and Business, HIMI 2013. Lecture Notes in Computer Science, vol 8018. Springer, Berlin, Heidelberg
Forrester, J. W. (1958). Industrial dynamics: a major breakthrough for decision makers. Harvard business review, 36(4), 37-66. Human Interface and the Management of Information: information and interaction for learning, culture, collaboration and business-Volume Part III (423-432). Springer-Verlag. Im KS, Pesaran MH, and Shin Y (2003). Testing for Unit Roots in Heterogeneous Panels. Journal of Econometrics, 115(1), 53-74. Lee, H. L., Padmanabhan, V., and Whang, S. (1997a). The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93. Lee, H. L., Padmanabhan, V., and Whang, S. (1997b). Information distortion in a supply chain: The bullwhip effect. Management Science, 43(4), 546-558. Levin A, Lin C. F., and Chu C. S. J. (2002). Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties. Journal of Econometrics, 108(1), 1-24. Maddala G. S., Wu S. (1999). A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics, 61(Supplement 1), 631-652. Nienhaus, J., Ziegenbein, A., and Schönsleben, P. (2006). How human behaviour amplifies the bullwhip effect. A study based on the beer distribution game online. Production Planning & Control, 17(6), 547-557. Simchi-Levi, D., Simchi-Levi, E., and Kaminsky, P. (1999). Designing and managing the supply chain: Concepts, strategies, and cases. New York: McGraw-Hill. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71061 | - |
dc.description.abstract | 在智慧化時代,各企業營運流程中已導入各種資訊和智慧化系統,來降低人為決策的偏誤,然而仍無法避免供需不平衡的狀況產生。長鞭效應描述終端顧客需求透過供應鏈的傳遞,最終變異增加,造成存貨或缺貨的產生,此效應由Forrester (1958) 年提出,而後諸多學者對此效應的原因和解方也有許多研究,但大部分的研究卻忽略人類行為的影響。Nienhaus et al. (2006) 和 Brauner et al. (2013) 連结人為決策行為和啤酒遊戲的結果,歸納出不同型的決策者,而本研究將延續以啤酒遊戲為基礎,探討不同資訊對於決策者的影響,了解決策者處於不同供應鏈層級時,是否會改變決策行為。本研究預先將決策者依照其實驗結果的存貨波動狀況分類,再利用面板數據進行迴歸模型分析,研究結果發現,越往上游的決策者越會利用各種資訊輔助其決策,然而決策者整體考量的重點因素卻不受限於供應鏈層級和存貨波動程度。 | zh_TW |
dc.description.abstract | Nowadays, most of corporates have implemented technical or intelligence system, such as ERP, POS, in their daily operation to avoid human error and keep supply and demand balanced. However, we still cannot escape from it. Bullwhip effect describes increasing swings in inventory in response to shifts in customer demand as one moves further up the supply chain. This phenomenon has been first described by Forrester in 1960s and replicated in a series of studies. There were also many solution, but most studies have neglected impacts of human behavior.
Nienhaus et al. (2006) and Brauner et al. (2013) studied how human decision affect supply chain operation by wed-based beer game. Them researches have shown that human behavior does amplifies the bullwhip effect in supply chain. In this research, we continue analyzing what factors affect human decisions. Will it matter if they were at different level of supply chain? We first Class decision makers in to several Class by the degree of inventory fluctuation. Then use panel data analysis to conduct our research. In this research, we found that the more upstream a decision maker be, they would have considered more information before making decisions. Furthermore, no matter which level or which class a decision maker be, they were affected by the same factors. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:50:58Z (GMT). No. of bitstreams: 1 ntu-106-R05741023-1.pdf: 953588 bytes, checksum: 9db7752101fc018ebbb1bb04fc22bfb9 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 vii 第一章 緒論 8 第一節 研究背景 8 第二節 研究動機與目的 9 第三節 研究架構 9 第二章 文獻回顧 11 第一節 長鞭效應的起因和解方 11 第二節 啤酒遊戲中的決策行為 13 第三章 研究方法 15 第一節 啤酒遊戲實驗簡介 15 第二節 面板數據分析 16 第三節 研究模型 18 (一) 資料來源 18 (二) 決策行為分類 19 (三) 變數設計 21 (四) 資料品質評估 21 (五) 模型設計 24 第四節 分析架構 25 第四章 模型分析 27 第一節 總體分析 27 第二節 變數顯著性分類交叉分析 28 (一) 零售商(Retailer) 28 (二) 批發商(Wholesaler) 29 (三) 配銷商(Distributor) 30 (四) 製造商(Factory) 31 第三節 綜合比較 32 第五章 結論 34 第一節 研究結論 34 (一) 存貨波動性 34 (二) 需求資訊價值程度下降 34 (三) 時間差 35 (四) 存貨影響力低 35 (五) 長鞭效應之可利用性法則 36 (六) 長鞭效應之近視症 36 第二節 研究限制與建議 37 參考文獻 38 | |
dc.language.iso | zh-TW | |
dc.title | 啤酒遊戲之決策行為分析 | zh_TW |
dc.title | Analysis of Beer Game Decision Behavior | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 孔令傑,楊曙榮 | |
dc.subject.keyword | 長鞭效應,供應鏈,啤酒遊戲,行為,決策, | zh_TW |
dc.subject.keyword | Bullwhip effect, Supply chain,Beer game,Human behavior,Decision making, | en |
dc.relation.page | 31 | |
dc.identifier.doi | 10.6342/NTU201802153 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-07-31 | |
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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