| 研究生: |
鄒易宏 I-Hung Tsou |
|---|---|
| 論文名稱: |
損耗性商品在供應鏈中之資訊分享和決策制定之協調 Information Sharing and Decision Coordination for Perishable Items in a Supply Chain |
| 指導教授: |
陳振明
Jen-Ming Chen |
| 口試委員: | |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 工業管理研究所 Graduate Institute of Industrial Management |
| 畢業學年度: | 95 |
| 語文別: | 中文 |
| 論文頁數: | 85 |
| 中文關鍵詞: | 動態規劃 、損耗性商品 、決策制定協調 、供應鏈 、資訊分享 |
| 外文關鍵詞: | dynamic programming, decision-making coordination, perishable items, supply chain, information sharing |
| 相關次數: | 點閱:12 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
本文探討具有損耗性的商品在一簡單的供應鏈之中,資訊分享和決策制定協調對其成本之影響。我們設定在單一製造商、單一運輸商及單一零售商的結構下,當下游零售商面對著跟時間相依的需求時的環境,研究三種不同的供應鏈整合策略,分別為無資訊分享和無決策制定協調策略、完全資訊分享和無決策制定協調策略及完全資訊分享和完全決策制定協調策略。
探討在三個策略之下,供應鏈成員會採取不同的補貨模式來最小化自身的成本,在無資訊分享和無決策制定協調策略的模式下,零售商產生對自己最佳的補貨排程而驅使製造商和運輸商進行逐批補貨;若完全資訊分享後,零售商產生的補貨排程會分享給製造商,因而使製造商採取最佳化的動態經濟批量模式;在完全資訊分享和完全決策制定協調策略的模式下,由於整個供應鏈分享資訊、統一決策,故可將供應鏈視為整合單一的計劃者,我們發展一個資訊相互分享且系統協調的模型,目標為產生最佳的補貨排程並使整體系統的成本最小化。
本文的目的在量化和分析當供應鏈採用上述不同整合策略且產品具有損耗性特質時,資訊分享和決策制定之協調所產生的價值,並且檢視供應鏈上因資訊分享和決策制定協調產生之價值是如何分配在成員之間,我們利用數值分析來輔助了解可能的情形為何,並透過敏感度分析來了解在參數變動下,對各整合策略績效的影響。
This study is to discuss the impact of information sharing and decision-making coordination for perishable items in a supply chain consisting of a single retailer, upstream manufacturer and transportation provider. We consisting of three varying-level strategies for integrating a supply chain—including “no information sharing and no coordination”, “full information sharing and no coordination”, and “full information sharing and full coordination”.
Depending on the three strategies, members of a supply chain tend to adopt different replenishment model to minimize their costs. We assume that the downstream retailer faces a continuous time-varying demand and generate his own optimal replenishment schedule to trigger the upstream manufacturer replenishment and the third part transportation under decentralized decision making. On the other hand, we develop a system coordination replenishment model with the objective of minimizing whole system costs to generate the common replenishment schedule under centralized decision making.
Our objective is to quantify and analyze value generated from a system of information sharing and decision coordination for perishable items in a whole supply chain according to aforementioned three different strategies, and then, to observe how the value is distributed among members of the chain. In the study, data simulation analysis is used to investigate all possible situations, and sensitivity analysis to find out effects of variable parameters on each strategy.
1.Chopra, S., & Meindl, P. (2002), 陳銘崑, 吳忠敏, 傅新彬 譯, 供應鏈管理, 滄海書局.
2.Handfield, R.B., & Nichols, E.L. (2001), 呂博裕譯, 供應鏈管理概論, 高立圖書.
3.葉蕙華(2006), Information sharing and decision -making coordination in supply chain(供應鏈中資訊分享與決策制定之協調),國立中央大學工業管理研究所碩士論文。
4.Alexander, E.R. (1993), Inter-organizational Coordination: Theory and Practice, Journal of Planning Literature. Vol.7, No.4, 328-343.
5.Alexander, E.R. (1998), A structuration theory of inter-organizational coordination: Cases in environmental management, The International Journal of Organizational Analysis. Vol.6, No.4, 334-354.
6.Asanuma, B. (1991), Coordination between production and distribution in a globalized network of firms: Assembling flexibility achieved in the Japanese automobile industry, Kyoto University.
7.Azoury, K.S. (1985), Bayes solution to dynamic inventory models under unknown demand distribution, Management Science, Vol.31, No.9, 1150-1160.
8.Balkhi, Z.T. & Benkherouf, L. (2004), On an inventory model for deteriorating items with stock dependent and tiem-varying demand rates, Computers & Operations Research, Vol.31, 233-240.
9.Bernstein, F., Chen, F. & Federgruen, A. (2006), Coordinating supply chains with simple pricing schemes: The role of vendor-managed inventories, Management Science, Vol.52, No.10, 1483-1492.
10.Bourland, K.E., Powell, S.G.. & Pyke, D.F. (1996). Exploiting timely demand information to reduce inventories, European Journal of Operational Research, Vol.92, No.2, 239-253.
11.Cachon, G.., & Fisher, M. (2000), Supply chain inventory management and the value of shared information, Management Science, Vol.46, No.8, 1032-1048.
12.Carter, J.R., & Fredendall, L.D. (1990), The dollars and sense of electronic data interchange, Production and Inventory Management Journal, Vol.31, No.2, 22-26.
13.Carter, J.R. & Ferrin, B.G. (1995). The impact of transportation costs on supply chain management, Journal of Business Logistics, Vol.16, No.1, 189-212.
14.Chandra, C. & Grabis J. (2005), Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand, European Journal of Operational Research, Vol.166, No.2, 337-350.
15.Chen, F., Drezner, Z., Ryan, J.K., & Simchi-Levi, D. (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information, Management Science, Vol.46, No.3, 436-443.
16.Chu, P., Yang, K.L., Liang, S.K. & Niu T. (2004), Note on inventory model with a mixture of back orders and lost sales, European Journal of Operational Research, Vol.159, No.2, 470-475.
17.Clark, A.J. & Scarf, H. (1960), Optimal policies for a multi-echelon inventory problem, Management Science, Vol.6, No.3, 465-490.
18.Cooper S. & Meindl P. (2000), Supply chain management: strategy, planning and operation, Prentice-Hall, Inc.
19.Croson, R. & Donohue, K., (2006), Behavioral causes of the bullwhip effect and the observed value of inventory information, Management Science, Vol.52, No.3, 323-336.
20.Dejonckheere, J., Disney, S.M., Lambrecht, M.R., & Towill, D.R. (2003), Measuring and avoiding the bullwhip effect: A control theoretic approach, European Journal of Operational Research, Vol.147, No.3, 567–590.
21.Dye, C.Y., Chang, H.J. & Teng, J.T. (2006), A deteriorating inventory model with time-varying demand and shortage-dependent partial backlogging, European Journal of Operational Research, Vol.172, No.2, 417-429.
22.Faraj, S. & Xiao, Y. (2006), Coordination in fast-response organizations, Management Science, Vol.52, No.8, 1155-1169.
23.Fiala, P. (2005), Information sharing in supply chain, Omega, Vol.33, 419-423.
24.Forrester, J.W. (1958), Industrial dynamic - a major breakthrough for decision makers., Harvard Business Review, Vol.36, No.4 , 37-66.
25.Gavirneni, S. (2006), Price fluctuations, information sharing, and supply chain performance, European Journal of Operational Research, Vol.174, No.3 , 1651-1663.
26.Gaur, V., Giloni, A., & Seshadri, S. (2005), Information sharing in a supply chain under ARMA demand, Management Science, Vol.51, No.6, 961-969.
27.Gavirneni, S., Kapuscinski, R., & Tayur, S. (1999), Value of information in capacitated supply chain, Management Science, Vol.45, No.1, 16-24.
28.Goyal S.K. & Giri B.C. (2001), Recent trends in modeling of deteriorating inventory, European Journal of Operational Research, Vol.134, No.1, 1-16.
29.Goyal S.K. & Giri B.C. (2003), The production–inventory problem of a product with time varying demand, production and deterioration rates, European Journal of Operational Research, Vol.147, No.1, 549-557.
30.Gentry J.J. (1996), The role of carriers in buyer-supplier strategic partnerships a supply chain management approach, Journal of Business Logistics, Vol.17, No.2, 35-55.
31.Ghare, P.M. & Schrader, G..F. (1963), A model for exponentially decaying inventories, Journal of Industrial Engineering, Vol.14, 238-243.
32.Hahn, K.H., Hwang, H. & Shinn, S.W. (2004), A returns policy for distribution channel coordination of perishable items, European Journal of Operational Research, Vol.152, No.3, 770-780.
33.Hariga, M. (1996), Optimal EOQ models for deteriorating items with time-varying demand, Journal of Operational Research Society, Vol.47, 1228-1249.
34.Harris, F.W. (1913), How many parts to make at Once, The Magazine of Management, Vol.10, 135-136.
35.Huang, B. & Iravani S.M.R. (2005), Production control policies in supply chains with selective-information sharing, Operations Research, Vol.53, No.4, 662-674.
36.Kim, G.J., Chatfield, D., Harrison, P.T. & Hayya J.C. (2006), Quantifying the bullwhip effect in a supply chain with stochastic lead time, European Journal of Operational Research, Vol.173, No.2, 617-636.
37.Lau, J.S.K., Huang, G.Q., & Mak, K.L. (2002), Web-based simulation portal for investigating impacts of sharing production information on supply chain dynamics form the perspective of inventory allocation, Integrated Manufacturing System, Vol.13, No.5, 345-358.
38.Lee, H.L., Padmanabhan, V., & Whang, S.(1997), The bullwhip effect in supply chains, Sloan Management Review, Vol.38, No.3, 93-102.
39.Lee, H.L. & Rosenblatt, M.J. (1986), A generalized quantity discount pricing model to increase supplier''s profit, Management Science, Vol.32, No.9, 1177-1185.
40.Lee, H.L., So, K.C., & Tang, C.S. (2000), The value of information sharing in a two-level supply chain, Management Science, Vol.46, No. 5, 626-643.
41.Lee, H.L. & Whang, S. (1993), Decentralized multi-echelon inventory control systems: Incentives and information, Stanford Univ.
42.Lee, H.L. & Whang, S. (1999), Decentralized multi-echelon supply chains: Incentive and information, Management Science, Vol.45, No.5, 633-640.
43.Lee, H.L. & Whang, S. (2000), Information sharing in supply chain, International Journal of Technology Management, Vol.20, No.3-4, 373-387.
44.Li, X. & Wang, Q. (2007), Coordination mechanisms of supply chain systems, European Journal of Operational Research, Vol.179, No.1, 1-16.
45.Mendelson, H. & Whang, S. (1988), Optimal incentive-compatible priority pricing for the M/M/1 queue, Operations Research, Vol.38, No.5, 870-883.
46.Min, K.J. (1992), Inventory and quantity discount pricing policies under profit maximization, Operations Research Letters, Vol.11, No.3, 187-193.
47.Monahan, J.P. (1984), A quantity discount pricing model to increase vendor profit, Management Science, Vol.30, No.6, 720-726.
48.Nahmias, S. (1982), Perishable inventory theory: A review, Operations Research, Vol.30, No.4, 680-708.
49.Nienhaus, J., Ziegenbein, A., & Schoensleben P. (2006), How human behaviour amplifies the bullwhip effect. A study based on the beer distribution game online, Production Planning & Control, Vol.17, No.6, 547-557.
50.Ozer, O. & Wei, W. (2006), Strategic commitments for an optimal capacity decision under asymmetric forecast information, Management Science, Vol.52, No.8, 1238-1257.
51.Padmanabhan, G. & Vrat, P. (1990), Inventory model with a mixture of back orders and lost sales, International Journal of Systems Science, Vol.21, No.8, 1721-1726.
52.Porteus, E.L. & Whang, S. (1991), On manufacturing/marketing incentives, Management Science, Vol. 37, No.9, 1166-1181.
53.Raafat, F. (1991), Survey of literature on continuously deteriorating inventory models, Journal of the Operational Research Society, Vol.42, No.1, 27-37.
54.Sahin, F. & Robinson, E.P. (2002), Flow coordination and information sharing in supply chains: review, implications, and directions for future research, Decision Sciences, Vol.35, No.4, 505-536.
55.Sahin, F. & Robinson, E.P. (2005), Information sharing and coordination in make-to-order supply chains, Journal of Operations Management, Vol.23, 579-598.
56.Samaddar, S., Nargundkar, S. & Daley M. (2006), Inter-organizational information sharing: The role of supply network configuration and partner goal congruence, European Journal of Operational Research, Vol.174, No.2, 744-765.
57.Sarmah, S.P., Acharya, D. & Goyal, S.K. (2006), Buyer vendor coordination models in supply chain management, European Journal of Operational Research, Vol.175, No.1, 1-15.
58.Shin, H., & Benton, W.C. (2007), A quantity discount approach to supply chain coordination, European Journal of Operational Research, Vol.180, No.2, 601-616.
59.Spengler, J. (1950), Vertical integration and antitrust policy, Journal of Political Economy, Vol.58, No.4, 347-352.
60.Sterman, J.D. (1989), Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment, Management Science, Vol.35, No.3, 321-339.
61.Simatupang, T.M. & Sridharan, R. (2001), A characterisation of information sharing in supply chains, Proceedings of the 36th Annual ORSNZ Conference, 16-25.
62.Wagner, H.M. & Whitin, T.M. (1958), Dynamic version of the economic lot size model, Management Science, Vol.5, No.1, 89-96.
63.Wang, J., Jia, J., & Takahashi K. (2005), A study on the impact of uncertain factors on information distortion in supply chains, Production Planning & Control, Vol.16, No.1, 2-11.
64.Wang, S.P. (2002), An inventory replenishment policy for deteriorating items with shortages and partial backlogging, Computers & Operations Research, Vol.29, 2043-2051.
65.Whang, S. (1995), Coordination in operations: a taxonomy, Journal of Operations Management, Vol.12, 413-422.
66.Wu, K.S. (2002), EOQ inventory model for items with Weibull distribution deterioration, time-varying demand and partial backlogging, International Jouranl of Systems Science, Vol.33, No.5, 323-329.
67.Xu, H.P. & Wang H.P. (1992), Optimal inventory policy for perishable items with time proportional demand, IIE Transaction, Vol.24, No.5, 105-110.
68.Yu, Z., Yan, H., & Cheng, T.C.E.(2001), Benefits of information sharing with supply chain partnerships, Industrial Management & Data System, Vol.101, No.3, 114-119.
69.Zipkin, P. (1990), Optimizing the supply chain: A model for operations-marketing planning, RWP-90-9, Graduate School of Business, Columbia Univ.