跳到主要內容

臺灣博碩士論文加值系統

(18.97.14.87) 您好!臺灣時間:2025/02/12 08:54
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:陳力維
研究生(外文):Li-Wei Chen
論文名稱(外文):The Optimal Pricing Strategy with Bayesian Updating in the Dual Channel
指導教授:曾富祥曾富祥引用關係
指導教授(外文):Fu-Shiang Tseng
學位類別:碩士
校院名稱:國立中央大學
系所名稱:工業管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:英文
論文頁數:69
中文關鍵詞:季節性商品雙通路貝氏方法價格更新
外文關鍵詞:seasonal productdual channelBayesian methodprice updating
相關次數:
  • 被引用被引用:0
  • 點閱點閱:134
  • 評分評分:
  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:0
隨著科技越來越進步,消費者可以選擇的消費模式也越來越多元化。不僅能透過傳統的零售通路購買商品,還能夠透過網路購物,郵購,或者是電視購物的方式取得商品。網路購物、郵購以及電視購物都有一個共通點就是,消費者欲購買商品時不需要親自到零售商店購買,而是透過網路或是電視,這些無形的通路直接向上游的商品供應商做購買的動作。消費者利用這些無形的通路直接向供應商購買商品,所以我們又將這些無形的通路稱之為直接通路。直接通路與傳統的零售通路差別在於,直接通路中消費者欲購買商品時,消費者直接向上游的供應商購買;至於在傳統的零售通路中,消費者欲購買商品時,只能向零售商購買,無法向供應商直接購買。在此研究中,我們主要探討的商品是季節性商品,季節性商品有兩大特點: 一個是補貨前置時間長,另一個則是需求變異大。因為補貨前置時間長的關係,因此決策者無法在短時間內進行補貨的動作,所以決策者必須在季節性商品銷售期開始之前就必須決定生產數量或是訂購數量,而且決策者所決定的生產數量或是訂購數量,最理想的情況是決策者所決定的數量能夠切中市場實際需求,但是由於季節性商品的另一特點性商品市場需求變異量大的關係,使得決策者無法預期當期的市場需求變異程度,也無法在銷售期初就準確的決定出商品生產數量或是商品訂購數量。所以決策者如何在有限的銷售期間利用價格更新機制,使得決策者能夠減緩季節性商品需求變異大所帶給自己的影響,並且幫助自己能夠下一個好的決策。
在此研究中,我們假設現在有一季節性商品的供應商,而且此供應商有兩個通路在銷售季節性商品,一個是傳統的零售通路,另一個則是直接通路。除此之外,我們將此季節性商品的銷售期分成兩個階段。接著,我們提出了一個利用貝式方法來更新季節性商品需求資訊的模型,供應商根據原始的機率模型來決定生產數量以及訂定直接通路的商品售價,另一方面在零售通路的零售商也是根據原始的機率模型來決定訂購數量以及訂定零售通路的商品售價。接下來,零售商利用在銷售期第一階段所獲得的需求資訊來更新原始的機率模型,並且利用此新模型來更新銷售期第二階段季節性商品的售價,提供給零售商一個更好的訂價決策模型,使得零售商能夠達到利潤最大化,甚至是能夠使得供應商獲得更多的利潤。
In the today’s society, the consumers can choose the consumption mode is more and more diversified with the technology more and more progress. The consumers purchase the products through not only the traditional retail channel but also the Internet shopping, mail order, or TV shopping. These way that I mentioned above, the Internet shopping, mail order, or TV shopping have a common point that if the consumers want to buy some products, they don’t need to go to the retail store in person, they just place orders to the upstream via the Internet, TV, or the mail.
Consumers use these invisible channels, like Internet, TV, or mail, to buy goods directly from suppliers, so we call these invisible channels as direct channel. There is a main difference between the traditional retail channel and the direct channel is that the customers can buy the seasonal product from the supplier, in the direct channel, but the customers can’t buy the seasonal products from the supplier in the traditional retail channel. In this research, we mainly discussed the seasonal products, for the most seasonal products have two characteristics: one is the lead time of the replenishment is long, the other is the variance of the demand is huge. Because of the lead time of the replenishment is long, the decision maker can’t replenish the seasonal goods in a short sale season. For the reason, the decision maker must decide the production quantities or the order quantities before the sale season. And the best case is that the production quantities or the order quantities are meet the demand of the market. But because of the variance of demand for the seasonal products in the market, the decision makers can’t expected the degree of market demand changes. In other words, the decision makers can’t determine the production quantities or the order quantities precisely. So how the decision makers use the updating price mechanism in the limited sale season to mitigate the impact of seasonal product demand variants and help the decision makers make the better decision.
In this study, we assume that there is a seasonal supplier of goods, and the supplier has two channels to sell the seasonal product, one is the traditional retail channel, the other is a direct channel. In addition, we divide the planning horizon into two parts. And then we propose a model that uses the Bayesian method to update the demand information of the seasonal product. The supplier determines the production quantities based on the original probability model and the selling price of the seasonal product in the direct channel. On the other hand, the retailer also determines the order quantities and the selling price of the seasonal product based on the original probability model. At the end of the first period, the retailer uses the demand information obtained at the first period of the sale season to update the original probability model and uses this new model to update the selling price of the seasonal product at the second period. Our aim is that providing a better decision model for pricing via using the Bayesian method to update the demand information and the selling price of the seasonal product, so that the retailer can achieve profit maximization, and even can make the supplier to obtain more profits.
摘要 i
Abstract iii
Contents v
List of Tables vi
List of Figures vii
Chapter 1 Introduction 1
1-1 Motivation and Background 1
1-2 Research Objective 3
Chapter 2 Literature Review 5
2-1 Seasonal products 5
2-2 Decision models based on utility 7
2-3 Bayesian method 9
2-4 Dual channel 12
Chapter 3 Model and Analysis 15
3-1 Environment and Notations 15
3-2 Decision model based on consumers’ utility 18
3-3 Bayesian Information Updating 21
3-3-1 Updating the arrival rate, λ 21
3-4 The retailer’s expected profit model with the
Bayesian updating 23
3-5 The supplier’s expected profit model with the
Bayesian updating 25
Chapter 4 Numerical Example 28
4-1 Data Setting 28
4-2 Numerical Analysis 29
4-2-1 Only the retailer uses the Bayesian updating 29
4-2-2 Only the supplier uses the Bayesian updating 33
Chapter 5 Sensitivity Analysis 37
5-1 The sensitivity analysis on c 37
5-2 The sensitivity analysis on θ 42
5-3 The sensitivity analysis on γ 46
Chapter 6 Conclusion and Future Research 52
Reference 54
[1] Aviv, Y. and A. Pazgal (2008). "Optimal Pricing of Seasonal Products in the Presence of Forward-Looking Consumers." Manufacturing & Service Operations Management 10(3): 339-359.
[2] Azoury, K. S. and B. L. Miller (1984). "A Comparison of the Optimal Ordering Levels of Bayesian and Non-Bayesian Inventory Models." Management Science 30(8): 993-1003.
[3] Bernoulli, D. (1954). "Exposition of a New Theory on the Measurement of Risk." Econometrica 22(1): 23-36.
[4] Bitran, G. R. and S. V. Mondschein (1997). "Periodic Pricing of Seasonal Products in Retailing." Management Science 43(1): 64-79.
[5] Brown, P. J. and R. Sundberg (1989). "Prediction Diagnostic and Updating in Multivariate Calibration." Biometrika 76(2): 349.
[6] Chatwin, R. E. (2000). "Optimal dynamic pricing of perishable products with stochastic demand and a finite set of prices." European Journal of Operational Research 125(1): 149-174.
[7] Chen, M., Xia, Y., Wang, X (2010). "Managing Supply Uncertainties Through Bayesian Information Update." IEEE Transactions on Automation Science and Engineering 7(1): 24-36.
[8] Cho, D. W. and Y. H. Lee (2012). "Bullwhip effect measure in a seasonal supply chain." Journal of Intelligent Manufacturing 23(6): 2295-2305.
[9] Choi, T. M., Li, D, Yan, H (2003). "Optimal Two-Stage Ordering Policy with Bayesian Information Updating." The Journal of the Operational Research Society 54(8): 846-859.

[10] Chun, Y. H. (2003). "Optimal pricing and ordering policies for perishable commodities." European Journal of Operational Research 144(1): 68-82.
[11] Chiang, W.-y. K., Dilip Chhajed, James D. Hess (2003). "Direct Marketing, Indirect Profits: A Strategic Analysis of Dual-Channel Supply-Chain Design." Management Science 49(1): 1-20.
[12] Das, S. and D. K. Dey (2010). "On Bayesian inference for generalized multivariate gamma distribution." Statistics & Probability Letters 80(19–20): 1492-1499.
[13] Donohue, K. L. (2000). "Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes." Management Science 46(11): 1397-1411.
[14] Gilbert, S. M. and S. Jonnalagedda (2011). "Durable Products, Time Inconsistency, and Lock-in." Management Science 57(9): 1655-1670.
[15] Gurnani, H. and C. S. Tang (1999). "Optimal Ordering Decisions with Uncertain Cost and Demand Forecast Updating." Management Science 45(10): 1456-1462.
[16] Harrison, J. M., Keskin, N. B., Assaf Zeevi (2012). "Bayesian Dynamic Pricing Policies: Learning and Earning Under a Binary Prior Distribution." Management Science 58(3): 570-586.
[16] Ha, A. Y., Lode, Li, Shu-Ming Ng (2003). "Price and Delivery Logistics Competition in a Supply Chain." Management Science 49(9): 1139-1153.
[17] Kamath, K. R. and T. P. M. Pakkala (2002). "A Bayesian approach to a dynamic inventory model under an unknown demand distribution." Computers & Operations Research 29(4): 403-422.
[18] Lee, C.-M. (2008). "A Bayesian approach to determine the value of information in the newsboy problem." International Journal of Production Economics 112(1): 391-402.
[19] Leider, S. and Ö. Şahin (2014). "Contracts, Biases, and Consumption of Access Services." Management Science 60(9): 2198-2222.
[20] Lu, J.-C. and G. K. Bhattacharyya (1990). "Some new constructions of bivariate Weibull models." Annals of the Institute of Statistical Mathematics 42(3): 543-559.
[21] Rabe-Hesketh, S., Skrondal, Anders, Pickles, Andrew (2005). "Maximum likelihood estimation of limited and discrete dependent variable models with nested random effects." Journal of Econometrics 128(2): 301-323.
[22] Rahman, M. A., Sarker, B. R, and Escobar, L. A (2011). "Peak demand forecasting for a seasonal product using Bayesian approach." The Journal of the Operational Research Society 62(6): 1019-1028.
[23] Şen, A. and A. X. Zhang (2009). "Style goods pricing with demand learning." European Journal of Operational Research 196(3): 1058-1075.
[24] Shalizi, C. R. (2009). "Dynamics of Bayesian updating with dependent data and misspecified models." 1039-1074.
[25] Swinney, R. (2011). "Selling to Strategic Consumers When Product Value Is Uncertain: The Value of Matching Supply and Demand." Management Science 57(10): 1737-1751.
[26] Tang, C. S., Kumar Rajaram, Aydın Alptekinoğlu, Ou, J. (2004). "The Benefits of Advance Booking Discount Programs: Model and Analysis." Management Science 50(4): 465-478.
[27] Taskin, S. and E. J. Lodree (2011). "A Bayesian decision model with hurricane forecast updates for emergency supplies inventory management." The Journal of the Operational Research Society 62(6): 1098-1108.
[28] Webb, E. L. and J. J. Forster (2008). "Bayesian model determination for multivariate ordinal and binary data." Computational Statistics & Data Analysis 52(5): 2632-2649.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top