The Optimal Pricing Strategy with Bayesian Updating in the Dual Channel

NCUIR > School of Management at National Central University > Graduate Institute of Industrial Management > Electronic Thesis & Dissertation > Item 987654321/73879

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/73879

Title:	The Optimal Pricing Strategy with Bayesian Updating in the Dual Channel
Authors:	陳力維;Chen, Li-Wei
Contributors:	工業管理研究所
Keywords:	季節性商品;雙通路;貝氏方法;價格更新;seasonal product;dual channel;Bayesian method;price updating
Date:	2017-07-14
Issue Date:	2017-10-27 12:28:17 (UTC+8)
Publisher:	國立中央大學
Abstract:	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.
Appears in Collections:	[Graduate Institute of Industrial Management] Electronic Thesis & Dissertation

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