The Optimal Pricing Strategy with Bayesian Updating in the Dual Channel

• Main Authors: Li-Wei Chen, 陳力維
• Other Authors: Fu-Shiang Tseng
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/7ys9rk

碩士 === 國立中央大學 === 工業管理研究所 === 105 === 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.