Optimal Multiple Attribute Decision Model for Key Parameters of Online Group Buying Product

Abstract

The rise of the internet has led to rapid development of online group buying, and with the various functions and analysis tools provided on the internet, consumers are making more purchases than ever before. In addition to attracting consumers to buy products or services via online shopping platforms, the industry’s online group buying market allows customers to enjoy preferential prices together. Consumers can bargain through large-scale purchases. Through quantity-based pricing, the effect of the decline on consumers’ expected price will be enhanced, and consumers will be able to purchase products at lower prices, which encourages more consumers to join group buying schemes. In terms of product cost, online shopping operations enable manufacturers to save shop setup and inventory costs, and some of these cost savings may be reflected in the selling price, which can enable customers to obtain products at lower prices. The grey decision model is used for further in-depth exploration. The purpose of this study is to use the grey correlation ranking and grey multi-attribute decision-making (TOPSIS) process to further determine the optimal shelf-time, inventory quantity, and selling price (key parameters) of commodities, in order to design a plan that makes maximum profit for the industry and provides the best service to customers.