Pricing Strategies of a Three-Stage Supply Chain: A New Research in the Big Data Era

Abstract

In the Big Data era, Data Company as the Big Data information (BDI) supplier should be included in a supply chain. In the new situation, to research the pricing strategies of supply chain, a three-stage supply chain with one manufacturer, one retailer, and one Data Company was chosen. Meanwhile, considering the manufacturer contained the internal and external BDI, four benefit models about BDI investment were proposed and analyzed in both decentralized and centralized supply chain using Stackelberg game. Meanwhile, the optimal retail price and benefits in the four models were compared. Findings are as follows. (1) The industry cost improvement coefficient, the internal BDI investment cost of the manufacturer, and the added cost of the Data Company on using Big Data technology have different relationships with the optimal prices of supply chain members in different models. (2) In the retailer-dominated supply chain model, the optimal benefits of the retailer and the manufacturer are the same, and the optimal benefits of the Data Company are biggest in all the members.

An overview of big data analytics application in supply chain management published in 2010-2019

“…Big data analytics was used by (Liu, 2017) to develop new e-commerce methods for marketing fresh products with a short shelf life by keeping in mind the critical aspects of humidity and temperature. In another research paper, Mishra et al (2017) used social media big data to determine factors that influence customers' beef purchasing decisions.…”

“…It shows that the available data can used for targeted advertisements in a supply chain's green environment. Another study in big data pricing application was done by (Liu, 2017), in which he considered the data company to be an echelon in the supply chain, and determined its benefits using the Stackelberg game.…”

Investment decision-making of closed-loop supply chain driven by big data technology

“…More specifically, Ji et al [14] combined the big data with the Bayesian network and deduction graph model and extracted value from them to make more informed production decisions in the supply chain. Furthermore, Liu [18] gave pricing strategies under four supply chain structures formed by big data investment in supply chain. In addition, BDT identifies the browsing records of customers and prices differently for consumers according to their preference information [19], which directly raises awareness of the problem of product returns.…”

Pricing of a Three-Stage Supply Chain with a Big Data Company

“…Particularly, the Big Data company can obtain demand information (namely, θ) of product m at the cost of c b m and sell it to manufacturer m at the price of p b m . Similar to many existing studies [10,24,25], the cost and the price for the Big Data company are counted by the number of information which is proportional to customer demands with a ratio of β (called conversion coefficient). In order to produce proper products to meet customers' demands, manufacturers need to buy demand information from the Big Data company and share this information with the retailer to achieve better sales.…”

“…However, Big Data companies are not explicitly involved in the mathematical models of these two studies though the importance is stressed. Liu and Yi [23] further study a three-stage supply chain with one manufacturer, one retailer, and one Big Data company, and a similar problem is studied in [24] with different assumptions. Liu [25] considers an investment decision-making problem of Big Data information for book supply chains with one book publisher, one retailer, and one Data Company.…”