Research on Supply Chain Inventory Replenishment and Pricing Model Based on Time in Big Data Environment

In recent years, data has become a special kind of information commodity and promoted the development of information commodity economy through distribution. With the development of big data, the data market emerged and provided convenience for data transactions. However, the issues of optimal pricing and data quality allocation in the big data market have not been fully studied yet. In this paper, we proposed a big data market pricing model based on data quality. We first analyzed the dimensional indicators that affect data quality, and a linear evaluation model was established. Then, from the perspective of data science, we analyzed the impact of quality level on big data analysis (i.e., machine learning algorithms) and defined the utility function of data quality. The experimental results in real data sets have shown the applicability of the proposed quality utility function. In addition, we formulated the profit maximization problem and gave theoretical analysis. Finally, the data market can maximize profits through the proposed model illustrated with numerical examples.

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Pricing policies of green supply chain considering targeted advertising and product green degree in the Big Data environment

Journal of Cleaner Production ◽

10.1016/j.jclepro.2017.07.049 ◽

2017 ◽

Vol 164 ◽

pp. 1614-1622 ◽

Cited By ~ 39

Author(s):

Pan Liu ◽

Shu-ping Yi

Keyword(s):

Big Data ◽

Supply Chain ◽

Green Supply Chain ◽

Targeted Advertising ◽

Pricing Policies ◽

Data Environment

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Analysis of Customization Strategy for E-Commerce Operation Based on Big Data

Wireless Communications and Mobile Computing ◽

10.1155/2021/6626480 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Su Chen

Keyword(s):

Decision Making ◽

Big Data ◽

Supply Chain ◽

Price Competition ◽

Price Impact ◽

Game Equilibrium ◽

Decentralized Decision Making ◽

Impact Coefficient ◽

Data Environment ◽

The Cost

In order to improve the efficiency of customization and reduce the cost of customization under Big data environment, this paper uses cost-sharing contract, pricing mechanism, Hotelling model, and game theory tools and research methods, for C2B Electronic Commerce (e-commerce) mode of Supply Chain Pricing Strategy for in-depth discussion. This paper first gives the architecture of the customization service system based on big data. The paper studies the game equilibrium of supply chain members under four scenarios: centralized decision-making, decentralized decision-making, C2B-dominated decision-making, and traditional enterprise-dominated decision-making in a supply chain composed of a supplier and C2B e-commerce enterprises with horizontal price competition, and examines the cross-price. Important parameters such as impact coefficient, impact coefficient of effort degree of personalized customization, and so on have an impact on variables such as effort degree of personalized customization, retail price, and profit of supply chain members of C2B e-commerce enterprises. Research shows that with the increase of cross-price impact coefficient, C2B e-commerce will enhance its personalized customization efforts in different situations in order to pursue higher profits.

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