Optimal Credit Period and Lot Size Policies for a Retailer at Risk of Customer Default Under Two-Echelon Partial Trade Credit

In business practice, suppliers and retailers frequently offer trade credit to down-stream members to decrease the inventory level and promote sales. Granting trade credit also increases retailers' credit risk and customers' default risk, which may reduce retailers' profit and in turn...

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Bibliographic Details
Published in:IEEE access 2018, Vol.6, p.54295-54309
Main Authors: Zhang, Chuan, Fan, Ling-Wei, Tian, Yu-Xin, Yang, Shu-Min
Format: Article
Language:English
Subjects:
Customers
Decision making
Default
default risk
Delays
Economic models
economic order quantity (EOQ)
Economics
inventory
Inventory management
Optimization
Order parameters
Order quantity
Parameter sensitivity
Retail stores
Risk
Sensitivity analysis
Supply chain management
Supply chains
two-level partial trade credit
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Summary:In business practice, suppliers and retailers frequently offer trade credit to down-stream members to decrease the inventory level and promote sales. Granting trade credit also increases retailers' credit risk and customers' default risk, which may reduce retailers' profit and in turn exert negative influence on the supplier's profit. Hence, for the sake of self-interest, both the supplier and retailer choose to provide partial trade credit for their down-stream enterprises or customers. Numerous researchers assume the retailer is so powerful in decision-making that he/she can achieve the full trade credit. However, very few academicians have studied two-echelon partial trade credit, which is closer to reality. In this paper, we establish an economic order quantity model for a retailer that receives a partial trade credit from its supplier and offers a partial trade credit to its customers based on the retailer's profit maximization. The demand and default risk are assumed to be dependent on the credit period provided by retailers. This paper proves that the optimal solution is existing and unique. Meanwhile, we propose discrimination terms to identify the optimal solution among possible alternatives. Lastly, through numerical examples and sensitivity analysis, we derive the impact of related parameters on a retailer's order decision and managerial insights.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2871838