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Optimal Credit Period and Lot Size Policies for a Retailer at Risk of Customer Default Under Two-Echelon Partial Trade Credit
Author(s) -
Chuan Zhang,
Ling-Wei Fan,
Yu-Xin Tian,
Shu-Min Yang
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2871838
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.

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