Decision of a trade credit policy in a supply chain model of growing items under linguistic fuzzy term set

ABSTRACT

This article deals with a novel trade credit policy for growing items where the buyer collects the new born items from a supplier and sells them to the consumers when they are grown up. The supplier provides a credit period to the buyer through negotiation. From the buyer’s view point first of all, a profit maximization growing items with carbon emission supply chain model has been studied under three different scenarios over the length of credit periods. To capture the non-random uncertainty of the model parameters and the learning effects we have assumed the demand rate of the consumers and all the cost parameters as triangular fuzzy linguistic terms set by means of a learning matrix. For defuzzification we have utilized the eigen values of the corresponding fuzzy learning matrices and solved the problem with the help of a solution algorithm under optimum number of new born items. However, for numerical computations we have utilized the data set studied by Alamri from a real case study. A comparative analysis has also been discussed for model validation. Finally, sensitivity analysis and graphical illustration are carried out to focus the novelty and to study the impact of the inventory parameters of the proposed approach.

KEYWORDS:

• Supply chain model
• trade credit policy
• triangular fuzzy linguistic term set
• matrix eigen value
• defuzzification rule
• optimization

Acknowledgments

The authors would like to thank the editor and anonymous referees for their positive and constructive comments, which help to improve the quality of the paper to a grant extent.

Disclosure statement

The authors declare that they have no conflict of interest regarding the publication of this article.

Data availability statement

No new data has been generated for this research.

Additional information

Funding

This research did not receive any specific grant from funding agencies in public, commercial, or not-for-profit sectors.