Green consideration in a closed-loop supply chain model with imperfect inspection under learning impact

Abstract

This study investigates a multi-echelon closed-loop supply chain model integrating learning impact with a single producer, single retailer, and single collector. Retail pricing, green innovation, and marketing efforts are assumed to have a linear relationship with end-user demand. The return of rejected goods occurs at random and is subject to an imperfect inspection procedure that is vulnerable to two different types of classification error. An S-shaped learning curve is considered to soothe the inspection process which is subject to inaccuracy. Numerical examples are used to illustrate the implications of the suggested model in order to achieve a goal that benefits both the consignor and the consignee. It is investigated whether the learning effect can outweigh the loss even when the inspection error has a detrimental effect on supply chain earnings. To identify the crucial parameters that can spark some truly exceptional managerial ideas, a sensitivity analysis is carried out.