Non-instantaneous deteriorating inventory optimization in green supply chain for environment savvy customer with learning effect

Abstract

In this paper, we establish a single item inventory model for deteriorating items in a Green Supply Chain under learning effect. We consider a single manufacturer and single retailer model having one manufacturing cycle followed by multiple retailer cycle. Customer is environment savvy and prefers products with low carbon emission. It is considered that product maintains its value for a period of time before there is a loss of value. Thus the deterioration is assumed to be non-instantaneous. This is a more optimal modeling as it allows retailer to utilize full profit on the product selling before it starts offering discounts. It is assumed that the used products as well as unsold products are transferred back to the manufacturer where they are recycled and remanufactured. Learning is taken into account in estimating the total average cost. Learning is a natural phenomenon that occurs everywhere. Naturally, a person doing task repetitively will perform better over period of time. This leads to reduction in various costs. Numerical analysis is carried out at the end to validate model.

Manufacturing/remanufacturing based supply chain management under advertisements and carbon emissions process

“…Kumar et al [22] derived a CLSC model for smart objects using a reverse logistics system, with the retailer taking carbon emissions into account. Rani et al [28] presented a green CLSC model for products that do not degrade instantly. Sepehri et al [39] created a model for degrading lowquality objects in which the pace of degradation is constant and may be controlled by investing in preservation technologies.…”