Data Mining on Return Items in a Reverse Supply Chain
In the reverse supply chain the basic concern is to categorize the returned materials in the usable or non-usable ones for performing the required operations to resend them in the supply chain. Companies can obtain more confidence from their consumers to carry out continuous revers logistics removing the defective products. To begin the reverse multi-layer multi-product supply chain and after collecting the returning commodities, users cluster them via k-mean algorithm in MatLab software environment. The results are used to perform a sampling process to deliver the commodities to the related layer for rework and repair operations.