System and Method to Predict the Number of Returns in a Supply Chain : A Reverse Logistics Case Study

Reverse Logistics (RL) has gained great prominence in recent years. The challenges involved in RL are considerable compared to Forward Logistics (FL). The paper intends to predict the number of returns (failed products) in a supply chain for any repairable system applied to a small-scale industry. Discrete event simulation methodology is used to predict the number of repaired products. The predicted returns are transferred to a cloud for proper information sharing between different stages in supply chain to plan the RL activities. Based on the number of returns predicted, an algorithm is prepared, which helps in planning and transporting the minimum cost logistics network back to industry’s warehouses. The paper aims at providing a solution for RL activities for the small-scale industry