A modelling and management approach to risks in reverse logistics implementation

This research aims to identify and model the reverse logistics (RL) risk variables to estimate the risks associated with their deployment. Furthermore, it suggests risk management techniques to execute the RL implementation effectively. The Delphi technique, interpretive structural modelling (ISM), and fuzzy cross-impact matrix multiplication applied to classification (F-MICMAC) create a hybrid research framework in this study. Delphi determines the RL risk factors and ISM creates a structural model to examine the contextual connection between them, followed by F-MICMAC classification. The key risk elements connected with RL implementation include government policy risk and management policy risk. Major RL risk management strategies include collaboration with network partners, risk-sharing with stakeholders, strong mutual trust among collaborators, improved forecasting techniques and continuous information sharing. The current evaluation is extremely beneficial in identifying the driving and dependence power and the efficacy of a certain risk, which helps in segregating them for RL implementation.