ABSTRACT
In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi' (peoples' medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers' workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research.
ABSTRACT
In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to 'Jan Aushadhi' (peoples' medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers' workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research.