A viable supply chain by considering vendor-managed-inventory with a consignment stock policy and learning approach

Abstract

Inventory management challenges are optimizing inventory levels, minimizing stockouts, and streamlining the supply chain process for the vendor and the customer. The study tries to improve these challenges by proposing a new Viable Supply Chain (VSC) incorporating Vendor-Managed-Inventory (VMI) with a Consignment Stock (SC) policy for managing inventory between SC components. These strategies access the inventory level of the customer in the vendor, and if the items are sold, the customer pays the vendor for the inventory that was sold. The Robust Stochastic Optimization (RSO) with Conditional Value at Risk (CVaR) as risk criteria is utilized to solve the model. The objective function of this model combines the expected value, maximum, and CVaR of cost. To promote viability against disruption, sustainability requirements such as CO2 emissions and energy consumption are considered. An agile approach that includes a learning factor is suggested. In addition, a resiliency strategy by addressing order disruption requirements is proposed. The case study of this research is a small healthcare industry that prepares drug products. It is one of the largest industries with high sales and added value. The results show that the inclusion of VMI and CS in the main problem-solving approach resulted in a 14.8 % cost reduction compared to the scenario where VMI and CS were not considered. Finally, sensitivity analysis is performed on CVaR confidence, conservatism coefficient, learning rate, agility rate and scale of the main problem.

Keywords

• Supply chain
• Vendor-managed-inventory
• Consignment stock
• Robustness
• Learning approach
• Conditional value at risk