A dynamic inventory model with supplier selection in a serial supply chain structure

• This article provides an inventory model for a serial supply chain with multiple suppliers and time varying demand.
• The model minimizes the total cost of purchasing, production, inventory setup and holding, and transportation.
• Considering lead times, necessary feasibility conditions regarding the demand at the last stage are established.
• Actual transportation costs are modeled by exact piecewise linear functions.
• Our results show that inventory setup and holding costs can affect supplier selection and order lot sizing decisions.

Considering the inherent connection between supplier selection and inventory management in supply chain networks, this article presents a multi-period inventory lot-sizing model for a single product in a serial supply chain, where raw materials are purchased from multiple suppliers at the first stage and external demand occurs at the last stage. The demand is known and may change from period to period. The stages of this production–distribution serial structure correspond to inventory locations. The first two stages stand for storage areas for raw materials and finished products in a manufacturing facility, and the remaining stages symbolize distribution centers or warehouses that take the product closer to customers. The problem is modeled as a time-expanded transshipment network, which is defined by the nodes and arcs that can be reached by feasible material flows. A mixed integer nonlinear programming model is developed to determine an optimal inventory policy that coordinates the transfer of materials between consecutive stages of the supply chain from period to period while properly placing purchasing orders to selected suppliers and satisfying customer demand on time. The proposed model minimizes the total variable cost, including purchasing, production, inventory, and transportation costs. The model can be linearized for certain types of cost structures. In addition, two continuous and concave approximations of the transportation cost function are provided to simplify the model and reduce its computational time.