Case study on simulation analysis of a logistics network with multiple products by applying lateral transshipments algorithms

Purpose The purpose of this paper is to develop a simulation model to evaluate inventory and distribution decisions like lateral transshipments in a network with multiple products. Design/methodology/approach Data are collected from a company, and a discrete-event simulation in Python is developed to support the decision-making process of managers through different algorithms of lateral transshipments. Findings The numerical results show that the periodic delivery-continuous reorder policy is more robust than the others because the reorder process is not affected by the higher saturation that is achieved by periodic reorder-based policies. The new lateral transshipment algorithm will lead to huge savings in logistics costs for any company and increase truck saturation without causing a decrease in the service level. Research limitations/implications This paper provides a novel institutional perspective on a complex logistics issue where COVD-19 is believed to complicate the context. Practical implications This solution is devised for any company to achieve even greater benefits in terms of customer service improvement and logistics costs reduction. Originality/value The main contribution of this paper is the proposal of a new lateral transshipment algorithm that shows performance improvement by simulating distribution network processes according to different configurations.