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.