This paper proposes an efficient solving method for a multi-echelon inventory control optimization problem applicable to real-world contexts. The method is tested on a real-world case study. We implement a metaheuristic based on a local search algorithm and develop custom neighborhood operators. To validate the proposed method and assess its performance in a realistic setting, we develop a simulation tool that reproduces the inventory control process over several time periods. This simulation tool is used to evaluate the performance of the multi-echelon approach and to compare it to existing single-echelon order engines used in production. The results of the experiments show the significant benefits of the proposed multi-echelon inventory control optimization method, including an improvement of between 13% and 40% in average service rate and a profit increase between 23% and 37% depending on the type of instance considered.