Simulation of in-house logistics operations for manufacturing

Abstract

Nowadays, manufacturing companies present complex and robust in-house logistics operations that support production lines, where high system efficiency is the primary goal. However, to achieve the desired degree of efficiency, the use of tools that can help decision-makers to identify the improved set of operations is required. This need is explored in this work through the development of a simulation model. The model is inspired by a real automotive plant, where a segment of a mixed-model assembly line composed by a supermarket, diverse kits, human pickers and automated guided vehicles (AGV) is explored. Different scenarios are studied to analyse the potential for production support operation improvement, where the introduction of automated technologies, like robots, is explored. Results show that the system, through the addition of intelligent dynamic carrier robots, can significantly improve efficiency while reducing resources deployed. Furthermore, sizing the human workforce at the supermarket is the key to having a well-balanced production system.