past, manufacturing simulation tools were classified as languages or simulators. Languages were very flexible tools, but rather complicated to use by managers and too time consuming. Simulators were more user friendly but they came with rather rigid templates that didn’t adapt well enough to the rapidly changing manufacturing techniques. Nowadays, there is software available that combines the flexibility and user friendliness of both, but still some authors have reported that the use of this simulation to design and optimize manufacturing processes is relatively low. One of the most used techniques by manufacturing system designers is the discrete event simulation. This type of simulation allows to assess the system’s performance by statistically and probabilistically reproducing the interactions of all its components during a determined period of time. In some cases, manufacturing systems modelling needs a continuous simulation approach. These are the cases where the states of the system change continuously, like, for example, in the movement of liquids in oil refineries or chemical plants. As continuous simulation cannot be modeled by digital computers, it is done by taking small discrete steps. This is a useful feature, since there are many cases where both, continuous and discrete simulation, have to be combined. This is called hybrid simulation, which is needed in many industries, for example, the food industry. A framework to evaluate different manufacturing simulation tools was developed by Benedettini & Tjahjono (2009) using the ISO 9241 definition of usability: “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use.” This framework considered effectiveness, efficiency and user satisfaction as the three main performance criterion as follow: The following is a list of popular simulation techniques: Discrete event simulation (DES) System dynamics (SD) Agent-based modelling (ABM) Intelligent simulation: based on an integration of simulation and artificial intelligence (AI) techniques Petri net Monte Carlo simulation (MCS) Virtual simulation: allows the user to model the system in a 3D immersive environment Hybrid techniques: combination of different simulation techniques. Applications The following is a list of common applications of simulation in manufacturing: References Manufacturing Simulation