Discrete-event simulation is an effective tool to evaluate manufacturing systems and support the development of improvement plans. However, the use of simulation alone to optimize inventory and throughput simultaneously can result highly complicated, unwieldy model, especially when multiple part types are present.

In order to effectively analyze these responses with less complexity, this research developed a structured methodology that concurrently applied mathematical equations and discrete-event simulation. The approach permitted inventory within the system to be an additional parameter of improvement since its evaluation could be conducted with a greater level of accuracy. A cost per piece breakdown formed the basis of the objective function, where improvements were analyzed through discrete-event simulation in the attempt to achieve a minimized unit cost.

Additionally, as manufacturing systems become more capable of producing multiple part types on a single system, the research approach focused on a multi-part system where maximizing the number of changeovers using capacity based methods are sought to reduce part specific inventories. The developed methodology was applied to a multi-part engine block machining system to prove its validity.

It was concluded that the methodology developed in this applied research can produce significant savings in terms of inventory reduction and productivity improvement as compared to using simulation alone. Furthermore, the technique can easily be applied to many other manufacturing systems in any industry.