In manufacturing processes, simulation parameters such as arrival times have traditionally been drawn from statistical distributions or from empirical datasets. Although this approach may lead to relatively accurate parameters, there may be applications in which a more precise methodology is required. IoT is a technology that enables for the processing of real time data through microcontrollers and servers. A simulation may ingest this real-time data to modify downstream simulation parameters towards values that will produce higher yield. This chapter will introduce two techniques that are made possible by the availability of real-time data in simulation. First, the chapter will discuss possible optimizations that may be made by selectively choosing parameters that lead to higher production based on real-time data input. Then, the chapter will focus on the ability of IoT-based simulations to dispatch real-time instructions to robots placed in the manufacturing process. The chapter introduces these concepts by the model construction of a drug manufacturing process using a discrete-event simulation software called Tao.