Process simulation uses models that introduce approximations and assumptions but allow the description of a property over a wide range of temperatures and pressures, which might be non-covered by available real data. Models also allow interpolation and extrapolation - within certain limits - and enable the search for conditions outside the range of known properties.

Model development to better represent real processes is the core of further development of simulation software. Model development is done through the principles of chemical and physical engineering but also control engineering for the improvement of mathematical simulation techniques. Process simulation is therefore a field where practitioners from chemistry, physics, computer science, mathematics, and engineering work together.

Prerequisite to product performance and process modeling, material modeling consists of a set of simple equations and correlations where parameters are fitted to experimental data. Predictive methods estimate rheological, physical, thermal, and chemical properties of materials. The equations and correlations are normally preferred because they describe the property (almost) exactly at the fundamental levels. To obtain reliable parameters it is necessary to have experimental data usually from factual databanks or, if no data are publicly available, from actual laboratory measurements.

Using predictive methods is more cost effective than experimental work and data from databanks. Despite this advantage, predicted properties are normally only used in early stages of process development to find first approximate solutions and to exclude false pathways because of higher errors than correlations obtained from real data.

Process simulation has encouraged the development of mathematical models in the fields of numerics and solving complex manufacturing problems. Manufacturing simulation represents one of the most important applications of simulation and is of-ten referred to as CFD (computational fluid dynamics) under CAM (computer-aided manufacturing). The techniques extend as valuable tools used by engineers when evaluating the effects of capital investment in equipment and physical facilities like factory plants, warehouses, and distribution centers. Simulation can be used to predict the performance of an existing or planned system and to compare alternative solutions for a particular design problem. Another important goal of simulation in manufacturing systems is quantifying system performance. Common measures of system performance include:

• Throughput under average and peak loads
• System cycle time (how long it takes to produce a part)
• Use of resource, labor, and machines
• Bottlenecks and choke points
• Queuing at work locations
• Queuing/delays caused by material-handling devices
• WIP (work-in-process) storage needs
• Staffing requirements
• Effectiveness of scheduling systems
• Effectiveness of control systems

Simulation solutions are being increasingly integrated with computer-aided solutions and processes (Computer-aided De-sign or CAD, Computer-aided Manufacturing or CAM, Computer-aided Engineering or CAE, etc.). The use of simulation throughout a product lifecycle, especially at earlier concept and design stages, has the potential of providing substantial benefits.