As a recent CP article  noted, discrete event simulation (DES) can play a significant role in the process industries. At Eli Lilly and Company, we've been using DES to model many of our manufacturing facilities for quite a few years.
Examples of typical applications include:
• capacity analysis and debottlenecking of an analytical laboratory where the simulation showed the main constraint was the number of high-pressure liquid chromatographs, not people as previously thought;
• equipment reliability analysis to help decide if a run-to-fail strategy is better than a planned repair strategy;
• analysis of a device manufacturing line to determine where to locate accumulators to have maximum impact on throughput;
• simulation of the design for a new biotech facility to fully understand capacity and sizing of key systems such as water-for-injection, clean-in-place (CIP) and buffer makeup;
• resource loading analyses to maximize throughput with a given work force;
• capacity analysis of a cartridge inspection facility to predict labor requirements to meet future predicted demand (including projected technology changes as well); and
• prediction of energy requirements for a large system of fermentors, where each fermentor may have different products (with different cooling profiles) and will be on a different loading cycle, to define cooling system requirements.
We've also applied DES in non-manufacturing areas such as research and development, e.g., to simulate patient enrollment in clinical trials .
While DES is a key method for simulating systems, other approaches are available -- so it's important to decide if DES is appropriate for a given situation. DES is a good choice when modeling systems that might include:
• multiple pathways in the process, where product can be processed in different ways depending on the type of product or what occurs along the processing route;
• complex resource constraints and allocation decisions that must be represented accurately to understand the process;
• large numbers of primitives (i.e., basic building blocks such as tasks, queues, batching/unbatching and routing) in the model; and
• stochastics overlaid on dynamics (complex queuing systems).
Taking advantage of DES now is far easier than in the past. Today simulation tools are available that are highly capable, cheap, easy to use, flexible and readily connectible to other tools such as statistical packages for data analysis. These tools have significantly broadened the number of people in an organization who can build sophisticated simulations of complex processes as well as reduced the time needed to develop such models. No longer is a degree in industrial engineering or operations research necessary to build DES process models. Now anyone with process knowledge can quickly build useful DE simulations. Further, because mathematical requirements have been significantly reduced, models can more closely resemble the real process with fewer simplifying assumptions.
DES has provided valuable insights on how best to run a dedicated-product bulk pharmaceutical plant in a constantly changing production environment. The plant has a complex flow, involving numerous production operations, a wide variety of unit operations (e.g., centrifugation, chromatography, batch reaction and evaporation), product recovery loops and a large number of support operations. DES was a key tool for debottlenecking and modeling potential capacity increase solutions, including some with more complex dynamics and interactions. Examples of capacity modeling performed include: