A growing number of business process management software vendors are offering simulation capabilities to extend their modeling functions and enhance their analytical proficiencies. Simulation is positioned as a means to evaluate the impact of process changes and new processes in a model environment through the creation of “what-if” scenarios. Simulation is promoted to enable examination and testing of decisions prior to actually making them in the “real” environment. Since simulation approximates reality, it also permits the inclusion of uncertainty and variability into the forecasts of process performance.
Less than a handful of business process software vendors offer optimization to supplement their simulation capability. However, the need for optimization of simulation models arises when the process analyst wants to find a set of model specifications (i.e., input parameters and/or structural assumptions) that leads to optimal performance. On one hand, the range of parameter values and the number of parameter combinations is too large for analysts to simulate all possible scenarios, so they need a way to guide the search for good solutions. On the other hand, without simulation, many real world problems are too complex to be modeled by mathematical formulations that are at the core of pure optimization methods. This creates a conundrum; pure optimization models alone are incapable of capturing all the complexities and dynamics of the system, so one must resort to simulation, which cannot easily find the best solutions. Simulation Optimization resolves this conundrum by combining both methods.
When changes are proposed to business processes in order to improve performance, the projected improvements can be simulated and optimized artificially. The sensitivity of making the changes on the ultimate objectives can be examined and quantified reducing the risk of actual implementation. Changes may entail adding, deleting, and modifying processes, process times, resources required, schedules, work rates within processes, skill levels, and budgets. Performance objectives may include throughput, costs, inventories, cycle times, resource and capital utilization, start-up times, cash flow, and waste. In the context of business process management and improvement, simulation can be thought of as a way to understand and communicate the uncertainty related to making the changes while optimization provides the way to manage that uncertainty.
For additional information read Simulation Optimization: Applications In Risk Management.