Discreet Event Simulation (DES)

DES was first described in the 1950’s and piloted in Steel Plants.  It is a simulation model that focuses on operational and tactical tweaks that can increase efficiency within a short timeframe based on the flow of a product through time and places.  Its key Simulation factors are as follows:

1. Stochastic—allows for random events to disrupt the flow
2. Passive Agent (usually a product or inactive person moving through a workflow)
3. Often focused on a single unit workflow, such as a hospital ward or emergency department when used in healthcare.

DES has been used successful to research workflow improvements in hospital wards, operating rooms, intensive care units, emergency departments, and for specific health conditions such as diabetes and hypertension.  Although successful in helping map and improve workflows to be more efficient, it has most often been used in a research framework and there is some indication that less than 10% of DES models in healthcare are ever fully implemented in the settings they are researched.

Although likely helpful in reducing healthcare waste via more efficient workflows in hospital wards and the like, the passive nature of the agents (products and people) in these simulations limits its capacity to realistically simulate live healthcare environments in which many decision-makers and professionals are interacting in vary active ways. Thus, it may more closely function in rather linear, complicated environments in which decisions play a very small role—for example performing a common procedure in an operating room in the most efficient way, says nothing about whether the procedure performed was necessary or of high-value to the person receiving the procedure.

System Dynamics Simulation (SDS)

SDS also emerged in the 1950’s from Systems Theories, incorporating non-linear dynamics to better understand complex behaviors in a system. First developed by Jay Forrester at M.I.T. and best known for its implementation by the Club of Rome for its study of the global environment (Meadows, et. al., 1972), SDS relies heavily on causal feedback loops and policy levers in developing mathematical models within organization systems.  Fundamental to SDS are several principles regarding the nature of systems (Meadows, 2008, p. 188):

Systems

• A system is more than the sum of its parts.
• Many of the interconnections in systems operate through the flow of information.
• The least obvious part of the system, its function or purpose, is often the most crucial determinant of the system’s behavior.
• System structure is the source of system behavior. System behavior reveals itself as a series of events over time.

Unlike DES models which tend to focus on single-unit workflows, SDS allows for broader simulations across several work units with dynamic feedback loops providing causal impacts on product flow. The key factors of SDS for complex systems are as follows:

1. Non-stochastic (non-random)
2. Passive agents (products, people, groups of people, work units)
3. Causal Feedback Loops (provides dynamic nature of the system)
4. Policy levers—allows for scenario planning via iterations using different policy approaches