The afternoon started with the section on Quantitative Analysis, beginning with a presentation by Anne Rozinat from the Technical University of Eindhoven on Workfow Simulation for Operational Decision Support Using Design, Historic and State Information, with the paper co-authored by Moe Wynn, Wil van der Aalst, Arthur ter Hofstede and Colin Fidge.
As she points out, few organizations are using simulation in a structured and organized way; I’ve definitely seen this in practice, where process simulation is used much more during the sales demo than in the customer implementations. She sees three issues with how simulation is done now: resources are modeled incorrectly, simulation models may have to be created from scratch, and there is more of a focus on design than on operational decisions through lack of integration of historical operational data back into the model. I am seeing these last two problems solved in many commercial systems already: rarely is it necessary to separately model the simulation from the process model, and some number of modeling systems allow for the reintegration of historical execution data to drive the simulation.
Their approach uses three types of information:
- design information, from the original process model
- historic information, from historical execution data
- state information, from currently executed workflows, primarily for setting the initial state of the simulation
They have created a prototype of this using YAWL and ProM, and she walked through the specifics of how this information is extracted from the systems, how the simulation model is generated, and how the current state is loaded without changing the simulation model: this latter step can happen often in order to create a new starting point for the simulation that corresponds to the current state in the operational system.
This last factor has the potential to turn simulation into a much more interactive and frequently-used capability, if you consider the capability of being able to run a simulation forward from the current state in the operational system in order to predict behavior over the upcoming period of time: consider, for example, being able to use the current state as the initial properties of the simulation, then adding resources to predict how long it will take to clear the actual backlog of work in order to determine the optimal number of people to add to a process at this point in time. This turns short-term simulation into a operational decision-making tool, rather than just a design tool.