Improving Throughput and Predictability of High-volume Business Processes Through Embedded Modeling

Being faster is good. Being predictable is better. A faithful model of a system, loaded to reflect the system\u27s current state, can then be used to look into the future and predict performance. Building faithful models of processes with high degrees of uncertainty can be very challenging, especially where this uncertainty exists in terms of processing times, queuing behavior and re-work rates. Within the context of an electronic, multi-tiered workflow management system (WFMS) the author builds such a model to endogenously quote due dates. A WFMS that manages business objects can be recast as a flexible flow shop in which the stations that a job (representing the business object) passes through are known and the jobs in the stations queues at any point are known. All of the other parameters associated with the flow shop, including job processing times per station, and station queuing behavior are uncertain though there is a significant body of past performance data that might be brought to bear. The objective, in this environment, is to meet the delivery date promised when the job is accepted. To attack the problem the author develops a novel heuristic algorithm for decomposing the WFMS\u27s event logs exposing non-standard queuing behavior, develops a new simulation component to implement that behavior, and assembles a prototypical system to automate the required historical analysis and allow for on-demand due date quoting through the use of embedded discrete event simulation modeling. To attack the problem the author develops a novel heuristic algorithm for decomposing the WFMS\u27s event logs exposing non-standard queuing behavior, develops a new simulation component to implement that behavior, and assembles a prototypical system to automate the required historical analysis and allow for on-demand due date quoting through the use of embedded discrete event simulation modeling. The developed software components are flexible enough to allow for both the analysis of past performance in conjunction with the WFMS\u27s event logs, and on-demand analysis of new jobs entering the system. Using the proportion of jobs completed within the predicted interval as the measure of effectiveness, the author validates the performance of the system over six months of historical data and during live operations with both samples achieving the 90% service level targeted