Quantifying the Effectiveness of Interventions in Workflows

Abstract

Workflows are common in today’s business world, and are an integral part of current enterprise content management, product lifecycle management, and enterprise resource planning systems. When a task assignee does not complete a task on time, workflow systems are commonly configured to send out reminders. Reminders are a form of intervention in the workflow. It is tacitly assumed that workflow intervention is effective, yet, to date, there has been no quantitative characterization of the benefits of workflow intervention. This study first develops a mathematical model for workflow intervention. The controlling parameters are identified: the choice of probability distribution, the skewness of the probability distribution, the intervention interval, and the effectiveness of individual interventions. To the extent that closed-form solutions are available (e.g., for uniform or triangular probability density functions), they are presented. More generally, results are presented by representing the wait time using the Weibull probability density function. Cases where closed-form solutions are intractable are simulated using the Petri net method. Results indicate that, while interventions always reduce the mean cycle time for a workflow, there are certain circumstances where the cycle time reduction is dramatic (i.e., >50%).