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Research Papers

Quantifying the Effectiveness of Interventions in Workflows

[+] Author and Article Information
Rainer Hoff

 President Gateway Consulting Group, Inc., 8610 Transit Road, East Amherst, NY 14051rhoff@gatewaygroup.com

J. Comput. Inf. Sci. Eng 10(2), 021002 (Apr 13, 2010) (7 pages) doi:10.1115/1.3330446 History: Received December 15, 2008; Revised September 14, 2009; Published April 13, 2010; Online April 13, 2010

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%).

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

High-level business timing diagram

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Figure 2

Wait time probability distribution

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Figure 3

Impact of intervention on probability of wait time ending in a specific interval, as per example 1

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Figure 4

The influence of skewness ratio on the Weibull probability density function

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Figure 5

Dependence of net intervention efficiency on skewness ratio of the probability density function

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Figure 6

Dependence of net intervention efficiency on intervention interval

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Figure 7

Dependence of net intervention efficiency on individual intervention effectiveness

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