Abstract

Discrete-event simulation (DES) is an invaluable tool which organizations can use to help better understand, diagnose, and optimize their operational processes. Studies have shown that for the typical DES exercise, the greatest amount of time is spent on developing an accurate model of the process that is to be studied. Process mining, a similar field of study, focuses on using historical data stored in software databases to accurate recreate and analyze business processes. Utilizing process mining techniques to help rapidly develop DES models can drastically reduce the amount of time spent building simulation models, which ultimately will enable organizations to more quickly identify and correct shortcomings in their operations. Although there have been significant advances in process mining research, there are still several issues with current process mining methods which prevent them from seeing widespread industry adoption. One such issue, which this study examines, is the lack of cross-compatibility between process mining tools and other process analysis tools. Specifically, this study develops and characterizes a method through which mined process models can be converted into discrete-event simulation models. The developed method utilizes a plugin written for the ProM Framework, an existing collection of process mining tools, which takes a mined process model as its input and outputs an Excel workbook which provides the process data in a format more easily read by DES packages. Two event logs which mimic real-world processes were used in the development and validation of the plugin. The developed plugin successfully extracted the critical process data from the mined process model and converted it into a format more easily utilized by DES packages. There are several limitations which will limit model accuracy, but the plugin developed by this study shows that the conversion of process models to basic simulation models is possible. Future research can focus on addressing the limitations to improve model accuracy.

Degree

MS

College and Department

Ira A. Fulton College of Engineering and Technology; Technology

Rights

http://lib.byu.edu/about/copyright/

Date Submitted

2015-11-01

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd8142

Keywords

Siyao Tony Liu, process mining, discrete-event simulation, ProM, ProModel

Technology Emphasis

Manufacturing Systems (MS)

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