Workflow simulation for operational decision support using event graph through process mining

  • Authors:

  • Ying Liu;Hui Zhang;Chunping Li;Roger Jianxin Jiao

  • Affiliations:

  • Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore;School of Software, Tsinghua University, Beijing 100084, China;School of Software, Tsinghua University, Beijing 100084, China;The G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, USA

  • Venue:
  • Decision Support Systems
  • Year:
  • 2012

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Abstract

It is increasingly common to see computer-based simulation being used as a vehicle to model and analyze business processes in relation to process management and improvement. While there are a number of business process management (BPM) and business process simulation (BPS) methodologies, approaches and tools available, it is more desirable to have a systemic BPS approach for operational decision support, from constructing process models based on historical data to simulating processes for typical and common problems. In this paper, we have proposed a generic approach of BPS for operational decision support which includes business processes modeling and workflow simulation with the models generated. Processes are modeled with event graphs through process mining from workflow logs that have integrated comprehensive information about the control-flow, data and resource aspects of a business process. A case study of a credit card application is presented to illustrate the steps involved in constructing an event graph. The evaluation detail is also given in terms of precision, generalization and robustness. Based on the event graph model constructed, we simulate the process under different scenarios and analyze the simulation logs for three generic problems in the case study: 1) suitable resource allocation plan for different case arrival rates; 2) teamwork performance under different case arrival rates; and 3) evaluation and prediction for personal performances. Our experimental results show that the proposed approach is able to model business processes using event graphs and simulate the processes for common operational decision support which collectively play an important role in process management and improvement.