Improving Productive Processes Using a Process Mining Approach

Today’s companies face great challenges when attempting to quest business markets with their demands on product quality and price. However, when a company maintains high efficiency levels on its productive processes usually it has this challenge quite simplified. The great availability of data we have currently on industry plants provides a very interesting support to face this challenge, when combined with new technologies such as process mining. This paper presents a case study where the very recent process mining techniques were applied to a very particular productive process characterized for its low frequency and heterogeneity. To do this, we made some changes to the “L * life-cycle model” methodology, for applying process mining in the identification of tasks with unsatisfactory performance levels, and analyzing the most relevant and critical aspects that influence it.

[1]  Sander J. J. Leemans,et al.  Using Life Cycle Information in Process Discovery , 2016, Business Process Management Workshops.

[2]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[3]  Christian W. Günther,et al.  Disco: Discover Your Processes , 2012, BPM.

[4]  Wil M. P. van der Aalst,et al.  Process Mining in the Large: A Tutorial , 2013, eBISS.

[5]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[6]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[7]  Gordon S. Blair,et al.  Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs , 2014, BPM.

[8]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.

[9]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[10]  Moe Thandar Wynn,et al.  Soundness of workflow nets: classification, decidability, and analysis , 2011, Formal Aspects of Computing.

[11]  Guido Schimm,et al.  Mining exact models of concurrent workflows , 2004, Comput. Ind..

[12]  Mathias Weske,et al.  Business Process Management: A Survey , 2003, Business Process Management.

[13]  Wil M. P. van der Aalst,et al.  Time prediction based on process mining , 2011, Inf. Syst..

[14]  Alessandro Sperduti,et al.  Heuristics Miner for Time Intervals , 2010, ESANN.

[15]  Hans-Georg Kemper,et al.  Application-Pull and Technology-Push as Driving Forces for the Fourth Industrial Revolution , 2014 .

[16]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[17]  Gordon S. Blair,et al.  Scalable Dynamic Business Process Discovery with the Constructs Competition Miner , 2014, SIMPDA.

[18]  Boudewijn F. van Dongen,et al.  Process Mining: Overview and Outlook of Petri Net Discovery Algorithms , 2009, Trans. Petri Nets Other Model. Concurr..

[19]  Jianmin Wang,et al.  A novel approach for process mining based on event types , 2007, IEEE International Conference on Services Computing (SCC 2007).

[20]  Wil M. P. van der Aalst,et al.  Process Mining: Overview and Opportunities , 2012, ACM Trans. Manag. Inf. Syst..

[21]  Boudewijn F. van Dongen,et al.  Replaying history on process models for conformance checking and performance analysis , 2012, WIREs Data Mining Knowl. Discov..

[22]  Guido Schimm Process Miner - A Tool for Mining Process Schemes from Event-Based Data , 2002, JELIA.

[23]  Surajit Chaudhuri,et al.  An overview of data warehousing and OLAP technology , 1997, SGMD.

[24]  Boudewijn F. van Dongen,et al.  The ProM Framework: A New Era in Process Mining Tool Support , 2005, ICATPN.