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BUSINESS PROCESS MANAGEMENT

2017-2018

IESEG School of Management ( IÉSEG )

Code Cours :

1718-IÉSEG-MBD1S2-OPS-MBDCI01UE

OPERATIONS MANAGEMENT


Niveau Année de formation Période Langue d'enseignement 
MSc in Big Data Analytics for Business1S2English
Professeur(s) responsable(s)L.STUDER
Intervenant(s)Léonard STUDER


Pré requis

Basic statistical and computer skills (descriptive statistics + Excel).
• Basic knowledge of some business process modelling techniques like Business Process Model and Notation (BPMN) or Event-driven process chain (EPC).
• Data manipulation with python or R scripting are welcomed skills for process miners but not required for the course.

Objectifs du cours

At the end of the course, the student should be able to :
Optimise and improve corporate performance by managing and optimising company's business processes through process mining.

Contenu du cours

As written by Prof W. van der Aalst: "Process mining is a bridge between data mining and business process modeling". With process mining, discovery of process maps, conformance of the real process with some model and enhancement of the real processes can be investigated. Concepts, algorithms and tools to support the process mining will be presented. Real-life logs will be analysed in tutored studies. Data quality conditions and constraints to carry on meaningful process mining analysis of real-life data will be given.


Modalités d'enseignement

Organisation du cours

TypeNombre d'heuresRemarques
Face to face
lecture16,00   includes small tutored process mining analysis sessions
Independent work
Reference manual 's readings4,00   reading some internet documents
Independent study
Estimated personal workload30,00  
Charge de travail globale de l'étudiant50,00  

Méthodes pédagogiques

  • Tutorial
  • Interactive class
  • Case study


Évaluation

Analyze real-life process log data and answer MCQ questions to validate the findings

Type de ContrôleDuréeNombrePondération
Final Exam
Written exam2,000100,00
TOTAL     100,00

Bibliographie

  • "Process Mining: Data Science in Action" by Wil M. P. van der Aalst, Springer Verlag, 2016, (ISBN 978-3-662-49851-4) - this is a reference book; this is NOT a must-read before the course ref. -

  • "The Added Value of Process Mining"(http://www.bptrends.com/the-added-value-of-process-mining/) -

  • "Novatica Special Issue on Process Mining" (http://www.ati.es/novatica/2014/ASA/NvS2014-Digital.pdf) -

  • "Why Process Mining is better than Excel for Process Analysis" (http://fluxicon.com/blog/2014/01/why-process-mining-is-better-than-excel-for-process-analysis/) -


Ressources internet



 
* Informations non contractuelles et pouvant être soumises à modification
 
 
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