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Laboratoire d'InfoRmatique en Image et Systèmes d'information

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Laboratoire d'InfoRmatique en Image et Systèmes d'information
UMR 5205 CNRS / INSA Lyon / Université Claude Bernard Lyon 1 / Université Lumière Lyon 2 / École Centrale de Lyon
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Maxime Chabert

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PhD student

Team Geometry Processing and Constrained Optimization
 
Institution Institut National des Sciences Appliquées de Lyon
Location Blaise Pascal (INSA)
 
E-mail maxime.chabert at liris.cnrs.fr
Contact details Publications Thesis
 
Subject Research and exploitation of parameter setting concepts to automate the customization process of an ERP software
Abstract ERP (Enterprise Resource Planning) softwares have a large functional area managing almost all the processes of an industrial company. While the same ERP can be used by many companies, a customization step is needed in order to adapt the software to the structural and organizational needs of the company. The customization process can be very complex from the fact an ERP has a huge number of parameters that can be implicitly dependent. To speed-up this step, we propose to use datamining techniques to identify standard sets (called concepts) of parameter settings that we associate to the needs of a company using knowledge engineering methods.
First, we sutdy conceptual clustering that is a way to partition customer settings into clusters and identify each cluster with a parameter settings set. We use a declarative framework based on constraint programming to resolve the problem efficiently and relax easily some constraints that are too strong for our application case.
Second, we sutdy hierarchical conceptual clustering, that consists on applying recursively conceptual clustering on the clusters we find, to fit better the industrial issue and represent the parameter setting concepts with a tree, as global concepts can have more specific variants.
Finally, we propose and experiment a method to represent functional needs of a company in order to associate parameter setting concepts with needs to reuse it during future customization steps.
Advisor Christine Solnon
Advisor Amélie Cordier

Last update : 2018-01-16 13:46:39