– The project uses machine learning methods to automate the configuration of ERP / CRM software, an endeavour which has never been attempted before. The training data encompasses interviews and software
configurations from over 400 companies from Brazil, Canada, China, France, Germany and: Romania. A decision tree based questionnaire has been designed and implemented to gather the training data. A twophases
correction system with integrated corrections recommendation assures the quality of training data. The technologies are being evaluated for efficiency
and efficacy with 750 simultaneous users.
– A new kind of business model tailored for SMEs:and the usage of Enterprise Open Source software has been developed: Open Source Starter. By automating part of the configuration of Enterprise Open Source Software,
it is now possible to enable SMEs to implement complex Open Source Software on their own for their company, if they get the right training. Open Source Starter is a structured e-learning process where small companies first configure their enterprise software automatically based on machine learning and are then trained to implement it on their own.
Number of scientific articles published: 0
Number of patents filed: 0
Number of product’s innovation: 3
– ERP5 Survey, a decision-tree based questionnaire provides a model to relate informations about the structure of a company to an ERP configuration. It is evaluated with 750 users.
– EAT, the ERP5 Artificial Intelligence Toolkit, implements machine learning methods to provide automatic questionnaire correction and to create an ERP5 category configuration. It is evaluated with 30 users.
– ERP5 Starter implements the Open Source Starter business model for ERP5 with the help of ERP5 Survey / EAT. It is evaluated with three customers.
Number of product’s innovation service: 0
Number of projected jobs created: 0
Number of jobs maintained: 0
Number of related companies creation: 0