Management of non-conformities and lessons learned

Semantic analysis for non-conformity management and lessons learned

The flow of information from the field (operational teams, operations, customers) is an essential source for conducting a continuous improvement policy (quality assurance, marketing, capitalization of technical facts). At the level of the information system strategy, the dilemma then arises as to the format for capturing these returns: either leave the format as free as possible (single text field) so as not to presume the nature of the returns and allow a “natural” recording by the stakeholders, or strongly format these returns (questionnaires and pre-defined fields) to facilitate the processing and evaluation.

Thanks to semantic technologies, TEEXMA® is able to use the best of these two strategies by going to the heart of free-text fields, or the contents of complete documentary corpuses (stacks of reports), the very meaning of feedback is to categorize them. In addition, the addition of machine learning algorithms allows the system to become increasingly relevant in its feedback analysis.

The introduction of these technologies opens up vast fields of applications and substantial benefits in the treatment of non-conformities and feedback.