Last decade recognizes a high job demand, more specialized trainings with very oriented jobs offers. This situation makes hiring and recruitments officers in the difficulty to select and find easily the appropriate candidate as well for candidates to choose the best practices and trainings to find later a respectable position. This work aims to help all actors in the job sector by modeling the Product Lifecycle Management (PLM) competencies and analyzing the demands especially in industry 4.0. First, the enterprises needs, in terms of skills, are identified through various job offers distributed on online media. Job offers are structured according to profile, geolocation and required competencies, etc. Then, the analysis is based on information retrieval and text mining through a statistical measure used to evaluate how important a competence to a job offer in a given collection. This contribution applies the Term Frequency Inverse Document Frequency (TF-IDF) to determine what skills in a corpus of job offers is the most requested in PLM jobs. This contribution addresses more than 1300 job offers, written in French, and posted in France during the period of (2015–2016). The offers cover more than 388 K words, from which 20 types of PLM job titles and 106 terms are related to the job competencies. The obtained results allow us to identify the most requested jobs, skills and classifying jobs and competencies for a better guidance of PLM job actors.