Advertisement

PLM Competencies Analysis Based on Industry Demand

  • Mourad Messaadia
  • Samir Ouchani
  • Anne Louis
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 540)

Abstract

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.

Keywords

PLM competencies PLM job offers TF-IDF Clustering 

Notes

Acknowledgement

This work is supported by the French Government’s Program “Investments for the Future”. Acknowledgement is made to APEC for the job offers database.

References

  1. 1.
    ICT for Manufacturing The ActionPlanT Roadmap for Manufacturing 2.0. https://setis.ec.europa.eu/energy-research/sites/default/files/static-projects/files/roadmap.pdf
  2. 2.
    Thoben, K.D., Wiesner, S., Wuest, T.: Industrie 4.0” and smart manufacturing–a review of research issues and application examples. Int. J. Autom. Technol. 11(1), 4–16 (2017)CrossRefGoogle Scholar
  3. 3.
    Schuh, G., Potente, T., Varandani, R., Schmitz, T.: Global footprint design based on genetic algorithms–an “Industry 4.0” perspective. CIRP Ann.-Manuf. Technol. 63(1), 433–436 (2014)CrossRefGoogle Scholar
  4. 4.
    Erol, S., Jäger, A., Hold, P., Ott, K., Sihn, W.: Tangible industry 4.0: a scenario-based approach to learning for the future of production. Procedia CIRP 54, 13–18 (2016)CrossRefGoogle Scholar
  5. 5.
  6. 6.
  7. 7.
    Kärkkäinen, H., Pels, H.J., Silventoinen, A.: Defining the customer dimension of PLM maturity. In: Rivest, L., Bouras, A., Louhichi, B. (eds.) PLM 2012. IAICT, vol. 388, pp. 623–634. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-35758-9_56CrossRefGoogle Scholar
  8. 8.
    Messaadia, M., Benatia, F., Baudry, D., Louis, A.: PLM adoption model for SMEs. In: Ríos, J., Bernard, A., Bouras, A., Foufou, S. (eds.) PLM 2017. IAICT, vol. 517, pp. 13–22. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-72905-3_2CrossRefGoogle Scholar
  9. 9.
    Casagrande, A., Gotti, F., Lapalme, G.: Cerebra, un système de recommandation de candidats pour l’e-recrutement. In: AISR2017 (2017)Google Scholar
  10. 10.
    Diaby, M.: Méthodes pour la recommandation d’offres d’emploi dans les réseaux sociaux. Doctoral dissertation, Sorbonne Paris Cité (2015)Google Scholar
  11. 11.
    Wittorski, R.: De la fabrication des compétences. Education Permanente, no. 135 La compétence au travail, pp. 57–70 (1998)Google Scholar
  12. 12.
    APEC – Tendance métiers dans l’industrie: Le PLM (2017). ISBN 978-2-7336-1030-5Google Scholar
  13. 13.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. (CSUR) 34(1), 1–47 (2002)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Leskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2014)CrossRefGoogle Scholar
  15. 15.
    Liu, H., Christiansen, T., Baumgartner, W.A., Verspoor, K.: BioLemmatizer: a lemmatization tool for morphological processing of biomedical text. J. Biomed. Semant. 3(1), 3 (2012)CrossRefGoogle Scholar
  16. 16.
    Girodon, J.: Proposition d’une approche d’amélioration des performances des organisations par le management opérationnel de leurs connaissances et compétences. Ph.D. thesis, Université de Lorraine (2015)Google Scholar
  17. 17.
    Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142 (2003)Google Scholar
  18. 18.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  19. 19.
    Kešelj, V., Peng, F., Cercone, N., Thomas, C.: N-gram-based author profiles for authorship attribution. In: Proceedings of the Conference Pacific Association for Computational Linguistics, PACLING, vol. 3, pp. 255–264 (2003)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.CESI/LINEACTRouenFrance
  2. 2.CESI/LINEACTAix En ProvenceFrance

Personalised recommendations