Advertisement

Industry 4.0 and Service Companies: The Case of the French Postal Service

  • Cyrine SelmaEmail author
  • Dalila Tamzalit
  • Nasser Mebarki
  • Olivier Cardin
  • Loic Bruggeman
  • Didier Thiériot
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 803)

Abstract

The aim of this article is to overview the transition of smart logistic companies towards the Industry 4.0. Industry 4.0 is a generic concept that subscribes to the idea of a general awareness of the importance of new technologies for the manufacturing industry. This reflection aims to conserve and develop a strong and innovative industrial activity to support the modernization of different fields. In the case of high volumes service companies, many reasons have led to this modernization; remarkable increase in the volume of items to deliver, complexity of managing the transportation system and the personnel scheduling, humanization of work conditions, etc. In this work, The French postal service company, La Poste, is given as a particular case of study. La Poste daily treats high volumes of mail and parcel items. In this article, the organization of the French postal service system from the collect to the delivery is explicitly described. A literature review of different concerned research axes is presented. Finally, we present what we think are the future needs for researches in these axes.

Keywords

Industry 4.0 High volumes Logistics 4.0 Warehousing 4.0 Cyber-physical production systems 

References

  1. 1.
    Lu, Y., Ju, F.: Smart manufacturing systems based on cyber-physical manufacturing services (CPMS). IFAC Pap. 50, 15883–15889 (2017)CrossRefGoogle Scholar
  2. 2.
    Droździel, P., Wińska, M., Madleňák, R., Szumski, P.: Optimization of the post logistics network and location of the local distribution center in selected area of the Lublin province. Procedia Eng. 192, 130–135 (2017)CrossRefGoogle Scholar
  3. 3.
    López, J.A.V., Dueñas, A.B.P., Ruiz, R.J.L., Córdoba, S.M.: System optimization courier and parcel in cities. Procedia Soc. Behav. Sci. 160, 577–586 (2014)CrossRefGoogle Scholar
  4. 4.
    Boloukian, R., Siegmann, J.: Urban Logistics; a key for the airport-centric development—a review on development approaches and the role of urban logistics in comprehensive airport-centric planning. Transp. Resour. Procedia 12, 800–811 (2016)CrossRefGoogle Scholar
  5. 5.
    Sim, T., Lowe, T.J., Thomas, B.W.: The stochastic p-hub center problem with service-level constraints. Comput. Oper. Res. 36, 3166–3177 (2009)CrossRefGoogle Scholar
  6. 6.
    Zäpfel, G., Bögl, M.: Multi-period vehicle routing and crew scheduling with outsourcing options. Int. J. Prod. Econ. 113, 980–996 (2008)CrossRefGoogle Scholar
  7. 7.
    Niroomand, I., Nsakanda, A.L.: Improving collection flows in a public postal network with contractor’s obligation considerations. Int. J. Prod. Econ. 198, 79–92 (2018)CrossRefGoogle Scholar
  8. 8.
    Chen, C., Pan, S.: Using the crowd of taxis to last mile delivery in e-commerce: a methodological research. In: Borangiu, T., Trentesaux, D., Thomas, A., McFarlane, D. (eds.) Service orientation in Holonic and Multi-Agent Manufacturing. Studies in Computational Intelligence, vol. 640. Springer, Cham (2015)Google Scholar
  9. 9.
    Clausen, U., Geiger, C., Pöting, M.: Hands-on testing of last mile concepts. Transp. Res. Procedia 14, 1533–1542 (2016)CrossRefGoogle Scholar
  10. 10.
    Devari, A., Nikolaev, A.G., He, Q.: Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers. Transp. Res. Part E Logist. Transp. Rev. 105, 105–122 (2017)CrossRefGoogle Scholar
  11. 11.
    Cesar, M., Shinghal, R.: An algorithm for segmenting handwritten postal codes. Int. J. Man-Mach. Stud. 33, 63–80 (1990)CrossRefGoogle Scholar
  12. 12.
    Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Deep big simple neural nets excel on handwritten digit recognition. Neural Comput. 22, 3207–3220 (2010)CrossRefGoogle Scholar
  13. 13.
    Niu, X.-X., Suen, C.Y.: A novel hybrid CNN–SVM classifier for recognizing handwritten digits. Pattern Recogn. 45, 1318–1325 (2012)CrossRefGoogle Scholar
  14. 14.
    Basu, S., Das, N., Sarkar, R., Kundu, M., Nasipuri, M., Kumar Basu, D.: A novel framework for automatic sorting of postal documents with multi-script address blocks. Pattern Recogn. 43, 3507–3521 (2010)CrossRefGoogle Scholar
  15. 15.
    Kamranian, Z., Monadjemi, S.A., Nematbakhsh, N.: A novel free format Persian/Arabic handwritten zip code recognition system. Comput. Electr. Eng. 39, 1970–1979 (2013)CrossRefGoogle Scholar
  16. 16.
    Basu, S., Sarkar, R., Das, N., Kumar Basu, D.: Handwritten bangla digit recognition using classifier combination through DS technique. Lecture Notes in Computer Science, vol. 3776, pp. 236–241 (2005)Google Scholar
  17. 17.
    Srihari, S.N.: Recognition of handwritten and machine-printed text for postal address interpretation. Pattern Recogn. Lett. 14, 291–302 (1993)CrossRefGoogle Scholar
  18. 18.
    Bard, J.F., Binici, C., deSilva, A.H.: Staff scheduling at the United States postal service. Comput. Oper. Res. 30, 745–771 (2003)CrossRefGoogle Scholar
  19. 19.
    Burns, R.N., Carter, M.W.: Work force size and single shift schedules with variable demands. Manag. Sci. 31, 599–607 (1985)CrossRefGoogle Scholar
  20. 20.
    Aykin, T.: Optimal shift scheduling with multiple break windows. Manag. Sci. 42, 591–602 (1996)CrossRefGoogle Scholar
  21. 21.
    Emmons, H.: Work-force scheduling with cyclic requirements and constraints on days off, weekends off, and work stretch. IIE Trans. 17, 8–16 (1985)CrossRefGoogle Scholar
  22. 22.
    Júdice, J., Martins, P., Nunes, J.: Workforce planning in a lot sizing mail processing problem. Comput. Oper. Res. 32, 3031–3058 (2005)CrossRefGoogle Scholar
  23. 23.
    Malhotra, M.K., Ritzman, L.P., Benton, W.C., Keong Leong, G.: A model for scheduling postal distribution employees. Eur. J. Oper. Res. 58, 374–385 (1992)CrossRefGoogle Scholar
  24. 24.
    Rabta, B., Wankmüller, C., Reiner, G.: A drone fleet model for last-mile distribution in disaster relief operations. Int. J. Disaster Risk Reduct. 28, 107–112 (2018)CrossRefGoogle Scholar
  25. 25.
    Zhang, X., Yue, S., Wang, W.: The review of RFID applications in global postal and courier services. J. China Univ. Posts Telecommun. 13, 106–110 (2006)CrossRefGoogle Scholar
  26. 26.
    Barreto, L., Amaral, A., Pereira, T.: Industry 4.0 implications in logistics: an overview. Procedia Manuf. 13, 1245–1252 (2017)CrossRefGoogle Scholar
  27. 27.
    Leitão, P., Rodrigues, N., Barbosa, J., Turrin, C., Pagani, A.: Intelligent products: the grace experience. Control Eng. Pract. 42, 95–105 (2015)CrossRefGoogle Scholar
  28. 28.
    Trentesaux, D., Thomas, A.: Product-driven control: concept, literature review and future trends. In: Borangiu, T., Thomas, A., Trentesaux, D. (eds.) Service Orientation in Holonic and Multi Agent Manufacturing and Robotics, pp. 135–150. Springer, Berlin (2013)CrossRefGoogle Scholar
  29. 29.
    Chalfoun, I., Kouiss, K., Huyet, A.-L., Bouto, N., Ray, P.: Proposal for a generic model dedicated to reconfigurable and agile manufacturing systems (RAMS). Procedia CIRP 7, 485–490 (2013)CrossRefGoogle Scholar
  30. 30.
    Cardin, O., Ounnar, F., Thomas, A., Trentesaux, D.: Future industrial systems: best practices of the intelligent manufacturing and services systems (IMS2) French Research Group. IEEE Trans. Ind. Inform. 13, 704–713 (2017)CrossRefGoogle Scholar
  31. 31.
    Witkowski, K.: Internet of things, big data, Industry 4.0—innovative solutions in logistics and supply chains management. Procedia Eng. 182, 763–769 (2017)CrossRefGoogle Scholar
  32. 32.
    Hofmann, E., Rüsch, M.: Industry 4.0 and the current status as well as future prospects on logistics. J. Comput. Ind. 89, 23–34 (2017)CrossRefGoogle Scholar
  33. 33.
    García-Domínguez, A., Marcos-Bárcena, M., Medina-Bulo, I., Prades-Martell, L.: Towards an integrated SOA-based architecture for interoperable and responsive manufacturing systems. Procedia Eng. 63, 123–132 (2013)CrossRefGoogle Scholar
  34. 34.
    Uhlemann, T.-J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP 61, 335–340 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cyrine Selma
    • 1
    • 2
    Email author
  • Dalila Tamzalit
    • 1
  • Nasser Mebarki
    • 1
  • Olivier Cardin
    • 1
  • Loic Bruggeman
    • 2
  • Didier Thiériot
    • 2
  1. 1.LUNAM Université, IUT de Nantes – Université de Nantes, LS2N UMR CNRS 60042CarquefouFrance
  2. 2.La Poste – Direction Technique Branche Services-Courrier-ColisNantesFrance

Personalised recommendations