Process Planning in Era 4.0

  • Kaushik KumarEmail author
  • Divya Zindani
  • J. Paulo Davim
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


The world is experiencing the Fourth Industrial Revolution over the years. With the continual progress, the working environment is also changing rapidly in a direction to achieve substantial benefits. Various manufacturing-related activities are now being automated and are linked with another within a company. However, with the network of machines, one of the major challenges is that of data management. Management of humongous data is done using cloud computing, Internet of things or cyber-physical systems. With the constantly changing manufacturing environment, the personnel involved will have to learn the new skills and adapt to these changes that will ultimately aid in the enhancement of their performances. Manufacturing environment with both the technological as well as the human improvements will result in enhanced productivity, quality of product, reduced manufacturing time and hence the product prices. With the advancements in the manufacturing environment, the concept of mass customization will be easy to realize. The present chapter deals to reflect the changing role of process planner to product planner. Product planner in the realm of Industry 4.0 is a software that is connected to other parts in a supply chain. This software generates the order of scheduling, order of operations and process plan using advanced optimization algorithms. This chapter aims to provide an overview of the product planner that aids in automatically planning, scheduling and operation sequencing.


Product planning Process planning Scheduling Industry 4.0 Lean manufacturing Data management 


  1. O. Andersson, D. Semere, A. Melander, M. Arvidsson, B. Lindberg, Digitalization of process planning of spot welding in body-in-white. Procedia CIRP 50, 618–623 (2016)CrossRefGoogle Scholar
  2. M. Brettel, N. Friederichsen, M. Keller, M. Rosenberg, How virtualization, decentralization and network building change the manufacturing landscape: an industry 4.0 perspective. Int. J. Mech. Ind. Sci. Eng. 8(1), 37–44 (2014)Google Scholar
  3. T.G. Cummings, C.G. Worley, Organization Development and Change (Cengage Learning, 2014)Google Scholar
  4. W.D. Engelke, How to Integrate CAD/CAM Systems: Management and Technology (CRC Press, 1987)Google Scholar
  5. D. Gorecky, M. Schmitt, M. Loskyll, D. Zühlke, Human-machine-interaction in the industry 4.0 era, in 2014 12th IEEE International Conference on Industrial Informatics (INDIN), IEEE, July 2014, pp. 289–294Google Scholar
  6. M. Gibbert, M. Leibold, G. Probst, Five styles of customer knowledge management, and how smart companies use them to create value. Eur. Manag. J. 20(5), 459–469 (2002)CrossRefGoogle Scholar
  7. D. Ivanov, B. Sokolov, M. Ivanova, Schedule coordination in cyber-physical supply networks Industry 4.0. IFAC-PapersOnLine 49(12), 839–844 (2016)CrossRefGoogle Scholar
  8. B.C. Lines, K.T. Sullivan, J.B. Smithwick, J. Mischung, Overcoming resistance to change in engineering and construction: change management factors for owner organizations. Int. J. Project Manage. 33(5), 1170–1179 (2015)CrossRefGoogle Scholar
  9. A. Mason, R. Lee, Reform and support systems for the elderly in developing countries: capturing the second demographic dividend. Genus, 11–35 (2006)Google Scholar
  10. L. Monostori, B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, K. Ueda, Cyber-physical systems in manufacturing. CIRP Ann. 65(2), 621–641 (2016)CrossRefGoogle Scholar
  11. V.M. Pedagopu, M. Kumar, Integration of CAD/CAPP/CAM/CNC to augment the efficiency of CIM. Int. Rev. Appl. Eng. Res. 4(2), 171–176 (2014)Google Scholar
  12. J. Qin, Y. Liu, R. Grosvenor, A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52, 173–178 (2016)CrossRefGoogle Scholar
  13. L. Thames, D. Schaefer, Software-defined cloud manufacturing for industry 4.0. Procedia CIRP 52, 12–17 (2016)CrossRefGoogle Scholar
  14. M.A. Waller, S.E. Fawcett, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. J. Bus. Logist. 34(2), 77–84 (2013)CrossRefGoogle Scholar
  15. S. Wang, J. Wan, D. Zhang, D. Li, C. Zhang, Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput. Netw. 101, 158–168 (2016)CrossRefGoogle Scholar

Copyright information

©  The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringBirla Institute of TechnologyMesra, RanchiIndia
  2. 2.Department of Mechanical EngineeringNational Institute of Technology SilcharSilchar, CacharIndia
  3. 3.Department of Mechanical EngineeringUniversity of AveiroAveiroPortugal

Personalised recommendations