Complexity reduction in engineer-to-order industry through real-time capable production planning and control

Production Management
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Abstract

The engineer-to-order industry is under constant pressure to optimise production and handle complexity in the delivery of the right components at the right time. In many cases, e.g. in the building industry, they have to install their components at the construction site. Synchronisation between fabrication and on-site installation is difficult to realise with traditional planning techniques and instruments. The purpose of this study is to outline the potential of real-time-capable production planning and control in engineer-to-order companies as a successful approach to minimise time-dependent combinatorial complexity in the value chain. This research is based on axiomatic design theory in order to explain and confirm the hypothesis of complexity reduction through a near real-time feedback request at the installation site. We have demonstrated this through the information axiom of axiomatic design which states that complexity can be reduced to a minimum through a digitally automated continuous (re-)planning in order to avoid the system range shifting outside of the design range. Thus, the research team has described the first results of an industrial case study to develop a digital software tool to overcome this limitation. Our research contributes to complexity management in engineer-to-order manufacturing companies and further provides future direction towards digitalisation.

Keywords

Complexity Production planning and control Engineer-to-order Axiomatic design Real-time capability Industry 4.0 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

References

  1. 1.
    Browne J, Harren J, Shivan J (1996) Production management systems. An integrated perspective. Addison-Wesley, HarlowGoogle Scholar
  2. 2.
    Ballard G, Arbulu R (2004) Making prefabrication lean. In: Proceedings of 12th annual conference of the international group for lean construction, Helsingør, pp 3–5Google Scholar
  3. 3.
    Bertrand JW, Muntslag DR (1993) Production control in engineer-to-order firms. Int J Prod Eco 30:3–22.  https://doi.org/10.1016/0925-5273(93)90077-X CrossRefGoogle Scholar
  4. 4.
    Matt DT, Dallasega P, Rauch E (2014) Synchronization of the manufacturing process and on-site installation in ETO companies. Procedia CIRP 17:457–462.  https://doi.org/10.1016/j.procir.2014.01.058 CrossRefGoogle Scholar
  5. 5.
    Hon KKB (2005) Performance and evaluation of manufacturing systems. CIRP Ann Manuf Technol 54:139–154.  https://doi.org/10.1016/S0007-8506(07)60023-7 CrossRefGoogle Scholar
  6. 6.
    Efthymiou K, Mourtzis D, Pagoropoulos A, Papakostas N, Chryssolouris G (2016) Manufacturing systems complexity analysis methods review. Int J Comput Integr Manuf 29:1025–1044.  https://doi.org/10.1080/0951192X.2015.1130245 CrossRefGoogle Scholar
  7. 7.
    Dallasega P, Rauch E, Matt DT (2015) Sustainability in the supply chain through synchronization of demand and supply in ETO-companies. Procedia CIRP 29:215–220.  https://doi.org/10.1016/j.procir.2015.02.057 CrossRefGoogle Scholar
  8. 8.
    Ballard G, Gregory H (1998) Shielding production: essential step in production control. J Constr Eng Manag 124:11–17.  https://doi.org/10.1061/(ASCE)0733-9364(1998)124:1(11) CrossRefGoogle Scholar
  9. 9.
    Lincoln HF, Syed MA (2011) Modern construction–lean project delivery and integrated practices. CRC Press, Boca RatonGoogle Scholar
  10. 10.
    Dallasega P, Rally P, Rauch E, Matt DT (2016) Customer-oriented Production System for Supplier Companies in CTO. Procedia CIRP 57:533–538.  https://doi.org/10.1016/j.procir.2016.11.092 CrossRefGoogle Scholar
  11. 11.
    Arbulu R (2006) Application of pull and conwip in construction production systems. In: Proceedings of the International Group of Lean Construction IGLC-14, Santiago-Chile, pp 215–226Google Scholar
  12. 12.
    Papadopoulou PC (2013) Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support. Thesis submitted for the degree of Doctor of Philosophy School of Engineering & Design Brunel University, 2013, pp 20–22Google Scholar
  13. 13.
    Ghimire S, Luis-Ferreira F, Nodehi T, Jardim-Goncalves R (2017) IoT based situational awareness framework for real-time project management. Int J Comput Integr Manuf 30:74–83.  https://doi.org/10.1080/0951192X.2015.1130242 Google Scholar
  14. 14.
    Schuh G, Brosze T, Kompa S, Meier C (2012) Real-time capable production planning and control in the order management of built-to-order companies. In: ElMaraghy H (ed) Enabling manufacturing competitiveness and economic sustainability. Springer, Berlin Heidelberg, pp 557–562CrossRefGoogle Scholar
  15. 15.
    Arica E, Powell DJ (2014) A framework for ICT-enabled real-time production planning and control. Adv Manuf 2:158–164.  https://doi.org/10.1007/s40436-014-0070-5 CrossRefGoogle Scholar
  16. 16.
    Zöbel D (2008) Echtzeitsysteme. Springer, BerlinMATHGoogle Scholar
  17. 17.
    Cundius C, Alt R (2013) Real-time or near real-time? Towards a real-time assessment model. In: Proceedings of the 34th International Conference on Information Systems, Milan, pp 1–18Google Scholar
  18. 18.
    Georgoulias K, Papakostas N, Makris S, Chryssolouris G (2007) A toolbox approach for flexibility measurements in diverse environments. CIRP Ann Manuf Technol 56:423–426.  https://doi.org/10.1016/j.cirp.2007.05.101 CrossRefGoogle Scholar
  19. 19.
    Schuh G, Gottschalk S, Höhne T (2007) High resolution production management. Ann CIRP 56:439–442.  https://doi.org/10.1016/j.cirp.2007.05.105 CrossRefGoogle Scholar
  20. 20.
    Mourtzis D, Vlachou E, Doukas M, Kanakis N, Xanthopoulos N, Koutoupes A (2015) Cloud-based adaptive shop-floor scheduling considering machine tool availability. In: Proceedings of the International Mechanical Engineering Congress & Exposition ASME 2015, Houston, Texas.  https://doi.org/10.1115/IMECE2015-53025
  21. 21.
    Lanza G, Stricker N, Moser R (2013) Concept of an intelligent production control for global manufacturing in dynamic environments based on rescheduling. In: Proceedings of the IEEE International Conference on Industrial Engineering and Engineering Management, Bangkok, pp 315–319Google Scholar
  22. 22.
    Georgiadis P, Michaloudis C (2012) Real-time production planning and control system for job-shop manufacturing: A system dynamics analysis. Eur J Oper Res 216:94–104.  https://doi.org/10.1016/j.ejor.2011.07.022 MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Vatankhah Barenji A, Hashemipour M (2017) Real-time building information modeling (BIM) synchronization using radio frequency identification technology and cloud computing system. J Ind Syst Eng 10:61–68Google Scholar
  24. 24.
    Suh NP (1990) The principles of design. Oxford University Press, New YorkGoogle Scholar
  25. 25.
    Park GJ (2007) Analytic methods for design practice. Springer, LondonMATHGoogle Scholar
  26. 26.
    Matt DT, Rauch E (2011) Continuous improvement of manufacturing systems with the concept of functional periodicity. Key Eng Mater 473:783–790.  https://doi.org/10.4028/www.scientific.net/KEM.473.783 CrossRefGoogle Scholar
  27. 27.
    Matt DT (2010) Axiomatic design of agile manufacturing systems. In: Aized T (ed) Future manufacturing systems. Intech, Rijeka, pp 179–194Google Scholar
  28. 28.
    Suh NP (2005) Complexity–theory and applications. Oxford University Press, New YorkGoogle Scholar
  29. 29.
    Matt DT (2010) Functional periodicity as a concept for the (re-)design to agility of production systems. Prod Eng Res Devel 4:363–369.  https://doi.org/10.1007/s11740-010-0247-0 CrossRefGoogle Scholar
  30. 30.
    Matt DT (2007) Achieving operational excellence through systematic complexity reduction in manufacturing system design. Key Eng Mater 344:865–872.  https://doi.org/10.4028/www.scientific.net/KEM.344.865 CrossRefGoogle Scholar
  31. 31.
    Arashpour M, Wakefield R, Lee EWM, Chan R, Reza Hosseini M (2016) Analysis of interacting uncertainties in on-site and off-site activities: Implications for hybrid construction. Int J Proj Manage 34:1393–1402.  https://doi.org/10.1016/j.ijproman.2016.02.004 CrossRefGoogle Scholar
  32. 32.
    Dallasega P, Rauch E, Matt DT, Fronk A (2015) Increasing productivity in ETO construction projects through a lean methodology for demand predictability. In: Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management, Dubai, pp 1–11Google Scholar
  33. 33.
    Dallasega P, Marcher C, Marengo E, Rauch E, Matt DT, Nutt W (2016) A Decentralized and Pull-based Control Loop for On-Demand Delivery in ETO Construction Supply Chains. In: Proceedings of the 24th Annual Conference of the International Group for Lean Construction, Boston, pp 32–42Google Scholar
  34. 34.
    Dallasega P, Marengo E, Nutt W, Rescic L, Matt DT, Rauch E (2015) Design of a Framework for Supporting the Execution-Management of Small and Medium sized Projects in the AEC-industry. In: Proceedings of the 4th International Workshop on Design in Civil and Environmental Engineering, 2015, pp 30–31Google Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2018

Authors and Affiliations

  1. 1.Faculty of Science and TechnologyFree University of Bozen-BolzanoBolzanoItaly
  2. 2.Fraunhofer Italia Research s.c.a.r.l., Innovation Engineering Center (IEC)BolzanoItaly

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