Skip to main content
Log in

Highly iterative technology planning: processing of information uncertainties in the planning of manufacturing technologies

  • Production Process
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

Highly iterative product development is a promising approach to continuously involve customers in development and to meet global challenges such as short product life cycles and increasing variant diversity. In this context, the planning of production technologies, which takes place in parallel to product development, faces the challenge of processing uncertain product information in early planning phases. This is due to the frequent change of the required product characteristics while the product is being developed. Technology planners must therefore adapt the effort of their planning methods to the existing information uncertainty. This paper presents a new methodology for processing uncertain information from various information sources in technology planning. Firstly, individual information are modelled using fuzzy sets. Afterwards, a new method based on the Dempster–Shafer theory of evidence is presented, which enables an aggregation of individual information from different sources considering their uncertainties. The aggregated information regarding the product characteristics are used to determine the product maturity in the current iteration loop of the highly iterative development process. Finally, the user of the methodology selects a suitable technology planning level based on the prevailing product maturity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Schuh G, Wetterney T, Lau F, Schröder S (2016) Next generation hardware development: framework for a tailorable development method. In: Kocaoglu DF (ed) Proceedings of PICMET’16: technology management for social innovation, Portland International Center for Management of Engineering and Technology, Honolulu, pp 2563–2572. https://doi.org/10.1109/PICMET.2016.7806807

  2. Cooper RG, Sommer AF (2018) Agile—stage-gate for manufacturers. Res Technol Manag 61(2):17–26. https://doi.org/10.1080/08956308.2018.1421380

    Article  Google Scholar 

  3. Goevert K, Lindner M, Lindemann U (2018) Survey on agile methods and processes in physical product development. In: ISPIM Innovation Forum, Boston, pp 1–13

  4. Böhmer AI, Hostettler R, Richter C, Lindemann U, Conradt J, Knoll A (2017) Towards agile product development—the role of prototyping. In: Maier A et al (eds) Proceedings of the 21st international conference on engineering design (ICED 17). Design methods and tools. The Design Society, Vancouver, pp 1–10

  5. Komus A (2014) Status Quo Agile. Success and forms of usage—hybrid and selective approaches. In: Berlin Days of Software Engineering, Berlin

  6. Sommer AF, Hedegaard C, Dukovska-Popovska I, Steger-Jensen K (2015) Improved product development performance through agile/stage-gate hybrids. The next-generation stage-gate process? Res Technol Manag 58(1):34–45. https://doi.org/10.5437/08956308X5801236

    Article  Google Scholar 

  7. Gartzen T, Brambring F, Basse F (2016) Target-oriented prototyping in highly iterative product development. Proced CIRP 51:19–23. https://doi.org/10.1016/j.procir.2016.05.095

    Article  Google Scholar 

  8. Milberg J, Müller S (2007) Integrated configuration and holistic evaluation of technology chains within process planning. Prod Eng Res Dev 1(4):401–406. https://doi.org/10.1007/s11740-007-0055-3

    Article  Google Scholar 

  9. Schuh G, Gartzen T, Basse F, Schrey E (2016) Enabling radical innovation through highly iterative product expedition in ramp up and demonstration factories. Proced CIRP 41:620–625. https://doi.org/10.1016/j.procir.2016.01.014

    Article  Google Scholar 

  10. Klocke F, Fallböhmer M, Kopner A, Trommer G (2000) Methods and tools supporting modular process design. Robot CIM Int Manuf 16(6):411–423. https://doi.org/10.1016/S0736-5845(00)00024-7

    Article  Google Scholar 

  11. Cooper RG (2017) Idea-to-launch gating systems. Better, faster, and more agile. Res Technol Manag 60(1):48–52. https://doi.org/10.1080/08956308.2017.1255057

    Article  Google Scholar 

  12. Zink L, Hostetter R, Böhmer AF, Lindemann U (2017) The use of prototypes within agile product development. Explorative Case Study of a Makeathon. In: Jardim-Goncalves et al (eds) Proceedings of 2017 international conference on engineering, technology and innovation (ICE/ITMC). Madeira, pp 68–77. https://doi.org/10.1109/ICE.2017.8279871

  13. Borsdorf R (2007) Methodische Ansatz zur Integration von Technologiewissen in den Produktentwicklungsprozess. Dissertation RWTH Aachen

  14. Klocke F, Buchholz S, Stauder J (2014) Technology chain optimization: a systematic approach considering manufacturing history. Prod Eng Res Dev 8(5):669–678. https://doi.org/10.1007/s11740-014-0572-9

    Article  Google Scholar 

  15. Stauder J, Buchholz S, Mattfeld P, Rey J (2016) Evaluating the substitution risk of production systems in volatile environments. Prod Eng Res Dev 10(3):305–318. https://doi.org/10.1007/s11740-016-0670-y

    Article  Google Scholar 

  16. Cooper RG (2014) What’s next?: after stage-gate. Res Technol Manag 57(1):20–31. https://doi.org/10.5437/08956308X5606963

    Article  Google Scholar 

  17. Schneider S (2015) Agile Prozessplanung im Produktentstehungsprozess am Beispiel der Motorenproduktion. Dissertation Technische Universität Dortmund

  18. Klein TP (2016) Agiles Engineering im Maschinen- und Anlagenbau. Dissertation Technische Universität München

  19. Diels F (2018) Indikatoren für die Ermittlung agil zu entwickelnder Produktumfänge. Dissertation RWTH Aachen

  20. Salomons OW, van Houten FJAM, Kals HJJ (1993) Review of research in feature-based design. J Manuf Syst 12(2):113–132. https://doi.org/10.1016/0278-6125(93)90012-I

    Article  Google Scholar 

  21. Klocke F, Müller J, Mattfeld P, Kukulies J, Schmitt R (2018) Integrative technology and inspection planning. A case study in medical industry. J Manuf Sci E-T ASME 140(5):1–10. https://doi.org/10.1115/1.4039114

    Article  Google Scholar 

  22. Klocke F, Brinksmeier E, Weinert K (2005) Capability profile of hard cutting and grinding processes. CIRP Ann 54(2):22–45. https://doi.org/10.1016/S0007-8506(07)60018-3

    Article  Google Scholar 

  23. Limbour P, Savic R, Petersen J, Kochs HD (2007) Fault tree analysis in an early design stage using the Dempster–Shafer theory of evidence. In: Aven T, Vinnem JE (eds) Risk, reliability and societal safety: proceedings of the european safety and reliability conference. Taylor & Francis, London, pp 713–722

  24. Trommer G (2001) Methodik zur konstruktionsbegleitenden Generierung und Bewertung alternativer Fertigungsfolgen. Dissertation, RWTH Aachen

  25. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  26. Heinsohn J, Socher-Ambrosius R (1999) Wissensverarbeitung. Eine Einführung. Spektrum Akademischer Verlag, Heidelberg

    MATH  Google Scholar 

  27. Schell H (1997) Bewertung alternativer Handhabungs- und Fertigungsfolgen. Dissertation, RWTH Aachen

  28. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton

    MATH  Google Scholar 

  29. Rebner G, Auer E, Luther W (2012) A verified realization of a Dempster–Shafer based fault tree analysis. Computing 94(2–4):313–324. https://doi.org/10.1007/s00607-011-0179-3

    Article  MathSciNet  MATH  Google Scholar 

  30. Boersch I (2007) Wissensverarbeitung. Spektrum Akademischer Verlag, Heidelberg

    Google Scholar 

  31. Beierle C (2014) Methoden wissensbasierter Systeme. Springer, Wiesbaden

    Google Scholar 

  32. Gordon J, Shortliffe EH (1990) The Dempster–Shafer theory of evidence. In: Pearl J, Shafer G (eds) Readings in uncertain reasoning. Morgan Kaufmann Series, San Mateo, pp 272–292

    Google Scholar 

  33. Rakowsky UK (2007) Fundamentals of the Dempster–Shafer theory and its applications to reliability modeling. Int J Reliab Qual Saf Eng 14(6):579–601. https://doi.org/10.1142/S0218539307002817

    Article  Google Scholar 

  34. Rao PK, Kong Z, Duty CE, Smith RJ, Kunc V, Love LJ (2016) Assessment of dimensional integrity and spatial defect localization in additive manufacturing using spectral graph theory. J Manuf Sci E-T ASME 138(5):1–12. https://doi.org/10.1115/1.4031574

    Article  Google Scholar 

  35. Feldhusen J, Grote KH (2013) Pahl/Beitz Konstruktionslehre. Methoden und Anwendung erfolgreicher Produktentwicklung. Springer, Berlin

    Book  Google Scholar 

  36. Klocke F, Mattfeld P, Stauder J, Müller J, Grünebaum T (2017) Robust technology chain design. Considering undesired interactions within the technology chain. Prod Eng Res Dev 11(4–5):575–585. https://doi.org/10.1007/s11740-017-0756-1

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the German Research Foundation DFG for the support of the depicted research within the project KL 500/211-1 “Methodology for the highly iterative design of production process sequences”.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jan Rey.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rey, J., Apelt, S., Trauth, D. et al. Highly iterative technology planning: processing of information uncertainties in the planning of manufacturing technologies. Prod. Eng. Res. Devel. 13, 361–371 (2019). https://doi.org/10.1007/s11740-019-00882-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-019-00882-7

Keywords

Navigation