Highly iterative technology planning: processing of information uncertainties in the planning of manufacturing technologies
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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.
Keywords
Manufacturing technology planning Highly iterative product development Information uncertainties Dempster–Shafer theory of evidenceNotes
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”.
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 CrossRefGoogle 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 1–13Google Scholar
- 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 1–10Google Scholar
- 5.Komus A (2014) Status Quo Agile. Success and forms of usage—hybrid and selective approaches. In: Berlin Days of Software Engineering, BerlinGoogle Scholar
- 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 AachenGoogle Scholar
- 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 CrossRefGoogle 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 CrossRefGoogle Scholar
- 16.Cooper RG (2014) What’s next?: after stage-gate. Res Technol Manag 57(1):20–31. https://doi.org/10.5437/08956308X5606963 MathSciNetCrossRefGoogle Scholar
- 17.Schneider S (2015) Agile Prozessplanung im Produktentstehungsprozess am Beispiel der Motorenproduktion. Dissertation Technische Universität DortmundGoogle Scholar
- 18.Klein TP (2016) Agiles Engineering im Maschinen- und Anlagenbau. Dissertation Technische Universität MünchenGoogle Scholar
- 19.Diels F (2018) Indikatoren für die Ermittlung agil zu entwickelnder Produktumfänge. Dissertation RWTH AachenGoogle Scholar
- 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 CrossRefGoogle 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 CrossRefGoogle 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 CrossRefGoogle 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 713–722Google Scholar
- 24.Trommer G (2001) Methodik zur konstruktionsbegleitenden Generierung und Bewertung alternativer Fertigungsfolgen. Dissertation, RWTH AachenGoogle Scholar
- 25.Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X CrossRefzbMATHGoogle Scholar
- 26.Heinsohn J, Socher-Ambrosius R (1999) Wissensverarbeitung. Eine Einführung. Spektrum Akademischer Verlag, HeidelbergzbMATHGoogle Scholar
- 27.Schell H (1997) Bewertung alternativer Handhabungs- und Fertigungsfolgen. Dissertation, RWTH AachenGoogle Scholar
- 28.Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonzbMATHGoogle 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 MathSciNetCrossRefzbMATHGoogle Scholar
- 30.Boersch I (2007) Wissensverarbeitung. Spektrum Akademischer Verlag, HeidelbergGoogle Scholar
- 31.Beierle C (2014) Methoden wissensbasierter Systeme. Springer, WiesbadenGoogle 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 272–292Google 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 CrossRefGoogle 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 Google Scholar
- 35.Feldhusen J, Grote KH (2013) Pahl/Beitz Konstruktionslehre. Methoden und Anwendung erfolgreicher Produktentwicklung. Springer, BerlinCrossRefGoogle 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 CrossRefGoogle Scholar