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A dynamic process-based cost modeling approach to understand learning effects in manufacturing

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  • Nadeau, Marie-Claude
  • Kar, Ashish
  • Roth, Richard
  • Kirchain, Randolph

Abstract

Informed technology decision-making requires a structured understanding of cost evolution over time. A dynamic approach integrating learning curves and process-based cost modeling is introduced to examine learning in manufacturing. The approach is applied to the case of a hydroforming process, and quantifies the cost impacts of learning improvements in cycle time, downtime, and reject rates. A comparison with cases of automotive assembly and wire drawing illustrates that variation in learning is tied to the individual process cost structure. The results show aggregate cost evolution is strongly dependent on cost structure and that major cost elements may not align with major cost improvement-through-learning opportunities. The analyses can be used to focus intentional learning activities on primary learning operational drivers.

Suggested Citation

  • Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
  • Handle: RePEc:eee:proeco:v:128:y:2010:i:1:p:223-234
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    References listed on IDEAS

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    Cited by:

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    2. Karali, Nihan & Park, Won Young & McNeil, Michael, 2017. "Modeling technological change and its impact on energy savings in the U.S. iron and steel sector," Applied Energy, Elsevier, vol. 202(C), pages 447-458.
    3. Sung, Bongsuk & Song, Woo-Yong, 2014. "How government policies affect the export dynamics of renewable energy technologies: A subsectoral analysis," Energy, Elsevier, vol. 69(C), pages 843-859.
    4. Jonathan Oesterle & Lionel Amodeo & Farouk Yalaoui, 0. "A comparative study of Multi-Objective Algorithms for the Assembly Line Balancing and Equipment Selection Problem under consideration of Product Design Alternatives," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-26.
    5. Hasan Özyapıcı & İlhan Dalcı & Ali Özyapıcı, 2017. "Integrating accounting and multiplicative calculus: an effective estimation of learning curve," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 258-270, June.
    6. Thomassen, Gwenny & Van Passel, Steven & Dewulf, Jo, 2020. "A review on learning effects in prospective technology assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    7. Duffner, Fabian & Mauler, Lukas & Wentker, Marc & Leker, Jens & Winter, Martin, 2021. "Large-scale automotive battery cell manufacturing: Analyzing strategic and operational effects on manufacturing costs," International Journal of Production Economics, Elsevier, vol. 232(C).
    8. Hasan Özyapıcı & İlhan Dalcı & Ali Özyapıcı, 0. "Integrating accounting and multiplicative calculus: an effective estimation of learning curve," Computational and Mathematical Organization Theory, Springer, vol. 0, pages 1-13.
    9. Anzanello, Michel J. & Fogliatto, Flavio S. & Santos, Luana, 2014. "Learning dependent job scheduling in mass customized scenarios considering ergonomic factors," International Journal of Production Economics, Elsevier, vol. 154(C), pages 136-145.

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