Abstract
A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient of determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.
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References
Zhang, Z., Gong, L., Jin, Y., Xie, J., Hao, J.: A quantitative approach to design alternative evaluation based on data-driven performance prediction. Adv. Eng. Inform. 32, 52–65 (2017). https://doi.org/10.1016/j.aei.2016.12.009
Komoto, H., Masui, K.: Classification of design parameters with system modeling and simulation techniques. CIRP Ann. 63(1), 193–196 (2014). https://doi.org/10.1016/j.cirp.2014.03.098
McComb, C., Cagan, J., Kotovsky, K.: Rolling with the punches: an examination of team performance in a design task subject to drastic changes. Des. Stud. 36, 99–121 (2015). https://doi.org/10.1016/j.destud.2014.10.001
Sundin, E., Sandström, G.Ö., Lindahl, M., Rönnbäck, A.Ö., Sakao, T., Larsson, T.C.: ‘Challenges for industrial product/service systems: experiences from a learning network of large companies’, p. 7
Van Horn, D., Olewnik, A., Lewis, K.: Design analytics: capturing, understanding, and meeting customer needs using big data. In: Volume 7: 9th International Conference on Design Education; 24th International Conference on Design Theory and Methodology, Chicago, Illinois, USA, Aug 2012, pp. 863–875. doi: https://doi.org/10.1115/DETC2012-71038
Bititci, U., Nudurupati, S.: Driving continuous improvement. Manuf. Eng. 81(5), 230–235 (2002). https://doi.org/10.1049/me:20020506
Wall, J., Aeddula, O.K., Larsson, T.: Data analysis method supporting cause and effect studies in product-service system development. Proc. Des. Soc. Des. Conf. 1, 461–470 (2020). https://doi.org/10.1017/dsd.2020.123
Hair, J.F. (ed.): Multivariate Data Analysis with Readings, 4th edn. Prentice Hall, Englewood Cliffs, N.J (1995)
Jobson, J.D.: Multiple Linear Regression. In: Applied Multivariate Data Analysis. Springer, New York, NY, pp. 219–398 (1991)
Tenenbaum, J.B.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000). https://doi.org/10.1126/science.290.5500.2319
Haykin, S.S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Upper Saddle River, N.J (1999)
Grunert, K.G.: Attributes, attribute values and their characteristics: a unifying approach and an example involving a complex household investment. J. Econ. Psychol. 10(2), 229–251 (1989). https://doi.org/10.1016/0167-4870(89)90021-4
Understanding Regression Analysis. Springer US, Boston, MA (1997)
Olden, J.D., Jackson, D.A.: Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154(1–2), 135–150 (2002). https://doi.org/10.1016/S0304-3800(02)00064-9
Devore, J.L.: Probability and Statistics for Engineering and the Sciences, 8th edn. Brooks/Cole, Cengage Learning, Boston, MA (2012)
Hassoun, M.H.: Fundamentals of Artificial Neural Networks. MIT Press, Cambridge, Mass (1995)
Baughman, D.R., Liu, Y.A.: Neural Networks in Bioprocessing and Chemical Engineering. Academic Press, San Diego (1995)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford; Oxford University Press, New York (1995)
Marwala, T., SpringerLink (Online service): Artificial Intelligence Techniques for Rational Decision Making. Springer International Publishing, Cham, Springer, Imprint (2014)
Gavin, H.P.: The Levenberg-Marquardt method for nonlinear least squares curve-fitting problems c © (2013)
Olden, J.D., Joy, M.K., Death, R.G.: An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecol. Model. 178(3–4), 389–397 (2004). https://doi.org/10.1016/j.ecolmodel.2004.03.013
Gevrey, M., Dimopoulos, I., Lek, S.: Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Model. 160(3), 249–264 (2003). https://doi.org/10.1016/S0304-3800(02)00257-0
Collopy, P.D., Hollingsworth, P.M.: Value-driven design. J. Aircr. 48(3), 749–759 (2011). https://doi.org/10.2514/1.C000311
Bertoni, M., Pezzotta, G., Scandella, B., Wall, J., Jonsson, P.: Life cycle simulation to support cross-disciplinary decision making in early PSS design. Procedia CIRP 83, 260–265 (2019). https://doi.org/10.1016/j.procir.2019.03.138
Price, M., Soban, D., Mullan, C., Butterfield, J., Murphy, A.: A novel method to enable trade-offs across the whole product life of an aircraft using value driven design. J. Aerosp. Oper. 1(4), 359–375 (2012). https://doi.org/10.3233/AOP-120028
Yip, P.S.L., Tsang, E.W.K.: Interpreting dummy variables and their interaction effects in strategy research. Strateg. Organ. 5(1), 13–30 (2007). https://doi.org/10.1177/1476127006073512
Kurz-Kim, J.-R., Loretan, M.: A note on the coefficient of determination in models with infinite variance variables. SSRN Electron. J. (2007). https://doi.org/10.2139/ssrn.996664
Acknowledgements
The research leading to these results has received financial support by the Swedish Knowledge and Competence Development Foundation (Stiftelsen för kunskaps-och kompetensutveckling) through the Model Driven Development and Decision Support research profile at Blekinge Institute of Technology.
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Aeddula, O.K., Wall, J., Larsson, T. (2021). Artificial Neural Networks Supporting Cause-and-Effect Studies in Product–Service System Development. In: Chakrabarti, A., Poovaiah, R., Bokil, P., Kant, V. (eds) Design for Tomorrow—Volume 2. Smart Innovation, Systems and Technologies, vol 222. Springer, Singapore. https://doi.org/10.1007/978-981-16-0119-4_5
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