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Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: A study of iPhone evolution

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  • Biswas, Sumana
  • Ali, Ismail
  • Chakrabortty, Ripon K.
  • Turan, Hasan Hüseyin
  • Elsawah, Sondoss
  • Ryan, Michael J.

Abstract

Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The proposed model considers the influence of market demand, customer needs and technological requirements that are time-dependent. The methodology is a four-phase model. In this model the evaluations of product family evolution are based on the Grey Relational Analysis and Fuzzy Analytical Hierarchy Process. Sensitivity is performed to investigate the reliability of the model. In addition, a data-driven neural network-based forecasting model is proposed that can forecast the specification of the most influential features of future product with a reasonable accuracy. This forecasting model utilizes the information of the previous generation’s product. For each phase, the effectiveness of the developed approach is demonstrated with numerical simulation results and validated with a case study of Apple’s iPhone product family. The case study shows that the approach is very effective to identify the most influential key design features and best performed products that will influence the evolution design of future product.

Suggested Citation

  • Biswas, Sumana & Ali, Ismail & Chakrabortty, Ripon K. & Turan, Hasan Hüseyin & Elsawah, Sondoss & Ryan, Michael J., 2022. "Dynamic modeling for product family evolution combined with artificial neural network based forecasting model: A study of iPhone evolution," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:tefoso:v:178:y:2022:i:c:s0040162522000816
    DOI: 10.1016/j.techfore.2022.121549
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    1. Egidijus Rytas Vaidogas & Jurgita Šakėnaitė, 2015. "Solving the Problem of Multiple-Criteria Building Design Decisions with respect to the Fire Safety of Occupants: An Approach Based on Probabilistic Modelling," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-18, August.
    2. Runliang Dou & Yubo Zhang & Guofang Nan, 2017. "Iterative product design through group opinion evolution," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3886-3905, July.
    3. Filippi, S. & Barattin, D., 2014. "Definition and exploitation of trends of evolution about interaction," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 216-236.
    4. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    5. Johnna Montgomerie & Samuel Roscoe, 2013. "Owning the consumer—Getting to the core of the Apple business model," Accounting Forum, Taylor & Francis Journals, vol. 37(4), pages 290-299, December.
    6. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    7. Liu, Heng & Özer, Özalp, 2009. "Managing a product family under stochastic technological changes," International Journal of Production Economics, Elsevier, vol. 122(2), pages 567-580, December.
    8. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    9. Haizhu Zhang & Shengfeng Qin & Rong Li & Yisheng Zou & Guofu Ding, 2020. "Progressive modelling of feature-centred product family development," International Journal of Production Research, Taylor & Francis Journals, vol. 58(12), pages 3701-3723, June.
    10. Fu-ying Zhang & Yan-shen Xu, 2007. "Research on technical strategy for new product development based on TRIZ evolution theory," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 4(1/2), pages 96-108.
    11. Meyer, Marc H. & Utterback, James M., 1941-, 1992. "The product family and the dynamics of core capability," Working papers #77-92. Working paper (Sl, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    12. Golmohammadi, Davood, 2011. "Neural network application for fuzzy multi-criteria decision making problems," International Journal of Production Economics, Elsevier, vol. 131(2), pages 490-504, June.
    13. Montgomerie, Johnna & Roscoe, Samuel, 2013. "Owning the consumer—Getting to the core of the Apple business model," Accounting forum, Elsevier, vol. 37(4), pages 290-299.
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    1. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    2. Wenbin Zhou & Xuhui Xia & Lei Wang & Zelin Zhang & Baotong Chen, 2022. "A Product Evolution Rules Based Method for Retired Mechanical Product Demand Acquisition," Sustainability, MDPI, vol. 14(23), pages 1-17, November.
    3. Zhao, Zichao & Li, Dexuan & Dai, Wensheng, 2023. "Machine-learning-enabled intelligence computing for crisis management in small and medium-sized enterprises (SMEs)," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    4. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.

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