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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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    References listed on IDEAS

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    Cited by:

    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. 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).
    3. 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.

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