Digital Transformation through Big Data: Implications for Global Product Development

Authors

  • Sunandar ITKES Muhammadiyah Sidrap

DOI:

https://doi.org/10.61978/novatio.v2i4.1005

Keywords:

Big Data, Predictive Analytics, Product Development, Innovation, Digital Twin, Predictive Maintenance

Abstract

Big Data and Predictive Analytics (BDPA) have emerged as transformative forces in product development, creating opportunities to enhance innovation, efficiency, and resilience. This review highlights BDPA’s contributions to value creation by improving customer insights, optimizing design, strengthening operational efficiency, and mitigating risks. It synthesizes empirical and conceptual studies from multidisciplinary databases to demonstrate how BDPA shapes competitiveness across sectors. Comparative findings reveal adoption disparities between advanced and emerging economies, where infrastructural and skill-related constraints limit effectiveness. Addressing these barriers requires investment in human capital, cross-departmental collaboration, and supportive policy frameworks. Future research should prioritize longitudinal and sector-specific approaches to better capture BDPA’s sustained impacts and contextual dynamics.

References

Charles, V., & Emrouznejad, A. (2018). Big data for the greater good: An introduction. 1–18. https://doi.org/10.1007/978-3-319-93061-9_1 DOI: https://doi.org/10.1007/978-3-319-93061-9_1

Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort‐Martorell, X., & Reis, M. (2016). How can SMEs benefit from big data? Challenges and a path forward. Quality and Reliability Engineering International, 32(6), 2151–2164. https://doi.org/10.1002/qre.2008 DOI: https://doi.org/10.1002/qre.2008

Damian, A., Piciu, L., Turlea, S., & Țăpuș, N. (2019). Advanced customer activity prediction based on deep hierarchic encoder-decoders. 403–409. https://doi.org/10.1109/cscs.2019.00074 DOI: https://doi.org/10.1109/CSCS.2019.00074

Iftikhar, R., & Khan, M. (2022). Social media big data analytics for demand forecasting. 902–920. https://doi.org/10.4018/978-1-6684-3662-2.ch042 DOI: https://doi.org/10.4018/978-1-6684-3662-2.ch042

Jin, K., Zhong, Z., & Zhao, E. (2024). Sustainable digital marketing under big data: An AI random forest model approach. IEEE Transactions on Engineering Management, 71, 3566–3579. https://doi.org/10.1109/tem.2023.3348991 DOI: https://doi.org/10.1109/TEM.2023.3348991

Kumar, V., Vijayakumar, V., Gupta, M., Rodrigues, J., & Janu, N. (2022). AI empowered big data analytics for industrial applications. JUCS – Journal of Universal Computer Science, 28(9), 877–881. https://doi.org/10.3897/jucs.94155 DOI: https://doi.org/10.3897/jucs.94155

Lainjo, B. (2019). Enhancing program management with predictive analytics algorithms (PAAs). International Journal of Machine Learning and Computing, 9(5), 539–553. https://doi.org/10.18178/ijmlc.2019.9.5.838 DOI: https://doi.org/10.18178/ijmlc.2019.9.5.838

Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber‐physical systems: Towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2(1), 34–36. https://doi.org/10.1049/iet-cim.2020.0009 DOI: https://doi.org/10.1049/iet-cim.2020.0009

Mathivanan, S., & Jayagopal, P. (2020). Recent development in big data analytics. 1640–1663. https://doi.org/10.4018/978-1-7998-7705-9.ch072 DOI: https://doi.org/10.4018/978-1-7998-7705-9.ch072

Mulunjkar, A., Deshpande, A., Steinke, S., Chartier, B., & Kuwertz, L. (2019). Operational excellence and product reliability enhancement through big data analytics. https://doi.org/10.4043/29513-ms DOI: https://doi.org/10.4043/29513-MS

Nikitjuk, D., Korosteleva, M., & Tarmaeva, I. (2025). Sports nutrition as an example of effective implementation of innovative trends in nutrition: Personalization and digitalization (literature review). Health Care of the Russian Federation, 69(1), 65–69. https://doi.org/10.47470/0044-197x-2025-69-1-65-69 DOI: https://doi.org/10.47470/0044-197X-2025-69-1-65-69

Proto, S., Corso, E., Apiletti, D., Cagliero, L., Cerquitelli, T., Malnati, G., … & Mazzucchi, D. (2020). REDTag: A predictive maintenance framework for parcel delivery services. IEEE Access, 8, 14953–14964. https://doi.org/10.1109/access.2020.2966568 DOI: https://doi.org/10.1109/ACCESS.2020.2966568

Schöggl, J., Rusch, M., Stumpf, L., & Baumgartner, R. (2023). Implementation of digital technologies for a circular economy and sustainability management in the manufacturing sector. Sustainable Production and Consumption, 35, 401–420. https://doi.org/10.1016/j.spc.2022.11.012 DOI: https://doi.org/10.1016/j.spc.2022.11.012

Sia, W., Ahmad, Z., Muhamad, S., Ali, A., & Hamdan, H. (2024). Effective risk management through data-driven HSE assurance program for safe execution project delivery. https://doi.org/10.2118/221994-ms DOI: https://doi.org/10.2118/221994-MS

Taifa, I., & Nzowa, J. (2025). Implementing supply chain management 4.0: Potential driving forces and strategies from an empirical study of pharmaceutical industries. Engineering Reports, 7(6). https://doi.org/10.1002/eng2.70190 DOI: https://doi.org/10.1002/eng2.70190

Tripoli, M., & Schmidhuber, J. (2020). Optimising traceability in trade for live animals and animal products with digital technologies. Revue Scientifique et Technique de l’OIE, 39(1), 235–244. https://doi.org/10.20506/rst.39.1.3076 DOI: https://doi.org/10.20506/rst.39.1.3076

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Published

2024-10-31

How to Cite

Sunandar. (2024). Digital Transformation through Big Data: Implications for Global Product Development. Novatio : Journal of Management Technology and Innovation , 2(4), 279–291. https://doi.org/10.61978/novatio.v2i4.1005