DAMPAK REVOLUSI INDUSTRI 4.0 DAN PENERAPAN COMPUTATIONAL INTELLIGENCE PADA GENERAL ELECTRIC

Authors

Pricyllia Herfianty Putri Universitas Airlangga

DOI:

Keywords:

Computational Intelligence, Industry 4.0, General Electric, Artificial Intelligence, Machine Learning, Operational Efficiency, Business Innovation.

Abstract

Revolusi Industri 4.0 telah mendorong transformasi industri melalui pemanfaatan teknologi cerdas, salah satunya Computational Intelligence (CI). General Electric (GE), sebagai perusahaan multinasional pada sektor manufaktur dan energi, telah mengadopsi CI untuk mengoptimalkan operasional dan meningkatkan daya saing. Penelitian ini bertujuan menganalisis peran dan dampak penerapan CI oleh GE dalam menghadapi tantangan Industri 4.0 dengan pendekatan kualitatif berbasis studi kasus penelitian ini menemukan bahwa penerapan CI dalam sistem produksi GE, melalui platform Predix meningkatkan efisiensi operasional, mengurangi downtime hingga 50%, serta menghemat energi sebesar 10-25%. Pemanfaatan Artificial Intelligence (AI) dan Machine Learning (ML) memungkinkan pemeliharaan prediktif, meningkatkan keandalan peralatan, dan mengoptimalkan rantai pasok. Implementasi CI juga berkontribusi terhadap pengambilan keputusan berbasis data dan fleksibilitas produksi, memungkinkan GE beradaptasi dengan perubahan pasar. Dengan demikian, adopsi CI tidak hanya meningkatkan efisiensi operasional, tetapi juga membuka peluang inovasi dan memperkuat daya saing GE secara global.

References

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W.W. Norton & Company.

Davenport, T. H., & Ronanki, R. (2018). Artificial Intelligence for the Real World. Harvard Business Review, 96(1), 108-116.

Engelbrecht, A. P. (2007). Computational Intelligence: An Introduction. John Wiley & Sons.

Foster, R. N. (2019). Innovation: The Attacker’s Advantage. Simon & Schuster.

General Electric. (2023). Industrial AI and Analytics. Retrieved from https://www.ge.com.

General Electric Digital. (2023). Predix: The Industrial IoT Platform. Retrieved from https://www.ge.com/digital.

Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0. Final Report of the Industrie 4.0 Working Group.

Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239-242.

Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for Industry 4.0 and big data environment. Procedia CIRP, 16, 3-8.

Pedrycz, W., & Chen, S. M. (2021). Computational Intelligence Systems in Industrial Engineering. CRC Press.

Porter, M. E., & Heppelmann, J. E. (2015). How Smart, Connected Products Are Transforming Companies. Harvard Business Review, 93(10), 96-114.

Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum.

Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941-2962.

Tortorella, G. L., & Fettermann, D. C. (2018). Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975-2987.

Zhao, N., Parthasarathy, M., Patil, S., Coates, D., Myers, K., Zhu, H., & Li, W. (2023). Direct additive manufacturing of metal parts for automotive applications. Journal of Manufacturing Systems, 68, 368–375. https://doi.org/10.1016/j.jmsy.2023.04.008