Forecasting Cerdas: Kunci Sukses Bisnis
DOI:
https://doi.org/10.30640/cakrawala.v4i1.3786Keywords:
intelligent forecasting, artificial intelligence, business managementAbstract
This research examines the role of intelligent forecasting as a key element in modern business success. By leveraging artificial intelligence (AI) technology, big data analytics, and machine learning, intelligent forecasting enables companies to predict market trends and optimize strategic decision-making. The research method was conducted through online webinars involving academics, practitioners, and business actors. The results showed that intelligent forecasting could improve accuracy by up to 98.2% and prediction ratio by 96.5%. However, MSMEs still face challenges in adopting this technology, including limited resources and technological understanding. This research concludes that intelligent forecasting is vital in improving operational efficiency and business competitiveness, but a more inclusive approach is needed to ensure wider adoption across various business scales.
References
Agarwal, A., & Ojha, R. (2023). Prioritising the determinants of Industry-4.0 for implementation in MSME in the post-pandemic period–a quality function deployment analysis. The TQM Journal, 35(8), 2181-2202. https://doi.org/10.1108/tqm-06-2022-0204
Brownlie, D. T. (1992). The role of technology forecasting and planning: Formulating business strategy. Industrial Management & Data Systems, 92(2), 3-16. https://doi.org/10.1108/02635579210009623
Daim, T., Bukhari, E., Bakry, D., Vanhuis, J., Yalcin, H., & Wang, X. (2021). Forecasting technology trends through the gap between science and technology: The case of software as an E-Commerce service. Форсайт, 15(2 (eng)), 12-24. https://doi.org/10.17323/2500-2597.2021.2.12.24
Elisa, E., Tukino, T., & Handoko, K. (2022). Penerapan forecasting methods untuk penjualan produk UMKM dengan algoritma k-nearest neighbor. Jurnal Tekinkom (Teknik Informasi dan Komputer), 5(2), 455-463. https://doi.org/10.37600/tekinkom.v5i2.629
Kotler, P., & Keller, K. L. (2016). Marketing management. Pearson Education.
Lesmarna, S. P., Alzami, F., Rizqa, I., Salam, A., Aqmala, D., Megantara, R. A., & Pramunendar, R. A. Development of time-series-based MLOps architecture for predicting sales quantity in micro, small, and medium enterprises (MSMEs). Transmisi: Jurnal Ilmiah Teknik Elektro, 26(2), 64-69. https://doi.org/10.14710/transmisi.26.2.64-69
Li, B., Yao, C., Zheng, F., Wang, L., Dai, J., & Xiang, Q. (2021). Intelligent decision support system for business forecasting using artificial intelligence. Arabian Journal for Science and Engineering, 1-11. https://doi.org/10.1007/s13369-021-05886-z
Li, X., Ang, C. L., & Gray, R. (1999). An intelligent business forecaster for strategic business planning. Journal of Forecasting, 18(3), 181-204. https://doi.org/10.1002/(SICI)1099-131X(199905)18:3<181::AID-FOR712>3.0.CO;2-3
Makridakis, S. S. (2020). The M4 Competition: Results, findings, and conclusions. International Journal of Forecasting, 36(1), 54-74.
Mia, M., Yousuf, M., & Ghosh, R. (2021). Business forecasting system using machine learning approach. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 314-318. https://doi.org/10.1109/ICREST51555.2021.9331114
Mishra, S., Deshmukh, S. G., & Vrat, P. (2002). Matching of technological forecasting technique to a technology. Technological Forecasting and Social Change, 69(1), 1-27. https://doi.org/10.1016/S0040-1625(01)00123-8
Santoso, S., Kusnanto, E., & Saputra, M. R. (2022). Perbandingan metode pengumpulan data dalam penelitian kualitatif dan kuantitatif serta aplikasinya dalam penelitian akuntansi interpretatif. OPTIMAL Jurnal Ekonomi dan Manajemen, 2(3), 351-360. https://doi.org/10.55606/optimal.v2i3.4457
Saragih, H., & Karyati, C. M. (2023). The development of West Java MSMEs as a form of economic defense: An analysis with forecasting methods. West Science Business and Management, 1(05), 371-385. https://doi.org/10.58812/wsbm.v1i05.493
Smith, J. W. (2018). Forecasting in SMEs: Challenges and opportunities. Journal of Small Business Management, 56(4), 567-584.
Taufiqih, R., & Ambarwati, R. (2024). Enhancing sales prediction for MSMEs: A comparative analysis of neural network and linear regression algorithms. Jurnal Teknologi dan Manajemen Informatika. https://doi.org/10.26905/jtmi.v10i1.11875
Wolfe, H. D. (1956). Forecasting for business. Financial Analysts Journal, 12(1), 17-19. https://doi.org/10.2469/FAJ.V12.N1.17
Yuan, F. C., & Lee, C. H. (2020). Intelligent sales volume forecasting using Google search engine data. Soft Computing, 24(3), 2033-2047. https://doi.org/10.1007/s00500-019-04036-w
Żbikowski, K., & Antosiuk, P. (2021). A machine learning, bias-free approach for predicting business success using Crunchbase data. Information Processing & Management, 58(4), 102555. https://doi.org/10.1016/J.IPM.2021.102555
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