Study of data science in the managerial decision-making of enterprises

Authors

  • Lingyao Zhu

DOI:

https://doi.org/10.54097/5qa0rq15

Keywords:

data science; decision-making; Zara; financial field.

Abstract

Big data has played an increasingly important role in global manufacturing, circulation, distribution, and consumption activities. The management mode based on data is remolding the management mode of enterprises. In the management control of enterprises, data science has become an important supporting tool for decision-making. It could help managers of enterprises to better understand and hold changes within and outside the environment of the enterprise so as to establish effective management strategies through data to excavate and discover correlations among mass data to predict management forms in the future. Firstly, this paper analyzes the difference between machine learning and deep learning from the technical level. Secondly, taking Zara for example to analyze how Zara takes advantage of data science to obtain competitive edge in the fashion industry. At last. This thesis has discussed advantages of data science in prediction and decision-making in the finance field through the method of literature review.

Downloads

Download data is not yet available.

References

Bala, & Pradip Kumar (2012). Improving inventory performance with clustering-based demand forecasts. Journal of Modelling in Management, 7(1), 23–37. https://doi.org/10.1108/17465661211208794

Chan, C. C. H., Cheng, Ch. B., & Hsien, W. (2011). Pricing and promotion strategies of an online shop based on customer segmentation and multiple objective decision-making. Expert Systems with Applications, 38(12), 14585–14591. https://doi.org/10.1016/j.eswa.2011.05.024

Coursera. (2023). Deep Learning vs. Machine Learning: A Beginner’s Guide. Coursera. https://www-coursera-org.translate.goog/articles/ai-vs-deep-learning-vs-machine-learning-beginners-guide?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc

Crabtree, M. (2023). Machine Learning (ML) vs Deep Learning (DL): A Comparative Guide. https://www-datacamp-com.translate.goog/tutorial/machine-deep-learning?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc&_x_tr_hist=true

Grieve, P. (2023). Deep learning vs. machine learning. Zendesk. https://www-zendesk-com.translate.goog/au/blog/machine-learning-and-deep-learning/?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc#georedirect

Hassani, Z., Meybodi, M. A., & Hajihashemi, V. (2020a). Credit risk assessment using learning algorithms for feature selection. Fuzzy Information and Engineering, 12(4), 529–544. https://doi.org/10.1080/16168658.2021.1925021

Hassani, Z., Meybodi, M. A., & Hajihashemi, V. (2020b). Credit risk assessment using learning algorithms for feature selection. Fuzzy Information and Engineering, 12(4), 529–544. https://doi.org/10.1080/16168658.2021.1925021

Jacqueline Priya, G., & Saradha, S. (2021). Fraud Detection and Prevention Using Machine Learning Algorithms: A Review. 7th International Conference on Electrical Energy Systems (ICEES), https://doi.org/10.1109/icees51510.2021.9383631

Middleton, M. (2023). Deep Learning vs. Machine Learning | Flatiron School. Flatiron School. https://flatironschool-com.translate.goog/blog/deep-learning-vs-machine-learning/?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc

Namir, K., Labriji, H., & Benlahmar, E. H. (2022). Decision Support Tool for Dynamic Inventory Management using Machine Learning, Time Series, and Combinatorial Optimization. Procedia Computer Science, 198, 423–428. https://doi.org/10.1016/j.procs.2021.12.264

Research, S. P.-. M. (2023). The secret of Zara’s success: data-driven decisions. https://www-linkedin-com.translate.goog/pulse/secret-zaras-success-data-driven-decisions-steadypace-sa?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc

Roy, A., Sun, J., Mahoney, R., Alonzi, L., Stephen, A., & Peter, B. (2018). Deep learning detecting fraud in credit card transactions. , Systems and Information Engineering Design Symposium (SIEDS), 129–134. https://doi.org/10.1109/SIEDS.2018.8374722

Silva, E. S., Hassani, H., & Madsen, D. Ø. (2019). Big Data in fashion: transforming the retail sector. Journal of Business Strategy, 41(4), 21–27. https://doi.org/10.1108/jbs-04-2019-0062.

Thomasson, E. (2013, September 27). Online retailers go hi-tech to size up shoppers and cut returns. U.S. https://www.reuters.com/article/net-us-retail-online-returns/online-%20retailers-go-hi-tech-to-size-up-shoppers-%20and-cut-returns-idUSBRE98Q0GS20130927

Wolfewicz, A.. (2023). Deep Learning vs. Machine Learning – What’s The Difference? https://levity-ai.translate.goog/blog/difference-machine-learning-deep-learning?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc

ZARA: Achieving the “Fast” in Fast Fashion through Analytics - Digital Innovation and Transformation. (2017, April 5). Digital Innovation and Transformation. https://d3-harvard-edu.translate.goog/platform-digit/submission/zara-achieving-the-fast-in-fast-fashion-through-analytics/?_x_tr_sl=en&_x_tr_tl=zh-TW&_x_tr_hl=zh-TW&_x_tr_pto=sc.

Downloads

Published

09-04-2024

How to Cite

Zhu, L. (2024). Study of data science in the managerial decision-making of enterprises. Highlights in Business, Economics and Management, 28, 506-510. https://doi.org/10.54097/5qa0rq15