Data Driven Decision Making in Manufacturing Businesses in China and Asia Pacific

Daniella Maya Haddab

doi:10.55938/ijgasr.v2i3.57

Authors

Daniella Maya Haddab Weizmann Institute of Science

DOI:

https://doi.org/10.55938/ijgasr.v2i3.57

Keywords:

Data-Driven, Decision-Making, Business Model, Analytics

Abstract

The objective of this study is to investigate the mechanisms of Big data - based business model development in Chinese standard industries. Deductive reasoning as well as case analysis were employed to evaluate manufacturing businesses in China and confirm the devices. This process created an integrated framework with 3 components: Business model perspectives, processes together with big data driven company model advancements. Three Chinese businesses put the framework on revealing that business model development is a constant and growing process impacted by big data. Nevertheless, the study shows that limitations have a small sample size, that is typical in qualitative studies. Ideally, businesses will develop a solid infrastructure that combines the internet of things, traditional manufacturing methods and front end buyers. Furthermore, management must make sure that their organizational structure, climate, and human resources are well prepared for the transformation.

Downloads

Download data is not yet available.

Author Biography

Daniella Maya Haddab, Weizmann Institute of Science

.

References

Akter J. S., Haque S. M. (2022). Innovation Management: Is Big Data Necessarily Better Data?. Management of Sustainable Development, 14(2), 27–33.

Wang C., Wang Y., Ye Z., Yan L., Cai W., Pan S., "Credit Card Fraud Detection Based on Whale Algorithm Optimized BP Neural Network," 2018 13th International Conference on Computer Science & Education (ICCSE), Colombo, Sri Lanka, 2018, pp. 1–4, doi: 10.1109/ICCSE.2018.8468855.

Deeplearningbook.org. (2019). Deep Learning. [online] Available at: https://www.deeplearningbook.org/ [Accessed 11 Jan. 2019].

European Central Bank (2018). Fifth report on card fraud, September 2018. [online]. Available at: https://www.ecb.europa.eu/pub/cardfraud/html/ecb.cardfraudreport201809.en.html#toc1 [Accessed 21 Jan. 2019].

Ghobadi F., Rohani M. “Cost Sensitive Modeling of Banking solutions Fraud using Neural Network strategy”, 2016 Signal Processing and Intelligent Systems (ICSPIS), International Conference of pp. 1–5. IEEE.

Facts Global (2019). Topic: Startups worldwide. [online] Available at: https://www.statista.com/topics/4733/startups-worldwide/ [Accessed 10 Jan. 2019].

Lakshmi J S. V. S. S., Kavilla S. D. “Machine Learning For Banking solutions Fraud Detection System”, unpublished

Awoyemi J. O., Adentumbi A. O., Oluwadare S. A. “Banking solutions fraud detection using Machine Learning Techniques: A Comparative Analysis”, Computing Networking and Informatics (ICCNI), 2017 International Conference on pp. 1–9. IEEE.

Kalaiselvi N., Rajalakshmi S., Padmavathi J., Karthiga J. B., "Credit Card Fraud Detection Using Learning to Rank Approach," 2018 International Conference on Computation of Power, Energy, Information and Communication (ICCPEIC), Chennai, India, 2018, pp. 191–196, Doi: 10.1109/ICCPEIC.2018.8525171.

Wang M. Xu, Wang H., Zhang J., "Classification of Imbalanced Data by Using the SMOTE Algorithm and Locally Linear Embedding", Signal Processing, 2006 8th International Conference on (Vol. 3). IEEE. 2006 8th international Conference on Signal Processing, Beijing, 2006