Science, Technology, Engineering and Mathematics.
Open Access

THE ECONOMIC POTENTIAL OF AUTONOMOUS SYSTEMS ENABLED BY DIGITAL TRANSFORMATION AND BUSINESS ANALYTICS

Download as PDF

Volume 2, Issue 2, Pp 33-42, 2024

DOI: https://doi.org/10.61784/wjebr3017

Author(s)

Rakibul Hasan Chowdhury

Affiliation(s)

CCBA certified & Member, International Institute of Business Analysis (IIBA), USA.

MSc. Digital Business Management, University of Portsmouth, UK.

MS. Business Analytics, Trine University, USA.

Corresponding Author

Rakibul Hasan Chowdhury

ABSTRACT

Autonomous systems, a cornerstone of digital transformation, have emerged as transformative tools across industries such as manufacturing, healthcare, and logistics. Enabled by advancements in technologies like IoT, artificial intelligence (AI), and machine learning, these systems promise unprecedented efficiency, precision, and scalability. Business analytics serves as a critical enabler, providing the intelligence layer that empowers autonomous systems to analyze vast datasets, predict trends, and make informed decisions in real time. This paper explores the economic potential of autonomous systems, highlighting their ability to enhance productivity, reduce costs, and drive innovation cycles. Key findings demonstrate significant economic benefits, including operational optimization, increased competitiveness, and resource efficiency, while also addressing challenges such as workforce resistance, regulatory hurdles, and high implementation costs. The study underscores the need for supportive policies and strategic frameworks to maximize these systems' benefits. Future research directions focus on integrating advanced AI, examining socio-economic impacts, and exploring emerging technologies like quantum computing to further advance autonomous capabilities.

KEYWORDS

Autonomous systems; Digital transformation; Business analytics; Economic impact; AI; Industry 4.0

CITE THIS PAPER

Rakibul Hasan Chowdhury. The economic potential of autonomous systems enabled by digital transformation and business analytics. World Journal of Economics and Business Research. 2024, 2(2): 33-42. DOI: https://doi.org/10.61784/wjebr3017.

REFERENCES

[1] Lim H S M, Taeihagh A. Algorithmic decision-making in AVs: Understanding ethical and technical concerns for smart cities. Sustainability, 2019, 11(20): 5791. https://doi.org/10.3390/su11205791.

[2] Wang J, Zhang Z, Zhao L. Logistics optimization through IoT-based intelligent tracking and monitoring systems. Journal of Transportation Research, 2016, 14(2): 45–58.

[3] Mayer-Sch?nberger,V, Cukier K. Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, 2013.

[4] Brynjolfsson E, McAfee A. The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company, 2014.

[5] Makridakis S. The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 2017, 90: 46–60. https://doi.org/10.1016/j.futures.2017.03.006.

[6] Manyika J, Chui M, Bughin J, et al. Jobs lost, jobs gained: Workforce transitions in a time of automation. McKinsey Global Institute, 2017.

[7] Cath C, Wachter S, Mittelstadt B, et al. Artificial intelligence and the ‘good society’: The US, EU, and UK approach. Science and Engineering Ethics, 2018, 24(2): 505–528.

[8] Renn O, Benighaus C. Perception of technological risk: Insights from research and lessons for risk communication and management. Journal of Risk Research, 2013, 16(3–4): 293–313. https://doi.org/10.1080/13669877.2012.729522.

[9] Kagermann H, Wahlster W, Helbig J. Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry. German National Academy of Science and Engineering, 2013.

[10] Topol E J. Deep medicine: How artificial intelligence can make healthcare human again. Basic Books, 2019.

[11] Chen H, Chiang R H, Storey V C. Business intelligence and analytics: From big data to big impact. MIS Quarterly, 2012, 36(4): 1165–1188. https://doi.org/10.2307/41703503.

[12] Lee J, Bagheri B, Kao H A. A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 2015, 3: 18–23.

[13] Mervin M C, Moyle W, Jones C, et al. The cost-effectiveness of using PARO, a therapeutic robotic seal, to reduce agitation and medication use in dementia: Findings from a cluster-randomized controlled trial. Journal of the American Medical Directors Association, 2018, 19(7): 619–622. https://doi.org/10.1016/j.jamda.2018.02.008.

[14] Kaplan J. Humans need not apply: A guide to wealth and work in the age of artificial intelligence. Yale University Press, 2015.

[15] Dablanc L, Rodrigue J P. The city logistics paradigm: Concepts, policies, and implementations. Elsevier, 2017.

[16] Zhang D, Hu Z, He J, Li Y. Precision agriculture and the use of IoT in farming. Sustainable Agriculture Reviews, 2019, 36: 231–246.

[17] Davenport T H, Kim J. Keeping up with the quants: Your guide to understanding and using analytics. Harvard Business Review Press, 2014.

[18] Basu M. How “virtual hospitals” are caring for Singapore’s elderly. 2019. https://govinsider.asia/digital-gov/how-virtual-hospitals-are-caring-for-singapores-elderly/.

All published work is licensed under a Creative Commons Attribution 4.0 International License. sitemap
Copyright © 2017 - 2024 Science, Technology, Engineering and Mathematics.   All Rights Reserved.