The Role of Artificial Intelligence in Supply Chain Management Transformation Economic and Business Implications
DOI:
https://doi.org/10.62012/jegar.v2i1.17Keywords:
Artificial Intelligence, Supply Chain Management, Economic Implications, Digital Transformation, Business Models, Workforce Reconfiguration, Predictive Efficiency, Supply Chain-as-a-Service, Supply Chain Resilience, AutomationAbstract
This study explores the economic and business implications of implementing artificial intelligence (AI) in supply chain management. Using a mixed-methods approach that combines in-depth case studies of eight multinational companies, a survey of 312 supply chain professionals from 23 countries, and a panel data analysis of 128 companies over a five-year period (2018-2023), the study identifies four key dimensions of AI-driven economic transformation. The results show that AI implementation results in operational cost restructuring with an average reduction of 24% after 36 months, predictive efficiency improvements with forecast accuracy increasing by 37.8%, workforce reconfiguration with a shift from routine operational positions (-18.7%) to highly technical and analytical positions (+24.3%), and business model transformation with the emergence of AI-powered services that have 42.7% higher profit margins than traditional businesses. These findings illustrate a fundamental shift in the supply chain economy, where value comes not only from cost efficiency but also from increased resilience, workforce productivity, and the creation of new value propositions. This research contributes to a more comprehensive understanding of how digital transformation is changing the economic and business landscape in the context of global supply chains, with important implications for corporate strategy, employment policy, and competition regulation.
Downloads
References
A. Toorajipour, V. Sohrabpour, A. Nazarpour, P. Oghazi, and M. Fischl, "Artificial intelligence in supply chain management: A systematic literature review," J. Bus. Res., vol. 122, pp. 502-517, Jan. 2021.
T. Davenport and R. Kalakota, "The potential for artificial intelligence in healthcare," Future Healthcare J., vol. 6, no. 2, pp. 94-98, Jun. 2019.
M. L. Tushman and C. A. O'Reilly, "Ambidextrous organizations: Managing evolutionary and revolutionary change," Calif. Manage. Rev., vol. 38, no. 4, pp. 8-30, Jul. 2020.
J. Lee, B. Bagheri, and H. A. Kao, "A cyber-physical systems architecture for Industry 4.0-based manufacturing systems," Manuf. Lett., vol. 3, pp. 18-23, Jan. 2022.
F. Wang, H. Zhang, and J. Wang, "Digital twin-based smart supply chain design and optimization," Int. J. Prod. Res., vol. 59, no. 21, pp. 6519-6546, Nov. 2021.
D. Agrawal, S. Panagariya, and L. Wienstroer, "AI-driven transformation in global supply chains: Macroeconomic implications and policy challenges," J. Int. Econ., vol. 134, no. 3, pp. 103567, Aug. 2023.
S. Min, Z. G. Zacharia, and C. D. Smith, "Defining supply chain management: In the past, present, and future," J. Bus. Logist., vol. 40, no. 1, pp. 44-55, Mar. 2022.
C. H. Lee, H. Kim, and J. Park, "Assessment of ROI challenges in AI-enabled supply chain transformation: A multi-industry analysis," Supply Chain Manag. Rev., vol. 27, no. 3, pp. 217-236, May 2023.
H. Palippui and M. Kadhafi, “Digitalization of Indonesian Offloading Management Systems from FPSO to Shuttle Tanker”, Journal of Maritime Technology and Society, vol. 1, no. 2, pp. 79–81, Jun. 2022.
J. W. Creswell and V. L. Plano Clark, "Designing and conducting mixed methods research," 3rd ed. Thousand Oaks, CA: SAGE Publications, 2018.
R. K. Yin, "Case study research and applications: Design and methods," 6th ed. Thousand Oaks, CA: SAGE Publications, 2018.
L. G. Tornatzky, M. Fleischer, and A. K. Chakrabarti, "The processes of technological innovation," Lexington, MA: Lexington Books, 1990.
J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, "Multivariate data analysis," 8th ed. Cengage Learning EMEA, 2019.
J. M. Wooldridge, "Econometric analysis of cross section and panel data," 2nd ed. Cambridge, MA: MIT Press, 2010.
K. Charmaz, "Constructing grounded theory," 2nd ed. London: SAGE Publications, 2014.
N. K. Denzin and Y. S. Lincoln, "The SAGE handbook of qualitative research," 5th ed. Thousand Oaks, CA: SAGE Publications, 2018.
T. M. Welbourne, H. M. Johnson, and A. Erez, "The role of organizational and individual characteristics in technology acceptance," Int. J. Hum. Resour. Manag., vol. 29, no. 2, pp. 313-333, Jan. 2018.
M. Chen, S. Mao, and Y. Liu, "Big data: A survey," Mobile Networks and Applications, vol. 19, no. 2, pp. 171-209, 2014.
G. R. Weiß and R. K. Bukkapatanam, "Artificial intelligence in supply chain management: A comprehensive review and future research directions," Int. J. Prod. Econ., vol. 243, 108316, Jan. 2022.
M. S. Sodhi and C. S. Tang, "Supply chain analytics: State-of-the-art and future directions," Eur. J. Oper. Res., vol. 291, no. 3, pp. 806-822, Jun. 2021.
R. Dubey, A. Gunasekaran, S. J. Childe, S. F. Wamba, and T. Papadopoulos, "The impact of big data on world-class sustainable manufacturing," Int. J. Adv. Manuf. Technol., vol. 84, no. 1, pp. 631-645, Apr. 2016.
J. Ross, "The business value of IT: Challenges and solutions," IEEE Engineering Management Review, vol. 48, no. 1, pp. 150-154, 2020.
M. Christopher and H. Peck, "Building the resilient supply chain," International Journal of Logistics Management, vol. 15, no. 2, pp. 1-14, 2004.
E. Brynjolfsson and A. McAfee, "The second machine age: Work, progress, and prosperity in a time of brilliant technologies," W. W. Norton & Company, 2014.
D. Acemoglu and P. Restrepo, "The race between man and machine: Implications of technology for growth, factor shares, and employment," American Economic Review, vol. 108, no. 6, pp. 1488-1542, 2018.
J. Bughin, E. Hazan, S. Ramaswamy, M. Chui, T. Allas, P. Dahlström, N. Henke, and M. Trench, "Skill shift: Automation and the future of the workforce," McKinsey Global Institute, May 2018.
D. J. Teece, "Business models, business strategy and innovation," Long Range Planning, vol. 43, no. 2-3, pp. 172-194, 2010.
F. Zhu, X. Ye, J. Fu, and Z. Xiao, "Contingent effects of technology uncertainty and market uncertainty on digital business strategy," IEEE Transactions on Engineering Management, vol. 69, no. 4, pp. 1437-1451, 2022.
Y. Chen, "Improving market outcomes: A dynamic model of market power and competition policy," RAND Journal of Economics, vol. 52, no. 2, pp. 386-423, 2021.
R. Baldwin, "The great convergence: Information technology and the new globalization," Harvard University Press, 2016.
