FACTORS AFFECTING THE APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) IN SUPPLY CHAINS OF SMALL AND MEDIUM-SIZED ENTERPRISES (SMES) IN THAILAND
Article Sidebar
Main Article Content
Abstract
This qualitative research aimed to examine the internal and external organizational factors influencing the application of artificial intelligence (AI) in the supply chains of small and medium-sized enterprises (SMEs) in Thailand and to develop an AI application model that enhances sustainable competitive advantage. The study employed documentary research by reviewing 88 documents, including relevant theories, concepts, and previous studies, as well as focus group discussions with five participants comprising supply chain scholars, AI experts, and SME entrepreneurs, selected by specific sampling method. The research instrument was a focus group discussion guide, which was validated by three experts using the Item-Objective Congruence (IOC) index, with values ranging from 0.67 to 1.00. Data were analyzed using content and thematic analyses. The findings revealed that the factors influencing AI application in the supply chains of Thai SMEs can be classified into two categories: internal and external organizational factors. Internal factors include human resources, digital technology readiness, organizational culture, information systems, and top management support. External factors consist of government policies and support, business competition, technological change, and sustainability orientation. AI applications also improve demand forecasting, inventory management, cost reduction, and customer responsiveness, thereby strengthening sustainable competitive advantage. However, Thai SMEs continue to face constraints related to financial resources, workforce capability, and digital knowledge. These findings indicate the need for continuous support from government agencies and higher education institutions to facilitate AI adoption. Based on these findings, the study proposes an AI application model for supply chain management in Thai SMEs, serving as a practical framework to strengthen organizational capabilities and improve competitiveness in the digital economy.
Article Details
References
สำนักงานส่งเสริมวิสาหกิจขนาดกลางและขนาดย่อม. (2566). SME White Paper 2023. กรุงเทพมหานคร: สำนักงานส่งเสริมวิสาหกิจขนาดกลางและขนาดย่อม.
K1. (15 มีนาคม 2569). การประยุกต์ใช้ปัญญาประดิษฐ์ในการบริหารห่วงโซ่อุปทานของวิสาหกิจขนาดกลางและขนาดย่อม. (กวินพัฒน์ เลิศพงษ์มณี, ผู้สัมภาษณ์)
K2. (18 มีนาคม 2569). การประยุกต์ใช้ปัญญาประดิษฐ์ในการบริหารห่วงโซ่อุปทานของวิสาหกิจขนาดกลางและขนาดย่อม. (กวินพัฒน์ เลิศพงษ์มณี, ผู้สัมภาษณ์)
K3. (20 มีนาคม 2569). การประยุกต์ใช้ปัญญาประดิษฐ์ในการบริหารห่วงโซ่อุปทานของวิสาหกิจขนาดกลางและขนาดย่อม. (กวินพัฒน์ เลิศพงษ์มณี, ผู้สัมภาษณ์)
K4. (22 มีนาคม 2569). การประยุกต์ใช้ปัญญาประดิษฐ์ในการบริหารห่วงโซ่อุปทานของวิสาหกิจขนาดกลางและขนาดย่อม. (กวินพัฒน์ เลิศพงษ์มณี, ผู้สัมภาษณ์)
K5. (24 มีนาคม 2569). การประยุกต์ใช้ปัญญาประดิษฐ์ในการบริหารห่วงโซ่อุปทานของวิสาหกิจขนาดกลางและขนาดย่อม. (กวินพัฒน์ เลิศพงษ์มณี, ผู้สัมภาษณ์)
Bag, S. et al. (2021). Role of technological dimensions of green supply chain management practices on firm performance. Journal of Enterprise Information Management, 34(1), 1-27.
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120.
Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169-196.
Bughin, J. et al. (2017). Notes from the AI frontier: Modeling the impact of AI on the world economy. San Francisco: McKinsey Global Institute.
Christopher, M. (2016). Logistics and supply chain management. (5th ed.). Harlow: Pearson Education.
Creswell, J. W. & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). New Jersey: Sage Publications.
Davenport, T. H. & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Davenport, T. H. (2006). Competing on analytics: The new science of winning. Boston: Harvard Business School Press.
Dubey, R. et al. (2020). Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture. British Journal of Management, 30(2), 341-361.
Elkington, J. (1997). Cannibals with forks: The triple bottom line of 21st century business. Oxford: Capstone.
Ellström, D. et al. (2022). Dynamic capabilities for digital transformation. Journal of Strategy and Management, 15(2), 272-286.
Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(2), 109-122.
Guest, G. et al. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59-82.
Hart, S. L. (1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), 986-1014.
Ivanov, D. et al. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.
Mentzer, J. T. et al. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1-25.
Mintzberg, H. (1989). Mintzberg on management: Inside our strange world of organizations. New York: Free Press.
North, D. C. (1990). Institutions, institutional change and economic performance. Cambridge: Cambridge University Press.
OECD. (2021). The digital transformation of SMEs. Paris: OECD Publishing.
Porter, M. E. & Kramer, M. R. (2011). Creating shared value. Harvard Business Review, 89(2), 62-77.
Porter, M. E. (1985). Competitive advantage: Creating and sustaining superior performance. New York: Free Press.
Schein, E. H. (2010). Organizational culture and leadership. (4th ed.). San Francisco: Jossey-Bass.
Storey, D. J. (2016). Understanding the small business sector. London: Routledge.
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319-1350.
United Nations. (2015). Transforming our world: The 2030 agenda for sustainable development. New York: United Nations.
Venkatesh, T. et al. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144.
Waller, M. A. & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84.
