Optimizing Supply Chain Efficiency for SMEs Using Data Analytics and AI: A Path to Scalability and Profitability
Abstract
As global business environments become increasingly data-driven, small and medium-sized enterprises (SMEs) are seeking innovative ways to optimize operations, enhance efficiency, and maintain competitiveness. This study examines the transformative role of artificial intelligence (AI) and predictive analytics in improving key business functions such as demand forecasting, supply chain management, logistics optimization, and predictive maintenance. By employing a mixed-methods research design, data was collected from 250 SMEs through surveys and supplemented with semi-structured interviews with key decision-makers to gain deeper insights into AI implementation challenges and successes. The findings indicate that AI adoption leads to substantial improvements in cost efficiency, operational performance, and customer satisfaction across various industries. Specifically, businesses that integrated AI-driven predictive analytics experienced a 30% reduction in shipping costs, a 25% decrease in food waste, and a 15% increase in profit margins. Furthermore, predictive maintenance systems helped manufacturing firms reduce downtime by 37.5% and cut annual repair costs by 15%. Despite these advantages, AI adoption remains hindered by high initial costs, a lack of technical expertise, and data integration challenges. This research contributes to the growing body of knowledge on AI adoption in SMEs by providing empirical evidence of its effectiveness in business optimization. The study offers practical recommendations for overcoming implementation barriers, including phased AI deployment, investment in workforce training, and collaboration with AI technology providers. It also underscores the need for government policies and financial incentives to support SME digital transformation. The study concludes that AI and predictive analytics are no longer optional but essential for SMEs seeking sustainable growth and resilience in an increasingly digital economy. Future research should explore the long-term financial impacts of AI adoption and its application in underrepresented industries.
Keywords
Artificial Intelligence (AI), Predictive Analytics, SMEs, Business Performance, Supply Chain Optimization, Digital Transformation
Citations
IRE Journals:
Opeyemi Oyinlola Olatunji
"Optimizing Supply Chain Efficiency for SMEs Using Data Analytics and AI: A Path to Scalability and Profitability" Iconic Research And Engineering Journals Volume 8 Issue 9 2025 Page 1457-1465
IEEE:
Opeyemi Oyinlola Olatunji
"Optimizing Supply Chain Efficiency for SMEs Using Data Analytics and AI: A Path to Scalability and Profitability" Iconic Research And Engineering Journals, 8(9)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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