Adaptive AI-Orchestrated Supply Chain Optimization for Service-Sector Companies

Abstract

Service companies have special challenges managing their supplies because they deal with digital services, people, and urgent needs rather than just physical products. This study explains how machine learning can help service companies improve their supply chains instantly. We check out how AI controls; where AI systems locate and manage many supply network activities automatically, and can improve performance, reduce costs, and improve service delivery. The study focuses on practical applications in healthcare, hospitality, and professional services, providing exactly what to do. Our findings show that when service companies use adaptive AI systems that learn and improve over time, they can respond faster to changing demands, predict problems before they happen, and deliver better services to their customers. This study proposes a mixed AI structure for instant decision support in average retail firms. By bringing together mixed computing and artificial intelligence, the structure allows evidence-based decision-making, automates tasks, and provides personalized customer experiences.
The structure's success is shown through a case study, showcasing improved sales, stock management, and customer satisfaction. This research contributes to the retail industry's digital transformation, offering average firms a competitive edge in today's busy market.