BS-4 The Impact of AI on Innovation and Operational Efficiencies for Small to Medium Manufacturing Businesses
SCURS Disciplines
Business
Document Type
Oral Presentation
Abstract
Artificial intelligence is revolutionizing the running of industrial machinery for SMEs, thus creating an opportunity to improve overall efficiency by reducing downtimes and increasing production. Effective adaptation to more intelligent devices allows companies to get the best out of their equipment. But with AI alone, it is not enough—it has to be done right. Deep Seek's success validates that the difference lies in the implementation plan. Without proper information and an integration agenda, AI creates more problems and inefficiency than real improvement. Recently, this has been demonstrated through Deep Seek's quick pace with innovation and putting available resources into efficient use. The future of SMEs lies in the adoption of AI and deriving strong intelligence from such data. This study delves further into how small and medium-sized enterprises can utilize AI to optimize machinery: firms would make better decisions, reduce waste, and conserve energy. This report identifies the best practices for AI adoption in manufacturing through actual case studies, given demand-driven decision-making analysis, quality improvements, and improved supply chains. One of the most significant challenges is to reach just the right balance between automation and human expertise. The skilled workforce should complement AI rather than substitute for it. SMEs lack the technical strength and strategic consulting necessary to harness AI systems correctly in their processes, making them more vulnerable to these issues. This project follows a qualitative methodology with case studies from SMEs which have adopted AI-based machinery. The cases include failed and successful implementations to search for patterns, pitfalls, and best practices. Preliminary findings are that SMEs with structured implementation processes, including phased rollout of AI, workers' training, and real-time performance monitoring, achieve better machinery uptime, energy efficiency, and product quality. Drawing inspiration from some of the success stories of AI-driven innovations like Deep Seek, SMEs can overcome all AI adoption challenges and realize their full potential for future transformation of industrial operations.
Keywords
Artificial Intelligence, Machinery, Sustainability, Small-Medium Businesses
Start Date
11-4-2025 3:10 PM
Location
CASB 101
End Date
11-4-2025 3:25 PM
BS-4 The Impact of AI on Innovation and Operational Efficiencies for Small to Medium Manufacturing Businesses
CASB 101
Artificial intelligence is revolutionizing the running of industrial machinery for SMEs, thus creating an opportunity to improve overall efficiency by reducing downtimes and increasing production. Effective adaptation to more intelligent devices allows companies to get the best out of their equipment. But with AI alone, it is not enough—it has to be done right. Deep Seek's success validates that the difference lies in the implementation plan. Without proper information and an integration agenda, AI creates more problems and inefficiency than real improvement. Recently, this has been demonstrated through Deep Seek's quick pace with innovation and putting available resources into efficient use. The future of SMEs lies in the adoption of AI and deriving strong intelligence from such data. This study delves further into how small and medium-sized enterprises can utilize AI to optimize machinery: firms would make better decisions, reduce waste, and conserve energy. This report identifies the best practices for AI adoption in manufacturing through actual case studies, given demand-driven decision-making analysis, quality improvements, and improved supply chains. One of the most significant challenges is to reach just the right balance between automation and human expertise. The skilled workforce should complement AI rather than substitute for it. SMEs lack the technical strength and strategic consulting necessary to harness AI systems correctly in their processes, making them more vulnerable to these issues. This project follows a qualitative methodology with case studies from SMEs which have adopted AI-based machinery. The cases include failed and successful implementations to search for patterns, pitfalls, and best practices. Preliminary findings are that SMEs with structured implementation processes, including phased rollout of AI, workers' training, and real-time performance monitoring, achieve better machinery uptime, energy efficiency, and product quality. Drawing inspiration from some of the success stories of AI-driven innovations like Deep Seek, SMEs can overcome all AI adoption challenges and realize their full potential for future transformation of industrial operations.
