Leveraging AI and cloud solutions for energy efficiency in large-scale manufacturing

Nnaemeka Stanley Egbuhuzor 1, *, Ajibola Joshua Ajayi 2, Experience Efeosa Akhigbe 3 and Oluwole Oluwadamilola Agbede 3

1 Columbia Business School, Columbia University, NY, USA.
2 The Wharton School of Business, University of Pennsylvania, PA, USA.
3 Booth School of Business, University of Chicago, IL, USA.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(02), 4170-4192.
Article DOI: 10.30574/ijsra.2024.13.2.2314
Publication history: 
Received on 18 October 2024; revised on 09 December 2024; accepted on 11 December 2024
 
Abstract: 
The integration of Artificial Intelligence (AI) and cloud solutions is revolutionizing energy efficiency in large-scale manufacturing, offering transformative potential to address the sector's pressing challenges. Manufacturing industries face growing pressure to optimize energy use, reduce operational costs, and meet stringent sustainability targets. This paper explores how AI-driven cloud technologies can enhance energy efficiency through predictive analytics, real-time monitoring, and intelligent automation, ensuring sustainable and cost-effective operations. AI-powered systems leverage machine learning algorithms and Internet of Things (IoT) sensors to collect and analyze energy consumption data across manufacturing facilities. By identifying patterns, anomalies, and inefficiencies, these solutions enable predictive maintenance and dynamic load balancing, reducing energy waste. Cloud-based platforms provide scalable infrastructure for centralized data storage and seamless communication between devices, fostering collaboration across distributed manufacturing sites. Furthermore, real-time analytics delivered through cloud dashboards empower managers to make informed decisions and implement proactive energy-saving measures. This study highlights the role of AI in optimizing energy-intensive processes such as heating, cooling, and material handling. For instance, deep learning algorithms can fine-tune production parameters to maximize output while minimizing energy consumption. Similarly, AI-enabled demand forecasting allows manufacturers to align energy procurement with production needs, mitigating peak load costs and ensuring operational continuity. Despite these advantages, adopting AI and cloud solutions presents challenges, including high initial investment, data security concerns, and workforce skill gaps. To overcome these barriers, this paper proposes a strategic implementation framework, emphasizing the importance of stakeholder collaboration, robust cybersecurity measures, and capacity-building initiatives to ensure the seamless adoption of AI and cloud technologies in manufacturing. The findings underscore the potential of AI and cloud solutions to redefine energy efficiency in manufacturing, aligning with global sustainability goals and economic competitiveness. By harnessing these technologies, manufacturers can achieve significant energy savings, reduce carbon footprints, and drive long-term operational excellence.
 
Keywords: 
Artificial Intelligence (AI); Cloud Computing; Energy Efficiency; Manufacturing; Internet Of Things (IoT); Predictive Analytics; Sustainability; Demand Forecasting; Real-Time Monitoring; Intelligent Automation
 
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