Manufacturers embrace gen AI, automation to drive sustainability: report
By DE Staff
Automation General Quality Sustainability AI Alithya Automation gen AI Microsoft Copilot report supply chain survey
Manufacturers are rapidly adopting new technologies to meet efficiency, process productivity, supply chain and sustainability goals, according to Alithya Group’s 2024 Manufacturing Trends report.
Automation, the top technology investment
One of the key findings of the report—which surveyed more than 150 professionals in the manufacturing sector—is that more than 60 per cent of manufacturers plan to adopt process automation in 2024, making it the top priority technology investment. Other top technologies include Microsoft Copilot, predictive analytics and supply chain management.
The adoption of AI and machine learning technology is seen as a quick path to enhance efficiency, business operations and supply chain resiliency.
Manufacturers are focusing on improving operational efficiency and process productivity. More than 40 per cent of respondents identified innovation in operations as a priority, while 25 per cent focused on innovating processes.
Sustainability initiatives and supply chain
When it comes to sustainability initiatives planned in 2024, more than one-third of manufacturers (37 per cent) are looking to enhance energy efficiency by upgrading manufacturing equipment or processes. What’s more, 56 per cent look to implement waste reduction measures and 39 per cent look to reduce carbon footprint by investing in renewable energy sources.
To enhance the resilience of their manufacturing supply chains, manufacturers are diversifying their supplier base, investing in technology solutions, and strengthening strategic partnerships with key suppliers.
Implementing advanced data analytics or AI-driven tools, and conducting regular risk assessments, are also part of their strategies for addressing potential disruptions and ensuring business continuity.
Data analytics and AI in manufacturing processes
Manufacturers plan to implement machine learning for demand forecasting and planning, employ data analytics to optimize production efficiency, explore the potential of digital twins, use AI for predictive maintenance, and integrate data analytics to monitor energy consumption.
