Manufacturing accounts for 50% of the world’s energy consumption and 20% of global carbon emissions. With the rising awareness for sustainability, the industry is under increasing pressure to transform through green manufacturing initiatives. Policy changes from the governments (e.g. EU’s ‘Fit for 55’) and initiatives from companies are a positive step towards achieving sustainability. Technology plays a pivotal role across these initiatives with AI leading the way as a critical tool in the journey towards a greener manufacturing paradigm. By intertwining AI with the objectives of enhancing efficiency, minimising waste and carbon footprints, and ensuring supply chain sustainability, the manufacturing sector has embarked on a transformative journey toward a future where green manufacturing is the norm, not the exception.
Following are some of the key areas in manufacturing utilising AI for green manufacturing initiatives.
AI for enhancing efficiency
Manufacturing efficiency is one of the key levers linked to emissions and sustainability. Recovering from the Covid-induced lockdowns, improving efficiency has been a focus of companies wherein digitisation and AI-based tools are being increasingly used to optimise resource usage and create a greener impact.
AI-driven solutions analyse extensive datasets, identify patterns and optimise resource utilisation, leading to significant efficiency improvements and creating a greener footprint.
AI for optimising energy consumption and waste reduction
The industrial sector’s energy consumption is expected to rise by 32% from 2022 to 2050, highlighting the need for effective energy management and waste reduction strategies.
AI applications, crucial for developing low-carbon materials and energy-efficient production processes, play a pivotal role in reducing the manufacturing sector’s carbon footprint by managing energy consumption more effectively.
AI-enabled solutions can dynamically optimize energy usage in manufacturing operations by analyzing production schedules, equipment loads, and facility data. Algorithms can be used to identify opportunities to shift energy usage to off-peak times (reducing peak demand charges), scale equipment based on real-time requirements, and tune HVAC settings according to weather and occupancy patterns (minimising electricity waste). Implementing AI-based approaches for smart energy optimisation can result in up to 10% energy savings.
AI for sustainability across the supply chain
Manufacturing industry has a complex supply chain ecosystem with multiple vendors, and logistics companies playing a role in the carbon footprint of a product. The entire process can account for over 75% of a product’s emissions making sustainability across the supply chain an important initiative.
AI-based solutions in supply chain work across material and vendor selection, location, packaging, routing, warehousing and logistics optimisation, helping in reducing the overall carbon footprint of the products while creating more sustainable and resilient supply chains. Through 2024, 50% of supply chain organisations plan to invest in AI and advanced analytics capabilities to enhance sustainability efforts.
Industry examples: AI’s Impact on green manufacturing initiatives
| Industry | AI Applications | Specific Examples | Impact |
| Heavy Industry | Energy Management | CITIC Pacific Special Steel, China uses AI to dynamically adjust energy use. | Reduced energy consumption by 10.5%. |
| Energy & Utilities | Predictive Maintenance | K-water, South Korea employs AI for water treatment facility maintenance. | 10% reduction in energy consumption. |
| Pharmaceuticals | Automated Process Verification | Johnson & Johnson, China uses AI for quality and efficiency in manufacturing. | 26% GHG emissions reduction. |
| Transportation | Voyage and Route Optimization | Maersk leverages AI for maritime route optimization. | Fuel cost reduction equating to 10% savings. |
| Renewable Energy | Enhanced Performance Forecasting | Ørsted applies AI in forecasting wind energy production. | 10% increase in annual energy production. |
| Oil & Gas | Drilling Efficiency and Fuel Quality Optimization | Aramco, Saudi Arabia utilizes AI for operational efficiency. | 14% GHG emissions reduction, 17% improved operational availability. |
| Supply Chain Sustainability | Logistics and Distribution Optimization | DHL Supply Chain, US, employs a digital logistics control tower. | 71% reduction in pick cycle time. |
| Chemicals & Materials | Material Recycling and Process Optimization | Siemens, China integrates AI for resource recycling. | 60% reduction in material waste from cartons. |
| Aerospace | Predictive Maintenance and Route Optimization | Emirates and Lufthansa use AI for maintenance and fuel efficiency. | Emirates: 20% reduction in unplanned maintenance events. Lufthansa: EUR 150 million annual fuel savings. |
| Electricity Distribution | Grid Efficiency and Reliability | Enel uses AI for energy consumption prediction. | Reduced wasted energy by 10%. |
These transformative examples of AI in enhancing operational efficiency and sustainability serve not merely as success stories but as blueprints for widespread change. This evolution demands not only the adoption of AI-driven solutions but also a commitment to continuous learning and adaptation to emerging sustainability challenges.
The way forward is clear: embrace AI, innovate relentlessly, and collaborate widely to ensure a sustainable manufacturing paradigm for future generations.
Raunaq Rakesh – Assistant Manager, Growth Advisory, Aranca.
