AI has emerged as a powerful tool to drive sustainable practices. A recent survey by McKinsey showed that 43% of organisations that have adopted AI solutions are using it to assist in their sustainability efforts. The same study shows that 40% of business leaders are actively seeking ways to reduce the environmental impact of their AI use.
Here we explore the role of AI in driving sustainable practices, with a focus on these four areas:
- Facilities management - Improving energy efficiency
- Manufacturing - Reducing waste in manufacturing processes
- Transport and logistics - Enhancing supply chain sustainability
- Agriculture - Increasing crop yield
We will provide examples of organisations that have successfully implemented AI for each use case, highlighting the benefits they have achieved.
Improving energy efficiency
Buildings are one of the biggest contributors to global greenhouse gas emissions, responsible for over a third of emissions worldwide. AI can be used to improve energy efficiency in buildings and reduce carbon emissions.
AI solutions can monitor HVAC (heating, ventilation and air conditioning), as well as overall energy use, occupancy, and downtime hours. This data can then be used to optimise energy consumption by predicting the habits of occupants and controlling HVAC-energy balance.
Case study: ASI use machine learning for efficient building management
Aberdeen Standard Investments (ASI) and KJ Tait Engineers used Ecopilot at their offices in Newcastle to improve energy efficiency. AI technologies like Ecopilot harness a building’s thermal mass to align with the building balance point temperature.
This technology can then make automatic, continuous HVAC energy management decisions based on short and long-term heating and cooling needs. Using this new solution, ASI achieved a 29% reduction in gas use and 15% reduction in electricity use versus the baseline year.
Waste reduction in manufacturing processes
Manufacturing processes generate at least 50% of global waste, a negative impact that largely comes from poor-quality products. Poor quality generates waste in the form of discarded goods and excess inventory.
AI is assisting workers in making critical, high-volume decisions such as predicting demand and optimising inventory levels. According to one Google Cloud survey, 39% of manufacturers are using AI for quality inspection, while 35% are using it for quality checks on the production line itself.
AI-powered computer vision systems use high-resolution cameras to monitor every aspect of the production process. The system can detect defects that the human eye might miss and automatically take corrective measures. Leveraging this technology, organisations can reduce product recalls while reducing environmental impact.
Case study: BMW’s AI-Powered Future
BMW has been implementing automated image recognition in series production since 2018 to reduce waste and recalls. The technology compares component images in real-time to hundreds of other images of the same sequence to detect deviations from the standard. This checks whether all required parts have been mounted, and whether they are in the correct place, reducing human error.
The system is easy to use and can be set up quickly. At the training stage, a high-performance server calculates the neural network from around one hundred images. The network immediately starts optimising and reaches 100% reliability after a test run and any necessary adjustments.
Enhancing supply chain sustainability
AI is helping organisations to optimise their operations by using advanced algorithms to analyse data from multiple sources, including IoT devices, weather forecasts and customer data. This data provides real-time insights on areas including demand forecasting, route optimisation and inventory management.
One way in which AI achieves this is through predictive analytics, which uses historical data and current trends to forecast customer demand accurately. By adjusting inventory levels, production schedules and transportation routes accordingly, organisations are enhancing efficiency while reducing waste.
Another sustainable use of AI in supply chain management is transportation optimisation. With real-time data from GPS trackers, traffic reports and weather forecasts, AI algorithms determine the most fuel-efficient routes.
Case study: M&S uses AI to reduce waste and emissions
British multinational retailer Marks & Spencer (M&S) has implemented artificial intelligence (AI) technology to enhance their supply chain sustainability. M&S uses AI solutions to optimise their forecasting and replenishment process, reducing food waste and greenhouse gas emissions.
Using predictive analytics to forecast demand and adjust orders accordingly, M&S were able to reduce food waste by 1.9 million items and greenhouse gas emissions by 3,200 tonnes over 12 months. The technology has had a positive impact by helping to ensure that stores are stocked with the right products at the right time.
Increasing crop yield
The United Nations’ Food and Agriculture Organisation (FAO) forecasts that global agricultural production must nearly double by 2050 to feed the growing world population.
AI solutions can help increase crop yields and reduce the amount of land needed to produce the same amount of food. Research by PwC estimates that AI applications in agriculture can reduce emissions by up to 160Mt CO₂e in 2030 while using fewer resources.
One key area where AI is being used is precision farming. This process involves using data analytics and AI to optimise crop production, reduce resource consumption, and increase yields. For example, AI can analyse soil samples and provide farmers insights into the nutrient content and pH levels of their soil. This information can then be used to make more informed decisions about what crops to grow and how to manage them.
Case study: Reducing soybean loss with a harvest vision system
Farmwave's Harvest Vision System (FHVS) uses artificial intelligence services to help farmers optimise crop production, reduce resource consumption, and increase yields.
During a harvest season, the FHVS covered almost 40,000 acres of soybeans in Northern Illinois damaged by a Derecho storm. The FHVS provided data to adjust settings in real-time to reduce actual loss from combines.
The system also allowed farmers to change the cutting direction to reduce losses and maintain efficiency. Stabilising losses at 15-18 beans/sq. ft (5 bushels) saved the operator 3 bushels/pass (~50% of the field).
Conclusion
AI solutions have shown great promise in driving sustainable practices across industries and organisations. As organisations continue to develop innovative solutions, organisations can leverage these technologies to improve their operations and achieve their sustainability goals.
Audacia is a UK-based software development company and digital transformation service provider, with significant experience working across a number of industries and organisations to help businesses reach their goals.
