So what can you do as a company to benefit from this? And how can organisations use AI to systematically reduce their carbon footprint? That's what we're getting to the bottom of today in this article. We look at both internal processes and the supply chain as an external process. Because the carbon footprint of the global supply chain is gigantic. As early as 2021, the WEF states that the 8 most important supply chains are responsible for more than 50% of global CO2 emissions. As we have already explained artificial intelligence in supply chain management, demand forecasting and decarbonisation in detail in our previous articles, this time we will look at another aspect that is often neglected.
Fundamentally, AI can help us in three areas: Monitoring emissions, predicting emissions and reducing emissions. Let's start with emissions monitoring.
(Figure 1: Benefits of AI in climate change up to 2023, BCG)
Monitoring Emissions with AI
Determining emissions has long been a challenge for companies committed to sustainability. This is because conventional methods often provide inaccurate data and do not offer real-time monitoring – because there is simply too much data. Big data, on the other hand, is the favourite problem for artificial intelligence. It is capable of tracking and measuring carbon emissions across various operational processes.
The hardware is devices and sensors from the IoT (Internet of Things) sector to collect real-time data from various points in a company's operations. These sensors monitor energy consumption, production processes, transport and plant emissions. The collected data is fed into the AI, which continuously analyses the data for patterns, anomalies and trends. This enables sensors in combination with AI in production facilities to measure the emissions of individual machines, for example. And this enables companies to recognise inefficiencies and eliminate them promptly. The collection and processing of large amounts of data used to be an insurmountable gap – but today, comprehensive emissions profiles of companies are possible.
Beyond simple data collection, AI can directly attribute emissions to specific products, departments or processes. This level of detail allows organisations to understand the impact of individual activities in terms of carbon emissions and therefore make more informed decisions about reducing emissions. For example, AI can analyse production data to determine which production processes are responsible for the most emissions. And in the next step, guide companies to make targeted changes to reduce emissions.
And it doesn't stop there. AI is also able to improve the accuracy of emissions data – by filling in gaps and estimating missing information. This reduces uncertainty and provides a more reliable basis for decisions on emission reduction strategies. That's right, this is about compliance, among other things. Companies can take proactive steps towards sustainability and adapt to legal requirements.
Predictive Analysis for Emissions Reduction
Once emissions have been monitored, the next step is to use predictive analytics to forecast future emissions trends. Another special discipline of AI: large amounts of data are analysed and emissions can be predicted based on current patterns and operational changes. This predictive power is crucial for setting and adjusting emissions reduction targets in order to achieve the required or desired sustainability goals.
Predictive analysis provides companies with a valuable tool for managing their carbon emissions. It helps to identify areas where emissions are likely to increase. And enables organisations to plan ahead to reduce their carbon footprint. This is particularly useful for organisations with complex processes, where adjustments to production schedules or logistics routes can have a significant impact on emissions. The predictive capability of AI enables organisations to take proactive measures. By anticipating an increase in energy consumption, AI can suggest changes to prevent this from happening (we will return to this “prescriptive” scenario below).
Another benefit of predictive analytics is its contribution to long-term sustainability strategies. By predicting emissions, companies can create long-term plans to meet carbon reduction targets over a longer period of time – especially in light of regulatory compliance. This gives them a clear roadmap for achieving sustainability in compliance with environmental regulations.
Last but not least, predictive analysis also plays a role in optimising resource allocation. By using energy more efficiently and reducing waste, companies contribute to lower emissions and a smaller environmental footprint. This emphasises the value of AI-supported predictions that lead companies to much more sustainable practices.
Predictive AI meets prescriptive AI
Prescriptive AI goes one step further than predictive AI. Rather than just making predictions, it gives companies practical insights and ideas to reduce emissions in real time. What does that mean?
An example: While a predictive system gives a company an idea of the likelihood of customer churn, a prescriptive system would proactively recommend measures to best prevent this from happening. Prescriptive is translated as "prescriptive" – but it can also be thought of as "preventive".
For instance, AI can recommend the best production plans and transport routes to reduce unnecessary energy consumption and emissions. By analysing data from different operational areas/departments, AI identifies inefficiencies and suggests solutions. This precision helps companies to further reduce their environmental footprint without jeopardising productivity or operational goals.
AI can also streamline production processes and ensure that resources are utilised efficiently. This helps companies both in terms of environmental footprint and profitability – a two-fold win for sustainability and the bottom line. Implementing AI-powered recommendations will further promote a culture of continuous improvement. And that's not to be sneezed at, as a change in awareness and mindset precedes any innovation.
Energy-Efficient Technology
The production of energy (electricity, heat) is the main source of CO2 in the atmosphere. Energy-efficient technologies are therefore key if companies want to further reduce their carbon footprint. AI helps companies to find and implement more efficient practices – enabling them to ensure that the technology in their data centres and other operations is as energy efficient as possible. This includes the use of energy-efficient servers, cooling systems and other infrastructure to reduce energy consumption and minimise CO2 emissions. Even AI itself is not necessarily resource-efficient and is part of the problem. The question companies should ask themselves is: "Does this application need AI?". Local AI models or specialised and therefore leaner AI-systems are also a way to consider.
Again, this is where the advantage of AI to monitor and control energy consumption in real time comes into play. This continuous control allows organisations to both change their energy behaviour and act more sustainably. In real time. Here too, reducing energy consumption not only reduces the ecological footprint, but also operating costs.
Renewable energy sources can also be utilised more efficiently. AI algorithms can predict energy requirements and thus facilitate the integration of renewable energies into the company's energy consumption. This reduces dependence on fossil fuels. This transition to clean energy is essential for long-term sustainability and supports global decarbonisation efforts.
Supply Chain Collaboration
Supply chain – clearly our favourite topic. What additional measures can companies take that we haven't already discussed in previous articles? The answer: Collaboration in the supply chain.
After all, AI can also facilitate this. That means collaboration between companies, suppliers and end consumers to achieve joint emission reduction targets. In particular, this involves the expanded recording of Scope 3 emissions. By sharing data and working together, companies can recognise potential for improvement and implement strategies to reduce emissions throughout the supply chain.
AI makes it possible to monitor emissions throughout their value chain and provides insight into which parts of the supply chain contribute the most to carbon emissions. With this knowledge, companies can focus their efforts where they have the greatest impact. And they can do this by working with suppliers who are willing to adopt more sustainable practices.
Schneider Electric is a prime example of how the supply chain can be integrated into a comprehensive emissions reduction programme. In 2021, the company launched partnerships with more than 1,000 of its suppliers to calculate emissions reductions and set targets. Through workshops and advanced training webinars, the company has engaged nearly 900 suppliers to report and actively reduce their emissions, driving progress beyond their own operations.
Collaboration can also lead to innovations in product development. The international consumer goods company Reckitt is focussing its approach on product development to achieve its sustainability goals and reduce emissions (see also Fig. 2, point 3). Changing consumer behaviour is a strong driver to develop innovative products that contribute to sustainability. AI enables the development of more environmentally friendly products, packaging, modes of transport and other factors. For example, Reckitt is setting an example of innovation with its Finish dishwashing detergent brand: Finish dishwasher tabs are designed to eliminate the need for pre-rinsing and allow customers to save water in their daily use of the product. Overall, these collective efforts contribute to a significant reduction in carbon emissions and support the overarching goal of sustainability.
(Fig 2: BCG guide for 9 key initiatives that every organisation can take, step by step, BCG)
Conclusion
There is a lot for companies to do when it comes to sustainability – but there are just as many opportunities. Let's get on with it. Or to put it in the words of the Boston Consulting Group:
Aim high, start small and scale fast.