Surely seen as an odd pair, AI technology and sustainability can be quite powerful when combined for the benefit of our planet.
The concept of sustainable AI will soon claim a big role in the plethora of initiatives to address environmental sustainability challenges previously considered impossible. Such initiatives and uses can also certainly change the outlook and management of sustainable logistics. Forbes explained it perfectly as, “committing to sustainable practices is no longer a nice to have but a must do as the negative impacts of climate change become more obvious and ominous, with the potential to alter everything from supply chains to profitability.”
As stated by the World Economic Forum in the context of the transition to low-carbon supply chains, artificial intelligence (AI), 3D printing, and digital twins are “some of the powerful tools enabling the next wave of climate change solutions”. Specifically, AI technology has the potential to optimise different aspects of the logistics process: from optimal route planning to efficient carbon counting analytics, to increased asset utilisation, and smart inventory management. The implementation of AI in these fields has the potential to make supply chains more efficient, cost-effective, and environmentally more sustainable.
Can AI make supply chains more sustainable?
AI technology can play a significant role within sustainable logistics, creating more environmentally sustainable transportation networks, by supporting with:
- Sustainability assessment: Carbon tracking in upstream activities is an upcoming trend, and AI can help with powering platforms that are used for identifying opportunities and rating risk in ESG ratings. Such end-to-end solutions can help with the full spectrum of sustainability and performance management recommendations through broad-scale supply chain risk screening and mapping, and reliable scorecards with actionable ratings.
- Circularity design: AI can be used to optimise product design for circularity such as creating suggestions for reuse, refurbishment, or recycling opportunities within the business’ supply chain. Software solutions can be used to optimise route planning, leveraging knowledge to improve last-mile deliveries and the return-management processes. AI can help improve the reverse logistics infrastructure required to “close the loop” on products and materials, by increasing visibility and actionability in the processes to sort and disassemble products, remanufacture components, and recycle materials.
- Delivery optimisation: AI-powered algorithms can help companies optimise their supply chain operations and emission intensity by forecasting their demand/supply patterns and identifying potential supply chain disruptions. By analysing data from multiple sources, including weather patterns, consumer behaviour, and transportation patterns; AI can help companies plan and adjust their operations to improve availability, minimise waste and reduce their delivery carbon footprint.
- Energy optimisation: AI can help companies optimise their energy consumption by analysing data on energy usage, identifying opportunities for efficiency improvements. This includes minimising the electricity needed in offices and facilities, based on capacity, or as an enabler for distributed or combined energy storage.
- Vessel, truck, plane, and port optimisation: AI-powered software solutions can improve the efficiency and the integration of ocean, inland, and air freight. New AI-enabled models can be developed to help drive down emissions, anticipate needs, and improve efficiency in operations.
- Demand prediction: AI can also be used to reduce surplus inventory or production based on more accurate demand sensing. This is significant since a better understanding of demand and buying patterns leads to lower production wastage resulting in the use of fewer raw materials. This also means that there is a decreased need for transportation, and fewer items will end up wasted.
- Partnership evaluation: AI can be used to assess the sustainability of partners and suppliers, while identifying potential areas for improvement. By analysing available data on supplier performance – including carbon emissions – businesses will be empowered to make informed decisions about their supply chain partners.
The development of sustainable AI will allow companies to minimise waste, reduce carbon emissions, and make better use of resources (from product design to recycling) while benefiting from all angles. Wunderman Thompson explains that “from a bottom-line perspective, we have proven that intelligent business optimization can generate significant efficiencies through the application of AI to packaging and logistics. We are seeing double-digit percentage improvements for clients, increased margins, and significant reductions in waste and GHG (greenhouse gas) emissions from these solutions”. Beyond this, network optimisation and carbon emission simulation will become a lot better supported by better demand forecasting and evaluation of circularity options at different product stages.
It’s important to remember that all that glitters is not gold. AI technology needs to be used with care. To reach fully decarbonised supply chains, access to data is critical, which is prompted by the need for targeted investments, but most companies around the globe still lack visibility into their supply chain beyond the first tier. Visibility has the potential to improve efficiency, reduce emissions and enable forecasting. However, a precondition for promising AI algorithms is the need to train them with relevant data from both the company, as well as public data that is free from alternative motives or inaccurate sources.
Also, importantly for sustainability, the environmental impact of AI itself cannot be ignored. As mentioned in Sustainability Mag, “AI systems can be energy-intensive, and there is a pressing need for those working in the field of AI to address the potentially large environmental impact” of using and running these programs. Fredrik Grill, Global Head of Contract Logistics Decarbonisation at Maersk, adds: “AI is essential in any future environmental toolbox. It can revolutionise sustainability efforts, fostering efficiency and accountability within supply chains. However, AI is in-and-of-itself not a silver bullet. It requires access to qualitative data, so businesses should invest time around the non-AI work needed to start leveraging its full benefits”. Therefore, sparking the call for legislation around AI and good sustainable standards in its use, making sure using AI for sustainability isn’t as much a double-edged sword, but rather a genuinely smart way to reach the much-needed environmental balance faster.
In conclusion, despite the concerns, AI technology has the potential to contribute significantly to sustainable development and environmental protection efforts, making it an essential tool for addressing the challenges facing our planet today. The Harvard Kennedy School agrees, stating that “meeting sustainable development goals will require action on a number of fronts, including harnessing and maximizing the potential of technological innovation”.
