Greening the supply chain

As businesses become more focused on mitigating their climate impact, they are seeking increasingly innovative ways to embrace environmentally friendly practices throughout their supply chains – where over 90% of organisations’ greenhouse gas emissions come from.

One tool to achieve this goal that is gaining popularity is the use of artificial intelligence (AI). Here are some examples of how it has been playing its part in reducing the environmental impact within the supply chain.

1. Inventory management

AI is incredibly good at analysing and deriving insights from extensive datasets. It helps to predict product demand more accurately by analysing historical data, market trends and external factors. This helps businesses optimise inventory levels and reduce waste caused by overstocking or understocking. At the same time, this can reduce unnecessary energy consumption caused by transporting and storing excess inventory.

For example, PepsiCo uses AI and machine-learning algorithms to predict consumer demand and optimise production, which it says results in less wastage from excess production.

2. Efficient logistics

The transport and logistics sector is one of the largest sources of global greenhouse emissions. According to a UN report, it accounts for about 25% of carbon emissions globally. By using AI, organisations can optimise their transportation routes and logistics operations, which can help reduce emissions.

For example, AI algorithms allow organisations to analyse factors such as traffic patterns, weather conditions and fuel efficiency to optimise delivery routes. They can also process real-time data, so transportation can avoid congestion and minimise disruption.

Walmart is one example of a company doing just this. The US retail giant has an AI-powered system that it uses to plan its delivery routes, resulting in lower fuel consumption and reduced carbon emissions. Similarly, DHL uses AI systems to monitor its delivery routes and identify issues such as stalled consignments in real time.

3. Energy reduction

AI systems are also helping businesses optimise their energy management across supply chains through monitoring and analysing energy usage patterns. AI can identify areas of inefficiency and recommend optimisation strategies from analysing data from smart meters, sensors and Internet of Things (IoT) devices to determine equipment and processes that consume too much energy.

4. Checking credentials

AI is being used to analyse vast amounts of data about parties in the supply chain, such as their certifications, energy usage, waste production, water consumption, carbon emissions and compliance with environmental regulations.

One way companies are doing this is by using blockchain technology to track and verify the origin of raw materials, and ensure that suppliers follow sustainable sourcing practices.

For example, the IBM Food Trust leverages AI, blockchain and IoT technologies to help companies verify whether food suppliers use sustainably sourced ingredients and promote fair labour practices. Sustainability ratings agency EcoVadis, meanwhile, uses AI to analyse huge amounts of data and rate suppliers based on their social, environmental and ethical practices.

5. Lifecycle analysis

Companies are under growing pressure to engage with the circular economy and assess the lifecycle of their products. As they analyse data from various stages of a product’s lifecycle – such as raw material extraction, manufacturing, distribution and disposal – they can identify areas where they can make improvements to reduce their environmental impact.

For example, AI systems can make recommendations on optimising material usage, implementing energy-efficient manufacturing processes, reducing waste generation, green budgeting and adopting sustainable packaging materials.

For instance, Thinkstep has AI-based software solutions that analyse the environmental impact of products throughout their lifecyle and recommends areas where improvements can be made, while SimaPro software uses AI and machine-learning algorithms to improve the accuracy of lifecycle analysis.

6. Real-time monitoring

A significant feature of AI systems is their ability to conduct monitoring and analysis in real time. By continuously analysing data from sensors, IoT devices and machine-learning algorithms, AI systems can detect anomalies, predict failures, and optimise equipment and machinery maintenance schedules. AI can then store this data in a cloud environment to help businesses better organise and access the data they receive. This proactive approach reduces unplanned downtime while enhancing operational efficiency and preventing resource wastage.

When maintenance needs are identified in advance, companies can schedule repairs or replacements during planned downtime, reducing the risk of unexpected breakdowns and minimising the need for urgent shipping or rush orders. This optimisation of maintenance activities also leads to energy savings and reduces resource consumption.

For example, PepsiCo has its own in-house AI system that monitors in real time all manufacturing equipment to predict failure and schedule maintenance, thus reducing downtime. Companies such as Uptake Technologies also have AI-based solutions that analyse various data points to predict failure and minimise downtime for machinery and vehicles.

Clearly AI’s potential is only just starting to be realised, but its application to support climate change goals, particularly in the supply chain, could be huge.