Businesses are under increasing pressure from eco-conscious consumers and regulators to meet net-zero commitments for greenhouse gas emissions from their supply chains. But they still have a long way to go.

A recent report from the Intergovernmental Panel on Climate Change (IPCC) outlined the stark reality of the climate crisis, and the disparity between where businesses are in their current efforts to “go green” and what that means for the success of climate-change targets.

Too often we see an eagerness to focus on passive fixes such as carbon offsetting that offer instant gratification, with little regard for the long-term efficacy of such solutions. Technology investments that seek to remodel the supply chain are more impactful in the long-term, but businesses often overlook them in favor of that quick fix.

The answer lies in a third way: a transformative solution that can be deployed and integrated at speed, with both short- and long-term results. 

The supply chain is a huge factor in the sustainability of an organization, and logistics and transportation are, unsurprisingly, major contributors to the carbon footprint. A 2016 McKinsey report found that more than 80% of a business's greenhouse gas emissions come from its supply chain. There’s an enormous opportunity for business leaders to enact positive change toward meeting sustainability targets by changing supply chain strategies. Yet the challenge is hugely complex, requiring access to multiple datasets from many sources.

For a supply chain manager, this level of complexity provides too many variables for individuals to calculate. It’s impossible for a human brain to adapt quickly to changing information that’s sourced from the hundreds of suppliers and other stakeholders that exist in a typical supply chain. The list of datasets is endless: operational capacity, previous performance, availability of “green” transportation modes, carrier prices, estimated shipping times, and the likelihood of route disruptions are but a few. The only way a business can effectively quantify this volume of data is with the assistance of artificial intelligence (AI) technology.

Using AI to Improve Sustainability

Using these datasets, AI technology can rank and identify the biggest levers for reducing the carbon footprint. The one that emerges as most significant, perhaps surprisingly, is fulfillment location. Choosing the right one has the potential to reduce a business’s carbon footprint by almost 30%, and it has a huge influence over other factors as well. Storing the right products or services closer to the end user means that vehicles travel over less distance for delivery, and loads are lighter; both of which are high-impact factors in reducing the carbon footprint.

However, fulfilment locations are only one component of the supply chain. For shipments, AI can also help calculate a route with the fewest miles spent in pollution-heavy transportation modes, and times when there will be least chance of delays. Businesses can also adopt a multi-carrier strategy in which AI automatically picks the carrier with the greenest fleet (although it should be noted that this only has a 7% impact on overall emissions), and is able to automate carrier-switching should problems arise in the supply chain.

Balancing Sustainability and Cost 

For many companies, cutting carbon emissions and costs have been viewed in isolation, with a bias toward the latter. After all, the business needs to be profitable, and shareholders kept happy.

It’s possible for a business to align its ESG and commercial goals. Historically, business leaders have been left in the dark about how much reducing their carbon footprint would affect their bottom line, but through AI they’re able to find the ideal balance. 

In a simulation of a fictional pharma business, we find that for every $1,300 businesses spend on last-mile logistics, they should be emitting no more than 284 pounds of carbon dioxide equivalent. This will vary between businesses, but AI is finally allowing business leaders to accurately forecast the cost of going green.

It can be tempting to look at the supply chain at a granular level, but optimizing it and cutting down on costs can only be achieved by adopting a holistic, data-first approach to logistics. Third-party AI-powered logistics platforms are easily integrated within existing systems, and can help supply chain managers make key decisions in real time.

Businesses can also create digital twins, which are AI-driven virtual depictions of supply chain components, including warehouses, suppliers and inventory. They provide greater insight into supply chain behavior, by running rapid and exhaustive simulations that can effectively calculate the cost of going green.

Climate regulations will only continue to increase, but business leaders no longer need to choose between the planet or profits. There’s a real opportunity to draw on the benefits of AI technology, and start making immediate and impactful changes to supply chains.

Phillip Ashton is co-founder and CEO of 7bridges.

Read more of SupplyChainBrain's 2022 Supply Chain ESG Guide here.