How an AI-Applied Supply Chain Enables Efficiency

Today’s supply chains are laden with inefficiencies, as most companies rely on antiquated practices to oversee and manage how goods get from place to place. The supply chain is delicate — even one disruption among suppliers, buyers, and logistics providers can have a trickle-down effect that causes waste, time loss, and increased carbon emissions.

With the supply chain still managed manually, logistics managers are operating under intense pressure, with the sheer amount of data about material supply, demand, and transportation routes overwhelming. Even with machine learning providing managers with intelligent analysis, logistics managers can only react so quickly to the thousands of changes along a single supply chain. As managers are overburdened, their slow reactions to real-time problems and disruptions cause the supply chain inefficiencies that create higher costs, waste, and even greater environmental impact.

One way supply chains can become inefficient is when the supplier/buyer relationship falls out of sync when either the supplier runs out of a product or material, or buyers purchase too little of a material and suppliers are left with an excess. Changing demand can cause supply chain malfunction which, in turn, creates more inefficiencies and waste.

The effects of change in demand were evident during the pandemic when, panicked by the onset of the pandemic, consumers rushed to stock up on groceries and essentials like milk and paper goods. Despite causing empty shelves and in-store shortages, it also caused more waste. Some suppliers couldn’t adjust fast enough to the rush in demand. On the supply side, a sluggish supply chain left perishable goods stranded and unable to make it to shelves on time: Dairy farmers ended up dumping thousands of gallons of fresh milk.

Inefficiencies also take place on the logistics side of the supply chain. The moving of goods from one place to another often depends on truckers hauling loads across the country. When these loads are delayed or there is a shortage of drivers to get the goods from point A to point B, buyers and suppliers can incur increased costs. Beyond that the environmental impact of the supply chain increases because the number of trucks on the roads and the distance they are traveling are not optimized for the best loads and routes.

The U.S. alone has 15.5 million freight trucks on the road, with the average truck emitting 161.8 grams of CO2 per ton-mile – totaling more than 1.9 billion tons of greenhouse gasses per year. According to the EPA, the transportation sector is currently responsible for over 50 percent of total NOx (nitrogen oxides) emissions in the U.S. and over 30 percent of total VOC emissions (volatile organic compounds). An antiquated and overtaxed supply chain cannot address those inefficiencies, but AI can make logistics planning more efficient.

Supply chain inefficiencies are costly both for the business and for the environment. But a smarter approach to the supply chain can both reduce the costs and challenges that supply chain managers face, as well as address the effect of inefficiencies on carbon emissions and waste. The supply chain, when managed more effectively, can benefit both their business goals and become the simpler route to sustainability. With artificial intelligence applied to supply chain analysis and decision-making, businesses can reduce their costs, waste, and emissions.

AI seamlessly links all players in the supply chain (logistic providers, suppliers, and buyers) to form analysis, which can be used to further efficiency with automatic prediction and decision-making. With the data analysis, an AI can react by automatically making decisions to address real-time supply chain challenges to more efficiently move goods between suppliers, buyers, and consumers. Instead of relying on managers to make decisions, the AI can purchase materials, change allocations, reroute trucks, and rebalance supply systems as needed to reduce inefficiencies. Not only can AI make decisions faster, these platforms can also prioritize sustainable efforts to significantly reduce freight detention, deadhead mileage, and linehaul mileage.

Because companies of all industries contribute carbon emissions through transport of goods and air, emissions from freight are only expected to grow by 2025. This is despite many companies setting their sights on becoming carbon neutral or reaching Net Zero by 2030. Supply chain data and management can be the secret weapon to decarbonization. But the supply chain management process needs to shift from antiquated manual analysis to faster, smarter analysis with machine learning. Models are being trained to take in hundreds of thousands of data points about product demand, material supply, truck routes, and goods allocations, and identify inefficiencies.

With AI interwoven into the supply chain process, the burden on analysis and supply chain managers is lifted, allowing them to shift to long-term strategic planning. Rather than jumping into situations as they arise in real-time, managers can expand their view beyond daily supply chain challenges to consider future decisions that will continue making the chain more efficient. Companies like Halliburton have adopted a supply chain managed by AI, which has already shown effectiveness in eliminating downtime, reducing stockouts and cutting costs.

The supply chain updated with artificial intelligence not only reduces costs for companies in any sector, it can also be prioritized to reduce waste of goods and carbon emissions. In the path to carbon neutrality, the supply chain can be easily upgraded with AI to make any business more sustainable.