Your supply chain is a complex living organism, pulsing with cargo that gives life to your business — and there is a lot of cargo being shipped around the world.
The total value of global trade is $28.5 trillion. It’s estimated that 12 billion tons of goods were transported globally via ocean shipments in 2021. There are more than 3.5 million truck drivers in the U.S. And Amazon alone has 525 million square feet of warehouse space.
Not only is a massive amount of goods being shipped each year, but the immeasurable variables at play – carriers, weather, traffic, forecasting, politics, capacity – adds a significant degree of uncertainty in supply chains.
In fact, supply chain is a bit of a misnomer. In reality, supply chains are massive, multifaceted tangles of complex networks — as a result, shippers face an equally massive and complex degree of uncertainty. And uncertainty in your supply chain network causes it to leak value through poor decision-making and frustrated customers and staff.
The root cause of this uncertainty? Data. And the difficulty of capturing the right data, at the right time and having the computational power to make sense of it all.
Over the last decade or so, supply chain visibility has peeled back that fog to extract as many data points as possible from any unique elements that could impact the flow of goods. While successful, this has brought in a new challenge – how can you identify what actually matters and how can you operationalize your insights immediately?
Humans are pattern-seeking creatures. We make sense of our world by comparing and contrasting the familiar. When a recognizable pattern emerges, our brain takes a shortcut to arrive at conclusions.
In the supply chain, patterns are also useful. Identifying them could answer questions like, “How do I provide an accurate and tight delivery commit date to orders?” Or, “What are my high-impact problems? What actions can I take, at a network-level, that will allow my supply chain to operate most efficiently under current conditions? Where am I spending more than I need to?”
But supply chain patterns are so complex that our brains cannot assess them with a high degree of accuracy. What’s more, you might not have collected enough data from your supply chain to achieve a high confidence interval (if we think back to statistics class), helping you know if the insight isn’t just accurate but valid.
Instead of relying on what’s effectively guesswork to answer these questions or hoping that a sample of a select few similar use-cases is reliable, what if you could pull from a massive archive of data and let artificial intelligence (AI) recognize and make sense of patterns for you?
Supply chain intelligence can be achieved by using what’s known as a neural network in the data science world.
Artificial neural networks come in many varieties, but the common thread is how they decompose and solve numerical problems. These networks are designed to model the human brain — multiple, interconnected neurons that each have an activation function that causes it to pass its calculation to other successive neurons or layers in the network.
Once a neural network is set up, it needs to be trained with examples of input and outputs. This training process adjusts the neural network’s weights – or the strength or amplitude of the connections between nodes – allowing it to adapt and map a sequence of inputs into known outputs, ultimately providing actionable results. All of this is inspired by how the biological brain works, with synapses adjusting their weights by learning. And it cannot be done without advanced pattern recognition.
By leveraging the insights from a supply chain neural network, analyzing complex problems becomes easy. Today, supply chain teams use data to simply determine what happened. With an artificial neural network, you have the ability to determine what could have happened – a platform capable of using trillions of data points to say, “If you intend to do X you will see Y, so we recommend Z.”
While lightweight, AI-enabled applications are already helping daily operators proactively manage exceptions on a transactional basis, AI has yet to have its chance to shine in supply chain.
Why? Because when you’re trying to model events in an industry where 12 billion tons of ocean freight is only one small piece of the puzzle, you need a lot of data to ensure a high degree of accuracy. Data that is used to further train the neural network and provide even better outputs going forward.
Earlier this summer, we announced our engagement with FedEx and the launch of FourKites X. At the time, we shared how and why the massive archive of supply chain data that FedEx has will be the rocket fuel that propels customers forward with actionable intelligence:
“Very few companies have as robust a data warehouse as FedEx. They’ve spent years and countless hours consuming and cleaning data from 16.5 million shipments each day — things like tagging and cataloging Harmonized System Codes for cross-border shipments. Traffic patterns and returns of parcels for final mile and reverse logistics. Fleet usage to better understand fuel consumption and sustainability targets. And much, much more.
So how much data does that amount to? Petabytes. To put that in perspective, one petabyte is equivalent to 1,024 terabytes. In terms of dollars, one petabyte would be equal to $1 quadrillion – the global economy is estimated to be worth around $85 trillion, or 8.3% of $1 quadrillion. It’s hard to even conceive of how much data that is — so can you imagine what having access to that treasure trove could unlock within the world’s most complex supply chains?”
By combining petabytes of data with the industry’s most-used advanced AI capabilities, we’ll go from root-cause analysis and tactical management to answering pressing supply chain questions. How do I provide an accurate and tight delivery commit date to orders? What actions can I take that will allow my supply chain to operate most efficiently under current conditions? What options do I have to address issues quickly? Which gives me the highest chance of success at the lowest cost?
Your supply chain holds a lot of potential. From a core component of customer satisfaction to a leading part of your overall operating budget and carbon dioxide footprint, your global network offers an untapped sea of opportunity.
What if you could harness a platform that can identify patterns in your supply chain and measure them against the planet’s largest supply chain dataset? Together, we can create the most advanced supply chain neural network – one capable of not only recognizing patterns, but using that pattern recognition to proactively identify solutions to the most pressing problems in supply chain. Problems that affect the availability of essential goods. Opportunities to dramatically reduce emissions.
Imagine how much more efficient, profitable and sustainable your business could be.
