Imagine a world in which trillions of individual pieces of information are gathered each day to create complex predictions about future supply chain disruptions and events. Extremely granular data on trade markets turns information about the movement of goods and services throughout the globe into cognitive systems with the power to illuminate new possibilities and intelligently predict changes in demand before they occur. While this may sound like science fiction, it’s increasingly becoming a reality as supply chains become more and more integrated with machine learning, artificial intelligence, and big data analytics. By 2020, IDC predicts that 50% of supply chains will utilize advanced analytics and artificial intelligence, and the effects on the global supply chain are sure to be widespread.
We’ve spoken a bit on this blog about the impact that AI and machine learning can have on a given company’s supply chaining planning and day-to-day operations, but what impact does this technology stand to make on the global supply chain as a whole?
Analytics in the Supply Chain
To begin with, let’s quickly review the ways in which advanced analytics are integrated into individual supply chains. Analytics generally fall into two distinct categories:
- Predictive analytics refers to analytics that create smart forecasts based on whatever data a given business provides, be it past demand information or information about historical disruptions.
- Prescriptive analytics refers to processes in which specific operational workflows are analyzed to determine any changes that could be made to increase efficiency or further optimize the functioning of a company’s value chain.
Though all manner of different methods of developing analytics exist on the marketplace, many of the most powerful resources being adopted right now utilize machine learning. Machine learning, a subset of AI, can refer to a number of different processes, but in general it involves training algorithms on datasets so large that a human planner couldn’t possibly extract meaningful insights from them. In an industrial context, this might take the form of Internet of Things (IoT) data from sensors attached to machinery and other equipment, or a constant flow of real-time pricing information across a variety of products or services that exist and change within a complex set of interrelations.
Cutting Through the Complexity
If your first thought when the idea of “too much data for a human being to analyze” came up was about the vast complexity of the global supply chain, you’re not alone. While individual supply chains certainly stand to benefit enormously from improved forecasting and process improvements, in some ways the perfect use case for machine learning is something as intricately complex and changeable as the global supply chain in its entirety. To be sure, worldwide supply streams result in the creation of more data than a human planner could possibly grapple with—and yet, understanding the relationship between global markets and one’s own supply chain is often crucial to meeting operational goals.
This is precisely where machine learning comes in. Applied to complicated, many-faceted value streams, these types of analytics solutions can cut through the complexity to provide unprecedented levels of inter-operational visibility. If, for instance, you were tasked with finding the most cost-effective transport options for your products, this level of visibility would enable you to make informed decisions based on information sources as varied as social media, weather reports, and historical data. The result is that related, interlocking supply chains work together more smoothly, owing to the more holistic overall view of those value streams that global analytics adoption is able to facilitate.
Predicting Disruptions
As artificial intelligence gains more widespread adoption, its predictive effects will only magnify. Not only does this have the power to reduce disruptions in individual supply chains, it also has the potential to make the global supply chain as a whole function more smoothly. Let’s say one of your suppliers has integrated AI into their value chain; as they get better at predicting potential bottlenecks, your business will be able to operate under increased confidence that any parts you may need from that supplier will arrive on time and in the correct condition. This, in turn, will lower your rate of bottlenecks and late deliveries, delighting your customers and continuing to make the entire worldwide value stream more efficient.
As this kind of practice evolves, some are predicting that we will see the rise of “anticipatory logistics,” in which shippers and manufacturers anticipate customer orders before they are actually placed by the customer. According to DHL, anticipatory logistics could increasingly empower businesses to analyze customer purchasing patterns in order to preemptively ready themselves for future orders, either by adjusting production ratios to accommodate future orders or by moving existing stock from warehouses to hubs located closer to expected buyers in order to decrease shipping time. More than a chance for companies to gain competitive advantages, the rise of anticipatory logistics represents the potential for a true paradigm shift in the way supply chains are administered. Suffice it to say that AI, machine learning, and big data analytics stand to change the nature of supply chain management in ways that we haven’t even imagined yet.
