Barcelona, October 18, 2022.- The magazine Supply Chain Quarterly has published an interesting diagnosis on the application of AI in the supply chain:
There is good reason for supply chain managers to explore how to apply artificial intelligence in their operations. The global management consulting firm McKinsey & Co. estimates that by adopting AI in the supply chain, companies and their customers stand to gain $1.2 trillion to $2 trillion in economic value globally. With such an opportunity on the table, it’s important to survey which areas of the supply chain are most ripe for benefiting from AI. The Institute for Experiential AI sees three core areas of opportunity: transportation and delivery, warehousing and inventory management, and analysis and decision-making.
1. Transportation and delivery. A complex supply chain is not necessarily a resilient one. Each junction in the movement of goods introduces new variables and logistical hurdles. In turn, decision-makers must select from an increasingly complex network of routing and delivery models. As the inputs stack up—think of adding to a growing tower of playing cards—the long-term resilience of the system begins to buckle. The task of supply chain managers then becomes to find and adopt end-to-end solutions that can forecast demand, mitigate risk, and account for multiple variables and distribution routes.
AI makes that possible. Supply chain managers can now use machine learning to process the complex data streams that undergird logistics networks. For example, they can take real-time traffic and global positioning system (GPS) data and use machine learning to identify and select from potentially trillions of delivery routes. They can also use predictive analytics solutions that are enabled by AI to anticipate and plan for demand surges, mechanical failures, shipping updates, or disruptive weather events. AI systems can also monitor news snippets, audio messages, sensor data, text alerts, and other unstructured data and inform decision-makers when a disruption has occurred.
Cold Chain Technologies—a company in the life sciences sector that ships and handles heat-sensitive drugs, pharmaceuticals, vaccines, and biologics—uses AI to monitor, route, and deliver thermal-assurance packages. The company requires transportation solutions that are able to maintain consistent temperatures across the supply chain. (This is critical for transporting COVID-19 vaccines, for example.)
Thermal packaging requires specialized internet of things (IoT) sensors and measuring devices that produce streams of data that algorithms can harness to map real-time conditions in the supply chain. But, as CEO Ranjeet Banerjee explains, the task for supply chain managers is not merely to automate processes, but to forge a path through the technological landscape with human decision-makers at the helm. Value, then, derives from top-level decision-making and human involvement.
“You have to start with the problems, define the use cases, define the value potential, and then come up with a cadence of solutions,” Banerjee says. “But it’s not one-and-done. It’s merely to provide a roadmap of new value.”
2. Warehousing and inventory management. Supply chain leaders have the demanding responsibility of balancing supply and demand. To support that effort, warehouse and inventory managers are turning to machine learning. Machine learning can be used to monitor supply routes, predict lead times, and fulfill orders. In many cases, machine learning can perform these tasks with near or absolute autonomy. However, from a risk management standpoint, it is crucial that the degree of autonomy be customizable so that mission-critical decisions remain in human hands while the ML supplies decision-makers with real-time data.
For instance, inventory managers tasked with balancing warehousing capacities with inbound and outbound deliverables can leverage machine vision to assist in stocking and fulfillment. Computer vision software can monitor the movement of goods and alert managers when supplies are low. The human managers then make the crucial decisions about how to address this low supply. Other tools like automated product classification and AI-powered robotics offer cost-cutting efficiencies that can help optimize the fulfillment process and improve lead times.
3. Analysis and decision-making. Across applications, AI empowers supply chain leaders with sophisticated data tools and end-to-end supply chain visualization. On-the-ground data can be quantified and delivered to AI-enabled systems that can then analyze that data and present it to decision-makers as actionable information. For example, details about how shipping containers are loaded or unloaded can be analyzed by AI to inform decisions about how deliveries should be ordered so that routes are created in the most efficient way possible. AI can also be used by supply chain leaders in the event of a disruption to locate alternative routes, suppliers, or delivery models, saving them time and energy when exploring remedies. Other algorithms and data sets can be used to streamline costs. It’s no surprise, then, that leading firms use data-driven AI to manage carriers, negotiate optimal rates, understand risks, and inform bottom-line financial decisions.
One promising development that is helping drive better decision making across the entire supply chain is the new field of cognitive analytics. Cognitive analytics gives structure to large data sets in forms more relatable to linguistic processing. Such systems can learn from interactions between data and human supervisors to provide detailed, contextualized insights. These insights can be used to connect different areas of the supply chain in a more transparent fashion. And that transparency is key. As Nada Sanders, Distinguished Professor of Supply Chain Management at the D’Amore-McKim School of Business at Northeastern University, points out, successful firms understand that technology that offers transparency between silos in the supply chain is superior to a sophisticated system whose analysis is narrow and deep. In other words, if you only have one very deep technology in one area, then you’ll likely be exposing your operation to variables that would only be visible from a broader, more systemic view.
“When you look at supply chains, the key is to understand that they’re a system; you need to have information transfer, and you need transparency because information flows, products flow,” Sanders says.
RESPONSIBLE AI IN THE SUPPLY CHAIN
On their own, data analysis and AI can point to bottlenecks, excesses, and oversights in the supply chain. In ideal circumstances, those insights lead to more efficient outcomes. But their true power lies in contextualization—a task generally more suited to humans than AI. For example, AI can fortify and streamline supply chain operations, but these improvements must be carried out in an ethical and responsible way. Having humans in the task loop can make sure that this occurs.
In many applications, algorithms have exhibited latent biases that exclude marginalized people while reinforcing power discrepancies. Facial-recognition tools, for example, have been shown to regularly misidentify people of color. Language models may likewise perpetuate linguistic hegemonies. If these algorithms can run afoul of ethical concerns in social contexts, then they can do the same in supply chains. One widespread example occurs in hiring and recruiting, where AI has been demonstrated to show biases toward privileged groups. Additionally, systems that are automated to select suppliers based on pricing or logistical efficiencies may overlook exploitative labor practices or even sanction regimes that human decision-makers would know to steer clear of.
That is why leading researchers and chief technology officers (CTOs) point to transparency and human-led AI as the only reliable way to secure the responsible use of algorithms. Cold Chain Technologies’ Ranjeet Banerjee acknowledges this, underscoring the value of AI in augmenting, rather than replacing, human intelligence.
“The easy decisions are the ones you automate first,” Banerjee says. “Then you use [automation] to increase the bandwidth of the human. Over time you create a feedback loop, and you see how the actual worked against the prediction, and then you can use the human intervention more thoughtfully.”
It’s crucial to understand that this process is continual. There is no “one and done” ethical AI solution. That means companies may need to upskill or retrain their employees or restructure their organization to secure the promised benefits of AI in supply chains.
As supply chains become even more complicated in response to ballooning data sets, political upheaval, climate disruptions, and increasingly sophisticated algorithmic tools, enterprises will need to look at the wider picture. As Nada Sanders says, it’s not just about logistics.
“It’s money, it’s people, it’s information,” she says. “It’s the linkage of marketing on the demand side and how we sell something, the messaging. They’re all connected, and understanding that system is really where the human element coupled with AI comes into play.”
AI in the supply chain offers scalable levels of visibility, granular oversight of logistics, and dynamic feedback to support human-driven decisions. But these opportunities may require organizational refocusing as companies seek the right tools to measure and quantify outcomes. When it comes to assessing the value of AI and which solutions to focus on, they may need to take a long-term investment approach rather than zeroing in on a few widely used metrics to measure their ROI.
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