THE FUTURE OF THE AUTOMATED SUPPLY CHAIN
AI / Gen AI Top Priorities for Digital Supply Chains
Avnet, a global technology distributor and solutions provider, has been using traditional AI for predictive modeling to improve the quality and visibility of its inventory management function for both suppliers and customers, says Doug Adams, senior vice president, global logistics and quality. The company is now investigating multiple potential uses for AI to both improve the customer experience and to help suppliers with forecasting. Within its global logistics function, Avnet has established an innovation and technology council that’s researching how AI can help all parties identify potential blind spots. The goal is to ensure Avnet is putting tools in place that can help its suppliers produce the right product, which Avnet can locate where it’s most needed, and in the optimal quantities. Avnet is investigating potential uses for generative AI in transportation, freight, and supply chain functionality, such as modeling Avnet’s distribution center network. For example, Avnet currently has four locations across Asia. Artificial intelligence could provide additional insight on these markets as they are today, as well as their expected growth, enhancing inventory allocation decisions. While it might seem there’s little difference between traditional
Artificial intelligence (including machine learning) Generative AI Internet of Things (IoT) Advanced analytics that are predictive or prescriptive Robots in manufacturing Big data/data lakes Robotic process automation Robots in logistics/distribution centers Wearable technology
20%
17%
11%
9%
8%
6% 6%
5% 5%
Mobile technology Augmented reality Blockchain Autonomous vehicles and/or drones 3D printing machines
4%
3% 3%
1%
0%
0%
10%
20%
Source: Gartner
automation and AI, that’s not exactly so. Traditional automation is task- oriented. “It’s ‘see this, do that,’”says Sujit Singh, COO of Arkieva, a supply chain solutions provider. Artificial intelligence can handle tasks, while encompassing advanced algorithms that can understand, reason, learn, and exercise some level of creative decision making, says Remington Tonar, co-founder of Cart.com, which offers a unified commerce platform. Cart.com, in partnership with select clients, currently uses AI to predict some customer demand.
BOOSTING PRODUCTIVITY The capabilities artificial intelligence offers can improve supply chain operations in multiple ways. It can leverage varying sources of information, including real- time data, to improve decision-making. Particularly in times of uncertainty, relying solely on historical data may lead to sub- optimal decisions. Early on, the technology probably will be leveraged more in execution actions than in planning. For instance, AI could help a supply chain professional determine whether to ship cargo today or tomorrow, and via one lane or another. While much of this is already within the realm of automation, AI would add a level of intelligence. Warehouse automation could be another early use case. “Warehouses are complex, but well-controlled environments,” says Matthias Winkenbach, principal research scientist at the Massachusetts Institute of Technology. For example, in order fulfillment, AI can analyze real-time data to predict the most efficient paths for picking items. DEMAND FORECASTING Applying AI to demand forecasting offers the “biggest lever,” says Ansgar Thiede, vice president, data science with Korber Supply Chain Software. Improved
Technology distributor Avnet leverages AI for predictive modeling to enhance inventory management and visibility for suppliers and customers.
28 Inbound Logistics • February 2025
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