Is unpredictability—the uncertainty that comes with ever-evolving supply chain disruptions—an unavoidable risk? Can technology help mitigate those issues?
Ongoing supply chain disruption, including the ongoing uncertainty of global tariffs, will increase adoption of AI in the supply chain. AI and other transformative technology untangles supply chain knots by modeling uncertainty and managing risk through AI applications rooted in reinforcement and unsupervised learning, stochasticity (randomness) and knowledge graphs.
Managing supply chain risk demands not only learning from history to address known uncertainties, but also tapping unsupervised learning to navigate scenarios that haven’t yet happened. AI and related technologies are just beginning to make waves in the supply chain planning and operations process. And the future offers not just unprecedented actionable insights and agility, but also AI-powered prescriptive solutions and more autonomous operations.
AI-based exception management
Your supply chain is vast, and you don’t need to know when every single thing is going right. Traditional alerts, even those based on intelligent thresholds, can lead to data overload. You just need to know what’s wrong, how to prioritize the most important challenges based on impact, and the steps to fix the most pressing issues. AI and machine learning can help by spotlighting the most important supply chain exceptions, including those that have downstream impact. AI can scientifically analyze historical data to generate thresholds for operational metrics, coupling that insight with inputs from users who capture domain knowledge through configurable business rules, to create robust exception parameters. This ensures your team doesn’t get bogged down in minutiae, suffer from alert fatigue and fail to respond to a critical alarm.
Generative AI helps operators plan how to route the movement of products from pickup locations to destinations with minimum cost, while considering pickup and delivery time windows, as well as the availability and constraints of different resources. Imagine this large exception: due to political changes, you suddenly need to source product from a supplier in a more tariff-friendly location. With AI, such midstream recalculations become easier. From port strikes to weather disruptions, AI can help you nimbly adapt manufacturing schedules, shipments, orders and other functions across your supply chain.
The new supply chain visibility
Ask yourself: How much inventory do you have right now, and where is it coming from? Will it be just the right amount at the right time to meet demand, or will you have costly excess inventory on your hands?
These are simple questions, but they’re tough for many supply chain leaders to answer with total confidence, every week and every month.
Companies need visibility into their entire value chain to manage day-to-day operations, identify and avert risk, and make the best decisions for future growth. But companies don’t always have the time or resources to realize this visibility when it’s needed the most. While many supply chain decisions have an immediate benefit, rash logistics decisions can lead to unforeseen downstream challenges. AI serves as a fail-safe, preventing operators from taking actions without considering all the value chain implications.
With generative AI, it’s comparatively easy to get quick answers on inventory levels, suppliers, disruptions and more. Too much inventory? Use AI in combination with other science-based models to see what happened, why and how to prevent it in future.
Read more from the Avathon white paper No more black swans: How AI is changing the game to drive supply chain visibility, agility and profit to see how companies are using new technologies to create resilient processes amid ever-increasing disruption.
