Many moons ago as a young engineering student I was enthralled by a lecture from my Operations Research (OR) professor. He introduced the topic of the Traveling Salesman problem (TSP) that day. He said the Traveling Salesman problem was “NP-hard,” Translation: impossible to solve at scale with classical computing. And it was also the first time I heard the word Heuristics and how it applied as a near optimal method to best solve the TSP. I’d call this my first lessons in SCM optimization and AI.
Supply chain Disruptions come in many avatars today and the world has suffered and healed from quite a few of them. AI and its agents are already at work solving critical SCM problems. But can the recent advances do one better and take on the deluge of disruptions too?
Disruptions – Everything everywhere all at once
Even before we arrive at how AI could spot and fix disruptions, let’s look at day to day forces outside patterns that algorithms like to learn.
Optimization is no longer just routing ships, it is resilience that rewards visibility and recovery. (Gartner)
Where AI shines today
Today AI is the SCM SME and co-pilot rolled into one, tackling problems such as
- Predictive analytics that fuses weather, news, supplier signals, and IoT data to sense risk early.
- Digital twins that run “what-if” scenarios for networks, inventory, and capacity.
- Dynamic routing that recalibrates logistics when ports clog, lanes shift, or demand spikes.
- Planning copilots that summarize exceptions and propose next actions.
This is not theoretical. Leaders use AI to spot issues sooner and recover faster.
And that is not all. Friendshoring and nearshoring require rapid supplier discovery. AI can shortlist suppliers, score resilience, and flag compliance gaps across a shifting patchwork of rules.
Even with these gains, supply chain complexity does not disappear. AI helps, but discipline, governance, and human judgment still decide the trade-offs. (Gartner)
Where we’re going, we don’t need roads: SCM with AGI and quantum computing
And hence my bold foray into a future where we are heading, agentic AI, and maybe one day something closer to AGI. To this mix I add quantum computing, or quantum inspired optimization.
So, let’s hop into the DeLorean time machine (yep, couldn’t resist the Back to the Future nod) and tackle supply chain optimization, no matter the size, complexity, or curveballs our way. Buckle up!
Now let’s fire up the DeLorean. (Gartner)
Quantum: the math buster
Remember the Traveling Salesman Problem? Quantum computing will not solve NP hard puzzles, but hybrid and quantum inspired methods may speed up optimization workloads. Routing fleets across constraints could improve, perhaps sooner than we think. (Reuters)
AGI: the autonomous strategist
Now add agentic AI. Unlike today’s narrow automation, agentic systems can plan steps, use tools, and execute tasks within guardrails.
- Autonomously reroute shipments within approved policies
- Trigger supplier qualification workflows when risk spikes.
- Adjust production and inventory plans based on constraints.
- Spot interdependencies across politics, climate, and demand.
An agentic supply chain would not just respond to disruption. It would become more self-healing over time.
Imagine this:
A political crisis erupts in one region. An agentic layer sees ripple effects on materials, reroutes flows, proposes alternates, and escalates the high impact calls, all before your first sip of that Americano.
Meanwhile, advanced solvers, sometimes quantum inspired, keep recalculating routes and schedules in the background. The supply chain does not just survive disruption, it learns to flow around it like water. (Gartner)
Why Kannan still matters!
Now, a confession. I didn’t write this blog alone. AI tools helped. They fed me structure, lists, and angles. But without my memories of that Bangalore lecture, my not so funny Back-to-the-Future jokes, my own perspective on AI + SCM , this would just be another AI-generated post and YOU would have spotted it
And that’s the catch. Until AGI truly takes over, if it ever does, human-in-the-loop remains vital.
- Humans set the priorities: efficiency vs. resilience, cost vs. ethics.
- Humans ask the “why,” not just the “how.”
- Humans bring creativity, judgment, and yes, humor to the mix.
AI can predict, optimize, and even execute. But it can’t (yet) tell us what kind of supply chain future we want.
Drive that DeLorean back to 2025
Okay, so quantum and agentic DeLoreans sound fun, but what about the real world of today? You don’t need a time machine to start making supply chains smarter right now. Here’s the pragmatic playbook if AI is already part of your SCM, to keep outcomes measurable safe:
And to bring it all back to where we started, the Traveling Salesman, I hope he will return home soon using the shortest cost effective path that AI can choose for him. (Gartner)
“Here he lies where he longed to be;
Home is the sailor, home from sea,
And the hunter home from the hill”
-Robert Louis Stevenson
