This Note analyzes complex linkages in a YouTube video from the MIT Center for Transportation and Logistics: The Corona Virus and the Impact on the Global Supply Chain.
COVID-19 provides an unfortunate example of a ”live case“ that’s relevant to anyone affected by human health. That’s everyone. Moreover, since the C0V-2 virus is a rational actor whose behavior we are still trying to figure out, how we problem-solve this crisis can be applied to other challenges.
Rather than watching the video for 46 minutes actively listening and taking notes, I spent 15 minutes actively thinking with an Artificial Intelligence (AI) platform. Machine and I analyzed relationships being discussed by experts in the video via “supervised machine-learning.” That means I was in charge of the questions, and the AI responded with answers.
Here’s a detailed fly-through and what the sortie yielded. In the end, I was able to quickly grasp aspects of the problem that I would have missed, and think better about what we should and can do in this situation.
The takeoff was simple: cut and paste the YouTube video link into the SCAN program of SavantX, a hyper-navigator that discerns relationships in data. Then we look at the following:
What do we see? A complex network of linkages among nodes. Visually scanning the linkages, I notice a relationship between supply chain and vulnerable. Selecting those two nodes generates a new picture with text evidence that shows up as “Results” in the dropdown menu on the right. The text describes how these two ideas are related.
Looking at supply chain and vulnerable (two nodes in the middle, above):
I cross-check the main display again to see other connections to supply chain and vulnerable and see companies as a node. I select companies to investigate any relevant linkages there.
Looking at supply chain and companies (the node above supply chain, above):
I want to find relationships being discussed that a company can actually influence now, as well as for future planning. Focus on what we can do, more than what we can’t do, by making decisions and taking action.
Ok, but I don’t want to burn gas reacting all of the time; how can I be proactive? I notice the node, reacting.
Looking at supply chain and reacting (small node to the right of yossi sheffi, above):
Now I search for more opportunities to influence, and notice communication as a node. Even a pilot should be able to influence that…
Looking at supply chain and communication (small node in between companies and customer, above):
Ok, got that point. I note shortages. What can we do about those?
Looking at supply chain and shortages (far right, above):
Ok, what about controlling agencies: governments – federal, state, local?
Looking at supply chain and governments (center, above):
Too late in a crisis? Not entirely. This one will be long enough for action now to matter, particularly given the exponential rate of spread. Taking advantage of public-private partnerships is important all the time. Decision making is a node—what’s there?
As a last maneuver, I deselect supply chain and all nodes, and search the morass again, looking for targets to influence or plan against.
I find manufacturing and labor.
Looking at manufacturing and labor (center right, above):
The point here is wherever we are highly dependent we can be vulnerable, so diversify or know our suppliers’ situation. That’s good general advice, but what are options? What are competitors doing? Finally, I see competitors as a node.
Looking at competitors (small node to the left of cannot):
The remaining relationships display dangerous terrain with respect to competitors: alternate suppliers connected to China; suppliers cannot supply everything. Returning to base.
We spent much more time understanding the complexities of the supply chain problem, and much less on what to do about it. Such is the reality of good mission prep. As for mission execution, there are a number of critical engagements—discern vulnerabilities, specify shortages, improve communications, expand partnerships, adjust manufacturing, remote labor. Overall, there is an overriding need for proactive strategy.
Here’s a checklist of what I learned:
For a 15-minute session with machine learning, that was time well spent. Human-led AI is useful for problems that require speed and a deep understanding of complex relationships, vulnerabilities and opportunities.