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Artificial intelligence


HOW AI COULD SOLVE SUPPLY CHAIN SHORTAGES AND SAVE CHRISTMAS

Just-in-time shipping is dead. Long live supply chains stress-tested with AI
digital twins.

By
 * Will Douglas Heavenarchive page

October 26, 2021

Ms Tech | Getty




With the supply-chain disruptions of the past two years showing no signs of
easing anytime soon, businesses are turning to a new generation of AI-powered
simulations called digital twins to help them get goods and services to
customers on time. These tools not only predict disruptions down the line, but
suggest what to do about it. Desperate companies struggling with the collapse of
just-in-time shipping are using them to find a crucial balance between
efficiency and resilience. 

The list of things that have been hard to get hold of at one time or another in
the last few months is as varied as it is long: new cars, new phones, contact
lenses, cleaning products, fresh produce, garden furniture, books, the color
blue. “It’s not like when everyone ran out of toilet paper in March 2020,” says
Chris Nicholson, founder of Pathmind, a company that applies AI to logistics
problems. “This time the missing items feel personalized.”


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humans to step in to set them straight.

Covid-19 has shined a spotlight on many of the world’s networks, from the
internet to international air travel. But the supply chains that crisscross the
world—the ships and trucks and trains that link factories to ports and
warehouses, bringing almost everything we buy many thousands of miles from where
it’s produced to where it’s consumed—are facing more scrutiny than they ever
have.

“It’s fair to say that whatever you’re selling, you’ve got a problem right now,”
says Jason Boyce, founder and CEO of Avenue7Media, a consulting firm that
advises top Amazon sellers. Boyce says he has clients who would be turning over
tens of millions of dollars a year if they could stay in stock. “We’re having
talks with clients every day where they’re just crying,” he says. “For months,
they haven’t been fully in stock for one 30-day period in a row.”

Digital twins seek to solve breakages in the supply chain by anticipating them
before they happen and then using AI to figure out a workaround. The name
captures the key idea of simulating a complex system in a computer, creating a
kind of twin that mirrors real-world objects—from ports to products—and the
processes they are a part of. Simulations have been a part of decision-making in
industry for some years, helping people explore different product designs or
streamline the layout of a warehouse. But the availability of large amounts of
real-time data and computing power means that more complex processes can be
simulated for the first time, including the chaos of global supply chains that
often rely on numerous vendors and transportation networks.

This kind of technology has given Amazon, which already has the advantage of
controlling its own trucks and warehouses, an extra edge for years. Now others
are embracing it as well. Google is developing supply-chain digital twins that
the car maker Renault announced it had started using in September. International
shipping giants like FedEx and DHL are building their own simulation software.
And AI firms like Pathmind are creating bespoke tools for anyone who can pay for
them. Yet not everyone will benefit. In fact, the powerful new technology could
widen a growing digital divide in the global economy.


WEATHER THE STORM

It’s easy to blame the pandemic for the current supply-chain problems. Factory
closures and labor shortages knocked out production and delivery hubs at the
same time that a leap in online shopping and comfort buying sent demand for home
deliveries rocketing.

But in truth, the pandemic only made a bad situation worse. “There are global
forces driving this, all combined into a perfect storm,” says D’Maris Coffman,
an economist at University College London who studies the effect of the pandemic
on supply chains.

Quelling this storm will require sinking trillions of dollars into global
infrastructure, expanding ports and delivery fleets, and investing in better
management, better working conditions, and better trade deals. “Technology is
not going to solve these problems. It’s not going to allow ships to carry more
containers,” says David Simchi-Levi, who leads the data science lab at the
Massachusetts Institute of Technology and has helped build digital twins for
several large companies. But AI can help companies weather the worst of it.
“Digital twins allow us to identify issues before they happen,” he says.

According to Hans Thalbauer, the managing director of the supply chains and
logistics team at Google, the biggest problem businesses face is an inability to
forecast events up the chain. “It doesn’t matter which company you talk to,” he
says. “Everyone in the supply-chain world will tell you they don’t have the
visibility they need to make decisions”

It’s supply-chain visibility that lets Amazon, for example, predict when an item
will show up on your doorstep. For every item that Amazon delivers itself—and
that includes the millions of items it delivers on behalf of third-party sellers
like Boyce and his clients—it gives an accurate estimate of when it will arrive.
It might not seem like much, says Boyce, but if Amazon got these predictions
wrong, it would start losing customers—especially around the holiday season,
when people are buying last-minute gifts and trusting Amazon to deliver them.
“It takes massive computing power just to show that simple little delivery day,”
he says. “But people freak the hell out when they don’t get their stuff on
time.”

According to Deliverr, a US company that manages delivery logistics for multiple
e-commerce firms including Amazon, Walmart, eBay, and Shopify, an estimated
delivery time of two days versus seven to 10 days increases sales by 40%; an
estimated delivery time of one day increases sales by 70%. 

It’s no surprise that others want a crystal ball of their own. Just-in-time
supply chains are nearly dead. The disruptions of the past two years sank many
businesses that chased hyper-efficiency to an extreme. Warehouse space is
expensive, and paying to store inventory you might not need for a week can seem
extravagant in times of plenty. But when next week’s stock doesn’t show up, you
have nothing to sell.

“Before the pandemic, most companies were focusing on cutting costs,” says
Simchi-Levi. Now they’re willing to pay for resiliency, but focusing on
resiliency alone is also a mistake: you need to find the right balance between
the two. This is the real power of simulations. “We’re seeing a growing number
of companies starting to stress-test their supply chains using digital twins,”
he says.


WHAT IF?

By exploring different possible scenarios, companies can identify the balance
between efficiency and resiliency that works best for them. Add deep
reinforcement learning, which lets an AI learn through trial and error what
actions to take in different situations, and digital twins become machines for
exploring what-if questions. What if there’s a drought in Taiwan and the water
shortage shuts down microchip manufacturing? A digital twin could predict the
risk of this happening, trace the impact it would have on your supply chain,
and—using reinforcement learning—suggest what actions to take to minimize the
harm.

If you’re a car maker in the US Midwest, a digital twin might suggest you buy
extra components from a distributer on the West Coast that still has surplus.
But thread multiple scenarios together and things soon become vastly complex.
For example, according to Simchi-Levi, Ford maintains more than 50 plants around
the world, which use 35 billion parts to produce 6 million cars and trucks each
year. There are around 1,400 suppliers spread across 4,400 manufacturing sites
that it interacts with directly, and a stack of suppliers and suppliers’
suppliers up to 10 layers deep between Ford and the raw materials that go into
its vehicles. Any one of those links could break, and a good stress test would
need to probe each of them.


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Digital twins draw on as much data as possible to run their simulations and
train their AIs. There’s logistical information about the company and its
suppliers, accounting for inputs such as inventory and shipping data. Then
there’s data on consumer behavior, based on market analysis and financial
projections. And data about the wider world, such as geopolitical and
socioeconomic trends. Simchi-Levi has even drawn data from social media to
predict people’s behavior, especially during the pandemic.

Google’s digital twin can be plugged into Google Earth and takes into account
global weather patterns. If you’re a vegetable farmer in California, you can run
simulations to see which of your fields are at risk from La Niña, says
Thalbauer. When Google sets up a digital twin for a client such as Renault, they
can choose which of the many available data sources to include.


RELATED STORY

How lockdown is changing shopping for good
Big online stores are based around vast automated warehouses. Smaller and
cheaper versions of this tech will be key if smaller stores are to survive
through a series of lockdowns.

Pathmind takes a less heavyweight approach. Its digital twin simply wraps around
a company’s existing supply-chain management tools, tapping into the data they
already produce. It then augments this data by running what-if simulations and
adding the resulting synthetic data to the pot on which it trains its AI. The
approach is similar to how AlphaZero mastered Go and chess by playing millions
of virtual games against itself. Instead of learning what piece to move on a
board, digital twins can learn what stock to order and when, or where to open a
new warehouse.

With the right synthetic data, a digital twin can learn to respond to previously
unseen events, even global pandemics. “This is where we get into the whole
secret of ‘Why is AI smart?’” says Nicholson. “It lives more than we do, in
these many different worlds, some of which have never existed before.”

In theory, anyone can benefit from this technology. In practice, there will be
winners and losers. “Digital-twin technology presents a powerful opportunity for
companies of any size,” says Rick Lazio, a lawyer and former US congressman who
is now senior vice president at Alliantgroup, a US-based tax consultancy. But he
notes that it’s larger companies, which are already the best protected from
losses, that are starting to use this tech fastest.

Lazio thinks that many smaller firms will need some help, perhaps through
government investment, to stop them from falling behind. “Companies that adopt
technology early see benefits greater than the sum of its parts,” he says.

And it’s not just smaller businesses. “A lot of the world’s ports run on paper;
if you’re lucky, they’re using PDFs and emails,” says Nicholson. “These are
major operators, not a candle maker in New Hampshire. But without digitization,
we don’t get AI.”

Simchi-Levi is more optimistic. Many businesses used to assume that setting up a
digital twin would take enormous investment and years to pay for itself, he
says, but that’s no longer the case: a million dollars and 18 months can give
you many of the benefits.

Simchi-Levi has no doubt that the buzz around digital twins will remain even
once the worst of the current disruptions are over. If it’s not the pandemic, it
will be something else, he says. The last couple of years have taught businesses
how to prepare better, and how to compete better. “When we go back to normal, it
won’t be the same as before,” he says. “The pandemic proved that the future is
here.”



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BY WILL DOUGLAS HEAVEN


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