September 29, 2023 • 39 mins
In this episode, MIT CTL Director Yossi Sheffi, an expert with nearly five decades of experience in the supply chain and logistics areas, sits down with Susan Lacefield, Executive Editor of Supply Chain Quarterly. Yossi and Susan discuss the miracle of modern global supply chains—a magic conveyor belt that moves goods from mines and forests to supermarket shelves. They also discuss supply chain resilience in the face of major disruptions, the growing role that AI will play in supply chains, and how that affects practitioners, businesses, and consumers alike.
Executive Producer: Benjy Kantor Marketing Writer & Producer: Dan McCool Sound Editor: David Benjamin Sound Audio Engineer: MIT Audio/Visual Services
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
(upbeat music)
- Welcome to MIT's"Supply Chain Frontiers,"
presented by the MIT Center
for Transportation and Logistics.
Each episode of "Supply Chain Frontiers"
features center researchers and staff
or experts from industry
for in-depth conversations
about supply chain management,
logistics, education and beyond.
(upbeat music)
Today's episode features a conversation
(00:21):
between CTL Director Yossi Sheffi
and Susan Lacefield, executive editor
at "Supply Chain Quarterly."
Today's conversation was recorded
in front of a live audience
and covers a wide range of topics
touched on in ProfessorSheffi's latest book,
"The Magic Conveyor Belt (00:32):
Supply Chains,
A.I., and the Future of Work."
But first, MIT CTL offers a variety
of educational programsfor graduate students,
seasoned industry professionals
and anyone at any levellooking to learn more
about the supply chainand logistics domains.
To find out more about all
of CTL's educational offerings,
visit ctl.mit.edu/education.
(00:54):
And now, without further ado,
here's what makes the magicconveyor belt so magical.
- Maybe a good place to start
is with the title of the book.
Can you explain the analogy
you make between the supply chain
and the magic conveyor belt,
and what makes it magical?
- So let's start with why
I wrote this book.
After the pandemic,
(01:14):
a lot of people
were getting to my wife
(Susan laughs)and asking her,
"We understand yourhusband is in supply chain.
What is this?"
And imagine if, even after the pandemic,
people heard a lot of supply chain,
didn't know what it is.
So rather than having one-on-one interview
with one of the several hundred friends
that my wife has, I decide
(01:35):
(Susan laughs)to write a book.
So the first part ofthe book is explaining
what supply chains are,
why they are complex and, in some sense,
why would people should not
be pissed off when something
is not on the shelf or not available
on Amazon, but should be amazed
(01:56):
and awe-inspired when it's there.
Once they understand what it takes
to get something from the mines
in China or somewhere
to a finished product on a shelf,
how many processes it has to go through,
how many people are involved,
how many different tax regimes,
custom regime it has to go through
before we get the final product.
(02:17):
So this was the rationale.
And the magic convey belt is because
once you understand what it takes,
you think it's magic.
- Mm-hm, and it's very true.
- That's the title.- That's true.
So it was to get awayfrom people asking you
why their cat food-
- Yeah, absolutely.(Susan laughs)
Absolutely. (laughs)
- So as you mentioned,
the first part of thebook really talks about
(02:37):
the growing complexity of the supply chain
over the past few decades.
And I was wondering,
do you think we're gonna reach a point
where companies aregonna push back and say,
"Things are getting too complex"
and we need to maybe take a step back
and look at simplifying?
Or is complexity here to stay?
- I'm not sure.
I think complexity is here to stay.
Complexity is here to grow
(02:59):
because of unexpectedevent that's happening.
And furthermore, I'm not sure
there's a pressure to do it because a lot
of the technology that is being available
help company deal with the complexity
and deal with the unexpected event.
So I'm not sure there'sa pressure to do it,
especially among large,sophisticated companies.
(03:21):
So the answer is no.- No.
(Susan and Yossi laugh)- It is here to stay.
(Susan laughs)- Here to stay.
- You talked about one ofthe most mind-blowing facts
about any product that we touch
is the thousands of organizations
that have been involved in creating it,
and that they have done that
without any central control.
And I was wondering ifdecentralization is,
(03:42):
do you feel that's crucial
to supply chain efficiency and operating
in this complex world?
- Categorically, yes.- Okay.
- The idea that somebody can control,
control of supply chain iscontrolling the economy.
We tried it once or twice.
Didn't work very well.
So we're talking about modern markets.
(04:04):
Supply chain is actually
a whole set of buyer-seller,
buyer-seller, buyer-seller negotiation,
transaction, operation.
It works because everybody's trying
to do the right thing to minimize costs
and maximize level ofservice, by and large.
Now there are other things people
are worried about, like sustainability
(04:24):
and resilience, but everybodyis worried about it,
so everybody's trying to get
the best outcome.
I don't see how central planning can work.
Even in China, we don't see,
it's not central planning.
Central control of certain aspects,
but not of the transaction.
In fact, the Chinese
seem to be leery ofvery large corporations
(04:48):
who control more of the larger part
of the economy.
Has happened to several, you know,
tech companies in China.
They actually seem to encourage
competition between companies.
So I think it works, the market works.
- But as you introduce decentralization,
there's an element of risk
that kind of enters the equation.
(05:09):
I was wondering how dowe balance that risk
with all the benefits?
- No, it's, au contraire.
The risk goes down.- Huh.
- Because the risk to a particular company
maybe goes up.
They are out there on the front line.
But the risk to the economy-- Ah.
- Goes down.- Okay.
- Look, you can always find
(05:30):
good restaurant in New York, always.
You walk to a random restaurant,
the chances are it's a very good one.
Why?
Because restaurants in New York,
if you open a restaurant in New York,
the chances are within a year,
you'll have to close it.
The competition is murderous.
There are so many good restaurants.
So you can say the chances
for individual restaurant to succeed
(05:50):
is not very high.
But going to New York andhaving a good restaurant,
you know, the environment is great.
It works.
There's no risk.
You don't risk going toNew York and not finding
a good place to eat.
I'm not saying a place toeat, a good place to eat,
because it is decentralized.
- But there is, when you outsource
(06:11):
to a supplier and they're outsourcing
to other suppliers,there is that added risk
of, you know, a quality defect
that you can't control
or a sustainability issue popping up.
Is that a concern withthis decentralization?
You know, how do youcontrol for that sort of?
- I don't see it as adecentralization issue.
- Okay, okay.- I see it
as the depth of the supply chain,
(06:31):
the lack of visibility.
It exists.
It get slowly better with new technology.
But there are limits here.
The limits are that
for suppliers to tell their customer
who their supplier is,
not every supplier is willing to do it.
It's a competitive advantage
to know who the suppliers are.
And there always the fear
(06:53):
that the customer will go around them,
will go directly to the supplier.
So there's a kind of built-in opaqueness
to the supply chain,
which we're trying toget through to visibility
and good relationship and all of this,
and some people are moresuccessful than others.
But this issue is not a technology issue
and it's gonna be veryhard to solve completely.
(07:14):
And it's not decentralization issue.
It's the depth of the supply chain.
- So in the second half of the book,
you spend a lotta time talking about
artificial intelligence and the effects
that AI is having on the supply chain.
And I was wondering, you know,
when ChatGPT hit the scene in November,
suddenly, generative AI
(07:35):
became a very hot topic.
And I was wondering ifyou could talk about
some of the applications for generative AI
that you are seeing in the supply chain.
- First of all,
let me just explain
that we have been using, even-
- Oh, yeah,- AI for a long time,
using that.
All the restaurants,
all the drive-through restaurants
are using chatbot.
(07:55):
But it's not only drive-through.
Every time you call, nowaday,
customer service function,
you're talking to a chatbot to interpret
the results and try to give you answer.
And if sometimes it gets stuck
or you get stuck and started screaming,
"Agent, agent, agent," orsomething to this effect,
a human comes on.
And just like when yougo to the drive-through
(08:16):
and you start ordering, you know,
Champagne(Susan laughs)
and McDonald doesn't have it,
a human comes onboard andsay, "Well, I'm sorry.
We don't yet serve Champagne."
An interesting application
is in risk management
and supply chain,
trying to look at suppliers
and finding out
how risky they are.
(08:37):
Turns out that
when you look at metrics like
(indistinct) then financial metrics,
they are backward-lookingby about two quarters.
You want to see what's going on now.
We know, for a long time,
that one of the warning signs
is having coverage about
(08:59):
executives' living,
about failing some projects,
failing some M and Aproject in particular,
having bank covenants
that are a little problematic.
So now we have several companies
are using large languagemodel, particularly,
to look at tens of thousands of suppliers
(09:19):
at the same time andanalyzing all of them,
analyzing all the mention in the media
of redundancy, of executive living,
whatever, in order to generate
an alert and have somebody visit there
and finding out what'sgoing on, if we need
to start looking foranother supplier or what.
So this is something that could not
have imagined beforewe had this technology.
(09:42):
Looking at, if you look at a company like,
I don't know, I'm working with Flex a lot,
and they've 18,000 suppliers.
It's just, first-tier suppliers,
just finding out what's going on
with them is an issue.
Having a much better alert
when something goes wrong
is something that we were not able to do
before this type of technology was able.
(10:04):
We could check, you know,
10 suppliers at a time.
Checking tens of thousand was impossible.
Now it's being done.
- So the AI is going to actually enable
even more complexity in the supply chain
in the future as we're-
- Yes.(Susan and Yossi laugh)
It just, it can enable more possibilities.
More possibilities create complexity.
(10:25):
So, of course, when people get into,
when there's pressure, economic pressure,
whatever pressure, we know
that during recessions, company reduce
the number of SKUs.
They're trying to simplify.
They're also trying toreduce cost, you know,
improve service, butthey're trying to simplify.
But, you know,
there's the accordiontheory of management,
(10:46):
that when recession happen,
the number of SKU goes down,
and then marketing comes up
with all the good reason why we need more
and more and more SKU to serve
more and more territories,
more and more, 'cause all kind of option.
And then it, so that's theaccordion theory of management.
And it works, actually.
- So it kind of, it'slike the pendulum swing
(11:07):
that kind of balances.
- Yeah, between recessionary period,
expansionary period.
- So we've talked a little bit about AI.
Is there anything aboutthe application of AI
to the supply chain that gives you pause
or areas of concern?
- Not about the supply chain.
The areas that give me concern, the areas,
other people call, the area of fake news
(11:30):
can be done very convincingly.- Yes.
- The area of giving instruction,
how to build improviseroadside explosives.
But while I'm saying this is a concern,
a concern around, and the media,and I'm not that concerned
about it because, just give you an idea.
Unlike the early days of the internet,
when we all, everybody thought
(11:50):
this is the greatest thingsince sliced bread, right?
Because we can communicate with everybody,
families can see each other.
"All the, you know, distance is dead,"
to quote Tom Friedman.
Nobody thought about identity theft
and stealing customer data
and terrorists communicatingto each other on the net.
(12:11):
But now, it's different.
With the generative AI,
the companies, the media,
the politician are allaware of the dangers.
So there's a lot of workis going on already.
Already, the companies themselves
are putting guardrails on this.
So if you get ChatGPT or any one
of the others and ask how to prepare
a Molotov cocktails,it's not gonna answer.
(12:33):
So this is not give you an answer.
So they already started to put guardrail,
and there'll be more of this.
- Are you seeing that also
with use of analytics in companies where,
you know, you might have an algorithm?
There's an example in the book about
two competing bookstoresand they're both using
a pricing algorithm on Amazon.
And as a result, that drove up the price
(12:55):
of the book very high.
Are you seeing?
Companies already hadthose human interventions
in place to make sure
that the algorithmsdon't go outta control.
- Let me give you a moreeven general answer.
- Okay.
- One of the most important type of work
in the future will be monitoring,
vetting the automation,
AI-infused or otherwise,
(13:15):
but having a human monitoring.
That's a tough job.
It's a tough job because you have
to monitor something, andif you don't do every day,
and actually you lose expertise.
- Right.- It's hard to keep sharp.
And we have cases when, you know,
things did go awry.
So it's important, how dowe train people to do it?
(13:36):
- Yes.- For example,
today, modern aircraft can basically fly
by itself, gate to gate.
Now, talking about autonomous vehicles,
not too many peoplewill go on a, you know,
aluminum cylinder that fly 35,000 feet
over the ocean without a pilot, right?
So the pilots are in the aircraft,
(13:59):
actually don't need to do anything.
They can just sit there and nap.
But what we do, we let them do
the communication, basically.
It's the number one job.
So they always have to communicate
and change frequency.
So they keep alert.
It's one way to do it.
Because flying the airplane,
it flies itself.
So you really don't, once you put
(14:20):
the crew in, it flies, it change route,
it goes automatically.
But you give some jobs to the human,
they are not gonna fall asleep.
That's part of thechallenge of the future.
There are several models how people
and machine can work together.
Now, one such model is what
we talk about, the chatbots.
The chatbot has a monitor because
(14:43):
you talk to McDonald, whatever,
in the drive-through,
you actually talk to achatbot in most places,
and they respond.
And then when they don'tunderstand something,
a human comes on and we talk to you.
So there's a monitoringof what's going on,
and the minute that thechatbot doesn't understand
(15:03):
what's going on or gives the wrong answer
or whatever, a human comes.
So that's actually a monitoring function
that we don't even think about,
but happens every day.
With most customer service function,
you know, it used to bethat press one for this,
press two for this, press three for this.
That's rare now.
Now you just talk to the computer
and it turns it into text that appear
(15:25):
on somebody's screen, and then they report
and try to find an answer.
That's AI.
- Do you have any goodexamples of companies
that are doing goodthinking around what should
be given to humans todo in the supply chain
and what should be outsourced to AI?
- To me, that's thequestion of the future.
- Yeah.- The question of how.
(15:46):
The integration of humans
and AI-infused automation
is a question of the future.
We talk about one model.
The monitoring is one model.
You can think about whenthe human is in the loop.
The human is in the loop, for example,
think about an Amazon warehouse.
When the picker stands in one place
(16:06):
and there's a, you know, the aisle comes
to the picker, does something,
then another aisle come to the picker.
So the human is in the flow of the work.
So that's another example.
And a third example is the human operates.
When you go to several automotive plants,
for example, you see workers
(16:27):
standing with iPad-like devices
and basically running the robots.
That's another way of working with AI.
So that's, as I said, thequestion of the future,
how to organize the work-- Exactly.
- And how to, in some sense,
how to get the best out of the machine
and out of the human because
they have complementary skills.
You know, machines work all the time,
(16:49):
don't get breaks,
don't go to the bathroom.
They just work and-- Get sick. (laughs)
- They don't get sick.
And they're usually very accurate.
They do, you know, repeatedwork over, and overtime.
What machines don't have is context,
understanding whensomething does not belong,
has to change.
(17:11):
When we start think about, you know,
something change in the economy,
and suddenly peopleorder things differently.
So many standard automated ordering system
use the point of sale data
and order based on this,
put it into some forecast.
But this forecast is based on,
(17:32):
at best, say, on machine learning,
which is basically looking at past data.
All forecasts are based on past data.
When something is changing structurally,
suddenly there's a pandemic,
suddenly there's something else happening
and people change their buying habits,
then humans have to intervene again
because the machine does not have context.
(17:55):
As the machine isconcerned, nothing changed.
I mean, it gets, you know,
point of sale data, I keep going.
But something has changed,
and people understand the context.
Now, there's otherthings they worry about,
empathy and bias and things in general
that human can make sure
that happen or don't happen.
It's harder for machines.
(18:16):
- Do you think we'regonna get to that point,
where machines are gonnabe better at mimicking
that empathy piece?
'Cause it feels like thepeople who are working on AI
are trying to get there, you know?
See AI used in mentalhealth these days and.
- Yes, there are some
actually automated psychologists that try
to help people.
Who knows.- Who knows?
(18:36):
(laughs)
- I'm not sure about this because
that's exactly a question of context.
- Yes.
- Two people coming and saying, you know,
"I hate my children."
(Susan laughs)
- Or, "I hate my supplier." (laughs)
- Or, "I hate my supplier."
Well, you hate a supplier, don't go to a-
(Susan mumbles)
a psychologist.
But, you know, you hate your children,
(18:56):
(Susan laughs)you go to a psychologist.
But the context may be entirely different.
You know, I hate my child because
he's a thief and a liar,
or I hate my child justbecause I don't like tall kids.
I don't know, who knows?(Susan laughs)
- I mean, it's the context that-
- Get a crick in my neck(laughs) when I talk to you.
(laughs)- It's hard to imagine
some of these things
moving to AI completely.
And they talk about supplier.
(19:17):
Again, it is hard to imagine,
or let me put it strongly,
I don't think in the next 10 years,
in five, 10 years,
we will be able to have an algorithm
setting up a contract with a supplier
in China or Vietnam, let's say.
To set up a contract and relationship
(19:38):
for a long while,
it requires somebody to fly to Vietnam,
to negotiate like hell for two days,
and then sit and havedinners or two dinners
and talk about their kidsand talk about the family
and create relationship.
I don't see it changingin the near future.
I mean, AI will have to be so much better
and have to, but not the quantum jump
(20:00):
in capability to be able to do it,
which, right now, not clear it's possible.
- It's interesting, though, because
there's been a movement with technology
of making decisions morefact-based as opposed
to, you know, I like Joe
over at such-and-such trucking company,
so we're gonna use him.
But it seems like thathuman relationship is,
(20:21):
you're saying is stillgonna be an integral part
of supply chain management in the future.
- Yes, it is still integral because,
for example, if something goes wrong
and there's some disruptions,
how do I make sure that this supplier
knows my situation, knows me?
(20:41):
And if I'm calling and say, "Look,
I really need it," and everybody else call
and say, "I need it."- I need it, yeah.
- But I know this guy and I know
that he really needs it.
So the knowledge is, I think,
still very important, thepersonal relationship.
Now, one has to be (indistinct).
There may be critical suppliers
(21:02):
and maybe suppliers thatare not so critical.
And if they, maybe supplier,
if I have some part,
some commodity that theyhave dozens of suppliers,
and if that supplier goesdown or I have some shortage,
there are many others,maybe that I don't need
to be close to them.
But for most important suppliers,
I don't see any other way.
(21:24):
- Sometimes it's hard to know which
are your critical suppliers.
You might need that littlescrew, and then, suddenly,
that screw goes down.
- Yeah, it's called, inthe automotive business,
they call it the golden screw.
It's one part that's missingand you cannot make a car.
- Right, right.
The example in your book about the Ford
not being able to ship out because
they didn't have the little Ford logo
that sticks on the truck.
- This was last year.
(21:44):
You know, Ford has the blue little oval
that they put in the front of the truck.
They didn't have themduring the shortages.
They couldn't make trucks.
I mean, the trucks werestanding (laughs) in the yard
and they couldn't sellthem for a month, actually.
- So can AI be helpful identifying
who it is that you need to spend your time
(22:04):
developing that human relationship with?
It might not be who you think it is.
You also have to.
- You can take AI, I think it's simpler,
but as an aside, let me say that AI
became the buzzword-- Buzzword.
- At the time.
We used to think about blockchain or RFID
or became, and, you know,
people who are doing blockchain project,
(22:25):
they're actually justfixing up their systems.
To get funding frommanagement, they call it,
that's a blockchain project.
Now they call it, that's an AI project
for doing some optimization.
- So that's the learning to go away.
When you go back to your company,
make sure your project is AI. (laughs)
- Use AI, okay, what exactly are you using
and is it appropriate?
(22:46):
Can that tell you howmany company out there
that tail is wagging the dog?
I used to go to boards,
and people would ask the CEO,
"What's your China strategy?"
Or "What's your blockchain strategy?"
Now you ask, "What's your AI strategy?"
And I always said, "Stopit, what's your problem?
(Susan laughs)Start with the problem.
- Maybe the solution is AI,
(23:07):
maybe it's just hiring another person.
(Susan laughs)
I mean, you don't start with the solution.
But it's amazing how many people still do
because, I don't know, in part
because Wall Streetpays premium for having
an AI strategy orsomething of this effect.
It's not clear to me. It makes no sense.
- Is figuring out the problem an AI issue
(23:29):
or a essentially human issue,
is that something that's gonna-
- AI issue, you know,operational research issue,
statistics issue, you know, people issue,
process issue, can be anything.
So that's why I don'tlike having an AI strategy
or a blockchain strategy or whatever
is the current fad.
(23:52):
I should say AI is not fad.
It's been growing for many, many years,
and we got to the point that it could
make substantial changesin how people work
and the relationshipbetween people and machine.
- Right.
Just like a year earlier,
I think the buzzword was all robotics.
So it's kind of, or cobots, and so
it's the same sorta thing.
(24:12):
- Robotics are also now fused by AI.
I mean, so,- Right.
It's not the actual hardware of the robot,
(crosstalk) it's the software.
- Of course.- Yeah.
So kinda taking a step back, to your point
about context and the pilots
and training, sometimes you have
to do all the low-level jobs
(24:33):
to get that context toknow what to do next.
So-- I do talk about it.
- Yes.
So what can we do
with our supply chain pilots,
so to speak, to make sure
that they have the background,
the knowledge to be able to take over
those unusual events?
- Again, I take the problem
a little further from your question.
- Okay.
(24:53):
- So I was interviewing a shop,
basically a software provider,
asking them about ChatGPT taking their job
because it can now program.
And so the senior computer scientists
are not worried about it,
but it may take the job
of the junior computer scientists.
Now we're saying, "Guess what?
(25:15):
Senior computer scientists don't come
as senior computer scientists. (laughs)
They start as junior computer scientist.
We don't hire junior computer scientists,
you don't have work for them,
you are not gonna havesenior computer scientists.
And even for monitoring,
you need people with experience.
In the book, I talk alot about how to do it
and how to upgrade skills,
(25:35):
but there has to be recognition
that you need to hire people
at the lowest level.
One of the suggestions that I made
is maybe pivot
in the United States
for more of the German system
of people spending half time in a company
and half time in a university.
And they come up, and it's called
the dual education system.
(25:57):
That's about 52% of theGerman high schoolers
go into this system, whichis government-controlled.
The government defines Germany.
So the government define 365 professions
where this can be done.
And the university,you apply to, actually,
to the company,
and they work with a localcollege or university.
You spend half the timestudying the theory,
(26:19):
basically, and half thetime doing the work.
70% of these people gethired by the company
that they do their internship with.
But they come with experience,knowing the culture,
knowing the company.
It's much higher to move them
and much easier to move them along.
The United States, wesuffer another problem,
is this, is every mother
wants to say that theirchild goes to college.
(26:40):
My child goes to so-and-so college,
and your child just goes to trade school."
I always say that peopleshould meet my plumber.
Yeah.- My plumber drives a Bentley,
and we don't have enough plumbers.
And they can set the price, and they do
set it high. (laughs)
So we send, there are too many people
who go to college in the United States
(27:00):
and unfortunately, in manycases, come back with,
have to call it debt for a long,
long period, ratherthan go to trade schools
and community colleges, or combination.
Actually, there's auniversity here that does it.
Northeastern.- Northeastern, yes.
- The combination of work,and it's not as organized
as in Germany, but it's the same idea.
(27:22):
You work one semester,
you study one semester,and you flip between them.
- It's interesting.
I feel like Northeasternis becoming a school
that more and more peoplewant to go to nowadays.
- I know.
- So that comes back to your main job
of training students.
How have what you feelare the necessary skills
for a supply chainmanager changed recently?
(27:46):
- If I go over the history,
this program here started
a very analytical program,and then we realized
that our graduates
are very analytically savvy,
end up working for Harvard MBAs
who are half as smart andget paid twice as much.
And said, "This is not working."
Furthermore, companies came to us and say,
(28:10):
"Your graduates don't go up the ladder
in the company because
"they need the soft skills."
They need to be able to communicate,
they need to be able to sell,
they need to be ableto explain a position,
they need to be able to work in a team.
So the programs change,
started doing a lot more of this.
I think that as AI and automation
is getting more andmore into the workplace,
(28:32):
is the soft skills that willbecome even more important.
How do you work in team?
How do you make sure that your people
can work with AI?
You know, the promise of AI is that
it will do the job thatnobody wants to do,
and people will do the more interesting
and fulfilling job.
How do you make sure thatthis actually happens?
(28:52):
So all of this is part of the challenge
of the future.
We don't have all the answer yet.
We don't even have some of the answer yet,
but we're thinking about it. (laughs)
So people will need to understand,
we're not training computer scientists,
but people need tounderstand the capabilities
and where it can go wrong.
(29:13):
So people need to be sophisticated users.
It's like my colleague Chris Caplice
always talks about driving.
There are mechanics whoactually can fix the car
and know what's inside.
And then drivers, you don'thave to know what's going on.
You can just operate it.
We like to train drivers,people who understand
what the system can and cannot do,
but they don't need to be builders of AI
(29:36):
or generative AI system.
But they need to know thepromise, the limitation
and how to best use them.
- Yes.
- People always ask me, in classes,
if we allow people to use ChatGPT.
That's a big debate in universities.
Some universities absolutely disallow it.
It's ridiculous.
You know, when I was your age
(29:56):
and actually younger,
they used to teach me howto take square root by hand.
None of you studied it
because there are calculators.
None of you are studying how to do
a financial statement by hand because
there's now Excel and spreadsheets.
So the question is, why do you need to do
(30:17):
to replicate what ChatGPT can do by hand?
What you need to do is when it goes awry,
you need to test it.
You need to make sure that the results
are not what they call hallucinations.
(Susan laughs)
So because ChatGPT canhallucinate and invent stuff,
invent sometimes referencethat don't exist.
(30:37):
So you need to be sure of this because
if you can submit to me a paper
written by ChatGPT, as long as you realize
that if something is wrong,
open AI is not getting anF, you are getting an F.
Just so we understand each other.
So in short, the responsibility
is still on the user,
but not using a tool that's available,
(30:59):
for me, it's a losing proposition.
It's very hard to work.
Another example, you know,
of how automation is de-skilling jobs,
but having other benefits.
So if you go to London
and you go to a black cab,
you know, to drive a black cab in London,
you have to study for three or four years
(31:21):
and pass an exam, which is considered
the toughest exam in the world because
you need to know every point of interest
in London and how to go fromeverywhere to everywhere.
And you sit in an exam that you have
to show that you can drive from everywhere
to everywhere in the shortest route.
And you have to understand congestion.
And people who are doing this
and spending four yearsof their life doing it.
(31:42):
And then came Google Maps and Uber.
Everybody can do it.
Now, there are still,
the number of black taxis in London
went from 25,000 to about 8,000,
but the number of Ubers available
is now about 60,000 in London.
Lots more of them are available.
So win some, lose some.
(32:04):
- Another thing you talk about in the book
is how technology has had an impact
on enabling supply chain strategy.
Like, we wouldn't have been able to do
all the outsourcing and offshoring
if we didn't have advancedcommunication technology.
Do you see some radical changes
on how companies will bestructuring their supply chain
(32:25):
or organizing it because of the AI
or other emerging tech, like robotics?
- It's already happening, in the sense
that the number one use of robotics
is in warehouse automation.
I mean, warehouses are putting robots
like there's no tomorrow.
Autonomous vehicles.
Autonomous vehicles are robots.
So there's a lot of workon autonomous tracking.
(32:46):
- Yeah,
- Let me just say, however, that I talk
to a lot of people, a lot of interviewing,
people are worried about their jobs.
It's the number one, you know, fear, jobs.
And, again, people should chill, at least
for the short term, becauseit doesn't happen fast.
Give you one example.
In 1892, AT&T invented
(33:07):
the automatic telephone exchange.
Until then, there were, you know,
women putting plugs.
"Where's Mrs. Smith today?"
"She went to the supermarket."
They'll connect you later.
Very personal service.
- That's what my grandma did.
- Yeah, okay.(Susan laughs)
By 1950, there werestill 350,000 operators
like this in the United States.
Only by the 1980s, it started to go
(33:31):
really close to zero.
Nine decades from the invention
until it really,
all the jobs went or mostof the jobs went away.
So it takes time, andit takes time because
there are many hurdles.
You see already hurdles.
You see what are the writers
and actors worried about?
They're worried about using AI.
(33:53):
And they are, you know,stopping the industry,
putting the industry down.
And the industry will have to come
to some kind of agreement.
My guess would be part of the agreement
will be somehow slowingdown or putting guardrails
on the use of AI.
- Kinda like dock workers with-
- I was about to say.- Sorry. (laughs)
- Dock workers also fight automation.
(34:13):
In Long Beach, it's nothing like Rotterdam
or Singapore or Dubai because
of the afraid for the job,
afraid for the immediate job,
and not taking into account
that you can increase the throughput
and get even more jobs.- Right.
- In general, that'sthe most difficult thing
in this area, in this, peopleare worried about jobs.
(34:35):
And I understand it.
It's anxiety because
you know that people aregonna lose their job.
You see it in the supermarket
when you get to, when youcan check out yourself.
People are gonna lose their jobs.
So these are people that you know.
What you don't know isall the new industry
and the new jobs will come.
So one quick example of this,
that is old example.
So Ford came up
(34:57):
with a assembly line system.
Changed manufacturing, of course.
But it used to be the specialty team
used to build one car at time,
and Ford employedseveral thousand workers.
During the height of the Model T,
using the assembly line,
Ford had about 150,000 workers.
But this is not the big impact.
The big impact was that automobiles
(35:19):
became less expensive.
Highway developed, hotels, motels,
restaurant, the whole hospitality industry
created millions of jobs.
This was not what Henry Ford had in mind.
(Susan laughs)
I mean, but it was a sideeffect of what happened.
That is why it is so hard to imagine
all the new jobs that will come.
(35:40):
Many of the jobs that exist today
did not exist, you know, few decades ago.
We talked few decades ago about people
who will optimize ads on Google or people.
There's so many jobs that are totally new
because of new industries that came up.
So it's hard to predictwhat would be (mumbles).
(36:02):
the one thing aboutsupply chain coming back,
because that's what you ask about,
is it still involve physical movement?
Product have to move.
So there are some thingsthat will be still grounded
for a long time until we start having
3-D printing at scale.
This can change supply chain,
(36:23):
but it will be a long time
because 3-D printing is still very slow.
Technology cannot replace mass production,
not even close to replace mass production.
So I don't see fundamental changes.
The changes that may come will come
because of geopolitical consideration,
resilience consideration,
(36:44):
sustainability consideration.
But to get this, done we'll need to have
some more system thinking,
which is in very short supply
among the politicalclass, the media class.
People are talking about.
Give you an example.
Rare earth minerals are used
in every sophisticated product now.
(37:04):
China controlled 80, 90%
of the world supply.
Aluminum, China controlledmost of the world supply,
and stone, most of thesmelters are in China.
You know which country hasmore rare earth mineral
in the ground than China?
The United States.
But we don't want to mine it because
it's environmentally problematic.
Even though one should say
(37:25):
if it were done in the United States,
it would be probably done ina lot more responsible way
than it's done in China, but, still.
So we have to decide.
We have to stop saying, and green is,
and we just go green.- Right.
- We go security, wego standard of living.
We have to think more holistically.
And this is systemthinking that, as I say,
(37:47):
in short supply, becausethere are pressure groups,
whether the green parties in Europe
or environmental lobbiesin the United States.
There are the security hawks
that want everything to be from here.
But, again, from supplychain point of view,
moving the assembly or thelast stage of manufacturing
(38:08):
to United States is meaningless,
or to Europe, is meaningless
because there's a whole supply chain
that was built afterinvestment of billions
of dollars and decades
that is still in China.
Very hard to get out of this.
It will take billionsof dollars and decades
to get out of there.
So we need to stop talking about
(38:31):
totally separating the Chinese
and the Western economies,
and starting to work better together.
It's just not realistic.
- So no two, what are they?
Two-pronged or?- Two-pronged supply chain.
It's a nice thought,it's just not realistic,
I think, because peopledon't realize how much
is there already
that is very hard to move.
(38:51):
And, by the way,
even if you move some (indistinct)
how much of the resourcesare coming are mined
not in the West?
So you still need that.
And as long as you depend on something,
you're not really independent.
- Thank you, Yossi.
This has been a great conversation.
I've enjoyed it.- Thank you.
(upbeat music)
(39:12):
- Thanks for listening to this episode
of MIT's "Supply Chain Frontiers,"
presented by the MIT Center
for Transportation and Logistics.
To check out other episodes,
visit ctl.mit.edu/podcasts.
And for more on the center's research,
outreach and education initiatives,
make sure to visit us at ctl.mit.edu.
Until next time.
(upbeat music)
(upbeat music)
- Welcome to MIT's"Supply Chain Frontiers,"
presented by the MIT Center
for Transportation and Logistics.
Each episode of "Supply Chain Frontiers"
features center researchers and staff
or experts from industry
for in-depth conversations
about supply chain management,
logistics, education and beyond.
(upbeat music)
Today's episode features a conversation
(00:21):
between CTL Director Yossi Sheffi
and Susan Lacefield, executive editor
at "Supply Chain Quarterly."
Today's conversation was recorded
in front of a live audience
and covers a wide range of topics
touched on in ProfessorSheffi's latest book,
"The Magic Conveyor Belt (00:32):
Supply Chains,
A.I., and the Future of Work."
But first, MIT CTL offers a variety
of educational programsfor graduate students,
seasoned industry professionals
and anyone at any levellooking to learn more
about the supply chainand logistics domains.
To find out more about all
of CTL's educational offerings,
visit ctl.mit.edu/education.
(00:54):
And now, without further ado,
here's what makes the magicconveyor belt so magical.
- Maybe a good place to start
is with the title of the book.
Can you explain the analogy
you make between the supply chain
and the magic conveyor belt,
and what makes it magical?
- So let's start with why
I wrote this book.
After the pandemic,
(01:14):
a lot of people
were getting to my wife
(Susan laughs)and asking her,
"We understand yourhusband is in supply chain.
What is this?"
And imagine if, even after the pandemic,
people heard a lot of supply chain,
didn't know what it is.
So rather than having one-on-one interview
with one of the several hundred friends
that my wife has, I decide
(01:35):
(Susan laughs)to write a book.
So the first part ofthe book is explaining
what supply chains are,
why they are complex and, in some sense,
why would people should not
be pissed off when something
is not on the shelf or not available
on Amazon, but should be amazed
(01:56):
and awe-inspired when it's there.
Once they understand what it takes
to get something from the mines
in China or somewhere
to a finished product on a shelf,
how many processes it has to go through,
how many people are involved,
how many different tax regimes,
custom regime it has to go through
before we get the final product.
(02:17):
So this was the rationale.
And the magic convey belt is because
once you understand what it takes,
you think it's magic.
- Mm-hm, and it's very true.
- That's the title.- That's true.
So it was to get awayfrom people asking you
why their cat food-
- Yeah, absolutely.(Susan laughs)
Absolutely. (laughs)
- So as you mentioned,
the first part of thebook really talks about
(02:37):
the growing complexity of the supply chain
over the past few decades.
And I was wondering,
do you think we're gonna reach a point
where companies aregonna push back and say,
"Things are getting too complex"
and we need to maybe take a step back
and look at simplifying?
Or is complexity here to stay?
- I'm not sure.
I think complexity is here to stay.
Complexity is here to grow
(02:59):
because of unexpectedevent that's happening.
And furthermore, I'm not sure
there's a pressure to do it because a lot
of the technology that is being available
help company deal with the complexity
and deal with the unexpected event.
So I'm not sure there'sa pressure to do it,
especially among large,sophisticated companies.
(03:21):
So the answer is no.- No.
(Susan and Yossi laugh)- It is here to stay.
(Susan laughs)- Here to stay.
- You talked about one ofthe most mind-blowing facts
about any product that we touch
is the thousands of organizations
that have been involved in creating it,
and that they have done that
without any central control.
And I was wondering ifdecentralization is,
(03:42):
do you feel that's crucial
to supply chain efficiency and operating
in this complex world?
- Categorically, yes.- Okay.
- The idea that somebody can control,
control of supply chain iscontrolling the economy.
We tried it once or twice.
Didn't work very well.
So we're talking about modern markets.
(04:04):
Supply chain is actually
a whole set of buyer-seller,
buyer-seller, buyer-seller negotiation,
transaction, operation.
It works because everybody's trying
to do the right thing to minimize costs
and maximize level ofservice, by and large.
Now there are other things people
are worried about, like sustainability
(04:24):
and resilience, but everybodyis worried about it,
so everybody's trying to get
the best outcome.
I don't see how central planning can work.
Even in China, we don't see,
it's not central planning.
Central control of certain aspects,
but not of the transaction.
In fact, the Chinese
seem to be leery ofvery large corporations
(04:48):
who control more of the larger part
of the economy.
Has happened to several, you know,
tech companies in China.
They actually seem to encourage
competition between companies.
So I think it works, the market works.
- But as you introduce decentralization,
there's an element of risk
that kind of enters the equation.
(05:09):
I was wondering how dowe balance that risk
with all the benefits?
- No, it's, au contraire.
The risk goes down.- Huh.
- Because the risk to a particular company
maybe goes up.
They are out there on the front line.
But the risk to the economy-- Ah.
- Goes down.- Okay.
- Look, you can always find
(05:30):
good restaurant in New York, always.
You walk to a random restaurant,
the chances are it's a very good one.
Why?
Because restaurants in New York,
if you open a restaurant in New York,
the chances are within a year,
you'll have to close it.
The competition is murderous.
There are so many good restaurants.
So you can say the chances
for individual restaurant to succeed
(05:50):
is not very high.
But going to New York andhaving a good restaurant,
you know, the environment is great.
It works.
There's no risk.
You don't risk going toNew York and not finding
a good place to eat.
I'm not saying a place toeat, a good place to eat,
because it is decentralized.
- But there is, when you outsource
(06:11):
to a supplier and they're outsourcing
to other suppliers,there is that added risk
of, you know, a quality defect
that you can't control
or a sustainability issue popping up.
Is that a concern withthis decentralization?
You know, how do youcontrol for that sort of?
- I don't see it as adecentralization issue.
- Okay, okay.- I see it
as the depth of the supply chain,
(06:31):
the lack of visibility.
It exists.
It get slowly better with new technology.
But there are limits here.
The limits are that
for suppliers to tell their customer
who their supplier is,
not every supplier is willing to do it.
It's a competitive advantage
to know who the suppliers are.
And there always the fear
(06:53):
that the customer will go around them,
will go directly to the supplier.
So there's a kind of built-in opaqueness
to the supply chain,
which we're trying toget through to visibility
and good relationship and all of this,
and some people are moresuccessful than others.
But this issue is not a technology issue
and it's gonna be veryhard to solve completely.
(07:14):
And it's not decentralization issue.
It's the depth of the supply chain.
- So in the second half of the book,
you spend a lotta time talking about
artificial intelligence and the effects
that AI is having on the supply chain.
And I was wondering, you know,
when ChatGPT hit the scene in November,
suddenly, generative AI
(07:35):
became a very hot topic.
And I was wondering ifyou could talk about
some of the applications for generative AI
that you are seeing in the supply chain.
- First of all,
let me just explain
that we have been using, even-
- Oh, yeah,- AI for a long time,
using that.
All the restaurants,
all the drive-through restaurants
are using chatbot.
(07:55):
But it's not only drive-through.
Every time you call, nowaday,
customer service function,
you're talking to a chatbot to interpret
the results and try to give you answer.
And if sometimes it gets stuck
or you get stuck and started screaming,
"Agent, agent, agent," orsomething to this effect,
a human comes on.
And just like when yougo to the drive-through
(08:16):
and you start ordering, you know,
Champagne(Susan laughs)
and McDonald doesn't have it,
a human comes onboard andsay, "Well, I'm sorry.
We don't yet serve Champagne."
An interesting application
is in risk management
and supply chain,
trying to look at suppliers
and finding out
how risky they are.
(08:37):
Turns out that
when you look at metrics like
(indistinct) then financial metrics,
they are backward-lookingby about two quarters.
You want to see what's going on now.
We know, for a long time,
that one of the warning signs
is having coverage about
(08:59):
executives' living,
about failing some projects,
failing some M and Aproject in particular,
having bank covenants
that are a little problematic.
So now we have several companies
are using large languagemodel, particularly,
to look at tens of thousands of suppliers
(09:19):
at the same time andanalyzing all of them,
analyzing all the mention in the media
of redundancy, of executive living,
whatever, in order to generate
an alert and have somebody visit there
and finding out what'sgoing on, if we need
to start looking foranother supplier or what.
So this is something that could not
have imagined beforewe had this technology.
(09:42):
Looking at, if you look at a company like,
I don't know, I'm working with Flex a lot,
and they've 18,000 suppliers.
It's just, first-tier suppliers,
just finding out what's going on
with them is an issue.
Having a much better alert
when something goes wrong
is something that we were not able to do
before this type of technology was able.
(10:04):
We could check, you know,
10 suppliers at a time.
Checking tens of thousand was impossible.
Now it's being done.
- So the AI is going to actually enable
even more complexity in the supply chain
in the future as we're-
- Yes.(Susan and Yossi laugh)
It just, it can enable more possibilities.
More possibilities create complexity.
(10:25):
So, of course, when people get into,
when there's pressure, economic pressure,
whatever pressure, we know
that during recessions, company reduce
the number of SKUs.
They're trying to simplify.
They're also trying toreduce cost, you know,
improve service, butthey're trying to simplify.
But, you know,
there's the accordiontheory of management,
(10:46):
that when recession happen,
the number of SKU goes down,
and then marketing comes up
with all the good reason why we need more
and more and more SKU to serve
more and more territories,
more and more, 'cause all kind of option.
And then it, so that's theaccordion theory of management.
And it works, actually.
- So it kind of, it'slike the pendulum swing
(11:07):
that kind of balances.
- Yeah, between recessionary period,
expansionary period.
- So we've talked a little bit about AI.
Is there anything aboutthe application of AI
to the supply chain that gives you pause
or areas of concern?
- Not about the supply chain.
The areas that give me concern, the areas,
other people call, the area of fake news
(11:30):
can be done very convincingly.- Yes.
- The area of giving instruction,
how to build improviseroadside explosives.
But while I'm saying this is a concern,
a concern around, and the media,and I'm not that concerned
about it because, just give you an idea.
Unlike the early days of the internet,
when we all, everybody thought
(11:50):
this is the greatest thingsince sliced bread, right?
Because we can communicate with everybody,
families can see each other.
"All the, you know, distance is dead,"
to quote Tom Friedman.
Nobody thought about identity theft
and stealing customer data
and terrorists communicatingto each other on the net.
(12:11):
But now, it's different.
With the generative AI,
the companies, the media,
the politician are allaware of the dangers.
So there's a lot of workis going on already.
Already, the companies themselves
are putting guardrails on this.
So if you get ChatGPT or any one
of the others and ask how to prepare
a Molotov cocktails,it's not gonna answer.
(12:33):
So this is not give you an answer.
So they already started to put guardrail,
and there'll be more of this.
- Are you seeing that also
with use of analytics in companies where,
you know, you might have an algorithm?
There's an example in the book about
two competing bookstoresand they're both using
a pricing algorithm on Amazon.
And as a result, that drove up the price
(12:55):
of the book very high.
Are you seeing?
Companies already hadthose human interventions
in place to make sure
that the algorithmsdon't go outta control.
- Let me give you a moreeven general answer.
- Okay.
- One of the most important type of work
in the future will be monitoring,
vetting the automation,
AI-infused or otherwise,
(13:15):
but having a human monitoring.
That's a tough job.
It's a tough job because you have
to monitor something, andif you don't do every day,
and actually you lose expertise.
- Right.- It's hard to keep sharp.
And we have cases when, you know,
things did go awry.
So it's important, how dowe train people to do it?
(13:36):
- Yes.- For example,
today, modern aircraft can basically fly
by itself, gate to gate.
Now, talking about autonomous vehicles,
not too many peoplewill go on a, you know,
aluminum cylinder that fly 35,000 feet
over the ocean without a pilot, right?
So the pilots are in the aircraft,
(13:59):
actually don't need to do anything.
They can just sit there and nap.
But what we do, we let them do
the communication, basically.
It's the number one job.
So they always have to communicate
and change frequency.
So they keep alert.
It's one way to do it.
Because flying the airplane,
it flies itself.
So you really don't, once you put
(14:20):
the crew in, it flies, it change route,
it goes automatically.
But you give some jobs to the human,
they are not gonna fall asleep.
That's part of thechallenge of the future.
There are several models how people
and machine can work together.
Now, one such model is what
we talk about, the chatbots.
The chatbot has a monitor because
(14:43):
you talk to McDonald, whatever,
in the drive-through,
you actually talk to achatbot in most places,
and they respond.
And then when they don'tunderstand something,
a human comes on and we talk to you.
So there's a monitoringof what's going on,
and the minute that thechatbot doesn't understand
(15:03):
what's going on or gives the wrong answer
or whatever, a human comes.
So that's actually a monitoring function
that we don't even think about,
but happens every day.
With most customer service function,
you know, it used to bethat press one for this,
press two for this, press three for this.
That's rare now.
Now you just talk to the computer
and it turns it into text that appear
(15:25):
on somebody's screen, and then they report
and try to find an answer.
That's AI.
- Do you have any goodexamples of companies
that are doing goodthinking around what should
be given to humans todo in the supply chain
and what should be outsourced to AI?
- To me, that's thequestion of the future.
- Yeah.- The question of how.
(15:46):
The integration of humans
and AI-infused automation
is a question of the future.
We talk about one model.
The monitoring is one model.
You can think about whenthe human is in the loop.
The human is in the loop, for example,
think about an Amazon warehouse.
When the picker stands in one place
(16:06):
and there's a, you know, the aisle comes
to the picker, does something,
then another aisle come to the picker.
So the human is in the flow of the work.
So that's another example.
And a third example is the human operates.
When you go to several automotive plants,
for example, you see workers
(16:27):
standing with iPad-like devices
and basically running the robots.
That's another way of working with AI.
So that's, as I said, thequestion of the future,
how to organize the work-- Exactly.
- And how to, in some sense,
how to get the best out of the machine
and out of the human because
they have complementary skills.
You know, machines work all the time,
(16:49):
don't get breaks,
don't go to the bathroom.
They just work and-- Get sick. (laughs)
- They don't get sick.
And they're usually very accurate.
They do, you know, repeatedwork over, and overtime.
What machines don't have is context,
understanding whensomething does not belong,
has to change.
(17:11):
When we start think about, you know,
something change in the economy,
and suddenly peopleorder things differently.
So many standard automated ordering system
use the point of sale data
and order based on this,
put it into some forecast.
But this forecast is based on,
(17:32):
at best, say, on machine learning,
which is basically looking at past data.
All forecasts are based on past data.
When something is changing structurally,
suddenly there's a pandemic,
suddenly there's something else happening
and people change their buying habits,
then humans have to intervene again
because the machine does not have context.
(17:55):
As the machine isconcerned, nothing changed.
I mean, it gets, you know,
point of sale data, I keep going.
But something has changed,
and people understand the context.
Now, there's otherthings they worry about,
empathy and bias and things in general
that human can make sure
that happen or don't happen.
It's harder for machines.
(18:16):
- Do you think we'regonna get to that point,
where machines are gonnabe better at mimicking
that empathy piece?
'Cause it feels like thepeople who are working on AI
are trying to get there, you know?
See AI used in mentalhealth these days and.
- Yes, there are some
actually automated psychologists that try
to help people.
Who knows.- Who knows?
(18:36):
(laughs)
- I'm not sure about this because
that's exactly a question of context.
- Yes.
- Two people coming and saying, you know,
"I hate my children."
(Susan laughs)
- Or, "I hate my supplier." (laughs)
- Or, "I hate my supplier."
Well, you hate a supplier, don't go to a-
(Susan mumbles)
a psychologist.
But, you know, you hate your children,
(18:56):
(Susan laughs)you go to a psychologist.
But the context may be entirely different.
You know, I hate my child because
he's a thief and a liar,
or I hate my child justbecause I don't like tall kids.
I don't know, who knows?(Susan laughs)
- I mean, it's the context that-
- Get a crick in my neck(laughs) when I talk to you.
(laughs)- It's hard to imagine
some of these things
moving to AI completely.
And they talk about supplier.
(19:17):
Again, it is hard to imagine,
or let me put it strongly,
I don't think in the next 10 years,
in five, 10 years,
we will be able to have an algorithm
setting up a contract with a supplier
in China or Vietnam, let's say.
To set up a contract and relationship
(19:38):
for a long while,
it requires somebody to fly to Vietnam,
to negotiate like hell for two days,
and then sit and havedinners or two dinners
and talk about their kidsand talk about the family
and create relationship.
I don't see it changingin the near future.
I mean, AI will have to be so much better
and have to, but not the quantum jump
(20:00):
in capability to be able to do it,
which, right now, not clear it's possible.
- It's interesting, though, because
there's been a movement with technology
of making decisions morefact-based as opposed
to, you know, I like Joe
over at such-and-such trucking company,
so we're gonna use him.
But it seems like thathuman relationship is,
(20:21):
you're saying is stillgonna be an integral part
of supply chain management in the future.
- Yes, it is still integral because,
for example, if something goes wrong
and there's some disruptions,
how do I make sure that this supplier
knows my situation, knows me?
(20:41):
And if I'm calling and say, "Look,
I really need it," and everybody else call
and say, "I need it."- I need it, yeah.
- But I know this guy and I know
that he really needs it.
So the knowledge is, I think,
still very important, thepersonal relationship.
Now, one has to be (indistinct).
There may be critical suppliers
(21:02):
and maybe suppliers thatare not so critical.
And if they, maybe supplier,
if I have some part,
some commodity that theyhave dozens of suppliers,
and if that supplier goesdown or I have some shortage,
there are many others,maybe that I don't need
to be close to them.
But for most important suppliers,
I don't see any other way.
(21:24):
- Sometimes it's hard to know which
are your critical suppliers.
You might need that littlescrew, and then, suddenly,
that screw goes down.
- Yeah, it's called, inthe automotive business,
they call it the golden screw.
It's one part that's missingand you cannot make a car.
- Right, right.
The example in your book about the Ford
not being able to ship out because
they didn't have the little Ford logo
that sticks on the truck.
- This was last year.
(21:44):
You know, Ford has the blue little oval
that they put in the front of the truck.
They didn't have themduring the shortages.
They couldn't make trucks.
I mean, the trucks werestanding (laughs) in the yard
and they couldn't sellthem for a month, actually.
- So can AI be helpful identifying
who it is that you need to spend your time
(22:04):
developing that human relationship with?
It might not be who you think it is.
You also have to.
- You can take AI, I think it's simpler,
but as an aside, let me say that AI
became the buzzword-- Buzzword.
- At the time.
We used to think about blockchain or RFID
or became, and, you know,
people who are doing blockchain project,
(22:25):
they're actually justfixing up their systems.
To get funding frommanagement, they call it,
that's a blockchain project.
Now they call it, that's an AI project
for doing some optimization.
- So that's the learning to go away.
When you go back to your company,
make sure your project is AI. (laughs)
- Use AI, okay, what exactly are you using
and is it appropriate?
(22:46):
Can that tell you howmany company out there
that tail is wagging the dog?
I used to go to boards,
and people would ask the CEO,
"What's your China strategy?"
Or "What's your blockchain strategy?"
Now you ask, "What's your AI strategy?"
And I always said, "Stopit, what's your problem?
(Susan laughs)Start with the problem.
- Maybe the solution is AI,
(23:07):
maybe it's just hiring another person.
(Susan laughs)
I mean, you don't start with the solution.
But it's amazing how many people still do
because, I don't know, in part
because Wall Streetpays premium for having
an AI strategy orsomething of this effect.
It's not clear to me. It makes no sense.
- Is figuring out the problem an AI issue
(23:29):
or a essentially human issue,
is that something that's gonna-
- AI issue, you know,operational research issue,
statistics issue, you know, people issue,
process issue, can be anything.
So that's why I don'tlike having an AI strategy
or a blockchain strategy or whatever
is the current fad.
(23:52):
I should say AI is not fad.
It's been growing for many, many years,
and we got to the point that it could
make substantial changesin how people work
and the relationshipbetween people and machine.
- Right.
Just like a year earlier,
I think the buzzword was all robotics.
So it's kind of, or cobots, and so
it's the same sorta thing.
(24:12):
- Robotics are also now fused by AI.
I mean, so,- Right.
It's not the actual hardware of the robot,
(crosstalk) it's the software.
- Of course.- Yeah.
So kinda taking a step back, to your point
about context and the pilots
and training, sometimes you have
to do all the low-level jobs
(24:33):
to get that context toknow what to do next.
So-- I do talk about it.
- Yes.
So what can we do
with our supply chain pilots,
so to speak, to make sure
that they have the background,
the knowledge to be able to take over
those unusual events?
- Again, I take the problem
a little further from your question.
- Okay.
(24:53):
- So I was interviewing a shop,
basically a software provider,
asking them about ChatGPT taking their job
because it can now program.
And so the senior computer scientists
are not worried about it,
but it may take the job
of the junior computer scientists.
Now we're saying, "Guess what?
(25:15):
Senior computer scientists don't come
as senior computer scientists. (laughs)
They start as junior computer scientist.
We don't hire junior computer scientists,
you don't have work for them,
you are not gonna havesenior computer scientists.
And even for monitoring,
you need people with experience.
In the book, I talk alot about how to do it
and how to upgrade skills,
(25:35):
but there has to be recognition
that you need to hire people
at the lowest level.
One of the suggestions that I made
is maybe pivot
in the United States
for more of the German system
of people spending half time in a company
and half time in a university.
And they come up, and it's called
the dual education system.
(25:57):
That's about 52% of theGerman high schoolers
go into this system, whichis government-controlled.
The government defines Germany.
So the government define 365 professions
where this can be done.
And the university,you apply to, actually,
to the company,
and they work with a localcollege or university.
You spend half the timestudying the theory,
(26:19):
basically, and half thetime doing the work.
70% of these people gethired by the company
that they do their internship with.
But they come with experience,knowing the culture,
knowing the company.
It's much higher to move them
and much easier to move them along.
The United States, wesuffer another problem,
is this, is every mother
wants to say that theirchild goes to college.
(26:40):
My child goes to so-and-so college,
and your child just goes to trade school."
I always say that peopleshould meet my plumber.
Yeah.- My plumber drives a Bentley,
and we don't have enough plumbers.
And they can set the price, and they do
set it high. (laughs)
So we send, there are too many people
who go to college in the United States
(27:00):
and unfortunately, in manycases, come back with,
have to call it debt for a long,
long period, ratherthan go to trade schools
and community colleges, or combination.
Actually, there's auniversity here that does it.
Northeastern.- Northeastern, yes.
- The combination of work,and it's not as organized
as in Germany, but it's the same idea.
(27:22):
You work one semester,
you study one semester,and you flip between them.
- It's interesting.
I feel like Northeasternis becoming a school
that more and more peoplewant to go to nowadays.
- I know.
- So that comes back to your main job
of training students.
How have what you feelare the necessary skills
for a supply chainmanager changed recently?
(27:46):
- If I go over the history,
this program here started
a very analytical program,and then we realized
that our graduates
are very analytically savvy,
end up working for Harvard MBAs
who are half as smart andget paid twice as much.
And said, "This is not working."
Furthermore, companies came to us and say,
(28:10):
"Your graduates don't go up the ladder
in the company because
"they need the soft skills."
They need to be able to communicate,
they need to be able to sell,
they need to be ableto explain a position,
they need to be able to work in a team.
So the programs change,
started doing a lot more of this.
I think that as AI and automation
is getting more andmore into the workplace,
(28:32):
is the soft skills that willbecome even more important.
How do you work in team?
How do you make sure that your people
can work with AI?
You know, the promise of AI is that
it will do the job thatnobody wants to do,
and people will do the more interesting
and fulfilling job.
How do you make sure thatthis actually happens?
(28:52):
So all of this is part of the challenge
of the future.
We don't have all the answer yet.
We don't even have some of the answer yet,
but we're thinking about it. (laughs)
So people will need to understand,
we're not training computer scientists,
but people need tounderstand the capabilities
and where it can go wrong.
(29:13):
So people need to be sophisticated users.
It's like my colleague Chris Caplice
always talks about driving.
There are mechanics whoactually can fix the car
and know what's inside.
And then drivers, you don'thave to know what's going on.
You can just operate it.
We like to train drivers,people who understand
what the system can and cannot do,
but they don't need to be builders of AI
(29:36):
or generative AI system.
But they need to know thepromise, the limitation
and how to best use them.
- Yes.
- People always ask me, in classes,
if we allow people to use ChatGPT.
That's a big debate in universities.
Some universities absolutely disallow it.
It's ridiculous.
You know, when I was your age
(29:56):
and actually younger,
they used to teach me howto take square root by hand.
None of you studied it
because there are calculators.
None of you are studying how to do
a financial statement by hand because
there's now Excel and spreadsheets.
So the question is, why do you need to do
(30:17):
to replicate what ChatGPT can do by hand?
What you need to do is when it goes awry,
you need to test it.
You need to make sure that the results
are not what they call hallucinations.
(Susan laughs)
So because ChatGPT canhallucinate and invent stuff,
invent sometimes referencethat don't exist.
(30:37):
So you need to be sure of this because
if you can submit to me a paper
written by ChatGPT, as long as you realize
that if something is wrong,
open AI is not getting anF, you are getting an F.
Just so we understand each other.
So in short, the responsibility
is still on the user,
but not using a tool that's available,
(30:59):
for me, it's a losing proposition.
It's very hard to work.
Another example, you know,
of how automation is de-skilling jobs,
but having other benefits.
So if you go to London
and you go to a black cab,
you know, to drive a black cab in London,
you have to study for three or four years
(31:21):
and pass an exam, which is considered
the toughest exam in the world because
you need to know every point of interest
in London and how to go fromeverywhere to everywhere.
And you sit in an exam that you have
to show that you can drive from everywhere
to everywhere in the shortest route.
And you have to understand congestion.
And people who are doing this
and spending four yearsof their life doing it.
(31:42):
And then came Google Maps and Uber.
Everybody can do it.
Now, there are still,
the number of black taxis in London
went from 25,000 to about 8,000,
but the number of Ubers available
is now about 60,000 in London.
Lots more of them are available.
So win some, lose some.
(32:04):
- Another thing you talk about in the book
is how technology has had an impact
on enabling supply chain strategy.
Like, we wouldn't have been able to do
all the outsourcing and offshoring
if we didn't have advancedcommunication technology.
Do you see some radical changes
on how companies will bestructuring their supply chain
(32:25):
or organizing it because of the AI
or other emerging tech, like robotics?
- It's already happening, in the sense
that the number one use of robotics
is in warehouse automation.
I mean, warehouses are putting robots
like there's no tomorrow.
Autonomous vehicles.
Autonomous vehicles are robots.
So there's a lot of workon autonomous tracking.
(32:46):
- Yeah,
- Let me just say, however, that I talk
to a lot of people, a lot of interviewing,
people are worried about their jobs.
It's the number one, you know, fear, jobs.
And, again, people should chill, at least
for the short term, becauseit doesn't happen fast.
Give you one example.
In 1892, AT&T invented
(33:07):
the automatic telephone exchange.
Until then, there were, you know,
women putting plugs.
"Where's Mrs. Smith today?"
"She went to the supermarket."
They'll connect you later.
Very personal service.
- That's what my grandma did.
- Yeah, okay.(Susan laughs)
By 1950, there werestill 350,000 operators
like this in the United States.
Only by the 1980s, it started to go
(33:31):
really close to zero.
Nine decades from the invention
until it really,
all the jobs went or mostof the jobs went away.
So it takes time, andit takes time because
there are many hurdles.
You see already hurdles.
You see what are the writers
and actors worried about?
They're worried about using AI.
(33:53):
And they are, you know,stopping the industry,
putting the industry down.
And the industry will have to come
to some kind of agreement.
My guess would be part of the agreement
will be somehow slowingdown or putting guardrails
on the use of AI.
- Kinda like dock workers with-
- I was about to say.- Sorry. (laughs)
- Dock workers also fight automation.
(34:13):
In Long Beach, it's nothing like Rotterdam
or Singapore or Dubai because
of the afraid for the job,
afraid for the immediate job,
and not taking into account
that you can increase the throughput
and get even more jobs.- Right.
- In general, that'sthe most difficult thing
in this area, in this, peopleare worried about jobs.
(34:35):
And I understand it.
It's anxiety because
you know that people aregonna lose their job.
You see it in the supermarket
when you get to, when youcan check out yourself.
People are gonna lose their jobs.
So these are people that you know.
What you don't know isall the new industry
and the new jobs will come.
So one quick example of this,
that is old example.
So Ford came up
(34:57):
with a assembly line system.
Changed manufacturing, of course.
But it used to be the specialty team
used to build one car at time,
and Ford employedseveral thousand workers.
During the height of the Model T,
using the assembly line,
Ford had about 150,000 workers.
But this is not the big impact.
The big impact was that automobiles
(35:19):
became less expensive.
Highway developed, hotels, motels,
restaurant, the whole hospitality industry
created millions of jobs.
This was not what Henry Ford had in mind.
(Susan laughs)
I mean, but it was a sideeffect of what happened.
That is why it is so hard to imagine
all the new jobs that will come.
(35:40):
Many of the jobs that exist today
did not exist, you know, few decades ago.
We talked few decades ago about people
who will optimize ads on Google or people.
There's so many jobs that are totally new
because of new industries that came up.
So it's hard to predictwhat would be (mumbles).
(36:02):
the one thing aboutsupply chain coming back,
because that's what you ask about,
is it still involve physical movement?
Product have to move.
So there are some thingsthat will be still grounded
for a long time until we start having
3-D printing at scale.
This can change supply chain,
(36:23):
but it will be a long time
because 3-D printing is still very slow.
Technology cannot replace mass production,
not even close to replace mass production.
So I don't see fundamental changes.
The changes that may come will come
because of geopolitical consideration,
resilience consideration,
(36:44):
sustainability consideration.
But to get this, done we'll need to have
some more system thinking,
which is in very short supply
among the politicalclass, the media class.
People are talking about.
Give you an example.
Rare earth minerals are used
in every sophisticated product now.
(37:04):
China controlled 80, 90%
of the world supply.
Aluminum, China controlledmost of the world supply,
and stone, most of thesmelters are in China.
You know which country hasmore rare earth mineral
in the ground than China?
The United States.
But we don't want to mine it because
it's environmentally problematic.
Even though one should say
(37:25):
if it were done in the United States,
it would be probably done ina lot more responsible way
than it's done in China, but, still.
So we have to decide.
We have to stop saying, and green is,
and we just go green.- Right.
- We go security, wego standard of living.
We have to think more holistically.
And this is systemthinking that, as I say,
(37:47):
in short supply, becausethere are pressure groups,
whether the green parties in Europe
or environmental lobbiesin the United States.
There are the security hawks
that want everything to be from here.
But, again, from supplychain point of view,
moving the assembly or thelast stage of manufacturing
(38:08):
to United States is meaningless,
or to Europe, is meaningless
because there's a whole supply chain
that was built afterinvestment of billions
of dollars and decades
that is still in China.
Very hard to get out of this.
It will take billionsof dollars and decades
to get out of there.
So we need to stop talking about
(38:31):
totally separating the Chinese
and the Western economies,
and starting to work better together.
It's just not realistic.
- So no two, what are they?
Two-pronged or?- Two-pronged supply chain.
It's a nice thought,it's just not realistic,
I think, because peopledon't realize how much
is there already
that is very hard to move.
(38:51):
And, by the way,
even if you move some (indistinct)
how much of the resourcesare coming are mined
not in the West?
So you still need that.
And as long as you depend on something,
you're not really independent.
- Thank you, Yossi.
This has been a great conversation.
I've enjoyed it.- Thank you.
(upbeat music)
(39:12):
- Thanks for listening to this episode
of MIT's "Supply Chain Frontiers,"
presented by the MIT Center
for Transportation and Logistics.
To check out other episodes,
visit ctl.mit.edu/podcasts.
And for more on the center's research,
outreach and education initiatives,
make sure to visit us at ctl.mit.edu.
Until next time.
(upbeat music)
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