The Magic Conveyor Belt: Supply Chains, A.I., and the Future of Work

Summary

本集MIT的'供应链前沿'播客邀请了MIT交通与物流中心的主任Yossi Sheffi和《供应链季刊》执行编辑Susan Lacefield，共同探讨了Sheffi教授新书《魔法传送带：供应链、AI和工作的未来》中的主题。他们讨论了供应链的复杂性、AI在供应链中的应用、以及技术对未来工作的影响。Sheffi强调了供应链的去中心化和自动化的重要性，并指出AI的引入将使供应链管理更加复杂，但同时也提供了更多可能性。此外，他还提到了AI在风险管理和监控自动化方面的应用，并强调了人类监督的重要性。

Takeaways

• 📚 《The Magic Conveyor Belt: Supply Chains, A.I., and the Future of Work》这本书由MIT的Yossi Sheffi教授撰写，旨在解释供应链的复杂性，并展望人工智能如何塑造未来的工作。
• 🔄 供应链的复杂性是持续增长的，由于不可预测的事件和技术的发展，这种复杂性是不可避免的，并且是未来趋势的一部分。
• 🌐 分散化是供应链效率的关键，没有中央控制的供应链通过买卖双方的谈判和交易实现运作，这种模式是有效的。
• 🔍 尽管分散化带来了风险，但整体经济的风险实际上降低了，因为市场机制能够通过竞争和合作来管理风险。
• 🤖 人工智能和机器学习正在被用于风险管理，例如通过分析供应商的媒体提及来预测潜在的风险。
• 🛠️ 人工智能的应用在供应链中是多方面的，它不仅可以处理重复性工作，还可以通过分析大量数据来提供深入的洞察和预测。
• 🚀 AI的引入将使供应链更加复杂，但同时也创造了新的可能性，需要在经济压力下找到平衡点。
• 👥 人类的工作重点将转向监督和验证自动化系统，包括AI系统，确保它们的输出是准确和可靠的。
• 🛑 需要对AI系统设置监管措施，以防止其被滥用，例如生成假新闻或危险指令。
• 👨‍🏫 未来的供应链管理教育需要强调软技能，如沟通、销售、团队合作和与AI合作的能力。
• 🌟 尽管技术不断进步，但人类在建立关系、理解背景和处理异常事件方面的作用是不可替代的。

Q & A

《The Magic Conveyor Belt: Supply Chains, A.I., and the Future of Work》这本书的主旨是什么？

这本书的主旨是解释供应链是什么，为什么它们复杂，以及为什么当产品出现在货架上时，人们应该感到惊奇和敬畏。书中还探讨了人工智能（A.I.）对供应链的影响以及未来的工作方式。

MIT CTL提供哪些教育项目？

MIT CTL为研究生、行业专业人士以及任何希望深入了解供应链和物流领域的人士提供各种教育项目。

Yossi Sheffi在他的书中如何比喻供应链？

Yossi Sheffi将供应链比喻为“魔法传送带”，强调了供应链的复杂性和高效性，以及人们通常不会意识到的幕后工作。

为什么供应链的复杂性是不可避免的？

Sheffi教授认为，由于不可预测的事件和新技术的出现，供应链的复杂性不仅会持续存在，而且还会增长。技术帮助公司应对复杂性，因此并没有压力去简化它。

去中心化对供应链效率和运营有何重要性？

去中心化对供应链至关重要，因为它允许众多组织在没有中央控制的情况下进行谈判、交易和运营，从而提高效率并降低经济风险。

Susan Lacefield和Yossi Sheffi讨论了哪些关于风险管理的话题？

他们讨论了如何利用大型语言模型来监控成千上万的供应商，并通过媒体提及的内容生成警报，以便在必要时采取行动，比如寻找新的供应商。

生成式人工智能（AI）在供应链中有哪些应用？

生成式AI在供应链中的应用包括风险管理、监控供应商状况、分析媒体提及的内容以及提高对当前发生事件的实时了解。

Yossi Sheffi对于AI在供应链中应用的担忧有哪些？

Sheffi教授对于AI在供应链中的应用并不担忧，但他担心AI可能被用于制造假新闻或提供危险活动的指导，例如制造简易爆炸装置。

为什么需要人类监督AI和自动化系统？

需要人类监督AI和自动化系统是因为这些系统可能无法处理异常事件或理解上下文变化。人类监督可以确保系统正确运行，并在出现问题时进行干预。

Yossi Sheffi如何看待个人关系在供应链管理中的重要性？

Sheffi教授认为个人关系在供应链管理中非常重要，尤其是在出现问题和中断时，个人关系可以帮助确保供应商了解情况并优先处理订单。

为什么公司不应该仅仅因为AI是一个趋势就制定AI战略？

公司应该从问题出发，而不是从解决方案出发。AI可能是一种解决方案，但也可能只是需要雇佣更多的人。关键是要明确问题，然后找到最合适的解决方案。

Yossi Sheffi对于供应链管理教育的看法是什么？

Sheffi教授认为，随着AI和自动化在工作场所的普及，软技能将变得更加重要。他强调了团队合作、沟通、销售和解释立场的能力，以及理解和有效使用AI系统的重要性。

为什么Sheffi教授认为在大学中禁止使用ChatGPT是荒谬的？

Sheffi教授认为，禁止使用ChatGPT就像禁止使用计算器或电子表格一样荒谬。学生应该学会如何使用这些工具，并在出现问题时能够识别和纠正错误。

技术如何影响供应链策略的形成？

技术，特别是先进的通信技术，使得外包和离岸外包成为可能。未来的技术，如AI和机器人技术，可能会进一步改变公司构建和组织供应链的方式。

Yossi Sheffi对于自动化和工作流失的看法是什么？

Sheffi教授认为，尽管自动化会导致某些工作流失，但它也会创造新的行业和工作机会。他强调了系统思维的重要性，并指出需要全面考虑环境、安全和生活水平等因素。

为什么Sheffi教授认为完全分离中国和西方经济是不现实的？

Sheffi教授认为，由于已经建立的供应链和所需的巨额投资，完全分离中国和西方经济是不现实的。他提倡更好的国际合作，而不是单方面追求独立。

Outlines

00:00

📚 供应链管理的魔力：MIT的'供应链前沿'

MIT的'供应链前沿'节目由MIT运输与物流中心呈现，旨在深入探讨供应链管理、物流、教育等话题。本期节目邀请了CTL主任Yossi Sheffi和'供应链季刊'执行编辑Susan Lacefield，共同讨论Sheffi教授的新书《魔法传送带：供应链、人工智能和工作的未来》。节目还介绍了MIT CTL提供的教育项目，旨在为不同层次的学生和专业人士提供供应链和物流领域的深入学习机会。

05:00

🔍 供应链的复杂性与未来的挑战

在对话中，Sheffi教授首先解释了为何撰写《魔法传送带》一书，旨在普及供应链的概念及其复杂性。他强调，尽管疫情后人们对供应链的讨论增多，但对其理解仍然有限。书中第一部分解释了供应链的复杂性，以及为何当产品出现在货架上时，人们应感到惊奇而非理所当然。接着，讨论转向供应链的日益增长的复杂性，Sheffi教授认为，由于不可预测的事件和技术的发展，复杂性将持续增长，而非减少。

10:02

🌐 去中心化供应链的风险与效率

对话进一步探讨了供应链的去中心化问题。Sheffi教授认为，尽管去中心化带来了风险，但从宏观经济角度来看，风险实际上是降低的。他以纽约的餐饮业为例，说明了高度竞争如何保证整体质量，即使单个企业可能面临更高的风险。此外，讨论还涉及了供应链的深度和可见性问题，以及技术如何帮助改善这些问题，尽管完全的透明度很难实现。

15:04

🤖 人工智能在供应链中的应用与前景

Sheffi教授和Lacefield女士讨论了人工智能（AI）对供应链的影响，特别是生成性AI的兴起。Sheffi教授提到，AI在风险管理中的应用，如通过大型语言模型分析供应商的风险，以及AI如何帮助实时监控和警报系统。他们还讨论了AI在供应链中可能导致的更多复杂性，以及如何在经济压力下简化流程。

20:06

🚀 AI的伦理和社会影响

在讨论AI的应用时，Sheffi教授表达了对AI可能被滥用的担忧，例如制造假新闻或指导制造危险装置。他指出，尽管存在这些风险，但社会各界已经意识到这些问题，并正在采取措施来防范，如在AI系统中设置防护措施。此外，他还强调了未来工作中监控和审查自动化的重要性，以及如何训练人员来执行这些任务。

25:08

🤝 人际关系在供应链管理中的重要性

Sheffi教授强调了人际关系在供应链管理中的重要性，尤其是在处理突发事件和确保供应商理解买方需求时。他通过福特汽车因缺少小零件而无法交付车辆的例子，说明了关键供应商的重要性。此外，他还讨论了AI如何帮助识别哪些供应商值得建立人际关系，以及如何避免在技术项目中本末倒置，即从问题出发，而非从技术出发。

30:09

🎓 供应链管理教育的演变

Sheffi教授讨论了供应链管理教育的变化，强调了软技能的重要性，如沟通、销售和团队合作。他提到，随着AI和自动化的普及，软技能将变得更加重要。他还提出了对教育系统进行改革的建议，比如采用德国的双元制教育模式，以及如何让学生理解AI的能力和局限，成为AI系统的熟练使用者。

35:11

🛠️ 技术进步与就业市场的变迁

在最后一段中，Sheffi教授讨论了技术进步如何导致某些工作的消失和新工作的创造。他用AT&T自动电话交换机的发明和它对电话接线员工作的影响作为例子，说明技术变革对就业市场的长期影响。他还提到了3D打印技术对供应链的潜在影响，以及需要更全面的系统思维来解决如稀土矿物供应等全球性问题。

Keywords

💡供应链

供应链是指产品从原材料到成品，最终到达消费者手中的整个流程。它涉及多个环节，包括生产、加工、运输、分销和销售等。在视频中，供应链的复杂性和重要性被多次提及，如教授提到供应链的复杂性以及如何通过供应链管理确保产品及时出现在货架上。

💡麻省理工学院运输与物流中心(MIT CTL)

麻省理工学院运输与物流中心是视频的主办方，致力于供应链管理、物流、教育等领域的研究和实践。视频中提到了CTL提供的教育项目，旨在培养供应链和物流领域的专业人才。

💡《魔法传送带：供应链、A.I.和工作的未来》

这是Yossi Sheffi教授最新出版的一本书，书中探讨了供应链的复杂性、AI技术对供应链的影响以及未来的工作方式。书名中的'魔法传送带'是一个比喻，用来形容供应链的高效和神奇。

💡人工智能(AI)

人工智能是指由人造系统所表现出来的智能，它在视频中被广泛讨论，尤其是在供应链管理中的应用。例如，AI在风险管理、供应商评估和客户服务自动化中的应用被特别提及。

💡去中心化

去中心化是指在一个系统中，决策权和控制权不是集中在一个中心点，而是分散在多个节点。视频中讨论了去中心化对供应链效率的重要性，以及它如何降低整个经济体的风险。

💡复杂性

复杂性在视频中被用来描述现代供应链的一个主要特征。随着全球化和技术的发展，供应链变得越来越复杂。教授认为，复杂性是不可避免的，并且新的技术，如AI，正在帮助公司应对这种复杂性。

💡风险管理

风险管理是指识别、评估和控制风险的过程，以减少潜在的负面影响。在视频中，提到了AI在供应链风险管理中的应用，比如通过分析大量数据来预测和警告供应商的风险。

💡教育项目

教育项目在视频中被提及，作为MIT CTL提供给不同层次学习者的学习机会。这些项目旨在教育和培养供应链和物流领域的专业人才，无论是研究生还是行业专业人士。

💡生成式AI

生成式AI是一种能够创造新内容的人工智能技术。视频中提到了生成式AI在供应链中的应用，如通过分析媒体提及来生成对供应商的风险警报。

💡供应链管理

供应链管理是指对供应链中的活动进行规划、组织、协调、控制和优化的过程，以提高效率和降低成本。视频中多次提到供应链管理，强调了它在确保产品及时供应和应对复杂性中的关键作用。

💡技术进步

技术进步在视频中被讨论，尤其是在供应链领域中的应用。技术如AI和机器人技术正在改变供应链的运作方式，提高效率和应对复杂性。同时，技术进步也引发了对未来工作和教育需求的讨论。

Highlights

MIT运输与物流中心推出的《供应链前沿》节目，由中心主任Yossi Sheffi与《供应链季刊》执行编辑Susan Lacefield深入讨论。

Yossi Sheffi的新书《魔法传送带：供应链、AI和工作的未来》中探讨的主题。

MIT CTL提供的教育项目涵盖研究生、行业专业人士以及希望深入了解供应链和物流领域的任何人。

《魔法传送带》书名的寓意，解释了供应链的复杂性和为何人们应对其运作感到惊奇。

供应链的复杂性是持续增长的，并且由于不可预测的事件而变得更加复杂。

去中心化对供应链效率至关重要，市场机制优于中央规划。

风险分散在去中心化的供应链中，对经济整体而言风险降低。

供应链的深度和缺乏透明度是主要问题，而不是去中心化本身。

AI在供应链中的应用，包括风险管理和通过大型语言模型监控供应商。

AI将使供应链更加复杂，但同时也带来更多可能性。

AI在供应链中应用的担忧，包括假新闻和不当指导信息的生成。

监控自动化和AI系统将是未来重要的工作，需要人类持续监督。

人类与AI的整合是未来的关键问题，需要找到最佳的合作方式。

人类在供应链中的作用是提供上下文理解，这是机器所缺乏的。

AI在模仿人类情感方面的局限性，特别是在需要深入上下文理解的情况下。

供应链管理中人际关系的重要性，尤其是在处理危机和确保供应时。

AI可以帮助识别关键供应商，发展人际关系的重要性。

技术发展使得决策更加基于事实而非个人偏好。

Yossi Sheffi对AI项目的批判性看法，强调从问题出发而非追逐技术趋势。

供应链管理教育的变化，强调软技能的重要性，如沟通和团队合作。

AI和自动化对工作的影响，以及如何通过教育和培训适应这些变化。

技术对供应链策略的影响，如外包和离岸的实现依赖先进的通信技术。

AI和其他新兴技术，如机器人技术，将如何影响供应链的结构和组织。

自动化和机器人技术在供应链中的应用，特别是在仓库自动化方面。

技术发展对就业的影响，以及如何平衡技术创新与工作保障。

系统思维在供应链管理中的缺乏，以及需要更全面考虑的领域，如地缘政治、弹性和可持续性。

对分离中西方经济体的想法的批评，强调需要更好的国际合作。

Transcripts

(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

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 Professor Sheffi's latest book,

"The Magic Conveyor Belt: Supply Chains,

A.I., and the Future of Work."

But first, MIT CTL offers a variety

of educational programs for graduate students,

seasoned industry professionals

and anyone at any level looking to learn more

about the supply chain and logistics domains.

To find out more about all

of CTL's educational offerings,

visit ctl.mit.edu/education.

And now, without further ado,

here's what makes the magic conveyor 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,

a lot of people

were getting to my wife

(Susan laughs) and asking her,

"We understand your husband 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

(Susan laughs) to write a book.

So the first part of the 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

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.

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 away from people asking you

why their cat food-

- Yeah, absolutely. (Susan laughs)

Absolutely. (laughs)

- So as you mentioned,

the first part of the book really talks about

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 are gonna 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

because of unexpected event 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's a pressure to do it,

especially among large, sophisticated companies.

So the answer is no. - No.

(Susan and Yossi laugh) - It is here to stay.

(Susan laughs) - Here to stay.

- You talked about one of the 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 if decentralization is,

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 is controlling the economy.

We tried it once or twice.

Didn't work very well.

So we're talking about modern markets.

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 of service, by and large.

Now there are other things people

are worried about, like sustainability

and resilience, but everybody is 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 of very large corporations

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.

I was wondering how do we 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

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

is not very high.

But going to New York and having a good restaurant,

you know, the environment is great.

It works.

There's no risk.

You don't risk going to New York and not finding

a good place to eat.

I'm not saying a place to eat, a good place to eat,

because it is decentralized.

- But there is, when you outsource

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 with this decentralization?

You know, how do you control for that sort of?

- I don't see it as a decentralization issue.

- Okay, okay. - I see it

as the depth of the supply chain,

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

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 to get through to visibility

and good relationship and all of this,

and some people are more successful than others.

But this issue is not a technology issue

and it's gonna be very hard to solve completely.

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

became a very hot topic.

And I was wondering if you 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.

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," or something to this effect,

a human comes on.

And just like when you go to the drive-through

and you start ordering, you know,

Champagne (Susan laughs)

and McDonald doesn't have it,

a human comes onboard and say, "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.

Turns out that

when you look at metrics like

(indistinct) then financial metrics,

they are backward-looking by 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

executives' living,

about failing some projects,

failing some M and A project in particular,

having bank covenants

that are a little problematic.

So now we have several companies

are using large language model, particularly,

to look at tens of thousands of suppliers

at the same time and analyzing 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's going on, if we need

to start looking for another supplier or what.

So this is something that could not

have imagined before we had this technology.

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.

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.

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 to reduce cost, you know,

improve service, but they're trying to simplify.

But, you know,

there's the accordion theory of management,

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 the accordion theory of management.

And it works, actually.

- So it kind of, it's like the pendulum swing

that kind of balances.

- Yeah, between recessionary period,

expansionary period.

- So we've talked a little bit about AI.

Is there anything about the 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

can be done very convincingly. - Yes.

- The area of giving instruction,

how to build improvise roadside 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

this is the greatest thing since 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 communicating to each other on the net.

But now, it's different.

With the generative AI,

the companies, the media,

the politician are all aware of the dangers.

So there's a lot of work is 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.

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 bookstores and they're both using

a pricing algorithm on Amazon.

And as a result, that drove up the price

of the book very high.

Are you seeing?

Companies already had those human interventions

in place to make sure

that the algorithms don't go outta control.

- Let me give you a more even general answer.

- Okay.

- One of the most important type of work

in the future will be monitoring,

vetting the automation,

AI-infused or otherwise,

but having a human monitoring.

That's a tough job.

It's a tough job because you have

to monitor something, and if 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 do we train people to do it?

- Yes. - For example,

today, modern aircraft can basically fly

by itself, gate to gate.

Now, talking about autonomous vehicles,

not too many people will 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,

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

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 the challenge 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

you talk to McDonald, whatever,

in the drive-through,

you actually talk to a chatbot in most places,

and they respond.

And then when they don't understand something,

a human comes on and we talk to you.

So there's a monitoring of what's going on,

and the minute that the chatbot doesn't understand

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 be that 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

on somebody's screen, and then they report

and try to find an answer.

That's AI.

- Do you have any good examples of companies

that are doing good thinking around what should

be given to humans to do in the supply chain

and what should be outsourced to AI?

- To me, that's the question of the future.

- Yeah. - The question of how.

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 when the 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

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

standing with iPad-like devices

and basically running the robots.

That's another way of working with AI.

So that's, as I said, the question 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,

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, repeated work over, and overtime.

What machines don't have is context,

understanding when something does not belong,

has to change.

When we start think about, you know,

something change in the economy,

and suddenly people order 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,

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.

As the machine is concerned, 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 other things 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.

- Do you think we're gonna get to that point,

where machines are gonna be better at mimicking

that empathy piece?

'Cause it feels like the people who are working on AI

are trying to get there, you know?

See AI used in mental health these days and.

- Yes, there are some

actually automated psychologists that try

to help people.

Who knows. - Who knows?

(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,

(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 just because 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.

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

for a long while,

it requires somebody to fly to Vietnam,

to negotiate like hell for two days,

and then sit and have dinners or two dinners

and talk about their kids and talk about the family

and create relationship.

I don't see it changing in the near future.

I mean, AI will have to be so much better

and have to, but not the quantum jump

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 more fact-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 that human relationship is,

you're saying is still gonna 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?

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, the personal relationship.

Now, one has to be (indistinct).

There may be critical suppliers

and maybe suppliers that are not so critical.

And if they, maybe supplier,

if I have some part,

some commodity that they have dozens of suppliers,

and if that supplier goes down 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.

- Sometimes it's hard to know which

are your critical suppliers.

You might need that little screw, and then, suddenly,

that screw goes down.

- Yeah, it's called, in the automotive business,

they call it the golden screw.

It's one part that's missing and 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.

You know, Ford has the blue little oval

that they put in the front of the truck.

They didn't have them during the shortages.

They couldn't make trucks.

I mean, the trucks were standing (laughs) in the yard

and they couldn't sell them for a month, actually.

- So can AI be helpful identifying

who it is that you need to spend your time

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,

they're actually just fixing up their systems.

To get funding from management, 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?

Can that tell you how many 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, "Stop it, what's your problem?

(Susan laughs) Start with the problem.

- Maybe the solution is AI,

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 Street pays premium for having

an AI strategy or something of this effect.

It's not clear to me. It makes no sense.

- Is figuring out the problem an AI issue

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't like having an AI strategy

or a blockchain strategy or whatever

is the current fad.

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 changes in how people work

and the relationship between 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.

- 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

to get that context to know 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.

- 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?

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 have senior computer scientists.

And even for monitoring,

you need people with experience.

In the book, I talk a lot about how to do it

and how to upgrade skills,

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.

That's about 52% of the German high schoolers

go into this system, which is 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 local college or university.

You spend half the time studying the theory,

basically, and half the time doing the work.

70% of these people get hired 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, we suffer another problem,

is this, is every mother

wants to say that their child goes to college.

My child goes to so-and-so college,

and your child just goes to trade school."

I always say that people should 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

and unfortunately, in many cases, come back with,

have to call it debt for a long,

long period, rather than go to trade schools

and community colleges, or combination.

Actually, there's a university 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.

You work one semester,

you study one semester, and you flip between them.

- It's interesting.

I feel like Northeastern is becoming a school

that more and more people want to go to nowadays.

- I know.

- So that comes back to your main job

of training students.

How have what you feel are the necessary skills

for a supply chain manager changed recently?

- 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 and get paid twice as much.

And said, "This is not working."

Furthermore, companies came to us and say,

"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 able to 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 and more into the workplace,

is the soft skills that will become 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 that nobody wants to do,

and people will do the more interesting

and fulfilling job.

How do you make sure that this actually happens?

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 to understand the capabilities

and where it can go wrong.

So people need to be sophisticated users.

It's like my colleague Chris Caplice

always talks about driving.

There are mechanics who actually can fix the car

and know what's inside.

And then drivers, you don't have 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

or generative AI system.

But they need to know the promise, 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

and actually younger,

they used to teach me how to 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

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 can hallucinate and invent stuff,

invent sometimes reference that don't exist.

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 an F, 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,

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

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 from everywhere 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 years of their life doing it.

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.

- 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 advanced communication technology.

Do you see some radical changes

on how companies will be structuring their supply chain

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 work on autonomous tracking.

- 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, because it doesn't happen fast.

Give you one example.

In 1892, AT&T invented

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 were still 350,000 operators

like this in the United States.

Only by the 1980s, it started to go

really close to zero.

Nine decades from the invention

until it really,

all the jobs went or most of the jobs went away.

So it takes time, and it 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.

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 slowing down 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.

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's the most difficult thing

in this area, in this, people are worried about jobs.

And I understand it.

It's anxiety because

you know that people are gonna lose their job.

You see it in the supermarket

when you get to, when you can check out yourself.

People are gonna lose their jobs.

So these are people that you know.

What you don't know is all the new industry

and the new jobs will come.

So one quick example of this,

that is old example.

So Ford came up

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 employed several 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

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 side effect of what happened.

That is why it is so hard to imagine

all the new jobs that will come.

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 predict what would be (mumbles).

the one thing about supply chain coming back,

because that's what you ask about,

is it still involve physical movement?

Product have to move.

So there are some things that will be still grounded

for a long time until we start having

3-D printing at scale.

This can change supply chain,

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,

sustainability consideration.

But to get this, done we'll need to have

some more system thinking,

which is in very short supply

among the political class, the media class.

People are talking about.

Give you an example.

Rare earth minerals are used

in every sophisticated product now.

China controlled 80, 90%

of the world supply.

Aluminum, China controlled most of the world supply,

and stone, most of the smelters are in China.

You know which country has more 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

if it were done in the United States,

it would be probably done in a 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, we go standard of living.

We have to think more holistically.

And this is system thinking that, as I say,

in short supply, because there are pressure groups,

whether the green parties in Europe

or environmental lobbies in the United States.

There are the security hawks

that want everything to be from here.

But, again, from supply chain point of view,

moving the assembly or the last stage of manufacturing

to United States is meaningless,

or to Europe, is meaningless

because there's a whole supply chain

that was built after investment of billions

of dollars and decades

that is still in China.

Very hard to get out of this.

It will take billions of dollars and decades

to get out of there.

So we need to stop talking about

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 people don't realize how much

is there already

that is very hard to move.

And, by the way,

even if you move some (indistinct)

how much of the resources are 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)

- 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)