From Automated to Autonomous Supply Chains

Summary

在这段视频脚本中，Inma Borrella，MIT运输与物流中心的研究科学家，与Laura Allegue共同主持了一场关于供应链数字化转型的直播活动。他们邀请了英特尔公司的高级首席AI工程师Dr. Mani Janakiram，分享了英特尔在自动化供应链向自主供应链转型过程中的经验。Mani讨论了数字化转型的必要性，展示了英特尔如何利用人工智能、物联网和大数据来提高供应链的可视性、敏捷性和韧性。他还强调了文化变革的重要性，以及如何通过数据和分析来优化供应链流程。此外，Mani还提到了英特尔在可持续性方面的努力，包括水资源管理和可再生能源的使用。最后，他为那些希望开始数字化转型之旅的公司提供了建议，强调了从解决实际问题出发，逐步采用技术，并最终实现颠覆性创新的重要性。

Takeaways

• 🌟 供应链数字化是英特尔公司的关键战略支柱，它通过提高客户满意度、增强供应链的敏捷性和韧性，以及降低成本和复杂性，帮助公司实现长期目标。
• 📈 英特尔利用先进的数据分析和人工智能技术，如预测分析、物联网(IoT)、自然语言处理(NLP)和机器学习(ML)，来优化其供应链管理。
• 🚀 英特尔的供应链转型旅程是从自动化到自主化的连续过程，公司正在积极开发和实施新的AI能力，以提高端到端的供应链可见性和行动性分析。
• 🤖 机器人流程自动化(RPA)和AI在简化和标准化业务流程中发挥着重要作用，它们使英特尔能够更智能地自动化任务，提高效率。
• 🌐 英特尔的全球供应链非常复杂，涉及大量的数据和设备，公司通过数字化转型来提高其供应链的智能化水平，以应对这一挑战。
• ♻️ 英特尔在可持续性方面也取得了进展，通过数字化工具监控水资源使用，实现高比例的回收，并利用太阳能农场等替代能源。
• 🛠️ 英特尔作为领先的半导体解决方案供应商，正在通过扩展其集成设备制造(IDM)能力，包括外部代工厂的合作，来支持其供应链的扩展。
• 📊 数字孪生技术允许英特尔在产品开发和供应链规划中进行模拟和优化，这有助于公司在不牺牲质量的前提下，缩短产品上市时间。
• 🧐 英特尔强调在进行数字化转型时，需要进行文化变革，包括愿意放弃旧的工作方式，接受新技术，并在整个组织中建立信任和合作。
• 📉 面对COVID-19大流行期间的半导体短缺问题，英特尔利用数字工具进行风险管理和缓解，确保了业务连续性和供应链的韧性。
• ⏱️ 英特尔认为，尽管自动化和自主化技术将在未来发挥更大作用，但人类干预在可预见的未来仍然是供应链管理中不可或缺的一部分。

Q & A

Inma Borrella在MIT的哪个中心担任研究科学家？

Inma Borrella是麻省理工学院（MIT）交通与物流中心的研究科学家。

Dr. Mani Janakiram在英特尔公司担任什么职位？

Dr. Mani Janakiram是英特尔公司的高级首席人工智能工程师，负责制造业供应链运营。

数字化转型在供应链中的作用是什么？

数字化转型在供应链中的作用是通过利用数字工具和技术提高供应链的效率和敏捷性，降低复杂性和成本，增强生产力和韧性。

英特尔是如何支持全球自主运营的？

英特尔通过提供半导体技术，支持计算、连接性、云计算以及人工智能的快速发展，从而支持全球自主运营。

为什么数字化转型现在发生，并且呈现出指数级增长？

数字化转型现在发生并呈现指数级增长的原因是数据与物联网的融合、数据收集技术的进步、存储的可负担性、计算能力的提升以及人工智能和机器学习算法的发展。

人工智能在供应链转型中扮演什么角色？

人工智能在供应链转型中扮演着使能者的角色，它可以帮助感知、推理、行动，并与系统和生态系统进行交互，提高供应链的速度、敏捷性和生产力。

英特尔如何利用其供应链的复杂性和规模？

英特尔通过全球内部工厂网络进行规模化制造，并利用第三方代工厂扩大产能。它还通过数字化和人工智能技术优化供应链，提高效率和响应能力。

英特尔如何管理其庞大的供应链和供应商网络？

英特尔通过使用先进的数据分析、人工智能模型和机器学习算法来优化其供应链。它还利用认知网络爬虫技术来主动管理风险并保持供应链的韧性。

英特尔如何从手动过渡到自动化，最终实现自主供应链？

英特尔通过简化、标准化、自动化、智能化和自主化的步骤来过渡其供应链。它利用数字孪生、模拟和优化模型来预测和规划其供应链网络。

英特尔如何利用数字工具来应对COVID-19期间的半导体短缺问题？

英特尔利用实时数据可视化、预测分析、库存优化模型和物流优化来应对COVID-19期间的半导体短缺问题。

在数字化转型过程中，英特尔如何衡量自动化水平和评估进展？

英特尔通过关键挑战、战略目标、项目治理模型、项目里程碑和价值影响来衡量自动化水平和评估进展。它还使用中心卓越和定期审查来确保项目的成功实施。

Outlines

00:00

😀 活动介绍与嘉宾欢迎

Inma Borrella，MIT运输与物流中心的研究科学家，以及MITxMicroMasters供应链管理课程的联合主持人，与Laura Allegue一起欢迎参与者加入此直播活动。他们介绍了活动嘉宾Dr. Mani Janakiram，Intel公司的高级首席AI工程师，负责制造业供应链运营。Mani简要地对参与者表示了欢迎。随后，Inma和Laura介绍了活动议程，包括Mani将分享的数字化转型背景、Intel供应链的数字化案例，以及如何从自动化供应链发展到自主供应链。他们还提到了活动的互动环节，包括实时问答和投票。

05:02

🌟 数字化转型与人工智能的作用

Mani讨论了Intel在自动化供应链向自主供应链转型的旅程，强调这是一个持续的过程。他提到了数字化世界的到来，包括COVID-19期间的远程工作、AR/VR技术的发展、数据的可用性，以及半导体在提供计算、连接性、云计算和人工智能方面的基石作用。他还强调了数据的爆炸性增长，预计到2025年将有超过580亿的连接设备。Mani解释了数字化转型的驱动因素，包括数据与物联网的融合、内存的可负担性以及计算能力的提升。

10:03

🤖 AI与机器学习在供应链中的应用

Mani强调了AI在促进转型中的作用，包括在医疗和技术行业中AI和协作机器人（cobots）的应用。他讨论了AI如何通过提高速度、敏捷性、生产力、弹性、减少复杂性和成本来改善供应链。他还提到了Intel作为世界上最大的集成设备制造商之一，如何利用其庞大的工厂网络和第三方代工厂的合作来扩展生产规模。Mani还描述了Intel供应链的复杂性和规模，包括其在建设新工厂、设备投资和年度支出方面的巨额投资。

15:05

📈 供应链的智能化与数据科学

Mani讨论了如何使供应链变得智能，包括利用数据科学、物联网和人工智能来提高预测可见性、行动分析和合同分析。他提到了Intel在供应链管理中使用的多种数据模型和机器学习模型，以及如何通过自然语言处理（NLP）和机器人流程自动化（RPA）来优化合同管理。他还强调了库存管理的重要性，并描述了Intel如何使用不同的库存模型来优化需求供应。此外，Mani还讨论了Intel如何管理其供应商生态系统，以主动管理风险并保持供应链的韧性。

20:05

🚀 从手动到自动化再到自主供应链

Mani通过类比展示了从手动清洁到使用吸尘器，再到Roomba这样的自主清洁设备的演变，来说明供应链的自主化目标。他讨论了实现这一目标的挑战，包括技术基础设施、数据基础设施、治理、度量和管理策略。Mani强调了从手动到自动化再到自主供应链的转变，以及这一过程中的数字化和AI的作用。他还提到了Intel的CEO关于如何利用危机的格言，以及Intel如何通过数字化工具实现更智能、更高效的供应链。

25:06

📊 数字化转型的策略与执行

Mani讨论了数字化转型的策略，包括简化、标准化、自动化、智能化和自主化的过程。他强调了在数字化之前先优化流程的重要性，并提到了精益六西格玛的概念。Mani还提到了数字孪生技术，这是一种终极的转型，涉及业务流程、资产或过程的虚拟映射。他讨论了Intel如何利用这些技术来提高客户满意度、执行效率和承诺交付。

30:08

🌐 应对COVID-19带来的供应链中断

Mani描述了Intel如何应对COVID-19带来的中断，包括利用数字化工具进行风险管理、业务连续性规划和供应链建模。他提到了Intel如何利用预测和场景规划来应对不确定性，以及如何调整库存管理、采购策略和运输合同。Mani还强调了在COVID-19期间确保员工和供应商健康的重要性，以及Intel如何向他们提供口罩和呼吸机。

35:09

📚 Intel的趣味知识问答

进行了一项关于Intel的趣味知识问答，其中包括Intel的处理器、手表销售、博物馆、公司原名以及创下的吉尼斯世界纪录等信息。大多数参与者正确地指出Intel的第一个处理器是用于计算器的，但实际上所有选项都是正确的，因为Intel确实涉足了这些领域。

40:09

📉 数字化如何改善供应链功能

Mani讨论了数字化如何改善特定的供应链功能，如预测、库存管理和运输。他引用了著名统计学家George Box的话来强调所有模型都是错误的，但有些是有用的。Mani解释了Intel如何利用数据分析和不同的预测模型来应对需求波动性，并如何使用库存优化模型和数字孪生模拟来优化库存和网络管理。

45:10

🔄 自主供应链的战略意义与变革管理

Mani讨论了向自主供应链过渡的战略意义，以及Intel如何从文化和战略上支持这一变革。他强调了变革管理的重要性，包括确保采用新技术、减少团队焦虑和重新技能培训。Mani还提到了Intel在可持续性方面的努力，包括水资源管理、回收利用、替代能源和冲突矿产倡议。

50:12

🛠️ 数字化转型的建议与优先事项

Mani为那些希望开始数字化转型之旅的供应链专业人士提供了建议。他强调了从解决实际问题出发的重要性，而不是单纯追求技术。Mani建议采取增量方法建立信誉，然后考虑颠覆性技术。他还提到了Intel在供应链优先事项上的考虑，包括客户至上、产能扩展和支持外部制造业。

📏 衡量自动化水平与文化变革

Mani讨论了衡量供应链自动化水平的标准，以及如何评估自动化实施的进展。他提到了Intel如何根据战略目标和关键挑战来选择项目，并使用治理模型来资助和管理项目。Mani还强调了文化变革的重要性，包括愿意放手并接受技术，以及在决策中信任技术。

🤔 人类干预在自主供应链中的作用

Mani讨论了在自主供应链中人类干预的必要性，他认为完全自主的供应链在不久的将来不太可能实现。他强调了决策支持工具的作用，以及在某些决策点上人类输入的重要性。Mani指出，随着数据和决策复杂性的增加，AI和数据分析将在决策中发挥越来越大的作用。

🏁 活动总结与感谢

Inma和Laura对Mani的参与表示感谢，并总结了他对供应链数字化的见解和建议。他们强调了Mani的分享对观众的启发作用，并希望看到更多公司受到Intel过去十年工作的启发，开始自己的数字化转型之旅。

Keywords

💡数字转型

数字转型指的是企业或组织通过整合数字技术和数据分析来改进或彻底改变其业务模式和运营流程。在视频中，Inma Borrella和Dr. Mani Janakiram讨论了数字转型在供应链管理中的应用，强调了它如何帮助企业提高效率和响应性。例如，Dr. Mani提到了Intel如何利用数字工具来增强其供应链的自动化和自主性。

💡供应链管理

供应链管理涉及协调和管理产品从原材料到最终用户的整个流程。视频中提到，供应链管理是Intel业务的关键部分，它通过数字化和自动化技术的应用来提高供应链的效率和适应性。

💡人工智能工程师

人工智能工程师是专门从事人工智能技术研究、开发和应用的专业人员。在视频中，Dr. Mani Janakiram作为Intel公司的高级首席AI工程师，分享了他在制造供应链运营中应用AI技术的经验。

💡自动化供应链

自动化供应链指的是利用技术来减少人工干预，自动执行供应链中的各种任务和流程。视频讨论了Intel如何通过自动化技术提高供应链的效率，包括使用预测分析和机器学习模型来优化库存管理和需求预测。

💡自主供应链

自主供应链是一个更高级的概念，指的是供应链能够自我学习和适应，无需人工干预即可做出决策。Dr. Mani在视频中提到了Intel在自主供应链方面的愿景，强调了AI在实现这一目标中的作用。

💡数字化

数字化是指将非数字信息转换为数字格式的过程，以便于更有效的处理和分析。视频强调了数字化在供应链中的应用，如通过数字化可以提高数据的可见性和可访问性，从而支持更好的决策制定。

💡数据分析

数据分析是指使用统计方法和技术来检查、清理、转换和建模数据，从而提取有用信息、发现模式或支持决策。在视频中，数据分析被提及为Intel供应链的关键组成部分，用于预测市场趋势和优化库存。

💡机器学习

机器学习是人工智能的一个分支，它使计算机系统能够从数据中学习并改进其性能。视频提到了机器学习在供应链中的应用，如通过机器学习算法来提高预测的准确性和自动化决策过程。

💡风险管理

风险管理是指识别、评估和优先处理风险，并采取适当的措施来减轻风险的负面影响。视频中，Dr. Mani讨论了Intel如何使用数字化工具进行风险管理，特别是在COVID-19期间如何利用这些工具来应对供应链中断。

💡持续改进

持续改进是一个持续的过程，旨在不断提高产品、服务、流程或功能的质量和性能。视频中提到，Intel不断寻求改进其供应链，通过采用新技术和方法来应对市场变化和挑战。

💡集成设备制造商

集成设备制造商（IDM）是指那些设计、制造、封装和测试其产品的公司，通常在半导体行业使用这个术语。视频中提到Intel作为世界上最大的IDM之一，强调了其在供应链管理中的全球网络和制造能力。

Highlights

Inma Borrella作为麻省理工学院运输与物流中心的研究科学家，介绍了本次活动和与会嘉宾。

Dr. Mani Janakiram，作为英特尔公司的高级首席AI工程师，分享了他在制造供应链运营方面的经验。

讨论了数字化转型的背景，包括数字化供应链的例子以及如何从自动化供应链发展到自主供应链。

强调了COVID-19期间数字化的重要性，以及它如何加速了全球数字化的进程。

提到了半导体在提供计算、连接性、云计算和人工智能的基础技术方面的作用。

预计到2025年将有超过580亿的连接设备，这将推动英特尔在支持这一增长方面的努力。

讨论了数据和物联网的融合，以及它如何使数据的利用呈指数级增长。

人工智能在供应链中的应用，包括提高速度、敏捷性、生产力、韧性，降低复杂性和成本。

英特尔作为全球最大的集成设备制造商之一，如何利用其庞大的工厂网络和第三方代工厂来提供综合解决方案。

英特尔的供应链不仅支持内部工厂，还支持代工厂和外部制造活动，这增加了复杂性和规模。

介绍了英特尔在供应链的不同方面所实施的AI能力，如端到端的预测可见性和利用大数据和物联网进行可操作分析。

强调了从手动到自动化再到自主供应链的转变，以及这一转变的战略和运营影响。

讨论了数字化如何改进具体的供应链功能，如预测、库存管理和运输。

Mani分享了关于如何开始数字化转型旅程的建议，强调了解决重复问题的重要性。

提到了英特尔在可持续性方面的努力，包括水资源管理、回收和可再生能源的使用。

讨论了文化变革在供应链转型中的重要性，包括领导层的支持和技术的接受。

强调了衡量自动化水平的标准和评估自动化实施进展的重要性。

探讨了在越来越自动化的系统中，人类干预何时是必要的，以及人机交互的平衡点。

Transcripts

- Welcome everyone.

Thanks for joining.

I am Inma Borrella,

I am a research scientist at the MIT center

for transportation and logistics,

and I'm part of the MITxMicroMasters

in supply chain management program.

So I'm co-hosting this live event with

Ms. Laura Allegue, she is also a Course Lead

at the MicroMasters.

And today we are very fortunate to have with us,

Dr. Mani Janakiram

He's a senior principal AI Engineer

of manufacturing supply chain operations

at Intel corporation.

Welcome Mani.

- Thank you Inma.

And good morning and good evening to all.

- So let's kick off the event with a famble

just to break the ice.

I'm going to land it now.

So we just want to know where, why you are here today.

So while you fill out the poll,

Laura will explain the agenda for this session.

- Awesome, thank you Inma and welcome Mani.

We are super happy to have you today.

So for about the next 15 minutes,

Mani will provide some context in digital transformation.

He will share examples about

the digitization of Intel supply chain,

and we'll discuss how to evolve from

automated to autonomous supply chains.

Inma and I will ask some questions we have prepared,

but we will make sure that the last 15 minutes

will be saved for your questions.

So please use the webinar Q&A feature to ask those questions

and be sure you're logged in with a name.

We will not answer any anonymous questions.

We will also share some more polls during the event.

So be prepared to participate on that.

And I don't know how many answers do we have already,

but maybe we can start by checking the poll results.

So Inma, you share them, thank you.

So most of you want to learn about

digitalization of supply chain, that's awesome.

And I also see that you want to improve

your supply chain using digitization.

So that's great.

Hopefully we will get to cover all these topics

if time permits and I'm sure you'll get

a lot of great insights from Dr. Mani's experience.

So with that in mind, Mani are you ready to kick it off?

- Yes, I am ready.

I'll go ahead and share my screen

and we can get started, so,

Okay, let me do this.

So Laura can you see my screen?

- Yes.

- Okay, perfect.

So as Inma and Laura indicated, I'll be talking about

our journey in the automated supply chain

to autonomous supply chain,

it's a continuous journey.

So if you are thinking that we are there already,

no, we are not there.

We are one of the travelers among many.

And so I want to just get started with where Intel

and what Intel and how it is supporting

this kind of autonomous operations across the world.

As all of you know, the entire world is becoming digital.

And primarily there are a lot of reasons

why it is becoming digital,

but we're also living in the era of COVID

and working from home, you know,

like having different technologies, AR/VR

and the availability of data,

it's all forcing us to get into faster

into the digital world.

And as you all noticed, you know,

semiconductors are primarily providing

the underlying technology for many of this compute,

connectivity, cloud computing,

as well as the advent of artificial intelligence

has actually exponentially increased

how data can be leveraged.

And another thing that's happening also is

the digital data has been skyrocketing.

And we expect that the connected devices

would be in the 58 plus billion devices by 2025,

connecting every person on earth.

And we are looking at Intel to power this growth.

Another question that might come up is

why this digital transformation is happening now,

or why it is having this exponential growth.

Again, repeating what I said earlier,

the fusion of data with IOT

and numerous other data collection,

we can easily capture and collect unstructured data,

not just structured and big data.

And also the memory being really, really affordable.

Thanks to Moore's law and with, you know,

like in process memory capabilities,

and also availability of compute power,

as well as very good sound reasoning with the AI

and machine learning algorithms when you put them together,

that is where we expect the magic is happening.

And so I mentioned AI,

and artificial intelligence is actually enabling

quite a lot of this transformation.

For some of you, you know, artificial intelligence

might bring an image of, like a movie like a transformer

movie, like going back, you know,

like it could be more like, you know,

like a few other science fiction movies,

but reality is AI is all around us, we are using it,

we are leveraging it

and we are probably developing it as well.

And the whole idea is

there is a ton load of information out there and you know,

like how we sense it and how do we, you know,

like harness the information to reason out of it.

And then once we reason what we see and sense,

then we take action.

And that's where, you know,

like we have interaction with the systems

and, you know, ecosystem around us

and also the ability of the AI doing all these things

imagine, as well as learning

and then integrating it into its next action.

That is where the AI is actually enabling us.

And we hear a lot in the medical industry,

in the technology industry,

how AI, the cobots are really helping us.

And this is where we don't like it is not necessarily

just a technology for the sake of technology,

but it is enabling improving velocity, agility,

which is critical for supply chain, increasing productivity,

increasing resilience, reducing complexity and your cost.

Those are some of the things

that we actually are benefiting out of AI.

And as I mentioned, how Intel like you know,

is delivering this particular capability of value

to the semiconductor solutions,

as you probably know,

we are one of the largest integrated device manufacturers

in the world.

There are very few left.

And then when you start looking at Intel is like

a huge Intel factory network

with a global internal factory network

at scale manufacturing.

We are also expanding and leveraging the foundries out there

to expand the use of third party foundry capacity because

Intel we're you know,

like it's not necessarily manufacturing every chip,

but we'll be willing to provide an integrated solution

to the external foundries.

And given to the current challenges

also with our aspiration to be you know,

like a one stop shop for everything,

but also opening up Intel Foundry.

And so we're building,

we used to have presence in the foundry,

but our CEO, Pat Gelsinger is really looking to expand

what we call us the IDM 2.0

to expand our solution in the end.

And for that supply chain is going to be very critical

because supply chain, Intel supply chain is not necessarily

looking internally to support internal factories,

but we're going to be looking at

how do we support a foundry?

How do we support external manufacturing activity?

So the complexity and the scale has gone up.

And I just wanted to give a quick a feel

for how big and how complex our supply chain is.

When you look at it from a skew perspective,

it is not necessarily at any order

compared to the Walmarts and the Amazons,

but when you start looking at the complexity, the lead time.

Now for example, in the construction of Intel fab

would take anywhere upwards of two years plus

with the 4-6 billion dollar investment.

And the equipment that we buy, you know,

our CapEx will be in the order of 10-15 billion dollars.

And our spends would in the order of 25-30 billion dollars.

And when you start to look at like

one equipment, for example, lithography tool would be

like hundreds of millions of dollars.

So imagine the data that we need to harness

to keep this equipment productive, keep our factory running,

not to mention you know, like

ensuring continuous supply to our suppliers,

sorry, our customers,

as well as working with our 10,000 plus suppliers.

So this is the scale at which we operate.

We have worldwide presence.

We have what's called as the way for fab,

where we fabricate and put the transistors

and then assemblies where we actually package them

and test them and get them to the warehouses.

And so this is a big operation

all the way from foundries to customer.

So it's a long process, it's complicated and,

but we are enjoying it and we're taking it as a challenge.

And when we start to look at

what are the different things we can do

within the challenging environment,

supply chain, particularly given the, you know,

like the situation that we ran into risk mitigation,

resilience matters, mitigating complexity matters,

enabling faster lead time matters,

and then ensuring that our supply chain is cost-effective

and agile matters as well.

So in that a particular aspect of

how we want to make our supply chain smart and intelligent,

the digitization and the AI are playing a key role.

We have several data scientists, the subject matter experts,

and, you know, several of the engineers and technicians

working together to really address

the various aspects of supply chain.

We look at supply chain as a hybrid function.

It is not just the sourcing function, procurement function,

manufacturing, or logistics or planning.

It is a combination of all.

So because it's in gang, right.

If I pull one, something else get pushed.

And so how do we ensure that what we do has

a global optimization versus a local optimization.

And having a better understanding of

what supply chain data is telling us,

having a better visibility that we can actually act on

and having a ability to predict what is going to happen

and ability to take advantage of what we have,

what limitations we have and plan accordingly, you know,

like prescribe, you know, like almost like an optimization.

And on top of it, learn from what we do

is what the whole effort is.

What I have listed here is a laundry list of things

that we have implemented working on

and primarily I have kind of boxed

some of those capabilities because these are the areas where

we recently developed some AI capabilities

like supply chain, end to end predictive visibility,

and leveraging IOT in combination with big data,

for actionable analytics.

And we have AI and ML models to provide

what's going to happen, hat's the best thing to do.

And then in the contact analytics,

which is hugely unstructured data, lot of texts,

we have to develop NLP models,

we have to actually up streamline our process with RPA

and machine learning with clustering

and looking at where and what, how the contract terms are,

you know, working for us, how do we audit them

so that we don't have to, you know,

like swift through thousands and thousands of contracts,

but we need actionable insight into

what is happening in the contracts.

And then inventory is a big deal for us.

So we have to manage and make sure that

we have the necessary inventory of our products

and spares and components at any given time.

And so we also develop self-serve inventory models,

because one inventory model is not going to cut it.

Maybe we have a multi echelon inventory optimization,

We have a, primarily you know, di attach based inventory.

So we have different types of inventory model

that gets kicked in.

And so we are optimizing spares,

demand supply using data science as well.

And then of course the other critical aspect for us is

managing our ecosystem or understanding what is going on

with the supplier, so that we can proactively manage risk

and keep our supply chain resilient.

And so we leverage the cognitive web scraping

and leveraging all the data to really understand

what is going on.

Is there an issue with the supplier financial,

or is there an issue with our, you know,

like with the COVID and everything is a safety issue.

If something is happening,

at one part remote part of the world,

what is the impact to our customers?

What is the impact to our supply chain

impact to our employees and also to the society.

So that Intel as a corporate citizen can step up and help.

So these are some of the things that we are looking at,

and you might be wondering,

so where exactly are we heading with all these things?

Our goal is to really go from

manual to automated, to autonomous supply chain.

If you're wondering, what do you mean by that?

I just had some cartoons to just show that,

hey, you know, what,

what I mean by manual, automated, autonomous is

for example, going from a broom to a vacuum cleaner

to a Roomba, kind of a autonomous cleaning.

Or you know, most of you are technology familiar

that I share this slide a lot.

Now, you know, the way our navigation system has evolved

from a paper map to, you know,

to go from a place to point A to point B to Siri

and navigation system that you just speak to it, you know,

it primarily putting, you know,

like indicating where you want to go,

and that takes you through traffic

and takes you where you want to go.

And of course there are some stumbling blocks,

but you know like that's a growing challenge

that we have.

And what we are facing, what we are looking,

is the autonomous vehicles

and in the future, I think it is,

they're already in a limited way.

Primarily the vehicle knows you,

it knows your, it is in sync with their schedule,

it connects and, you know,

primarily it takes you to where you want to go

without even blinking an eye.

And you probably, you know, like in the future,

you will be sitting in the, you know,

like in the passenger seat.

Today, you could, but I think there is little more

infrastructure and things like that that need to happen.

So now think of the same technology

that we can apply for supply chain.

Currently, you know, like we, you know,

we have planners, we have logistics experts,

we have a sourcing experts, so we tend to operate in silos.

And so, as I was indicating earlier,

going from a silo kind of thinking to connected,

intelligent, what I call as the hybrid supply chain

is where we should be looking at.

And of course there are challenges that like,

all of us have, you know,

do we have the foundation ready?

Do we have the data infrastructure?

Do we have the right kind of governance in place?

And what are the metrics that we want to manage?

How do we, you know,

like make sure that culturally we are ready for this?

You know, like from a people and from a business process

and systems perspective,

as well as what is the strategy

to go from wherever we are to where we want to go

and how do we go about it?

It's not, you know,

like close your eyes and you're there kind of a deal

and foundationally as well as operationally,

what we want to do.

And then leveraging, you know,

like it's not the technology for the sake of technology,

where, and how we apply the advanced analytics, and AI

is also critical.

So that those are the kind of things we're looking for,

but we expect a value out of this is going to be pretty big

and increasing customer obsession

from our perspective to deliver the best value

and driving execution delivered to our commitments.

Those are all the critical things we're looking for.

So with that, I'm going to pause, and this is last,

my last slide.

So primarily, you know,

like as our past Andy grow, our CEO used to say, you know,

like we are going through crisis,

but don't let the crisis drive you,

you take charge of the crisis and survive it

and then improve upon it

and it is how we are looking at it.

So I'm gonna stop here and I'm going to stop sharing,

and then we'll go back to Inma and Laura.

- Thank you Mani.

I love that motto from your CEO, super inspiring.

So thanks for a great introduction to

supply chain digital transformation,

supply chain digitalization,

and for sharing interesting initiatives that

Intel is implementing to actually achieve

like this smarter, more efficient supply chain through

by using digital tools.

So now we will, we want to dive into some questions

because this is a great appetizer,

but we really want to keep talking about this topic.

And you know, we know you're an expert,

you've been at Intel for more than 20 years,

mainly working or leading the digital transformation

or the manufacturing and supply chain areas.

So we would love to see, to listen to

what you can tell us that how digital supply chain landscape

has evolved during this all these years.

And where did you see it going?

- Absolutely. I think that, you know,

like if you look at the evolution of digital supply chain,

even 30-40 years ago, we had, for example,

a robot, a pick and a place robot,

was supposed to be a big deal at that time.

Fix locations, X, Y, and Z, and then programming it using,

I remember doing that with some of the robots,

just primarily being very happy building one,

which was able to pick and place.

But today the robots are, you know, like in fact,

I was seeing the Boston dynamics,

they actually danced to the tune, it can jump,

it can move, it can think.

So the technology has evolved significantly

and it is not just for fun, right?

I mean, it is also, we also hear about

primarily having AI engine in transactions,

in you know, stock market,

having an advisor kind of a deal and then AI admins.

So what it is, as I indicated,

it is evolving.

Within the semi-conductor within the supply chain,

we see that our warehouses have, I think Inma and Laura

you can probably go into a lot of details there,

but you know, like using a robot, location analytics

and things like that have improved significantly.

So I think where it's going is really

going from helping or assist or you know

taking and helping out the mundane task.

Like you're, I'm talking about doing the software,

like, for example, if look at RPA,

robotic process automation, robotic desktop automation,

what used to be an Excel macro many years ago,

is already a huge industry and software with an RPA.

And where I see RPA is going is primarily, you know,

if you kind of look at it from a lean six Sigma perspective,

we talk about, hey, you have to look at a business process.

You have to simplify it first,

so that you don't go automate something that is stupid.

And then once you simplify it,

then you have to really look at,

is there an opportunity for me to standardize things,

because that way it is easier for machines to learn,

for people to like adopt and things like that, right.

So my goal is simplify, automate, and then autonomate.

And in somewhere in between,

you have to make it intelligent.

So simplify, standardize, automate,

make it intelligent and autonomate.

And this is where the digitization is going.

If you look at RPA, RPA fits in there.

If you look at digital transformation and AI,

it fits in there.

And then where it's going is also more like a digital twin,

which probably is an ultimate transformation of

where, you know, like our business processes

it's industrial, like it is asset twinned

or a process twinned.

So some of those things are happening in that fashion.

- And you made a very relevant point Mani,

that I would just like to highlight about this idea of

do not just go and digitize your process.

Just think about the processing,

if you can simplify it, make it better

and then you go on standardize and digitize it.

Don't just digitize whatever you have now,

because that's not the way to go.

So that's a very important previous step to digitization

that not many people think about

when they start the transformation journey.

And yeah, thank you.

Thank you very much for your answer, Laura.

- Yeah.

Yeah, so let's go now for launching the second poll Inma.

And the idea now is to

bring you some information about Intel.

So we are doing an Intel trivia,

which is one of fun facts,

and we wanted to learn what you know about Intel.

So you will have something there on selling watches,

owning a museum or some Guinness record out there.

And while we gather some of your responses,

I would like to go back to Mani.

So we have seen all the disruptions of this past year,

and I would say a little bit more than that.

And those have been pushing forward innovation.

And I think this is related with what you mentioned on

trying to survive and also trying to improve

as your CEO mentioned.

And I was wondering if you could tell us

how Intel was affected by the disruptions of the 2020

and how having a digital supply chain

may have helped you on that period?

- Absolutely.

The disruption that we lived through, or living through

is something that I don't think anybody expected the scale

or the impact, but we had, you know,

like to your question,

our supply chain, we have primarily

business continuity planning in place.

In fact, we have a risk and resilient team that, you know,

like we kind of do some of this,

not necessarily in a COVID like kind of an exercise.

We do natural disaster.

What happens like for example,

some of the currency issues are, yeah,

we are also going through some of the cyber security issues

and things like that, right.

So we have business practices in place

and the business practices were actually helped with

the digital activity in the sense that

we could actually leverage modeling,

and we could leverage some of the, you know,

like a business process models

and data and projection and prediction to say that,

like a decision tree, for example, right.

You know, in simple terms that we could leverage to see

what happens if you do this

and what would be the end result look like,

the scenario planning and how do we, you know,

what would be the best answer for this one?

So does this mean,

do we have to plan our inventory differently?

Does it mean we have to respond differently?

And do we have to look at you know know,

like alternate sourcing for that?

We have to really look at what the warehouse

and the cost of transportation going

is a longer lead contract

and need to be in place versus, you know,

fixed versus variable.

So those are all the things from

a supply chain perspective, we look at it.

And also when we had this COVID situation,

we were really looking at what are the different products?

How do we respond to the customer?

How do we make sure that our suppliers,

not just from a product delivery perspective,

but also from over all health, they're good.

We also got into providing them with masks and ventilators

and ensuring the health of our employees

as well as our suppliers was, you know,

like taken care of from that point of view.

- Awesome, thank you Mani.

And it's amazing to see,

okay, we had the disruption caused by COVID,

but how are we prepared,

And culturally also to have the risks and resilient team

and to be ready to address any kind of disruption.

So it has been a great push of growth.

And now I think we're ready to do much more in the future.

So thank you for your insights on that.

- Okay, so let's take a look at the poll.

Most people have, I don't know Mani if you know the answers,

the right answer to the poll,

some yeah, Intel insights,

but most people answer that

Intel's first processor powered a calculator,

51% of people believe this is the one that is true

and that's true, so you're all right.

But actually this was a trick question

and all the options were right.

So Intel also used to sell watches,

Intel has it's own museum in California.

It wasn't the company's original name,

even though we are know Intellus Intel forever.

I know, so in 2018,

Intel set a new Guinness world record title

for most drones flown simultaneously.

I need to find out if there is any video for that,

because it must be amazing.

So now you know, a little bit more about Intel corporation,

let's continue with our discussion,

Mani, did you know all these facts?

- You know, like I had to look up for the watch part.

I knew that, but I had to look up, if I'm being honest here,

but everything else yeah,

And I think if you are in Santa Clara,

I would strongly suggest that

you go to visit our Robert Noyce building.

The museum is exceptional, so.

- We'll go next time we travel to California.

So digitalization is a very fancy term

and digital transformation,

now digital transformation discussion is

full of technology buzzwords.

So we hear IOT, block chain, digital twins, cobots

and many people don't really know how this applies

to supply chain management.

So I really think that at the end of the day,

the digital transformation is about

improving supply chain processes by using digital tools.

But the focus should always be on the supply chain processes

and not just on the technology itself.

So some of our audience,

members of our audience are currently taking the C1x

in which we cover the basic pillars of any supply chain,

that's forecasting, inventory management,

and transportation.

So could you tell us how digitalization has improved

specific supply chain functions

such as forecasting inventory or transportation at Intel?

- Absolutely.

Let's take forecasting.

You know, we all know that

the famous statistician George box said

all models are wrong, but some are useful.

And that what it means is

you can have the best forecasting model out there,

but if a situation arises, it's going to,

you know, the demand volatility and the supply volatility

might put you in,

what did they forecast really?

And as you also know, as if you are forecasting within the,

you know, like within a year,

your demand, you know, like a forecast error,

it would be smaller,

but as you project further out,

it is like an outward funnel.

So what it means is you have to really elaborate

the power of people, data analytics,

to really, to come up with

what the data is telling us around us.

And then how often do I need to go make adjustment to it

and then try out different scenarios.

Some of the techniques, you know,

like a proven time-series models, we of course leverage.

Sometimes it is not just the one algorithm

or one you know, analytic model

that we are going to go with,

we look at an ensemble.

We need to learn from what has happened in the past.

And we also know that some of the changes

that happen either, you know, like a trending or a drifting,

you know, we can capture it.

But if it is a huge step function,

what Clayton Christensen calls as you know,

disruptive change, that is sometimes hard to capture.

But if you were to look at it, you know,

like in a cycle, you know, like cyclical fashion

in semiconductor, for example

in the past, you know,

the cagr or the component of growth rate is around 68%.

And then every, you know, like every six years,

there's a huge, you know, like a shift on a swing

because that is the lead time for building factories

and putting capacities.

So you could actually start to think about

how it is going to change,

not to mention how the technology is changing,

what the adoption rate is,

and of course, last year, as you noticed,

there was a lot of semiconductor shortage.

It is not necessarily because of the growth.

It is also because of some of the supply shortages

that culminated, and also some, you know,

like a mad rush for some of the products

thinking that it may not be available

you know, like I want it now.

That there was an explanation for it

because you're working from home,

you need more compute power, more PC,

you want more bandwidth, things like that,

that has changed what people are looking for.

So from our perspective, we leverage our forecasting models.

We leverage different scenario models.

We have inventory optimization models.

And top of it, if we have you know,

like having a new product, we have a

the digital twin kind of starts with a simulation,

if you will, right.

Modeling of what's going to happen,

how it's going to happen, it could be a simple,

you know, like a Monte Carlo model.

It can go into a more sophisticated

discrete event simulation model to really understand

how my network looks like,

what are the different echelons in my network

that is going to be constraint?

How do I manage my inventory buckets across the board?

What are the metrics that I need to really understand?

And then once we have that kind of a model

from a optimization perspective,

you can align capacity and demand through, you know,

like a big optimization engine that we leverage.

And so it could be linear programming.

And we also coupled that with machine learning

to explain what the optimization engine is telling us

so that we could do not only predictive scenarios,

we can also explain the decisions we're making.

And we are also leveraging it within our models,

within our business processes, RPAs

to automate, to understand data, to synthesize

and ensure that it is, you know, like it is right,

it is governed right, it has got the right metadata.

So it's a combination of all those things

where we are leveraging.

And we are looking at the metrics like

safety stock, and we're looking at service level, you know,

like what's the service level you are improving,

and satisfaction rates,

we're looking at inventory points.

And of course we're also modeling cost you know,

from a strategic point of view.

- I think it's really interesting the way

you just bring it down to earth with the specific examples,

I think that's much needed when we talk about digitalization

and all of this idea of using these new technologies

to really augment the capabilities that we have.

So really like having better optimization models,

through which you can get better insights into

machine learning,

or just using RPAs to optimize the management of data.

It's really like a very interesting way of just expanding

the amount of things and the insights

that you can get from the data.

So thank you Mani.

- Thank you Mani.

So in your presentation,

and moving forward to the next question,

you talk about the transition from automated

to autonomous supply chain, that is,

what's bringing everyone today here with us.

And this transition of course will have

important strategical and operational implications

for Intel.

So we would like to ask you,

why is this a key strategic pillar for Intel

and how is the company working on it,

that we would like to know about

the change management perspective,

we're thinking on how to ensure the adoption,

how to reduce the anxiety of your team,

how to re-skill the workforce.

- Absolutely, I think, you know, like,

just because we are automating our tasks

or you know, like going for the autonomous goal,

it does not necessarily mean that

it is going to impact people.

There will be some impact in terms of skills

and ability to move up in the, you know,

like in the learning and things like that.

And that is where, you know, like, as I indicated earlier,

the transformation, technically we have the pieces together

that we can make it happen.

Culturally, you know, like broadly speaking,

culturally and strategically, we have ways to go.

To the extent of this is my data, this is my process,

this is how I do it.

or the moniker that I'm a planner

and I'm attached to planning versus,

okay, you know, you do the best planning

and if you're not able to deliver the product,

what good is there, right?

So thinking holistically versus you know like in silo,

that is a cultural shift as well.

Having the right leadership to support,

because this is not going to happen overnight.

This is an effort that is a longterm effort

that needs to be supported.

And luckily we have that kind of a support within Intel,

as you might've noticed that we are being, you know,

like the top 10 supply chain leaders

as recognized by Gartner, all the 10 plus years.

And those are the, you know,

the reason why they're looking at is

number one, is the supply chain,

customer enabling and supporting.

Is it the ability to adopt technology

and be agile and leverage the technology

for the sake of solving problems

and enhancing the supply chain.

So those are the kinds of things we are looking at it.

And this also is from a you know, sustainability

and a good corporate citizenship perspective, right?

We, for example, we have systems and tools that monitor

and understand our water usage

and we have 90 plus percent recycling.

And our goal is to be a hundred percent.

And then we also have alternate energy.

In fact, we have the solar farms,

in most of our parking lots to leverage

and to harness energy from that,

it is just to name of a few.

And then we all of you are very familiar with

the conflict free mineral initiative.

And so zero waste is not necessarily something

that has happened, but it's happening as we go.

And so those are all the things that we can know

with the power of data, with the power of analytics,

we are understanding what is going on,

we plan for what can happen, and then look at, you know,

visually understand and predicatively manage these things.

- Thank you Mani.

I think it's a super interesting to speak about

the company culture and the cultural change

that is required.

And the fact that the top management

should be super committed to it

so that the full company is seen to that.

So that's a great addition above everything

that we usually teach that is

a little bit more technical sometimes.

So it's great to learn about that part of the strategy.

And I also think you have answered some of our learners

and audience questions because they are super interested

in the application of sustainability.

So thank you for bringing those examples.

- Great.

Yeah, no, Intel is doing a lot in the sustainability space,

also upstream with our suppliers and all these initiatives.

You've seen this sourcing intelligence

just to have a better visibility.

- Sourcing Intelligence,

Inclusion and diversity initiatives.

And, you know, we're spending billions of dollars where,

you know, like minority suppliers and things of that nature,

improving the diversity.

So there are a lot of efforts in that space as well.

You got it.

- Great.

And we shall have a live event about that another day.

So most of our participants today

are supply chain professionals,

and they maybe look into involve themselves

in the digital transformation of their own supply chains.

So these may seem as a daunting task

for many companies that are starting

or have not even started this journey.

So what advice can you give them?

Where should they start

or how should they start thinking about it?

- Absolutely, I think some people like,

in my initial career, I was very fascinated by technology.

Like, oh, this robot it's beautiful.

It works, it picks up things and places.

So I was more focused on

how can I make this technology to work?

I think the thinking should always be

what is the problem that needs a solution?

The solution need not have to be, you know, high-tech,

it needs to solve the problem.

But if the problem is repeating and if you're solving,

you know, like on a regular basis,

then you need to think about,

is there a better incremental approach to it?

And then once you have the credibility

and the ability to solve this kind of things

and things are going well,

then you really need to start thinking about,

can you do something disruptive?

Because the solutions that we have

incrementally will give you value,

but something that is disruptive would take you way forward.

I'm talking about

some of the technologies that have transformed, for example

the way we watch videos, streaming videos

never thought of it.

We talked about some of the, you know,

like the map to digital transformation.

Those are for me, you know,

like big disruptive technologies.

In supply chain you may be thinking about

what are the different things we can do

incrementally adding value and developing credibility.

And then, you know, a long-term,

I'm looking at disruptive technology

that built on my credibility I can go make it happen.

And I also understand that this is something

that I cannot go from zero to one.

That means I got to make sure that I have the right,

you know, like adoptive you know,

like mentality folks working with me.

I have models that shows what it can happen.

Like for example, if I have to go make some big changes

on a 50-100 million dollar kind of tool,

I could, well, some of the things I could do is

work with suppliers, you know, do design of experiment,

develop some of those things,

actually run the physical product.

I'd imagine if you have an asset twin

that actually mimics your physical model

and people trust what you're doing that

you know, what, if you change this particular location

of the particular thermal processor,

you could reduce the time by 10%, for example,

that if you were to model it in an asset twin

and show that that is how it is going to happen

without impacting quality,

then you can actually disruptively go from

what would have taken physically months to maybe,

you know, like days to weeks.

So those are the ways you make sure that you understand

where the big problems are,

prioritize the problem,

get the buy-in from not just the leaders,

but also from your community, because you know,

like adopting and leading and people willing to try it out.

Your peers also is critical.

And then experiment and, you know, and then go from there

is how I look at it.

- Awesome, thank you Mani.

I couldn't put it in better words than you,

so I leave it there.

And I think we can go to our audience's questions now,

right Laura?

- Yeah,

we should run our last poll maybe before we go to the Q&A

so we'll let it pre-populate while we speak.

So our last poll it's about

what you've learned from today's event.

We would really like to know

if we'll fulfill your expectations

or if you're going home with something else

that you expected.

And then meanwhile, let's go to the Q&A.

I don't know if in my field we have one already.

- Hi, can I start it?

So we have one question about, of course,

shortage of semiconductors,

because it's been in the news for so long.

So it's a very well-known topic that COVID-19 impacted.

So could you elaborate how Intel used digital elements

or digital tools, I guess

specifically planning visibility

and autonomous inventory management

in mitigating these impacts.

So I guess expanding a little more

of what you already shared.

- I think, you know, making data visible,

you know, real time is something of high value for us.

Because things are changing on the fly

and how we act on it,

what are the different things we can do,

that is where having the power of data,

digitalization and analytics is helpful

and then understanding and playing out different scenarios,

the inventory positioning, do we want to push?

Do we want to pull?

Those are some of the things we are leveraging

and also having a boardroom of thoughts

and then having the right people come in

and look at this data, look at it from overall,

what the impact of the customer and suppliers

and our faculty and employees in a wholesome fashion

is where the data analytics came together.

What actions we can take is something that

we were able to leverage.

And then what are the mitigations that we can do

and how do we communicate it?

And things of that nature were very helpful

and useful tools, whether it is an optimization,

whether it is a prediction models,

whether it is a control tower with, you know,

like data visualization, leveraging them, or, you know,

sourcing intelligence that would come in and tell us,

this is what happened here, this is what we need to do,

things of that nature we're all putting together.

And then of course from a logistics perspective,

what are the, you know,

like we hear about the ports and situations

are not getting delayed, our product is getting stagnated,

the supplier, not able to ship.

How are the things, what is the contract,

you know, like, do we have the right things in place?

All those things need to be looked at in combination

to look at how do we ensure uninterrupted delivery.

- Thank you, Mani.

So just going back to the poll

and sharing some of our audience comments.

So expanding my knowledge on digital transformation

is the most interesting part of today's event.

And also understanding the impact of digitalization

in supply chain functions.

Of course, getting ideas about improving supply chains,

learning about automation and autonomous systems

and the difference.

So thank you for bringing all that to our audience.

I would like to add one question so.

- Sorry Laura,

just mentioning that just,

people just selected almost all the options in this poll.

So that means that Mani covered

almost all the expectations from everyone.

So that's, that's great.

I think it was a very complete discussion and presentation.

Go ahead Laura.

- Totally, totally.

So thank you Mani for that.

So some will offer audiences asking about,

we know digitalization, and you also mentioned

a lot of possibilities and how to apply

digital transformation or the different tools

that there exists.

And they say, while we can apply to almost every aspect

of a supply chain, we want to know

where is Intel prioritizing that implementation?

Do you have any comment on that?

- That's a great question.

Primarily, you know,

like our priority starts with the customer obsession,

meaning what does,

what are the critical aspects customer want?

So understanding customer needs or changes in priorities,

and how do we go from there from a product

to planning to capacity management is where we start with.

So we have you know, SNOP and SNOE processes

that we are prioritizing heavily

and we are leveraging the power of data analytics

and modeling in that space.

And then from that point onwards, we're also looking at

what it means to expand our capacity.

That's where IDM 2.0 comes in.

Our customers not only are asking for

just the Intel CPU products or server products,

they are also asking for expanding the product horizon.

That's where we are moving into the external manufacturing,

as well as how do we support, investor related questions,

semi-conductor, you know, like being a shortage

and everything,

what are the different areas we can help the industry,

our customers as well as our government

through the foundries is another area we are prioritizing.

And as you can imagine,

there's a lot of planning that is going to happen.

And there's a lot of focus around construction.

We're going to be spending like 20 plus billion dollars

this year just in US.

And then we have expansion plans there.

So the supply chain is very central

to how we do that expansion, how do we support,

what are the SNOP.

So those are the critical areas we're looking at right now.

- Another question from Lim, Brian said,

hi Mani, I noticed that you mentioned several times that

culture change needed when transforming

to the next level of supply chains.

So what exact culture change are you referring to.

- There are a couple,

but one thing that comes to my mind right now is

ability to let go

It's, you know, like you should not think that

I control things in supply chain.

And if you were to think that I am part of the supply chain

and I can play a significant role in controlling

would like to leverage everything, you know, like around me

to make our supply chain better.

That to me is a big change.

You know, like if you were sitting on a data,

if you are managing a particular function

and if somebody were to come and say that

a technology can make that happen for you.

For example, if someone, a commodity manager

is pulling a supplier risk data,

and I have a ecosystem sensing tool

that actually does the web scraping and comes and provides

similar plus enhanced information,

if the commodity manager is not willing to adopt that,

then that's going to be a stumbling block.

So to me, you know, like willing to let go

and willing to embrace the, you know,

like technology and the solution around you

to enhance the supply chain would be a big cultural change.

- Willing to let go for operations people is a tough one.

So I love that, yeah,

it's a trusting more like technology

where they can, it can bring just,

just to contribute to move in the supply chain forward.

Great advice, thanks Mani.

- Thank you, Mani.

So we have some, a couple more questions, maybe.

So Remi is asking about how's the criteria,

how to select the criteria

to measure the level of automation that

you have on a supply chain,

if that exists and how would you evaluate your progress

on the implementation of automation?

- Great question again,

because there are always competing priorities,

there are always, you know,

like multiple more projects than you can handle.

So it comes down to, you know,

like for us, we really look at

what are the critical challenges,

you know, Intel supply chain has,

we have what's called strategic initiatives.

We have, you know, primarily we have targets

in terms of customer product,

you know, like a capacity supply chain.

We have the metrics, right?

Looking at agility improvement,

looking at cost improvement,

looking at, you know, so those targets,

we look at how do we go about doing that?

And then in supply chain,

what are the things we can support

and what are the, you know, like big challenges

that are pain points for us.

So that's our starting point.

And then we look at strategically,

what are the targets that we want to go accomplish.

It not necessarily have to be pain points,

but some of the technology changes that,

okay, now I need to bring digitization in the SNOP process.

But the question is,

it is not the technology for the sake of technology.

It is you know, what solution am I solving?

What metrics is it going to move?

And is that moment going to be significant enough

that I can actually get a buy-in from, you know,

all the decision makers.

So that is, we have a chartering document.

We have like a management review committees

and we kind of want to go fast,

but we also want to make sure that

we have the right areas where we want to go fast.

So we use that kind of a governance model to leverage

and you know, like fund the various projects.

And then we have good program management tools

to monitor and then milestones.

And then we actually, once we develop an implement,

we also monitor the progress and the value or the impact.

And before we start another project, if it is connected,

we look at what did the value from there brought in.

And we also have center of excellence

as where we leverage the resources, as well as the learning

to look at, did we deliver what we delivered?

And in fact, we also do something like,

okay, imagine that I'm funding you today,

six months from now, you know, what are the timeline is,

How do you see changes happening?

What are the metrics that are going to move?

And then we go back after six months and see,

this is what we said is going to happen.

This is where we are.

It's not that always, we hit the target,

like the way we said,

because our eyes are sometimes bigger than our appetite,

but we at least want to make sure that

what the variances are

and how we can adjust and move forward.

- Awesome, thank you Mani.

I truly believe that's a great advice

and a recommendation for our audience

and the fact that we understand where we are

before we know where we are going to go

and also how to connect what we do

to the impact it will make.

I think it's a great advice.

So thank you for that.

- I think we can answer one more question

before we wrap up.

So Carmel Tua says, hi, Mani

thank you for sharing your insights.

May I know at which point or an area human intervention

required in an autonomous supply chain.

This is a discussion that is ongoing, right?

Like where it is a human machine interaction.

When is the human input needed

in a increasingly autonomous system?

I don't know what are your ideas on that.

- I think if the question is trying to ask,

are we going to reach a singularity point?

My perspective is probably we're not.

Not at least in the, you know,

like in some areas, like, for example,

managing inventory, like if you have thousands of skews,

hundreds of thousands of skews,

and would you be looking at reorder point

and safety stock for all those components and skews?

Probably not, then you're going to have prioritization

and then others, you're going to have

some kind of a model and a map that says, you know,

when you hit 80%, I mean,

I'm just giving an example right

there, trigger reorder point, for example.

So those kinds of things that are not necessarily

at the same time,

if you're buying a hundred million dollar tool,

you're not going to let automation

and autonomous system takes over,

you will have some kind of,

it becomes more like a decision support.

It is not, decision-making kind of a tool.

So that means you really need to understand

what your supply chain wants,

what your priorities are,

and where you need to make sure that

the human decision is required

and where you don't have to.

So that it kind of comes down to the metrics

that they are going to drive by.

And also the governance model you're going to put together.

But short answer is I don't see

a complete autonomous supply chain happening

in the near future.

- Thank you Mani.

Yeah, that would be a tough one.

It would be nice to have like

more where autonomous parts of the supply chain

for certain skews as you mentioned.

But yeah, it's going to take time.

That's very interesting debate, right?

Where the human input is going to be required.

Even when you have like AI expanding,

it's influencing in our decision making.

- Yeah.

They're making your life a lot more easier

because when you look at it, the complexity is expanding

and the data and the decision-making,

you know, like data is exploding.

Basically it is going at an exponential growth

and the decisions need to be made now, not later.

So at some point it becomes harder

when you have to decide on thousands

and tens of thousands of variables

to come up with the right answer.

It becomes beyond human comprehension.

So that is where like leveraging data analytics

and the power of AI will come into big play.

And it is happening already.

- Yeah.

Great, so thank you so much Mani.

I think this is great, like last discussion,

just to wrap up the event,

we really enjoyed your presentation,

your insights around supply chain digitalization.

This is such a hot topic right now,

and such a complex area to go into.

I ensure our, our audience has appreciated

your insights and your suggestions and your advice

on how to think about it.

And hopefully we'll see many more companies

starting this journey, maybe being inspired by

what Intel has been doing in this past decade.

So thank you so much Mani for being our guest.

Once again, in the MicroMasters,

we really enjoy our discussions with you every time

during our live events.

And hopefully we'll see you again and in the future.

- Great, thank you Inma and Laura for the opportunity.

And hopefully the audience enjoyed it as well.

Thank you.

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