The New Competitive Edge: Analytics-Driven Supply Chain Design for Value Creation

Summary

本集MIT供应链前沿播客邀请了Mike Bucci和Milena Janjevic，他们分别来自Coupa（前身为LLamasoft）和MIT运输与物流中心。他们深入讨论了供应链设计的新旧实践，指出许多公司仍在使用已有30年历史的标准供应链设计方法，这种方法通常基于事件驱动，周期性更新，重点在于成本最小化。然而，随着数据可用性的增加和计算能力的提高，现在可以更频繁地进行更细致的分析。他们提出了四个主要机会来重新构想供应链设计：扩大考虑范围、纳入战术层面、考虑风险和规划弹性以及采用新技术和商业模式。这些机会可以帮助公司更好地适应快速变化的市场需求和供应条件，提高竞争力。

Takeaways

• 📚 传统供应链设计方法通常是基于事件的，周期性地重新评估重大供应链问题，如仓库网络设计或生产能力规划。
• 🔍 传统方法存在数据粒度低和实施后预期与实际表现差距大的问题，这与过去有限的计算能力和数据可用性有关。
• 🚀 现代供应链设计强调更频繁的分析和更新，以适应需求和供应方面的快速变化，如客户需求的增加和供应链的多样化。
• 🌐 新的供应链设计实践包括使用大数据分析、云计算、人工智能和机器学习技术来提高决策能力和响应速度。
• 🛠️ 供应链设计现在更注重客户中心性，考虑交货时间和可靠性作为关键的竞争因素，而不仅仅是成本最小化。
• 🔄 供应链设计的范围正在扩大，不仅包括传统的成本和物理结构，还包括长期价值创造和市场竞争力。
• 🔗 战术决策与战略决策之间的界限正在缩小，需要同时考虑两者，以确保数据和决策过程的一致性。
• 🛡️ 风险管理和弹性规划成为供应链设计的关键部分，企业需要识别和应对各种风险源，如自然灾害、大流行病或市场变化。
• 🌟 供应链设计现在包括更广泛的参与者和更复杂的组织关系，如利用众包资源和按需使用资源，而不是投资资产。
• 🔑 启动供应链设计改进的第一步是选择对业务重要的小项目，展示价值，并建立可重复的过程，以加快问题的回答和解决。

Q & A

MIT供应链前沿播客是关于什么的？

MIT供应链前沿播客是由麻省理工学院运输与物流中心(MIT CTL)主办的，每一集都会邀请中心的研究人员、工作人员或该领域的专家，就商业教育及其他话题进行深入对话。

Mike Bucci在Coupa（前身为LLamasoft）的角色是什么？

Mike Bucci在Coupa（前身为LLamasoft）主要负责专业服务团队的工作，他与全球各行业的公司合作，帮助他们理解如何利用Coupa软件解决供应链设计问题。

Milena Janjevic在MIT CTL的供应链设计计划中扮演什么角色？

Milena Janjevic在MIT CTL领导供应链设计计划，该计划旨在改善供应链设计的决策过程，通过行业赞助的研究和教育项目帮助公司更好地解决供应链设计问题。

传统的供应链设计方法有哪些特点？

传统的供应链设计方法主要是基于事件或偶发性的过程，通常每两到三年进行一次，重点在于重新评估重大的供应链问题，如DC网络设计或生产能力规划问题，主要关注成本最小化。

为什么传统的供应链设计更新周期较长？

传统的供应链设计更新周期较长是因为过去计算能力有限，数据收集困难，需要大量的工作来寻找、收集、清洗数据并获取组织内部的支持。

供应链设计中提到的“事件驱动”方法有哪些缺点？

“事件驱动”方法的缺点包括数据粒度低、无法及时反映市场变化，以及在实施新设计后预期性能与实际性能之间存在较大差距。

为什么现在的供应链设计需要更频繁地进行？

由于市场需求和供应方面的快速变化，公司需要更频繁地重新适应其供应链设计，以满足日益增长的客户需求，并应对供应链中的各种变化。

Milena和Mike提到的“客户中心性”在供应链设计中扮演什么角色？

“客户中心性”已成为供应链设计中的关键要素和竞争优势的来源，无论是在B2C还是B2B领域，交货时间和交货可靠性都成为了关键的竞争优势。

在供应链设计中“扩展范围”是什么意思？

“扩展范围”意味着在供应链设计中考虑更广泛的目标，不仅关注成本最小化，还要考虑如何创造长期价值，增加市场份额和收入。

为什么需要将战术层面的决策纳入供应链设计中？

将战术层面的决策纳入供应链设计可以使战略和战术决策之间的差异缩小，利用相同的数据基础解决两种类型的问题，提高决策效率。

在供应链设计中考虑风险和弹性规划的重要性是什么？

考虑风险和弹性规划可以帮助公司识别和应对潜在的供应链中断，确保供应链的稳健性和适应性，特别是在面对自然灾害、大流行病等不可预测事件时。

采用新技术和商业模式在供应链设计中有哪些机遇？

采用新技术和商业模式可以提高供应链设计的效率和效果，例如利用大数据、云计算、人工智能和机器学习等技术，以及探索新的组织关系和资源共享模式。

Mike和Milena提到的数字化转型如何影响供应链设计？

数字化转型通过提供更多的数据、云基础设施和AI机器学习能力，使供应链设计更加灵活和响应迅速，同时也促进了新的组织关系和业务模型的发展。

如何开始在公司内部推动供应链设计的改进？

公司可以通过选择对业务重要的小项目开始，确保所选项目具有可重复性，并能够展示价值，然后逐步建立可重复的过程，以加快回答业务问题的速度。

供应链设计的未来趋势有哪些？

供应链设计的未来趋势包括大数据的规模和粒度的增加、战略与战术规划的融合、持续的流程驱动设计、多企业解决方案的发展、风险管理和可持续性问题的重要性增加，以及AI和机器学习在解决方案中的应用。

Outlines

00:00

🎙️ 欢迎来到 MIT 供应链前沿播客

本段落介绍了 MIT 运输与物流中心的供应链前沿播客节目。节目邀请了中心的研究人员、工作人员以及领域专家进行深入的对话，内容涵盖商业教育及其他领域。在本期播客中，特别邀请了 Mike Bucci 和 Milena Janjevic 作为嘉宾。Mike 来自 Coupa（前 LLamasoft）的专业服务团队，专注于帮助全球各行业公司使用 Coupa 软件解决供应链设计问题。Milena 是 MIT CTL 的研究科学家，领导供应链设计倡议，旨在改善供应链设计决策过程。本期讨论主题为传统供应链设计方法及其新实践。

05:00

🔄 传统供应链设计方法及其局限性

本段落讨论了传统的供应链设计方法，这些方法通常是基于事件的、周期性的，每隔两到三年进行一次，以重新评估重大的供应链问题。传统方法涉及数据收集、清洗、获取组织支持、提供建议和实施解决方案的过程，通常以最小化成本为目标。然而，这种方法存在局限性，例如数据粒度低、实施后预期性能与实际性能之间存在差距，以及计算能力的限制。现在，尽管数据可用性增加，但供应链设计实践尚未跟上这一变化。

10:02

📈 供应链设计的新机遇与挑战

随着市场变化的加速，公司面临着日益增长的客户需求和供应端的挑战，这要求供应链设计能够更频繁地适应变化。传统的供应链设计方法由于计算能力、数据收集和系统分散等问题而受到限制。但现在，随着计算能力的提升和系统基础设施的改进，可以更频繁、更有效地进行供应链分析。此外，B2C和B2B领域中客户中心性成为关键元素和竞争优势的来源，这在传统供应链设计方法中并未得到充分体现。

15:04

🛠️ 重新构想供应链设计的四大机遇

本段落提出了重新构想供应链设计的四个主要机遇：扩展设计范围、纳入战术层面、考虑风险和规划弹性以及采用新技术和商业模式。这些机遇意味着供应链设计应超越传统的成本最小化，考虑长期价值创造、市场互动、客户中心性、税收、法规和可持续性等多方面因素。

20:06

🌐 扩展供应链设计的范围

扩展供应链设计的范围意味着在设计过程中考虑更广泛的目标，如长期价值创造、市场份额增长和收入生成。这包括将客户中心性纳入考量，而不仅仅是集中于成本最小化。例如，在选择设施位置时，不仅要考虑成本，还要考虑如何更好地服务客户，以提高市场份额。

25:09

🔍 纳入战术细节以增强战略决策

将战术层面的细节纳入供应链设计可以增强战略决策的有效性。这涉及到使用相同的数据基础解决战略和战术问题，减少两者之间的差异。例如，可以在同一模型中考虑销售和运营计划、短期粗略容量规划、补货库存计划等战术决策。

30:10

🚨 考虑风险和规划供应链的弹性

风险管理和弹性规划是供应链设计中日益重要的方面。企业需要识别和表征不同风险源，如市场需求变化、自然灾害或大流行病等，并在设计中考虑这些风险。这涉及到运行更多的情景分析，以测试网络对不同变化的弹性，并可能将外部风险指标纳入模型中。

35:11

🤖 采用新技术和商业模式

新技术和商业模式为供应链设计带来了新的可能性。大数据、云计算、人工智能和机器学习等技术的应用，可以帮助企业更好地分析数据、发现趋势和潜在问题，并提供解决方案。此外，数字化转型还促进了新型组织关系的发展，例如利用众包资源进行运输和仓储，以提高供应链的弹性。

40:13

🛑 克服障碍，推动供应链设计的创新

尽管供应链设计面临着许多机遇，但也存在一些障碍需要克服。这些障碍包括组织内部的支持和领导力承诺、缺乏清晰的项目发展路线图、缺乏数据基础设施以及对业务成果的明确理解。企业需要建立跨功能的团队和中心，明确所有权，并确保有高层的支持和参与。

🚀 供应链管理的未来趋势

供应链管理的未来趋势包括大数据的规模和粒度的增加、云基础解决方案的互联、战略和战术规划的融合、持续过程驱动设计的重要性、多企业解决方案的发展、风险管理的持续重要性、可持续性问题以及人工智能和机器学习在解决方案中的扩展应用。

💼 如何开始供应链设计的变革

对于刚开始进行供应链设计变革的公司，建议从对业务重要的小项目开始，展示价值并建立可重复的过程。利用外部合作伙伴的支持，加快交付时间，并利用现有的解决方案，找到适合自己用例的正确工具。

Keywords

💡供应链设计

供应链设计是指规划和管理产品从原材料到成品，最终到达消费者的整个流程。它是企业优化成本、提高服务水平和增强市场竞争力的关键环节。视频中提到传统的供应链设计方法已沿用约30年，主要侧重于基于事件的项目或流程，重点在于最小化成本。

💡数据分析

数据分析是利用统计和逻辑推理方法对数据进行研究和解释的过程，以提取有价值的信息。在视频中，数据分析被视为推动供应链设计的新竞争优势，强调通过分析来改进决策过程，如通过分析来识别风险和提高网络的适应能力。

💡风险管理

风险管理是指识别、评估和控制可能对企业造成负面影响的风险的过程。视频讨论了在供应链设计中考虑风险的重要性，比如通过映射不同的风险来源并为不同类型的风险制定不同的策略。

💡客户中心

客户中心是一种以客户的需求和满意度为中心的商业策略。视频中提到，无论是B2B还是B2C领域，交付的领先时间和可靠性都成为了关键的竞争优势，这要求供应链设计必须更加关注客户需求。

💡技术采纳

技术采纳指的是企业接受并应用新技术以提高效率和竞争力的过程。视频强调了采用新技术如人工智能、机器学习、大数据分析和云基础设施在供应链设计中的重要性，以提高数据处理能力和决策质量。

💡战略与战术决策

战略决策是指对企业长远发展有重大影响的决策，而战术决策则关注短期行动计划。视频中提到，现在战略和战术决策之间的界限越来越模糊，需要更频繁地重新评估战略决策，并且将战术层面的细节整合到战略模型中。

💡可持续性

可持续性涉及在满足当前需求的同时，不损害未来代际满足其需求的能力。视频中以CO2排放为例，说明了在供应链设计中考虑环境影响的重要性，这反映了现代企业在设计供应链时对社会责任的关注。

💡组织学习

组织学习是指组织通过经验、反馈和新信息来改善其决策和策略的过程。视频中提到，为了应对不确定性，企业需要不断改进其供应链设计和决策过程，这需要组织学习来适应不断变化的市场和环境。

💡多企业解决方案

多企业解决方案指的是跨越多个企业边界的供应链解决方案，它强调不同企业间的合作和资源共享。视频预测，未来供应链设计将继续发展，以包括更多跨企业合作的解决方案，以提高整体供应链的效率和韧性。

💡敏捷性

敏捷性是指企业快速适应市场变化和客户需求的能力。视频中提到，供应链设计需要更加敏捷，以应对需求和供应方面的快速变化，这要求企业能够快速重新设计和调整其供应链。

💡中心化与去中心化

中心化是指决策和资源集中在一个中心或少数几个点的过程，而去中心化则是将这些决策和资源分散到更多的点。视频中讨论了传统供应链设计倾向于中心化以降低成本，但现在需要考虑客户中心和市场变化，可能需要更分散的策略。

Highlights

MIT供应链前沿播客介绍了供应链设计的新实践。

Mike Bucci分享了他在Coupa（前身为LLamasoft）的专业服务经验，帮助公司解决供应链设计问题。

Milena Janjevic领导供应链设计计划，旨在改善供应链设计决策过程。

传统供应链设计方法通常基于事件驱动，周期性更新，重视成本最小化。

数据粒度和计算能力限制了过去供应链设计的精确性。

现在，数据可用性增加，供应链设计实践需要与时俱进。

行业趋势显示需求和供应侧变化加速，需要更频繁地适应供应链设计。

新的供应链设计景观包括更强的计算能力、系统基础设施改进以及更频繁的适应性。

B2B领域同样面临需求变化，需要考虑客户中心性和交付可靠性。

全球化和多样化的供应链需要更频繁的战略决策重估。

公司可以通过扩展范围、纳入战术、考虑风险和采用新技术来重新构想供应链设计。

扩展供应链设计范围意味着考虑长期价值创造和市场互动。

纳入战术决策有助于战略和战术决策的一致性和数据共享。

风险和弹性规划是当前供应链设计中的重要考虑因素。

采用新技术如AI和机器学习可以提升供应链设计的效率和效果。

数字化转型促进了新型组织关系的发展，如众包资源的使用。

供应链设计的未来趋势包括大数据、云基础设施、AI和机器学习的应用。

组织学习、流程改进和跨职能协作是推动供应链设计发展的关键。

Transcripts

(upbeat music)

- Welcome to MIT Supply Chain Frontiers

from the MIT Center for Transportation and Logistics.

Each episode features center researchers and staff

or experts from the field for in-depth conversations

about business education and beyond.

(upbeat music)

- Thank you for joining today's,

"MIT CTL Supply Chain Frontiers," podcast.

So happy to have Mike Bucci and Milena Janjevic.

Milena is a research scientist here at MIT CTL.

And Mike, can you tell us a little bit about yourself

and your role?

- Yeah, sure thing.

Mike Bucci, I work for Coupa, formerly LLamasoft

as a few years back.

Been largely in the services, professional services group

during that time.

Mostly working with companies

around the world in all industries.

Kind of helped them understand how

to leverage the Coupa Software

to solve their supply chain design problems.

So I've been fortunate to work with many companies

around the globe and looking forward

to sharing some of that experiences here today.

- Excellent.

And Milena, can you tell us a little bit about your work

and how it relates to supply chain design

and the work that Mike is doing?

- Yes, thanks for having me here.

So I am leading the supply chain design initiative,

which is basically looking at the ways

in which we can improve the decision making process

around supply chain design.

So here at MIT CTL, we do a variety

of industry sponsored research and educational programs

with the aim of helping companies learn how

to better address their supply chain design problems.

- Great, great, great.

So for today we're gonna be talking a little bit about sort

of old world or past use supply chain design and what some

of your research and some what new practices are

that you're doing in a supply chain design space.

So you recently co-published a white paper called,

The New Competitive Edge Analytics

Driven Supply Chain Design.

And in the white paper you mentioned

that most companies are still

using standard supply chain design practices

that have been in place for around 30 years.

What is the traditional supply chain design approach?

- From the Coupa's side, I mean really what we've seen

over the years and there has been a transition,

but yeah, the traditional approach and practice here

for supply chain design has really been

around more event-based or episodic kind of processes

that occur at some schedule,

but usually less frequently every year

through two to three years.

And the idea behind that is really

to reassess a significance supply chain problem,

strategic supply chain problem.

It could be a DC network design problem.

It could be production capacity planning problem,

but it's more of that event-based project or process.

And there's really a very significant heavy lift

to go find the data, collect the data, cleanse the data.

You know, get organizational buy-in and the process.

Provide recommendations and then go implement the solution.

And so that's kind of a process that reoccurs,

but it's kind of again, this more of event-based problem.

And the focus there is typically on minimizing cost.

It includes, you know, obviously balancing service

and inventory, but it's just that general problem.

But again, it's more of that event-based activity.

- Does the periodicity or the time in

between updates affect the difficulty of you to get the data

in a situation like that or to get accurate information?

- So I'd say that when you are redesigning

your supply chains every five years or 10 years, the level

of granularity that you will be able to incorporate

in those supply chain studies, it's typically very low.

So typically companies will aggregate data

at a very high level and that will lead

to all kinds of approximations that will ultimately result

in a big gap between the expected performance

of your supply chain

and then the realized performance

once you implement a new design.

And some of that has been, I would say has resulted because

of simply the computational power

that was limited in the past.

But now we see that the data availability

has significantly increased,

but the supply chain design practices

have not necessarily caught up with that.

- Right. Anything else to add on that point?

Like as far as the length of time between?

- Yeah, yeah, I think as Milena said,

I mean historically we've been somewhat constrained

by computational power

and other things are certainly looking forward

to what we see in the future.

What we see coming into play now is kind of

that more frequent analysis study which brings a whole bunch

of pros and cons related to that.

But we can talk through that.

- Yeah, well I mean let's talk about that.

So before we get to the pros, let's imagine that it's 1995

and every five years we're doing a supply chain design.

The cons to that that I heard are

that you're just, you're gonna have highest level data,

you're not gonna have any granularity.

Are there any other cons to that length in between?

- So one big industry trend

that we see is basically a much higher pace of change.

And that's gonna be both on the demand side

and on the supply side.

Companies are facing increasing customer demands

and the need to readapt the supply chains

in much more frequent matter to basically cater

to those increasing demands.

And that requires to have a much higher speed

of adaptation of their

supply chain designs.

- Yeah, I mean thinking back,

as Milena said, I mean if we go

back decades here, the computational complexity

or the challenges we had for computational power, horsepower

as well as data collection, right?

Because systems were dispersed, uncentralized, unstructured

in many ways to try to consolidate the data

and effectively leverage it for these kind of studies,

that was largely the challenges.

So today we obviously have a significant difference,

right, where as Milena said,

supply chains are much more diverse, both from a supply

and demand perspective, there's more continual change

and the frequency of adapting a supply chain is required

to be much more frequent than in the past.

And all that requires, of course

than the infrastructure around that.

So I think the newer landscape is

that we not only have a computational capability, we

also have the system infrastructures to be able

to pull the data more frequently and more efficiently

to kind of do these studies more frequently

and therefore leverage those results obviously and react

to them much more quicker than we could in the past.

- Great.

And this is gonna lead

to the conversation about supply chain design.

Just before that, you mentioned consumer demand.

So people want things now.

They want things in the format, they want 'em

on their front door or they want 'em in a box somewhere

or maybe they want 'em delivered

to their summer home or whatever it is.

Is there a B2B demand that's changing?

I think is one question that I have.

And then what are some of the other trends

that are pushing towards this more tight timeframe

and advanced supply chain design models?

- So I would say that customer centricity has become

a key element and a key source of competitive advantage

and both into B2C and B2B realm.

And that's, as Mike mentioned, that's not something

that is typically covered

by the traditional supply chain design methods,

which are really focused on this cost minimization.

And so whether it's in the B2C or B2B space, we see

that delivery lead time

and delivery reliability are becoming key order winners

and cannot be ignored from supply chain design studies.

- And from the Coupa side,

are you seeing people changing their sourcing models

or is there anything happening with globalization

or does that also influence this process?

- Yeah, I would say absolutely.

I mean, we all know that supply chains have been diversified

and global now in nature.

And as we've seen obviously in the pandemic and other things

along the last several years, one disruption along

that supply chain can have a significant impact

on your entire system.

And so all of these questions now that were maybe

in the past more long-term questions

that we could answer once and not have

to revisit them frequently now they are questions

that we need to revisit often because

of all the things Milena described, customer behavior

as well as on the supply chain, all the changes

that are occurring on the supply side.

I would also say that the need

to reassess these strategic decisions

more frequently leads kind of a shrinkage

between the strategic and tactical level decisions

that are occurring in companies.

And so now you're making strategic decisions

more frequently, maybe quarterly,

and you're making tactical decisions maybe monthly

or quarterly.

And now there's kind of a connection there

that we were trying to capture.

And that's one thing we see a lot with the companies

that we're working with is

that though there's a sniff overlap

between those kinds of questions

and those time horizons were typically

were separate kind of questions that were asked.

- I think we're understanding the challenge now.

We know where the market is sitting,

we know where business players are sitting

and what they're up against.

And so in the white paper you mentioned

that there are four opportunities, primary opportunities

that companies and and managers can take

to reimagine the design of their supply chains.

And they are extending the scope,

incorporating the tactical.

Accounting for risk and planning for resilience

and adopting new technologies and business models.

So I'd love to get a couple minutes in on each of these

to find out more about what they mean

and maybe what they mean on the ground

and what you've been seeing with the people

that you've been working with.

So let's start with extending the scope.

What do you mean by extending the scope?

- Yeah, so the first and most immediate opportunity

that we see here is extending the objectives

that we are considering in our supply chain design.

So we've already mentioned that traditional methods focused

on cost minimization on physical structure

of a supply chain.

And that's a very limiting way of considering supply chains

and supply chain design in the contemporary market.

And so supply chain design should have a much broader scope,

which basically focuses on long-term value creation

and basically consider the interactions that happen

between the choices we make in the supply chain design

and our ability to generate value, increase market share,

and generate revenue.

So for example, one key question in supply chain design

that we have been answering

for the past 30 years is where should I look

at my facilities?

How many of those facilities should I have?

And if you have purely a cost based perspective,

you know the answer's probably going to be to centralize

as much as you can and to gain some economies

of scale, have centralized inventory with lower cost.

But if you consider this idea of customer centricity

and the fact that the market share

that you can capture will depend on your ability

to actually serve your clients

in a reliable and fast fashion, then the answer is going

to be completely different.

I do not see that a lot of companies are currently

using approaches that fully account for

that aspect of value creation.

- Yeah, I think Milena covered a lot

of the items I would mention.

I would also say that, you know,

as far as extending the scope, I mean historically

as Milena kind of outline, if we're looking

at a supply chain problem, if we're for example looking

at a DC a distribution network kind of study, we'll put

in the distribution network, some

of the customers maybe a little bit on the sourcing,

but now we have the ability to go further back, right?

Maybe we can pick up some more supplier detail.

We may even include options

of supplier direct kind of shipments, other ways

that we can kind of extend the breadth

of the scope of the supply chain problem.

That's one thing.

And then of course adding in other components or costs

or considerations to the model as well.

Milena obviously discussed the idea of being closer

to the customer, the impact on how that can have on demand.

We of course can incorporate things like taxes and duties

and other kind of new government regulations as well as far

as understanding how our supply chain design should be,

sustainability is another component as well, right?

Companies are more concerned with their CO2

or total emissions and other components.

We can incorporate that as well kind of into the problem

and evaluate that in conjunction with cost, et cetera.

- So that's a little bit about extending the scope

and broadening what you traditionally would consider

as a supply chain design problem, right?

So tell us a little bit more about what you mean

by incorporating the tactical?

- Yeah, I'll start here.

I mean, I think incorporating the tactical really is again,

this combined or shrinking of the difference

between the strategic and tactical decisions.

And in all the work that we do with our clients

and customers that we work with, we see when we try

to address both the strategic question

and the tactical questions, you know, we estimate

that 75% of the data requirements are the same.

And so why not take advantage and get a multiplier of

that effort to solve both kinds of problems and have

that same kind of data foundation

where there's not discrepancies

between the tactical decision making process

and the strategic process.

So we see a lot of convergence there

of incorporating tactical level detail

into our strategic models.

We're kind of overlapping

the two kind of decision making processes.

And examples of that would be kind of things

like an S&OP process, short-term, rough cut,

capacity planning, replenishment inventory planning

kind of policies.

You know, how can we address those more

in our supply chain design kind of problem optimizations.

- One thing that I would add to that is that

from the modeling perspective

and data collection perspective, the incorporation

of these tactical decisions will

often require a certain effort.

And the idea is not

that every company should incorporate each type

of tactical decision with the same level of granularity,

but really identify those area

that are key for their value creation.

So if I am competing in the last mile space, I probably need

to have a much more granular and precise integration

of my routing decisions and my inventory decisions

in the last mile than if I am a manufacturer

that is mainly going to be looking

at production planning decisions

as a key driver of their advantage.

And so this idea that we can have a single model

and a single approach

that would fit all different industry context

and companies is now outdated.

- Excellent. I like that, I like that.

So not only is it tactical,

but the tacticalness is gonna depend on where you are

in the supply chain, what your business is

and that sort of thing.

So the next of the four opportunities that you recognize

in the white paper is to account for risk

and plan for resilience.

And I think this is on everybody's mind

after the last few years.

So can you tell us more about accounting for risk

and planning for resilience?

- So yeah, so I think the first step here is for companies

to basically map out the different sources

of risk, different sources of vulnerability

and to characterize those.

And there we need to recognize

that when we talk about risk, there are different categories

of risk that will be accounted for

in a very different manner in our supply chain design.

So if I'm talking about, you know, natural demand

that just comes from some variations in the markets,

that's very different than a risk linked

to a major natural disaster or pandemic.

And so the way I will account for that

in my supply chain design and

in my tools should be very, very different.

And so I think that

at this point companies are very much aware

of the requirement to incorporate the risk,

but they don't necessarily have the right tools to employ

and address each type of risk that they might face.

- Yeah, to add to what Milena said,

I mean what we're seeing is historically companies

will run scenarios for their supply chain design

or tactical problems and they'll do a few sensitivities of,

well what if Transportation Costco up or down 5%.

But we're seeing obviously those sensitivities

increase significantly a two to three x increase

in number of scenarios companies are running today.

Partially due to this risk related problem.

So the first thing we see is just running more scenarios

to look at the resiliency of your network

to different changes.

They can be maybe their cost changes

or they could be changes to constraints.

I have a certain port volume

that I'm expecting to move through.

What if that port volume were to decrease by 50%?

What would my supply chain, how would my supply chain react

and what is the impact of that on my network?

A second theory to that as well is we're now able to pull

into some of these models risk scores

and risk metrics, you know, from external sources.

And so by pulling that in some ways we can kind of put that

into the objective function or we're trying

to minimize certain amount of risks in our supply chain,

or at least report that out as far

as certain nodes, maybe we're flowing 95% of our volume

through a certain node.

We wanna decide to highlight that is a potential risk

because we were kind of single sourced

or single node kind of constrained

if there was a risk event related to that node.

Also, again, based on those external risk metrics,

we can provide some risk score for our supply chain design,

you know, based upon a supplier's risk,

based on different transportation risks, et cetera.

And so we're seeing that more and more

in the kind of the models that we're running

and the requirements our customers

are having regarding risk and resilience.

- I think what's fascinating about this

then is it kind of leads us directly

into the fourth opportunity because I'm sensing

that people are maybe collaborating more

or maybe they're getting data from resources

that didn't exist before that are public or

that are, you know, able to be purchased

and maybe there are new ways

of analyzing and new technology.

So can you tell us about the fourth opportunity

to adopt new technologies and business models?

- Yeah, I'll start here.

So for what we're seeing,

and you're right on there, Arthur, I mean as far

as what we're seeing is, again, risk is one good example.

We're able to maybe incorporate external data more,

consider that, or at least, you know, define

that in our solutions.

I would say a couple things here in play,

of course as you mentioned, it's this big data, right?

There's a lot more data we can capture

and bring into our analysis.

The cloud infrastructure is certainly helping us there

as well, right?

With more information around the cloud,

we can do some interconnectivity.

It's easier to get data than it was in the past

from different sources and different systems

within a company and externally.

AI machine learning play into that as well, right?

Now we have models where we're doing the optimization

or the simulation, but maybe on the front end

and the back end of that we're doing machine learning

and AI to kind of tease out other things in the solution

to better provide results to the customer, which some of

that can be related to that risk and resiliency as well.

And I even think further we're seeing situations where

that AI machine learning can actually look for things

in our supply chain

that may be I'll say cognitive blind spots maybe

to the modeler, but we don't really see that issue

or we don't see that problem.

But machine learning or AI can kind of pick up

on potential issues with our supply chain.

It could be risk related or it could be kind of trends

and costs and other things that we're seeing over time

in their networks.

So those are some examples

where I think the new technologies are kind of morphing

into this kind of supply chain design problem.

- I would add one thing to that is

that the digital transformation that Mike has just mentioned

also enables a new types of organizational relationships

between different companies and relationships

that now include actors that were previously not part

of our supply chain ecosystem.

One example of that is the use

of crowdsourced resources, it being transportation

or warehousing and basically everything that is targeted

towards an on-demand use of the resources rather

than investment in assets,

which is a really interesting mitigation strategy

that can build higher resilience into your supply chain.

When we talked about the traditional approaches

to the supply chain design a day would typically assume

a very classical supplier buyer relationship

that did not reflect these

new complex organizational relationships, did not,

for example, incorporate the ways

in which we would share revenue, share costs

and share risks among those different parties.

So that's another opportunity that we see.

It's extending basically the way

that we represent the organizational structure

of the supply chains to take advantage

of these new business models

that have arised in the last few years.

- So we've seen the four opportunities are

to extend the scope of the supply chain design

to incorporate tactical, more granular information and data.

Accounting for risk and adopting new technologies.

Do you have any examples of many of these

that you wanna share from your recent work?

- One would be a mining company that we've worked with

for many years and they're doing a lot

of strategic analysis, even some tactical studies

on their capacity planning.

But recently they wanted to add CO2 kind of

to their analysis and so we were able to bring in CO2

into both their production facilities.

They use a lot of rail as well as truck incorporate CO2

into the network and be able to provide to them

as kind of a almost free byproduct

of the model, kind of that CO2 analysis.

And they could run scenarios as well as they were looking

at different sites at different sourcing strategies,

different customer kind of assignments, you know,

what the impact would be on CO2.

So that was an example where kind of extending the scope

to other factors was one example there.

I'll give another example where a medical drug company

is evaluating their production locations around the world.

And in this example, there's obviously some tariffs

and duties, but also there is some country rules

around local production and how the government provides

and tenders and the win rates that you'll achieve.

So at the Milena's point about demand, this was a very much

a situation where depending

on where the production location was,

the demand could shiftly change.

And so we helped evaluate not only the network design

from a typical traditional sense, but also how would

that location decision affect demand?

Which was significant in their example.

- And if the time cycle is speeding up as we work

on supply chain design, are these iterative processes then?

Do they become almost a constant in the background

or how would you describe like, you know, these sound

like some pretty massive projects.

They multi-year, multi-month?

Do you plan on a certain periodic rate?

- Yeah, so I think, you know really from what we're seeing

in Coupa's perspective and what we're seeing

in the environment is not only

are the strategic decisions happen more frequently,

again, we've extended now into that tactical scope

and we see a repeatability requirement

that I would say 95% of our customers are requiring,

and we certainly agree with that, right?

Instead of doing that episodic event based kind of process,

it's now how do we make this repeatable so

that the data process can go from,

in many examples it's significant 30

to 60, 90 days down to hours, right,

to refresh a model. - Wow.

- And that maybe seem like a snik of improvement.

It is, but it's really not that hard

if we really just work through it, you know, one time

and then we get a compounding benefit.

'Cause now we can answer those strategic questions

more frequently.

We can answer the tactical questions.

In many cases now we answer a lot of additional questions

that we couldn't answer before.

So one example is cost to serve, we often get answers,

okay now I just need to know cost to serve.

Can you help me with that?

Well if we build this repeatable process,

we already have the models in place.

Cost to serve is a natural byproduct, we kind of get

for free again, right?

Can you answer now the

more tactical production planning problem?

Yes, because we can refresh the data quickly

and we can provide those results.

And what we see in addition to that is not only are we able

to solve these strategic and tactical problems frequently

because we have a set of data that's validated

and kind of refreshed and clean, we see

to your comments earlier, a lot of other parts

in the organization wanna go access that data

and leverage just from a BI perspective

and data analytics perspective, which we love

because now we're all working on the same page

and to, from an organizational buy-in perspective,

now there's more incentive for all the departments

and functions to provide good data to the system

because they wanna see the results out of the systems

if you will, so we see this very much compounding benefit

if we can get that repeatability put in place.

So again that's a, I would almost say a must have

in this day and age.

- So Milena, do you also have some examples

from the work that you're doing?

- Yes, so one example is a pharmaceutical company

that we are currently working with and

that is directly related to this idea

of extending the scope of supply chain design

and having a more customer-centric approach.

So in the pharma sector we have a few industries trends

that are really redefining the way

that these companies are thinking about their supply chain.

And these are portfolio shifts towards different types

of drugs that are targeting much smaller audiences

but have potential much higher revenue.

Increase market competition with some generic competition

that could directly impact the market share

for their products.

And basically what they are starting to realize is that

in order to increase their revenue

and market share, the drug is only one of the component

of the patient experience.

And so the way that you'll actually fulfill that drug

and bring it to your patient is a key element

that they want to consider going forward.

So first project that we worked with them actually looked

at different last mile strategies

to deliver a certain drug to the patients

and they were exploring various supply chain configurations,

but also basically the response of the consumers

to things like home delivery versus getting your drug

at a traditional channel, which would be a hospital

or pharmacy.

And we built a model that was basically incorporated

that customer response

with our overarching supply chain decision.

So that was an interesting exercise.

And in the second iteration of that, what we realized is

that in the specific space there's actually

a much broader healthcare ecosystem

that we need to think about.

And so rather than just focusing on a company

and a patient, we are now incorporating

additional stakeholders like healthcare authorities,

insurance companies, et cetera, et cetera,

which also have a say in this space.

And so we're basically moving

towards a kind of a multi-actor model

that captures those relationships that exist

between those problems.

And as you see, it's highly customized

and highly specific to the operations of this company.

And it's also going to be derived

into a series of different models according

to the country where you're operating

because of course the regulations around drug distribution

and such will be very different.

The second example that I have is linked

to this idea of incorporating the tactical

and the strategic.

And so there we are working with a company

that is a global shipping company.

We started with them by looking

at their overall distribution network.

So you know, I'm spanning from Asia many to the US and

as the time went by we actually realized that

in the current context we needed to incorporate into

that strategic model a much more granular description

of the specific events that could arise, such as strikes

at ports or different weather conditions

that can delay shipments in certain areas.

And so now we are moving towards a model

that basically is incorporating the tactical scheduling

and planning and route optimization as well

as more strategic decisions on network design.

- I'm also curious, what are you personally excited about?

Like what gets you up in the morning related

to these two questions?

Like you come to work and you say, you know what,

I'm gonna help this company overcome this obstacle.

What is that obstacle and why does it excite you?

- Yeah, so as far as you know, what keeps us excited here

at Coupa and me personally, I mean I've been doing this now

for 10 plus years at Coupa, LLamasoft.

And to your question, the reason I'm still here is

'cause I get more and I'm excited 'cause I think

like we can make a difference and we can help companies

and they can really find value.

We have the tools, the algorithms, you know,

those algorithms are evolving,

but there are solid algorithms that we know how to use

and it's really kind of taken advantage

of the data that's not available,

the computational capabilities we have.

And then all of that infrastructure to kind of make

that a seamless process so

that we can answer questions quickly

for customers where before that took a long time to do.

Additionally, with cloud infrastructure, you know,

we now have the ability to roll out apps and other things

that have kind of a user interface

that's much more customized to

that business function or that user.

But under the hood it's all the math that we love and know.

All the algorithms and stuff are running

underneath the hood that we developed for them,

but now we're presenting it to more users in

that organization to take advantage of

that capability, run their own scenarios

within some guardrails typically,

but run their own scenarios, get their own results,

and allow the modelers to not be still excited and be part

of that, but allow more people to take advantage

of these capabilities in the future.

- How do you incorporate designing for uncertainty

and anticipating the way the discipline will evolve?

- So that's a really good question because we are

at a phase where, as mentioned before,

we do not have this one model

or one framework fits everything.

And so we need to constantly basically reinvent the way

that we are thinking about supply chain design.

And the first way that we would do that would be not

so much about replacing the types of tools

that we are using, but really focusing

on the organizational processes,

the decision making processes around supply chain design.

And here I think the key is really

to enable organizational learning in a way

that we can always adapt both our designs and our processes.

And when we talk about organizational learning,

there are several levels at which we can characterize it.

So the first and most immediate one is to say,

well we implement a design.

We observe a performance of that design.

For example, I know demand was higher,

capacity was exceeded, something like that.

And then we tweak our design to adapt to this new condition.

The second level is, I would say is more complex.

It's basically reframing the problem.

So we've mentioned some

of that, extending the scope, et cetera.

And so it's basically saying, I'm no longer focusing

on cost, but incorporating risk,

incorporating value creation, et cetera, et cetera.

And then the third level I would say

is even more challenging.

And it's about basically reflecting

on our decision making process in the organization

and monitoring how that process happens and trying

to improve that design process itself.

And that means who is involved

when we are defining our objectives?

Are we including the right people in the organization?

The right people outside of the organization?

Who's responsible for monitoring the results?

How are enabling these feedback loops?

And I say that most companies currently may be

at the level one or level two and are not still at

that level where they are actually reflecting

on their process itself.

And that's one of the things

that I find actually the most rewarding.

It's when we get people from functions in the organization

in the same room and we have them come up with new problems,

with new ways of solving those problems

and understanding the value of actually changing the way

that they are currently performing

their supply chain design.

- So what stops people from knowing

that they need to do that?

I mean, I believe the white paper probably is groundbreaking

in that respect.

People have never stood back and asked themselves like,

hey, I need to know, oh I've gotta talk

to people across my domains.

Or oh, I have access to this other data,

or oh, I need to do this more periodically than I have been.

What are the biggest obstacles?

'Cause we don't know what we don't know, right?

If we're running an organization

and we've been doing it this way.

- Yeah, so I think from what we see,

I would say there's probably three things

that get in the way of customers kind of moving forward.

The first relates to what I'll call organizational buy-in.

Which from the leadership perspective, you know,

we talked about earlier cross-functional kind of involvement

and commitment.

IT as well as the analytical resources.

You know, we can say a CoE, a center of excellence related

to kind of supply chain design.

All of those pieces need to be in place

and agreed upon such that

from an organizational perspective,

we are gonna sustain this process over time.

And just from a CoE perspective, we know today obviously

with some of the challenges in the job market is, even

as CoE you have to have a process

of training the people, giving 'em more opportunities

to learn, recruitment, retention.

All of those things are critical

to keep the CoE kind of progressing and lively and growing.

The second thing is, I would say is

that companies do not have a clear roadmap

of the types of problems they wanna solve

and how they're gonna grow that over time.

I think the Milena's point is, they're solving a problem

but they really don't know what problem two or problem three

and how those are connected and what the sequence should be

and all of those things.

In addition to that project roadmap we talked about earlier,

the idea that there needs to be an automated process

to kind of do the data collection

and the data foundation of this whole process.

So they lack kind of that infrastructure

to do the data collection and data automation.

And so therefore every projects is a challenge.

And so people come to them and say,

"I need this question answered."

And the response is,

"Great, I'll have it between you in six months."

Well we know that's no longer even acceptable.

So if you don't have that common data infrastructure

in place, then you're not gonna be able to answer

to the questions timely and therefore the value proposition

to the company is less.

The third thing is really that we have a sound understanding

in that organizational buy-in of the metrics

and the deliverables that we're gonna be providing

to the business.

So we know that there's an investment

in resources, an effort to do all this.

We need to be able to measure and show that to the business

that we are delivering the results.

I mean, historically I think we've been very good

at the analytics side, you know, maybe most

of the modeling team

and those data analytic people are not good salesmen, right?

So we need to help support them to make sure

that hey, you are delivering value to the business

and we actually prove that ROI to the business.

So those are the things that I would say we see

as some of the common obstacles

to kind of really get him moving forward.

- So what steps should companies take then

to get the ball rolling?

- So there are many steps,

and I think Mike has already hinted too few

of them, such as, you know, increasing data availability,

making sure we have automated data processing

and sharing structures in order to really get

that end-to-end view and transparency.

However, and here I'm seconding Mike

in what he previously said is that I really do think

that the most important step is

at the level of organization.

And so we really need to have a clear ownership

of the supply chain analytics and design.

For example, establish a dedicated team

or center of excellence who's going to have

that long-term vision around supply chain design and

also empowering that team by the top levels of leadership.

So we wanna avoid to have that be a separate cell

that has to battle with each individual structure function

in the organization.

And we want to have a top level sponsoring of

that supply chain design, central of excellence

or a dedicated team.

- We see things evolving very quickly

in many domains, including supply chain management.

Where do you think we're heading?

- What we're seeing as far as where we're heading

in the next several years is really several things.

And we touched upon some of those

throughout this discussion.

I mean, first of course is we continue to see

that scale data, big data, the scale of the data,

the granularity of the data that we need as well

as the cloud-based connected solutions

will continue to increase.

We already talked about the idea

that the strategic tactical/planning solutions

are kind of continuing to merge

as far as what those problems we're trying to solve.

I think as Milena noted though,

that doesn't mean that there's one size fits all,

but there's a library of solutions

that are kind of interconnected as far as their data,

but there are maybe slightly different variations

to solve different problems.

Kinda related to all that is, you know, the persona

of continuous process driven design, right?

That this is a continuous process

and we talked about the idea of

that requires then this repeatable kind of foundation to it.

We talked a little bit earlier

about this multi enterprise kind of solution

that's gonna kind of grow.

So we do think that multi enterprise kind of solutions

will continue to be play a part in the future here.

Risk of course, we don't think that's gonna go away.

We think risk will continue to be, play a prominent role

in kind of these network designs and how to be able to react

and respond quickly to those

as well as proactively planned for those things.

Sustainability, of course, those kind of pieces

to the puzzle will continue to, I think be important.

And again, we talked a little bit earlier

as well about the AI machine learning.

We see again extending the solutions

to include more AI machine learning components

to these problems, whether it's kind of on data analysis

and trends or other components as well

as prescriptive kind of findings

and solutions that they might provide.

- So I think Mike covered quite a broad range of things

that we are expecting to happen over the next few years.

One thing that I would want to add is relevant

to what we are observing now.

I would say that the last few years really triggered

a change in a way that companies are thinking

about supply chain design.

And I think a lot of companies are starting to be aware

of the different problems, starting to realize the necessity

to be more customer centric, to incorporate risk.

And we are at a stage where the problems are there

and well defined,

but we don't necessarily have the solution.

And I would say that supply chains are

in a very specific place.

They're really now at the center

of the corporate strategy and decision making.

There's a lot of excitement going on

and I see a lot of experimentation happening, you know,

particularly with new products

and services, how do we differentiate from our competitors?

How do we use supply chain design to do that?

I do oversee that this experimentation is

not necessarily always supported

by this data-driven approach and

that the level of decision making maturity is often limited.

And so I'm expecting that

in next few years we'll kind of see more clearly

what are some of the winning strategies among those range

of different things that people are trying out.

And basically identified kind of the winners

and losers of this process.

And there we will think that having an intentional,

well-structured and analytics driven approach

is gonna be key to being

in the winning side of the equation.

- Excellent. So how do people get the ball rolling?

If I'm an operator

in a company, how do I get the ball rolling?

- Yeah, so obviously we work with a lot of companies

that are just getting started and

so what we typically wanna tell them to do

or how we coach them through that process is pick something

that's small but important to the business

so you can kind of work on something

that clearly is gonna demonstrate value to the business

and kind of show some of that value

as far as the effort that we do.

We certainly prefer something that has some repeatability,

like we wanna get answers, you know, more frequently

and we wanna then build on top of

that kind of a repeatable process.

So let's get in place something

that not only answers questions, but answers it

in a repeatable way so customers can begin

to see kind of how that, the value of that repeatability

and how it can speed a lot of the speed

to answer a lot of the questions.

Certainly there's many organizations out there, you know,

CTL, Coupa, other companies that can provide some support

to those companies to help them guide them along the way.

We certainly have experience with customers

that struggle maybe a little bit more than they should.

An external kind of partner can help kind us speed

that, you know, time into delivery.

I think overall being more efficient for everyone.

And then of course,

leverage the existing solutions out there.

There's plenty of solutions out there

that already can kind of solve these problems.

Take advantage of what's out there and make sure you fit it

to what you need, but you know, find something

that's the right fit for your use case and and get going.

- So Mike, Milena, thank you very much for being here today.

- Thank you.

- Thank you. My pleasure.

Glad to participate.

- We really appreciate your time and super valuable to us

and hopefully it'll be valuable to everyone.

So yeah, thank you.

- Thank you. Yeah, awesome.

- Thank you so much.

(upbeat music)

- All right everyone, thank you for listening.

I hope you've enjoyed this edition

of, "MIT Supply Chain Frontiers."

My name is Arthur Grau, Communications Officer

for the center, and I invite you

to visit us anytime at ctl.mit.edu or search

for, "MIT Supply Chain Frontiers,"

on your favorite listening platform.

Until next time.

(upbeat music)