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[Title], originally presented by MITX Micro Master's Program in Supply Chain Management, opens with the question of how AI is transforming supply chain decision-making.
As the academic year begins here in Cambridge, Massachusetts, we are excited to have several of our courses opening at this time that align with our discussion today.
More details will be shared through the chat, and today, we are going to explore how AI is transforming supply chain decision-making with Vijay Sankaraman, chief AI officer at Novant Health.
Vijay, welcome and thank you so much for joining us today; it's a pleasure to be here.
It's a bright day, especially the way we have all registered ourselves into the new transformative AI era and how we are powering ourselves to move through the supply chain and more things.
I'm very happy to share that perspective with you all today.
Thank you, Vijay. Really, I'm honored to have you joining us today, and we have a big audience joining live too, so welcome, welcome everyone.
In this session, we will start by looking at how AI and advanced analytics are being used to make better predictions and improve inventory decisions.
Then we are going to explore the evolution from predictive AI to generative and agentic AI.
We will also focus on how these technologies are changing the way supply chain decisions are made.
We will also talk about what these changes mean for supply chain professionals and the skills that will be important moving forward in the future.
But before we begin, we really want to learn more about our audience, so let's try to launch a fan poll to know how often you are using AI for work.
Let us know, in the last month, how often you use AI for work.
While you are responding to this poll, let me briefly share the plan for this webinar.
We will start learning about Vijay's leadership journey and his current role.
Then we will explore two use cases that show how AI tools are transforming the supply chain decision-making process.
And finally, we will discuss what capabilities supply chain professionals need to develop to lead these transformations.
Let's have a look at the first poll and see how often you are using AI for work.
Okay, Vijay, more than 60% of our participants use it daily or almost daily, and 25% use it several times per week.
So definitely, most of our participants are indeed using AI for their work.
But now we really want to know a little bit more about that use, so please let us know in which areas of your work you are currently using these AI tools.
Let's see if it is for demand forecasting, inventory management, transportation, or supply chain risk management.
Poll two is already open, please go to that poll and let us know.
While you are responding to this poll, I'll be sharing some guidelines for this webinar.
So if you have questions, please use the Q&A feature; we want this webinar to be interactive.
So be sure that you log in with your name, and we will also launch additional polls during this session, so try to keep engaged with the speaker and with me during this webinar.
With several of our courses having just started or opening soon, we will also share program and course resources in the chat throughout today's event, so keep an eye on the chat.
And let's try to take a look at the results of the second poll.
Let's see. Okay, Vijay, this is interesting because I think we have 22% of our participants using AI for demand forecasting and planning, which we are going to talk about.
20% for inventory management and optimization, 16% for transportation and logistics, 14% used for procurement and sourcing, and I know we are going to discuss how you are using agentic AI for procurement and sourcing at Novant Health.
13% used in manufacturing and operations, and 15% for supply chain risk management.
Super interesting. Thank you for sharing with us how you are using AI in your day-to-day job.
But now let me formally introduce our industry speaker, Vijay.
Vijay is the chief AI officer at Novant Health and has been in that role for about two years.
His previous experience was in the retail industry; he was the vice president of product and technology at Lowe's for five years and also held the role of head of product management and technology at Walmart for three years.
Vijay has extensive experience working with technology advancement and product development in different sectors.
So Vijay, healthcare is a sector very different from retail.
Working to improve people's lives definitely opens a completely new paradigm.
Could you tell us more about that and also about your leadership journey to get there?
Eva, again, thank you for having me here and for the kind introduction.
First things first, the way I have been brought up and been humble and fortunate to be part of a number of institutions you mentioned, but all through my journey, I've held onto one belief: that who we serve and what problems we are solving are far more important than the means to getting there because it evolves.
Given that, the perspective that we have held is working through a variety of consumer-focused companies like Walmart, Home Depot, and Lowe's has given me the perspective to look at what an everyday person, including myself, by putting myself in their shoes, experiences as friction to complete what they want to complete and how to get past that faster.
Healthcare makes it more personal, personal in the sense that each and every one of us, at some point during our lifetime, and/or for our loved ones, may have to make a trip for even a flu shot or something of that sort.
So when you go through a variety of phases in our lives, if you will, those journeys through the healthcare systems, especially in the era that we are all in, are laid with friction.
Friction in terms of how easy or not it is to get an appointment, how easy or not it is to complete a surgical journey, how easy or not it is to pay a bill, to understand the economics behind it.
So when you go through all of that, there is an element of supply chain, there's an element of marketing, there's an element of operations, all running behind the scene.
How can AI, technology, and advancements help move and eliminate the friction to make it easier for us?
That's the core purpose of me doing what I'm doing.
That's a very interesting angle, Vijay, how to eliminate this friction for patients and for people who really need to have a smooth process through that.
Let's start by taking a step back and looking at how supply chain analytics has evolved and how companies have been using these tools to support decision-making.
So personally, I have been working in supply chain management for 25 years, and truly, statistics, forecasting, optimization, and operations research have always been at the heart of supply chain decision-making.
But in many ways, AI is also building, I believe, on that foundation of all of these operations research techniques for decades, correct?
So Vijay, in the last, let's go back, the last 10, 15 years, what did supply chain analytics look like and how has this evolved since then?
Mainly, my question focused more on what companies were primarily using analytics for at that time.
Yeah, I think if all of us date ourselves back to your point, say 15 years ago, even in the line of it, we were very busy creating dashboards.
We were very busy creating scorecards.
We were busy understanding what happened last month, what happened last year, what happened yesterday, what was my inventory, how much traffic did I run through, how much do I need to keep up with what potentially could come.
All this was descriptive in nature, so essentially, if I have to encapsulate it in one word, it was descriptive analytics.
Now, that was in vogue, that was popular; we all heard the word "big data," we have all heard the word "ERP," we've heard a lot of things, and by the way, all of them are still around, nothing wrong about them.
But the word largely was fixated about leveraging that.
The other piece I'll bring to the connotation is, back in the time, 15 years ago or so, supply chain, from its genesis, from my eyes at least, was largely a pure cost center.
Your purpose in life as a supply chain leader was to keep the cost as low as possible, which is perfectly fine.
But as technology evolved, as advancements evolved, more and more we walked into what we called deeper analytics, aka, the early innings of AI, machine learning, and all the fun stuff we can talk about later came about.
And at the crux of it, the move went from being what I would call purely descriptive to a bit more predictive.
Now, I'm not just merely looking backward with the available technologies in front of me as a planner, as a supply chain leader; now I'm also being bold enough to make more predictions.
Now, in the process, I'm not just merely obsessed over cutting costs; I'm also looking at where I can make a profit, we can talk more about it.
So those are the big changes.
Yeah, yeah. And 100% I know about this evolution from descriptive to prescriptive analytics.
And also what has changed is not simply that we have more data or more computing power; we also have that, but also the types of patterns we can identify and the kind of decisions we can support is also evolving.
So how do you see machine learning and what change machine learning is bringing to the table in that evolution?
Yeah, see, first things first, all the definitions of machine learning out there, my simplest way of explaining it to, at the time, my fifth grader, now she's grown up to be a junior here.
If a job is best done by a machine, let the machine do it.
With that philosophy in mind, the advancements allowed the machine to be more and more – I use the word "machine" in a very figurative way – to become more and more immersed in what we do and learn the patterns, learn how we do what we do.
And to your point, there are a few other friends of the family of machine learning that came about.
Cloud computing became more prevalent, cloud computing became cheaper; all that allowed for a gargantuan amount of data to be ingested.
You're no longer married to your computer, your physical limitations of your computer; the cloud was wide open for you.
So what that did was, and the other pieces where in the traditional world, at least 15 years ago or so, you think about demand planning, think about inventory, think about transportation, everything that we saw in the poll a few minutes earlier, these were humans with, at the time, available computing power, computing knowledge.
ERPs had dealt with one, two, three, four, call it eight variables at a time.
For example, I'm looking at last year's sales history; I'm looking at the lift I would get with a promotion that I might be able to do; I'm also looking at a causal factor like weather.
Three or four factors were largely dominating how a science married with the art of the human would help predict transportation planning or demand planning or inventory.
Fast forward, machine learning comes about, learns and ingests all of this.
Now you have an element of physicality or physical intelligence getting into it: RFID, IoT, data sensors, the list goes on and on where the data that was only available otherwise on a weekly basis.
How many of us can put your hand up and say, "Hey, do you remember the weekly batch runs?"
Every Saturday night and a Friday night, something would run to predict what needs to happen that week.
Gone are those days where the amount of learning, the compute power, and the intelligence where you could do anything you want within the hour.
I can predict the one route, I can predict the one store; you get the point.
So what happens is that now it has collapsed the window of how often anything can be done and situates more and more into shifting the decision powers a little bit more equally.
Now, in the process, the variables are no longer limited to a handful.
Your variables could now be, in my quest as a supply chain planner to predict how long would it take for me to go from point A to point B, I can look at social events to say, what else is going to be happening in the route that day?
Which event is happening? Maybe there is a presidential campaign that's going to come by; I'm just going to throw one out there that may have a traffic impact to something.
All those factors are now a lot more sourceable at the tip of an intelligence's hands, factors that give far more prediction.
So I think machine learning created that element of jump from being reactive to a lot more predictive, if you will, with the level of accuracy that in the last 15 years we've seen significantly go up.
Yeah. And one thing I found particularly interesting is how you connect the dots between prediction and better decision-making.
A forecast by itself doesn't create value, but when we connect this prediction to make better decisions is when we really are bringing this value that you were mentioning.
So you saw this firsthand at Lowe's.
So can you walk us through the approach you used then, and more specifically, how did you move from predicting demand to using those predictions to make better inventory decisions, just to connect these two dots?
Absolutely. I'll give you a specific use case.
When we think about Lowe's, there are many products that make up Lowe's.
One key category that is highly valuable for the company, highly valuable for the consumer out there, is what they call the appliance sector: your refrigerators, your washer dryers, your dishwashers, what have you.
No points for guessing, Lowe's remains the market leader in the US for the appliance category for a long time and obviously would love to remain in that space.
But buried under the supply chain aspects of it, appliance is also one of the most highly unpredictable sourcing categories.
It is unlike certain other categories where the supply is plenty; this is the opposite of it, especially the fact that some of these have been sourced from the Far East, some of these have to be coming from Asian manufacturing facilities, if you will, and you are now looking at different ports they have to traverse through.
The number of, no longer is a refrigerator just a refrigerator with the site of kind of bespoke models coming through.
So all of that put together, it's a constrained supply chain.
So when it becomes a constrained supply chain for us, back in the time the problem was no longer I could rely on traditional inventory management and transportation to keep the product.
For context, we had roughly about 1700 locations where the point of sale would happen, plus the giant.com, which means I could ship anything anywhere.
Think of every zip code in the country, and you only have certain amount of shipping points to get there.
So physically, the idea was to go multiply the last mile shipping points, but the last mile shipping points cannot become an inventory holding point because then now you're only multiplying your constraints, you're not deducting them.
So then the science was applied to say, look, how do I predict what I need, when?
What categories within the appliance are needed?
Which ones, for example, you think about certain categories of refrigerators or what we call as "distress."
Look, I run an apartment, my refrigerator broke, I don't care which one; I need something ASAP.
I'm not looking for the $2,500 bespoke model; that's a category which is separate from, around a household.
Look, I don't buy a refrigerator every other year; I buy once every five, six years or 10 years, what have you.
And I want to make sure I pick the right one and it keeps my wife happy or my wife thinks, "Keep the guests happy."
So that is a different category.
So understanding and nuancing out who these are, how do I then apply, reusing the word "bespoke," bespoke supply chain through each of these.
And now I need the science, in this case, a machine learning model, to be performed through each of these, and that's how we kind of look through.
That's an excellent example.
Vijay, thanks for sharing that.
We have almost 900 live attendees, so it's really very interesting, and we are receiving some excellent questions from the audience.
Let me bring one from Sohavi, he's from Pakistan, and his question is very interesting.
He's saying, "Classical models consistently beat more sophisticated models like recurrent neural networks, for example, in retail demand planning, at least in his experience."
What are your thoughts on that?
Because definitely, I think he's highlighting an important point: sometimes more sophisticated models don't automatically mean better results.
What are your thoughts on that?
Absolutely right. I think spot on, right? Horses for courses.
Just because we have a very sophisticated model that can ingest a whole bunch of variables and spit something out doesn't necessarily mean that it's going to be that much better than everything.
It all depends on what is the situation we are trying to predict models for.
I'll give you an example: if we are looking at an everyday supply that we are trying to stock up in a store or stock up in a warehouse, a traditional model, in fact, you could even get away with the way back when MinMax, nothing wrong about it.
Now you're looking at constraints, now you're looking at something that has even perishability that gets into it.
Now you look at other variables that may apply, and I'll give you one context to that.
Back in the time in my Walmart, as many of you would know, Walmart still runs the largest grocery chain in the country.
We walk in there, you would see strawberries and bananas.
Every strawberry that you see there is not made alike, and every banana you see is not made alike in every store.
What I mean by that is when they come from the sources, before we had any level of intelligence we applied almost a decade ago now, all bananas would generally be routed, treated alike, and go to every store.
The store that was closest to the supplying warehouse got the freshest pack, and the store that was farthest out got a day or two older, hence the perishability of that particular batch of bananas was faster.
In other words, the shelf life was smaller.
So now a simple logic that was put in place to say, "We'll also factor that into which one gets routed," all of a sudden changed the narrative, and there's more to it.
So where I'm going with that is, you're absolutely right.
It comes down to what use case are we solving for, then pick the right one.
In our world, we use what we call ensemble models.
So not every model runs everything; the ensemble decides which one I need to use.
Excellent, excellent. Yes. Not every technology fits for all the problems.
So let's move now to the second part, Vijay.
So you have also framed very well with real examples that we really value that, the use of AI as predictive, descriptive, and predictive tools.
Let's try to move now to generative AI and agentic AI.
So from a supply chain practitioner's perspective, how would you explain the difference between these types of AI, generative and agentic AI?
Yeah. It's very interesting. We are gradually increasing our vocabulary of what these AIs are, right?
So I mean, in the simplest way I've learned myself is when we think of predictive AI, it answers the question, "What will happen?"
I look at all the content, I want to know how much is going to happen, when, how fast can I get somewhere; "What will happen?" is the question.
Predictive AIs do all the models and all the data that looks into it.
Generative crosses another boundary and says, "Help me to create something new. Get me use all the context you have."
The word "generative" here literally allows us to go create something net new that I don't have it before, synthetic included.
Agentic is simply saying that I get that. I get what you're going to know when.
I get to know, I also know you need to create something; let me do it for you. I put all the pieces together.
It is not one is more intelligent than the other; it's just fundamentally predictive, generative, and agentic in the order that you have, increasing scope of autonomy that we decide to give.
One of the use cases we look at in supply chain for us is when I look at how much I need to purchase to fulfill a category within a store or a hospital or a DC, wherever the need is; predictive tools come into play.
Now, if I have to go use that to help negotiate with a supplier, negotiate with how much do I vary based on the contracts, look at that and look at the contract and adjust for prices.
Generative AI now helps me to create that draft, that conversation, that amount of articulation to get there.
But if I'm saying all that is fine, I simply don't have the time to go look at a thousand suppliers every morning and do all of this.
Agentic, you take it from there.
So it's a matter of autonomy that opens up; that's how we look in supply chain.
And you have seen this transition, Vijay, very clearly in your current role at Novant Health, where you are using AI agents in a healthcare system and in the healthcare industry.
Can you give us an example of how agentic AI is being applied to procurement in particular in that environment?
Absolutely. Now, first things first, being in healthcare, I want to underscore the fact that we believe in having a human in the loop to get the right, remarkable patient experience.
There is no compromise on that, which means when we look at where AI and the forms of AI, like agentic AI or agents, come into play is exactly where it has the most relevance, where it is closer and closer to a clinician or a patient, closer and closer the human in the loop is involved.
Given all that, when we look at procurement, one of the use cases for us was similar to many other large healthcare systems.
Here, we are made up of thousands of contracts, thousands of supplies, and each of these supplies has a genesis of when they started, why they started.
I'll give you a very poor example, but it hits the point: think of a surgical set of instruments or scalpels that we use.
For instance, we have about seven different orthopedic surgeons operating at our seven different… the seven is just a notional number, different, I mean, by.
Each of these surgeons, through a variety of choices they and practices they bring to the table, they have a particular choice of an instrument they want to use, and some of these instruments are made by three different suppliers.
Now, pretty quickly, the number of the same procedure done by different surgeons, choice of scalpels made by different providers, suppliers I mean, multiplies.
And each of them may carry a contract or two, depending on when they were written, what was negotiated.
So suddenly, you see for one product, you've got multiple contracts before you know it, and there is variance in each of these.
Dr. Eva may have signed up for $50, making numbers appear, for a scalpel.
Dr. Vijay may have done it for 45, and maybe somebody else would have done it for 65.
And given the breadth and the scale of things out there, it is not humanly feasible to be able to look at each of these and align and synchronize.
Maybe should we get everything at 45, or the quality is better for 55?
All those things have to be looked at.
We have deployed agents to help analyze those variances, help make a recommendation of what makes the most sense for a particular category, that obviously ensures the human in the loop, the clinician in the loop, ensures, "Okay, that's the right thing for us to use."
And then help draft a conversation with the vendor and the supplier and say, "Hey, how could we help align closer to..."
So we reduce the variance in terms of procurement I have to do, so that reduces the contract variances.
So that's one of the spaces where we look at agents.
Yeah. And this raises also an important question about how much autonomy we should actually give to this system.
Because you also mentioned at the very beginning, the human needs to be in the loop.
And here is a very interesting question from Sing Yang from France.
He said, "In procurement, which decisions are most suitable for agentic AI?"
You just brought that example, but which decisions are the ones that should remain under human control?
Yeah. No, that's a great question, right? Not just in procurement, but I can also extend it to other parts of supply chain.
But in general, when a decision is routine, reversible, and it's a bounded cost, you are more likely than not at some point to help an automation or an agent to take over.
But when something is, for whatever rhyme or reason, irreversible or something that is too one-off, something that truly is more, let's call it strategic to the aspects of the company, aspects of what you do, you're branching into a brand new venture of business within your organization, or you want to try something out at large scale.
Those things significantly require a human in the loop because the reversibility aspect to me is the most important.
Yeah. And I think this distinction between what AI can do and what AI should do is really important.
And this is a very important point you are bringing here, Vijay.
There is another, this is probably a more technical question, but also very interesting from Regine from India.
He said, "How would our agents handle unstructured data when the data is good, but unstructured?"
Yeah. So, so here's the beauty of data, right?
We talk about AI a lot. We talk about agents, everything, all the glamor and the glitz that that aspect of the science brings to the table, which is very popular out there.
But the reality is none of this is any good if the underlying data is of no use; that's the weakest link.
Given that, the question is all about if you had more unstructured data, how do you use it?
Now, by nature, a lot of the data that we get to see in our lives is unstructured.
I'll give you one example, in the clinical world, I'll give you an example, I'll give you in a retail world.
In the clinical world, many of us go get x-rays. Your x-ray, by design, is an unstructured data; it's a picture.
Well, it's an image, it's a radiology image.
Having to read an unstructured data like an image, like a picture, and deduce information from it, through all the knowledge on it, is a highly complex piece to it.
As much as an AI would have in it, there is an element of human required to ensure that's validation.
So you've got to balance who you are.
In the retail world, when you think of an unstructured data, you can think of lots of product catalogs.
How many of us here can think of a retailer, you don't have to name it, think of a retailer where you saw a picture on the catalog on the web and what you got at your home, two different things, right?
When you think about any of these use cases, the idea here is ultimately, what use case are we having this AI to solve for?
And what is the underlying data?
If the underlying data is unstructured, how do I put the decision loop in a way that it is meaningful versus doing it for the sake of tech?
Never do anything, in my opinion, never do anything for the sake of tech.
Yeah. I'm glad that Regine brought that question because it also brought how important data is, data quality, and the type of data.
I think that's a great place to leave this part of the conversation because we have gone from AI helping us to predict, to recommend, and now potentially to act and take action.
But as these technologies take more and more work, I think the question and the concern becomes, is about what happened to the people doing that work and what skills become more important?
So let's now talk about this evolution from supply chain analytics to agentic AI and how this is impacting the workforce.
And let's start hearing from our global audience because we still have almost 900 people live.
So please share with us poll three and let us know how do you expect AI to affect your own career during the next three years.
And while the audience is responding to this poll, Vijay, as AI takes over more routine analytical and execution tasks, how do you see supply chain jobs and the workforce changing? Yeah.
I think, see, the reality is as more and more AI and automation come about in everything we do and everything we see, the quantitative grind shrinks.
Meaning the need for somebody to run a dashboard every day, the need for somebody to make a thousand phone calls to do something, what have you; the quantitative grind will shrink.
When the quantitative grind shrinks, the judgment gets elevated.
That'll be a shift, not an elimination, in my opinion, of functions.
Shift, I mean by the need for now the humans in the loop to say, "Is what's coming out the right thing? Do I, how do I ask better questions?"
So the way I think about it is that the most important skill that we all should prepare ourselves for as we get ahead of us is realizing that the automation and the AI can do what they can do with the power of compute and all that fun in place.
There is going to be a part where the fundamentals aren't changing; let's not, let's not assume that is going away.
If we don't know to ask the right question, you're not going to get the right answer, no matter, no matter what level of intelligence that's going to be out there.
If you don't know to feed the right data, structured or unstructured, you're not going to get the right answer.
To do that, you need a level of human intelligence to play in it; it's the shift that is required to help to get to that part of the stage.
So my ask of raising folks who want to participate in AI in the supply chain, what to do from here would be: not everybody needs to be the most strongest AI developer or coder.
In fact, many of us don't need to be; there are places for it.
What is required though is the ground understanding of the business fundamentals, the ground understanding of what supply chain, regardless of the function you're part of, that is not changing.
How do you use that and create? In the world of AI, we use the word "prompt," right?
That is, how do you prompt for the right question? How do you prompt yourself to get, to validate the answer?
Just because you get an automation answer doesn't mean it's always right, right?
How do you know to ask the right question? How do you know to see what things are to be able to get to that?
That is the skill that, that's the muscle, and that's the skill we have to develop.
I see a shift, not an elimination.
Definitely. And I think this is 100% aligned with the responses.
Let's try to share the results of poll three.
But most of our attendees, they see this, 42% more as an opportunity than a threat, 33% as a significant opportunity, which is great.
You also brought, Vijay, the importance of business acumen and this understanding of the business that needs to be there for sure, as well as the quality of the data and the type of data.
Yeah. One of our attendees from Vietnam, Mivo, he said, "Okay, if, how do you see companies now hiring junior analysts to learn on the job?"
"Is the expectation of new graduates to already work alongside AI agents from day one?"
I think you already answered the question because definitely the prompting and bringing the right questions to the tools is part of that.
But I don't know if you want to elaborate more on that interesting question.
Yeah. I mean, definitely an interesting question, and this is the turn in the transformation era we're all witnessing in front of us, right?
I mean, quite candidly, a certain population gets to become a bit more vulnerable than the others, particularly the newer entrants to the business because they are new, they're graduating from wherever they are, they're just walked in, walked into the workforce.
And the most entry-level jobs sometimes become the most obvious candidates for automation and AI to sort of take over.
So it's almost like a, like a mishmash.
So my ask of that situation or the entrants in this case is: do not skip the fundamentals.
Do not, don't assume everything is only going to be AI, and AI is only going to be as good as how you're going to know how to use it.
So that is always going to be a role; it's just a shift in the narrative.
Be prepared, understand enough to know how to ask the right questions, how to retain it.
You don't have to be an expert in building dashboards; you obviously need to know what, how to ask the right questions.
So I think it's preparing yourself rather than relying on what was working 20 years ago.
I couldn't agree more on the need of understanding these fundamental techniques and the foundations in order to build on that and be augmented by these tools.
Now let's move to the last poll because we want to learn from our global audience about specifically that.
Which skills do you believe are most critical for future supply chain professionals?
Vijay, you already mentioned the importance of foundational supply chain skills, analytics, statistics, optimization, supply chain fundamentals.
What should also companies be doing in order to prepare their workforce for this transition?
I mean, here's the core argument for me: you can't judge what you don't understand.
Meaning the population that drives the companies at any level has to ensure that they are equipped with the knowledge of what is going to be success for that function to be, which includes understanding the core aspects of the function, understanding the principles of other companies want to go, all of the above.
Only when you have either a physical or a virtual automation, you're not talking about just AI coming in the form of an LLM or a ChatGPT with you.
It is also the robotics and the physical sensors that are going to be hitting you all day, every day.
Regardless of that, that is an element of "how do you know if that automation is doing the right thing?"
Only if you know the function, you'll be able to ask that question.
So the companies have to prepare their audience, grounding themselves again and again in the solidarity of what they're going after with the basics and give them the enrichment, for lack of a better word, with AI and automation along the way.
AI and automation cannot be a substitute or an alternative for not having grounded fundamentals.
Yeah. Yeah.
And, and also to just challenge this output, to understand the output and being able to challenge that output.
Let's try to see now the results of this poll because our audience, 75% of them vote for critical thinking, that is totally aligned with what you are bringing here.
55% analytical skills, yes, these fundamental and basic tools that we need to understand in order to understand the solution and the technological and AI literacy, 57% vote for that one.
With that Vijay, what advice would you give to professionals who are building and also advancing their capabilities in supply chain management?
I mean, the first and the foremost, Eva, I would tell you is that the business of how we, or the art of business, the conducting of business is evolving as well.
The business itself is not static. What was otherwise, let's call it 20, 24 years ago, a linear way of doing things: you plan, you then make, and then you ship.
All that is now largely, with the automation and AI in place, becoming more secular in nature.
I'm going to be planning a lot more factors based on how that's going to get shipped, how that's going to, you know, get potentially returned, and every function is looking much more circular in nature.
That transformation and how the business is also being conducted is an extreme element that we, as professionals, should adapt to.
AI, automation, and whatever other fun things that are going to be coming ahead of us that we do not know, do not predict, will always be there.
Technology is only there to advance.
But the most important thing is if we get a grasp of how the business is shifting, what questions do we ask?
It is less important what answers I'm going to give because there are tools and automation out there that can give a good answer.
But if I do not know how to ask better questions, the, the, the answers are not going to be.
No matter what AI is out there, the answer is going to be largely irrelevant, right?
So first, my only advice for myself and anybody in this audience is stay grounded with what you can learn in terms of the business.
Stay grounded with how effective of a questions we can ask that improves the response that comes from wherever it comes from.
Yes, become a lifelong learner.
Thank you, Vijay, for such an insightful and engaging discussion.
We really appreciate you sharing your experience and giving us a practical perspective on how AI is changing supply chain decision-making and also how this is impacting the workforce.
And thank you to our audience for all the great questions and for being part of this conversation.
As you reflect on the topics we covered today, from supply chain analytics to generative and agentic AI, and also the changing skills required in the field, if you are thinking about continuing your learning journey, our supply chain analytics and supply chain fundamentals courses, part of the MITX Micro Master's Program in Supply Chain Management, are now open for enrollment.
You have a. We have also shared more details in the chat and also invite you to explore to continue investing in your learning journey that we have been discussing today is really critical and important.
I would also like to thank my team, Elise Jonika, and Kara Grini for supporting this event behind the scenes.
And finally, we would love to hear from you.
We are launching a final poll now to get your feedback on today's webinar.
Your inputs help us to continue improving these events and provide valuable learning opportunities to our community.
So thank you again to our global audience for joining us today and keep an eye out for future, future webinars where let's continue to grow and learn together in this exciting field of supply chain management.
Thank you, Vijay, and thank you everyone.
Merci d'avoir écouté ce podcast Podhoc.
