AstraZeneca, a global, science-led biopharmaceutical company focused on developing life-changing medicines, is using decision intelligence to help build more resilient and self-healing supply chains. In this episode, Arun Krishnan, SVP, Global Supply & Strategy, shares how AI and human expertise are transforming clinical and commercial supply chain operations, enabling faster, more informed decisions and creating a continuously learning operating model that helps ensure medicines reach patients when they're needed most. The conversation explores operational leadership, human-machine collaboration, and what it takes to scale decision intelligence across a global enterprise.
Transcript
Fred Laluyaux (00:00)
Hello and welcome to the Decision Intelligence podcast where we have real conversations with thought leaders, practitioners and tech experts about AI, decision intelligence and the future of work. I'm your host, Fred Laluyaux, co-founder and CEO of Aera Technology. And today I am delighted to welcome Arun Krishnan, Senior Vice President, Global Supply and Strategy at AstraZeneca, a global science-led biopharmaceutical company whose medicines are sold in more than 125 countries and used by millions of patients worldwide. Arun has more than two decades of experience leading global supply chain transformation and today he oversees AstraZeneca global supply. Arun, welcome to the podcast. It's really great to have you.
Arun Krishnan (00:48)
Hi Fred, nice to meet you. Great to be here. Nice to have this engagement.
Fred Laluyaux (00:52)
Great, great, great. Yeah, we've been working together with your team for quite some time, but it's the first time you and I are connecting, not really in person, but virtually. I think we'll be connecting in person soon. But before we start, tell us a little bit about your role. I gave the high level highlights, I know the clinical and the commercial supply chain leadership, but tell us a bit about your journey leading one of the most complex clinical and commercial supply chains and the company's growth journey. So tell us a bit about yourself.
Arun Krishnan (01:20)
So thank you, Fred. So a brief note about myself. I'm very passionate about supply chain. If supply chain is the business, business is the supply chain, I always say that. We are at the intersection point of growing through innovation in AstraZeneca, bringing 20 new medicines to our patients, a great ambition for the company towards 2030. In solving patient needs and also it's serving with purpose, right? The purpose is bringing medicines to patients, purpose towards the planet, towards our scope 3D carbonization and carbon commitments. And also with the rise of AI, it's how do we operate and orchestrate the supply chain with the power of decision intelligence? What do we think about the response to geopolitical strategies? What do we think about the response to economic and tariff strategies? It's always happening in the supply chain. When we put the currencies of supply chain, the technology, the data, the decision intelligence, it's an amazing place to be. And this amazing place along with the purpose makes it even more transformative, even more enjoyable and even more powerful.
Fred Laluyaux (02:38)
So, so fantastic. So you talked about globalization, touched on pandemic and disruptions and now AI. So why do you think decision intelligence, like the automation, the augmentation of decision making is the next frontier for enterprises and for AstraZeneca?
Arun Krishnan (02:57)
It's a great question, Fred. In the supply chain, every minute, every hour, every week, every day, every month, every time bound, there is a decision to be made.
Fred Laluyaux (03:07)
Mmmm.
Arun Krishnan (03:08)
Right? It's a continuous orchestration process. It's a continuous management process. Something is changing all the time. Something is changing all the time. Gone are the days of what I call the legacy ways of looking at it as an SNOP or a planning cycle. It's not a cycle based process. It's a decision centric process.
Fred Laluyaux (03:28)
Yep.
Arun Krishnan (03:28)
It's a time sensitive process. It's what I call event management and response management and variability management process. So it's all about what are the events happening in the supply chain? What are the variables we are dealing with? Both operational, tactical, and strategic, and how do we think about what's the next decision to make, what's the next course of action, and what's the next response strategy. Right? So that's the whole paradigm shift and the paradigm change with the rise of AI and the power of data and the information and the technology potential. It's not technology anymore, the constraint. How do we leverage this technology to orchestrate decisions, to think about what's the best response in the shortest possible time?
Fred Laluyaux (04:18)
But from your vantage point, right, you've been leading the commercial and clinical supply chain for a long time within this phenomenal company. What are the decisions that are becoming almost impossible to make or harder and harder to make, you know, as a result of the increased complexity, right? So everything is getting more complex. At the end of the day, where do you feel the struggle in terms of decisioning? And keeping the consistency at scale. And of course, how is decision
Arun Krishnan (04:47)
Yeah.
Fred Laluyaux (04:49)
intelligence helping? But I think it's interesting to say, you talk about technology not being the issue anymore, but the issue is with the decisioning. So why is it getting so complex? Why is it getting so complicated?
Arun Krishnan (05:00)
It's a great question, Fred. I won't say it's complex. It's multiple variables happening. For example, in the supply chain, there are internal variables and external variables for example, the volume of decisions to make, the velocity of decisions to make, the variety of decisions to make, both in three or four time bounds and time horizons, operational, tactical, strategic. Some could be design related decisions five years from now. Some could be operational decisions for this week. Some could be tactical decisions for next month. Some could be budgetary type decisions for next quarter. Some could be financial decisions or a launch type of decision, or some could be an optimization type of decision. So the N number of decisions, the volume, the variety, the velocity. Plus, those are internal, plus if you couple that with what is happening around the world, right? It's an assumptions-based process, but the power of joining the assumptions and the world around us is continuously changing. The world is moving faster than the assumptions. So the crux of this game or the crux of this whole decisioning, the speed of the decisioning,
It's the velocity of the decision. You can't afford to wait. You gotta move. You gotta keep the pace. You gotta keep the momentum. Rather than waiting for N number of decisions, what's a quick next decision to make? What are the options? What are the alternatives? What are the recommendations? Make that, make the move, and then make the next best decision as things and conditions and variables change.
Fred Laluyaux (06:38)
I like the framework that you provide because you're looking at the way the velocity, the complexity is increasing internally, like organically. And then you compound that with the external factors and the threat that if you don't decide fast enough, someone else will.
Arun Krishnan (06:55)
someone else will and in our case a patient is at stake right? It's because it's serving the patient with purpose so we can't put a patient at stake.
Fred Laluyaux (07:04)
So that's an amazing segue to my next question. And it's something that we've had the pleasure to welcome some of your colleagues at our events. I think we have the one coming in November. Hopefully we'll have you on stage. But, they were talking about the impact of better decisions, faster decisions, real time decisions on the patient's life.
Fred Laluyaux (07:25)
AstraZeneca was one of the early adopters of DI and one of the firms that we have the privilege to work with that has the biggest impact when you think about the technology. Can you talk about that impact first, right? What impacts have you seen with the technology and then what you've learned about introducing this way of working across the organization?
Arun Krishnan (07:49)
Great question, Fred. So I'm so happy to say the partnership and the collaboration between Aera and AstraZeneca has been fantastic. We started this journey last year in terms of our alignment to our intelligent supply chain mission, what we call the self-healing supply chain strategy and the self-healing supply chain ambition to transform patient outcomes, to transform bringing 20 new medicines to our patients.
So we started this journey in terms of bringing a couple of use cases in our transportation management process, in our lead time calibration process, and we are seeing some real time impact to call out a few in terms of, for example, prior to this DI capability, decision intelligence or decisioning management capability, we had a planning system, a scheduling capability, a transportation management system, a lead time tracking system, a risk sensing system. Each one had their own power and the process to do their things in their individual domains or in their individual spaces. Now with the power of DI, you have a full orchestration mechanism. You have the full end-to-end connecting mechanism. The planning system will know where the transportation orders are and where the freight delay is. Is it early or late? Those two systems will then connect into my risk management sensing system. Those three will connect into my lead time calibration and it's a bidirectional feed. So it's the cutting across the multi-eco system in order to orchestrate the full end-to-end decisioning, that's one. So that's the power that we are starting to see, plus, were the silos,
Fred Laluyaux (09:29)
That's very powerful. You're breaking the silos.
Arun Krishnan (09:33)
Breaking the silos, bringing the power of integration together, and which is to a larger purpose of orchestrating an outcome. Because at the end of the day, it's about what's the next decision to make? What's the velocity of that decision? How far should we go? When do we make it? What's the next best recommendation? Which can then inform what's the next best action in order to influence the outcome and the impact. So that's the secret sauce or that's the connection point. It's simple in nature, but I also say along with that comes the, I'm so grateful to our people, our organization who are learners, who are innovators, who thrive in a transformation environment, thrive in a learning environment and make that switch and pivot to new ways of working.
Fred Laluyaux (10:24)
I had the privilege of presenting and talking with your teams in multiple areas in the world and doing some kind of lunch and learn sessions and the curiosity is there. I think the mission, you can really get a sense of mission-driven people here.
And it's not technology, the feeling that I got, it was very energized. It was right the day after our user conference. You know, after the user conference, your energy as a CEO is a bit low. You worked really hard for that. It was literally the day after and I've left completely energized because again, the team has that purpose that this is not technology for technology's sake. This is not a top-down thing. This is something that will help us perform better. And when AstraZeneca performs better, you make critical you know, medicines and treatments available to individual patients, which is super important. But let's talk about that because, you know, when we talk about AI, it's always, I'm talking about models and interfaces and co-pilots and all this good stuff. And this is all very important, obviously. But what else had to come together from the people, from the process, from the data, from the governance perspective, cultural standpoint to translate this capability, this intelligence capability into what you've been driving, which is real business outcomes. So technology is one thing, but there is a lot more and we're touching on that. But what did you do? How did you, for people who are listening today and they are at the beginning of that journey, what can they learn from you, Arun,
Arun Krishnan (11:55)
Great question, Fred. First is to be very clear on the purpose. Purpose matters, right? So our purpose is very clear in terms of patient centricity, serving global product supply to patients flawlessly on time in full, number one. Number two is mission and speed. So what we call the value system. We are entrepreneurial. innovations. play to win. are patient centric. So our value systems underpin this drive and mission. innovation, our company mandate is to grow through innovation. So here is supply chain innovation, intelligent supply. And ways of working can be innovative. Process can be innovation. The way we connect the systems can be innovative. can be all encompassing. connecting data, people, ways of working, process, and then culture. So that's that ingredient. And the last part is the hard part, data. Data, data, data. And data is never perfect, but the mantra or the mindset is never aim for perfection, but aim for excellence. And as long as it's 80-20, as long as it can drive the right outcome, because we know there are variables in the supply chain that will change. So rather than putting the energy in boiling the ocean, you focus on a swim lane or a work stream that has say, look, for this use case, for this process work stream, what's the critical to quality, critical to success data objects. Focus on that and really go deep on that length and breadth and width of the data to make sure that that data is solid in order to enable this use case, because we know at the end of the cycle are the decisions that we need to make. And those decisions, it's also not just a decision for the sake of decision. Are those recommendations making business sense? So it's the lens at which we look at it. So
Fred Laluyaux (13:33)
Yeah.
Arun Krishnan (13:49)
If we take a step back, does it make business sense? Yes. The recommendation is okay. So then the machine can start self-learning and then it will auto recommend. And then this cycle continues, this iteration continues. Then you make the data a little bit better. It's like the fuel engine. You do the combustion and the ignition. You make the data better. The process works well. The people are trained and the ways of working are getting better.
Fred Laluyaux (14:13)
I love it. I always say the best is the enemy of good. But I think the way that you framed it, which is, you know, we shoot for excellence, not for perfection is actually even better. I love that. There is a lot of passion in the way you're explaining things. And I think that might be also one of the driving factor that explains the success that
Arun Krishnan (14:42)
Yeah. Yeah. Yeah.
Fred Laluyaux (14:58)
drive that intentionality that's quite unique to you. I mean, other companies do it. I can name others in other industries that have that, or even in your industry. But this is something maybe from the outside in partnering with you that I've seen is your, have a very strong sense of intentionality. The other thing I would say, which you probably don't, or maybe you do appreciate
Arun Krishnan (15:16)
Haha.
Fred Laluyaux (15:18)
if you know how to get the best out of your partners. You
Arun Krishnan (15:21)
Right.
Fred Laluyaux (15:21)
ask anyone at our company at Aera, and they will just go, you know they will do anything to make sure that AstraZeneca is successful. So you know how to partner, you know how to give, you know how to receive, but you're generous in your approach to your partnerships and we really appreciate that. But I think you got this payback because now you've got this ecosystem around you that is absolutely all in. And I think the last thing that comes to my mind, I don't want to steal your answers because I asked you the question is the fact that you're so at your level, very senior, but so connected with what's happening is also a good driver. I see other companies where it's not always the case that executives that are in your position don't necessarily know everything that's going on. But you took that topic as very critical. It's the way you perform is the way you decide, right? The way you perform as a company is the sum of all the decisions that you make. So if you can improve the way you decide, you improve your performance. And that's fantastic. So I just gave you a lot of praise, but they're all very sincere. and they're all heartfelt. So I just want you to let you know that. The one question that I have for you now as we wrap up, I know we're getting out of time. No, it's all heartfelt. The one question I have for you now is where you are, you got momentum, you got dynamism, you've got aligned resources from top to operations of where you want to go. Where are you going? Where do you see the future?
Five years from now, I know it's super hard, five years seems like very far, but will you define and identify those companies that have adopted this approach, that modern approach to decision making and building autonomous self-fitting operations from those that have not started or not as deliberate as you are?
How do you see the gap growing in the next five years?
Arun Krishnan (17:04)
So the world is becoming an intelligent world. It's an intelligent ecosystem. And the businesses and the supply chains, we can call it autonomous, automatic, automated, augmented. Those are just words. It's all about as the world around us is changing faster than our assumptions. Can we orchestrate an intelligent supply chain of the future that is smart, that is data-driven, that is helping make the right recommendations at speed, that is driving the decisions at speed?
I see the supply chain of the future as an ecosystem of connected partners, suppliers, customers. trading partners, so the possibility of both internal and external connections, whether, you know, we have all the data, it's not about the availability of data or the information it's about what do we do with all this information put together? How do we generate the insights? How many insights are translating into decisions? How fast are we making those decisions? What's the next best decision we make?
Arun Krishnan (18:07)
And what set of recommendations we do, and how do we turn those recommendations into action, to impact. So it's about the so what aspect of the business impact with all the ingredients and the currencies and the inputs that we have. The fundamental supply chain system or the fundamental supply chain orchestration management is still the same. It's about you know, it had technologies reducing the latency, technologies increasing the velocity, the speed blah blah blah all of that type of stuff. But it's more about what we are doing.
Fred Laluyaux (18:42)
My last question: I promise I know we're out of time. You talked earlier about breaking the silos within your supply chain. As you project, do you see the silos breaking across the enterprise? Not just within the supply chain, but connecting the supply chain to the other functions more seamlessly as well.
Arun Krishnan (19:02)
Absolutely, because if everybody is within the supply chain, you break down the silos and bring everything together. As I say, supply chain is the enterprise, enterprise is the supply chain and the business. So as the supply chain connects with the cross functions, everybody is looking at the same information the same way. Again, everybody has access to information. It's what you do with the information, what type of decisions to make and what recommendations to act when and where. So it comes down to what problem you're trying to solve and what problem you're trying to find. And how do we turn this problem into an impact, into an action, into a solution.
Fred Laluyaux (19:37)
You're literally talking about what we talk about all the time, which is the shift from data-driven to decision-centricity. And the way you think about all of this is through the decision and the business impact. So, Arun, I know we're out of time. I could speak with you for another hour, but you got to go. Thank you so much for joining today. Really appreciate
Arun Krishnan (19:55)
Thank you Fred.
Fred Laluyaux (19:57)
It was fun talking with you. Hopefully we can connect in person very soon. And I look forward to that.
Arun Krishnan (20:03)
See you Fred, have a nice evening. Bye bye.
Fred Laluyaux (20:04)
Thank you, thank you. This concludes our Decision Intelligence podcast. AstraZeneca how decision intelligence is transforming global supply chain operations. You can watch today's episode and more decision intelligence resources at AeraTechnology.com Thank you very much. Until next time, bye bye.