Find out how Johnson & Johnson is building the future of intelligent supply chain management with cognitive automation.
These days, there's nothing simple about supply and demand. With goods being manufactured all over the world, and customers expecting a very personal experience when they purchase goods and services, the idea of an intelligent, efficient supply chain becomes an increasingly vital part of doing business. Neil Ackerman, Senior Director of Global Supply Chain Enterprise Planning and Innovation at Johnson & Johnson, sees the future of intelligent supply chain management (SCM) as one of the most exciting and innovative paths that can keep Johnson & Johnson ahead of the competition.
Supply chains may seem pretty straightforward to the layperson. You order something, you pay for it, and you get it delivered to you. In reality, it is a far more complex process, with an incredible amount of moving parts that need to be managed and controlled, with predictable, dependable outcomes. That's the challenge and the promise that confront Neil and his team at Johnson & Johnson.
Watch the video to see how the Advanced Planning team at Johnson & Johnson is focusing on the end-to-end infrastructure of the supply chain in order to develop the greatest efficiencies to improve the customer experience. Specifically, learn how it is developing a self-driving automated system that gathers and indexes data and then creates an analytics layer, which in turn develops tools that can produce the most accurate forecast of output for a product. Beyond that, Neil talks about taking supply chain analytics to the next level by exploring cognitive automation, a combination of robotic process automation (RPA) and Artificial Intelligence (AI) that could revolutionize how Johnson & Johnson serves its customers, and give the company a winning edge.
- About Supply Chain and Advanced Planning at Johnson and Johnson
- Customer experience and Supply Chain
- Supply Chain Creates Competitive Advantage
- Supply Chain Management and Cognitive Automation
- What is the Meaning of "Self-Driving"
- Impact of Supply Chain Transformation on Johnson and Johnson
This transcript was lightly edited.
Michael Krigsman: We're at MIT, speaking about supply chain innovation and cognitive automation with Neil Ackerman from Johnson & Johnson. Hey, Neil. How are you doing?
Neil Ackerman: Terrific. Thanks for having me today. I'm real excited to be here. This is my favorite topic. I believe that supply chain is the most important part of a business and to be able to talk to my peers and friends about it is a great opportunity, especially here at the MIT Engine.
Michael Krigsman: Neil, tell us about Johnson & Johnson and tell us about your role.
Neil Ackerman: I work in advanced planning. It's part of the global supply chain. I focus on delivering an improved customer experience and creating a competitive advantage by levering advanced capabilities within the supply chain. That's globally.
To really take a step back for a second, J&J started in 1886. The company obviously has been around a long time, unlike some of the more, I would say, other types of companies like the FANG stocks that have been there, the Facebook, Amazon, Netflix, Google. Let's talk a little bit about really how big it is because people don't realize.
First of all, J&J is a market cap, the ninth largest in the world, of $372 billion. It operates in 175 countries. It has 250 subsidiaries. That's why the supply chain is so important within the company.
I think another important factoid would be that, of the thousands and tens of thousands of employees, over half are in supply chain, so over 60,000 employees just in supply chain alone globally at J&J. I'm responsible for this advanced planning function within supply chain, that innovation aspect of it, in all of our divisions.
Michael Krigsman: Well, let's talk about advanced planning. For people who are not supply chain experts, what does that mean?
Neil Ackerman: What makes up the J&J supply chain? How do we think about it? Well, we have some big, strategic ideas about supply chain. First of all, we want to fast track our innovation. We want to be an agile solution provider for customers. We want to have an end-to-end value, and we want to inspire our employees and people.
When you're in advanced planning, you're pioneering these aspects of our strategic initiatives in different ways as you try to create this end-to-end value. What do we mean by end-to-end value? What we're talking about is whether you're sourcing something, you're making something, you're delivering that product, that purpose of healthcare, that's part of the end-to-end infrastructure.
A lot of people don't really understand how these pieces and parts get to you. You just think that your doctor gives you a prescription and you go to the store and get it or it's mailed to you. Actually, all of these things are all part of a supply chain and advanced planning is actually looking at this end-to-end and saying, "Where can we have the greatest efficiencies to improve the customer experience, to create a competitive advantage?"
Michael Krigsman: Neil, when you talk about customer experience in this context of supply chain and advanced planning, please elaborate on that for us.
Neil Ackerman: The world of the customer and what their expectations are has changed a lot. I remember not too long ago when my only option was to go to the store and get something. Now, things are delivered.
I remember years ago when you had to wait on a phone call and customer service took a long time. Now, it's not the same. A lot of things are self-service, if you think about what's happened.
The customer has greater expectations. Patients have greater expectations. Doctors have them. Why is that? It's because their whole lives around them around have also become more efficient; effective, you could argue. As a result of that, in their whole lives they want that same efficiency.
We're responsible for the creation of the personalization. We're responsible for the shipping and delivery. We're responsible for the procurement and getting those items from all over the globe where people want to get it. Now that the world has become flat, and you can get anything you want from anywhere, we have those responsibilities.
Supply chain now has come from this, "Oh, we're kind of here, like we don't really know," to, "We're the sexy thing now," right? We are it.
What happens is, people don't realize that marketing, everyone could be like a marketeer, right? Is it purple? Is it pink? Is it orange? We don't like that color. What should it be? Actually, they all can't be marketeers, but they think they can.
In supply chain, not many people can be just supply chain expertise. You need a lot of knowledge about how things work from processes, cost structure, variable, fixed, trans cost, to fully understand how you can meet customer expectations, give them a great price, broad selection, and make sure that they get it in the most convenient and effective way.
Neil Ackerman: That is a challenge that I love and I know my peers who work on it love.
Michael Krigsman: Okay.
Neil Ackerman: All that, though, is not done by one person. The one thing that's fun about supply chain is it has to be a collaboration.
Michael Krigsman: Supply chain is a core operation at J&J. In fact, a very significant number of your employees are involved in supply chain, so why is that?
Neil Ackerman: Supply chain is really a competitive advantage when you can really knock it out of the park and so, luckily, J&J has had great success with supply chain; always ranked in the top tier of companies.
Michael Krigsman: What does it mean to knock it out of the park when it comes to supply chain?
Neil Ackerman: It means that not only are you exceeding your customer expectations, but you're at the leading edge of technology, cost structure, efficiency. There's a series of metrics in supply chain, that maybe don't matter for today, related to what they call forecast bias or mean absolute percent error, pieces that are different types of metrics. What really matters is, are all your stakeholders getting what they want when they want it delivered, so on time, in full, delivered? Are you minimizing your out-of-stock rates and maximizing utilization?
Now, every company has different problems at different times over the years. Fine. Over the long term, we've been able to be a winner and our return on assets have demonstrated such matters.
Really, what I think matters here is what's made us so good lately. We've been able to move towards a self-driving supply chain. We've been able to understand that the ideas of silos and these one-off decisions that used to be made or were made in the past now are becoming more autonomous.
They're more automated. They're more end-to-end within the infrastructure. You have procurement still talking to folks in deliver. You have make understanding what's happening with source and they know where to ship it afterward with deliver.
These different pieces that come together, they're one team. This is the collaboration that I'm really trying to talk about, this idea that no one has the exclusivity on supply chain and genius. That does not exist.
My own team, I work for them. I work for that team.
Michael Krigsman: I want to explore this notion of the cognitive automation but, before we do that, historically, how have the supply chain teams worked so that it sets the stage for the differences that cognitive automation can make?
Neil Ackerman: In the past, you had a human. The human would create this idea of what was the historical forecast. Let's come up with the demand equation.
The spectrum of the folks listening to this talk today could be from, "I'm going to use just an Excel spreadsheet," to, "I'm going to have a sophisticated piece of software." At the end of the day, they were creating a demand and supply plan, right?
The thing is that this actually can be automated and actually really accurate. Why? Because it could take different pieces of data, not just your forecast, the historical forecast, but it can also take what are some of the expectations in the external world that are going to happen, what's happening in your forecast for the future, where you're going to get source from, what's that going to look like, and you could put together, actually, a really wonderful, self-driving, automated system.
Now, does that mean that it's perfect all the time? No. Does it mean that the human was perfect all the time? No. It does mean that, over the long-term, you're going to get better and better and better. Therefore, by doing that, you're going to improve your bias, so you're not going to have a big range of what you think it was; what it could be. You're going to have a really low, mean, absolute percent error, so your error rate will be lower, and you're going to improve your on time, in full, and delivered to the customer.
Now, what are some of the things that need to happen? Well, first of all, even if you get all the data, you have to be able to understand that data is not linear in this world. It's an analytics layer of linear mixed integers and tools that are coming together to actually create that demand plan.
Now, a demand plan alone is nice. You also have to make sure that you can source the material, that you can make it so your manufacturing is ready to go, and that you have enough trucks to deliver it. This is a self-driving plan I'm really trying to talk about. That's where I find the most excitement to be.
Is it hard? Yeah. Yeah, it's really hard, but it's really fun, and it's worth doing because, when you do it right, you lower your fixed cost, your variable cost, your trans cost. You improve efficiency and customers are happier.
Michael Krigsman: When you talk about self-driving, explore that for us. Explain for us, what is that? What does that mean? What's involved with that? Maybe give us a couple of examples.
Neil Ackerman: There is a machine and the machine uses a series of data crawlers. These data crawlers go across and they collect data and they index all this data. That's step one.
Step two: the machine then says, "Let's create an analytics layer." The analytics layer is the piece that is going to then create those different types of tools, whether it be a linear or, basically, a series of integers that will come together to create a number or a forecast, so the output of whatever you're looking for.
What drives outputs? Inputs. And so, the self-driving machine is saying, "Give me all the inputs from end-to-end of the supply chain, and then I'm going to give you the most accurate output that I can give you."
Michael Krigsman: Give us some examples of these inputs.
Neil Ackerman: Let's take something simple -- well, relatively simple. You order something. You want to know or the person listening wants to know when they're going to get it. Very simple, right? Except, actually, really hard.
What happens is, they'll say, "Over the past six months, when someone has ordered widget A, when has widget A arrived?" When we told them it was going to be this date, were we actually accurate with that date? Actually, you'll find that most of the time people are not.
Minus, of course, the other, I would say, e-commerce companies that we can talk about, even them are not accurate all the time. Even when they're at a 5% to 8% error rate, that's kind of a lot for all their packages, right?
Think about other companies, too. You want to know what they call "available to promise." When is it going to be available so it can be promised with a date?
The self-driving machine would say, "We now know when this has all happened in the past. We now know what we're sourcing from around the world. We know with our manufacturing what are the machines making and we know when that's going to be ready. Then we know, based on historical context, when it's delivered and when it's going to be in a distribution center."
With all that data--this is multi-integers that are all coming together--they spin it. They come through and they say, with accuracy, let's say 90% accuracy, "This is going to be your ATP date," available to promise date.
What's fascinating about it is, that's not good enough. In the self-driving supply chain, you also need a capable to promise date. You didn't make it yet, right?
Imagine this. It was not made, but the customer still wants the date of when it's going to be there. Now, that is also very difficult, especially in a global world when you're making things all over the world. People don't realize this stuff goes on boats and planes, and it goes through customers, and it goes through regulations. There are a million things that could go wrong--just watch the news--a million things.
When that happens, we also want to create an accurate CTP date, the capable to promise date. We have found, as a large supply chain in a healthcare company, that we've been able to really, really improve our ATP and our CTP date with different partners.
One of those partners happens to be Aera. With Aera, they have helped us self-drive this machine in one of our divisions. Our accuracy was good, but it wasn't great before.
Now, we can define it as, hey, it's great in this particular division. Why is it so great? It's because we worked with them to create a cognitive platform, a self-driving machine that takes a bunch of important data pieces that are not necessarily structured, structured and unstructured, that come together to create an index. We then put on top of it our own algorithms that we developed to give us this forecast, with their help, of course. Then, together, we create an accurate ATP and CTP date.
Now, what does that look like? It looks like this. "Hi. I'm a customer service agent and I'm picking up the phone." Maybe I have thousands of customer service agents, which I do, a change agent.
You're a customer, and you want to know when widget A is going to arrive. You pick it up and you say, "When is widget A going to arrive?"
The customer [service agent] in the past might have said, "I have to look into it." It could take me 15 minutes. I have to find out where it is, what we're making. I've got to make a couple of phone calls. This is not just a typical item. This could be anything. There are medical devices. We have hundreds of thousands of SKUs, right? What is it?
Now, here's a new world. You call up the agent and the agent says, "Oh, I'm going to look." They look right on the portal. There's the date, and it tells the customer where it's coming from.
Now, what would be really cool is if it says, "There's a date. This is where it's coming from," but, actually, maybe there's a backorder, or maybe they don't have it. What's another path to get that done? Now you start focusing on the future of supply chain, the idea that this cognitive machine is going to literally tell you, in the future, and say, "Actually, we know there's a back order, but you can tell the customer that it's going to come from these three different locations, it's going to be made on these dates, and it's going to deliver here."
How does it know it? Because this machine has created, from the structured and unstructured data, the cognitive thinking to make that happen. Yeah? And so, how I define cognitive automation is robotic process automation, RPA, plus AI, artificial intelligence, equals cognitive automation.
Now, what's RPA and why is it so important? What matters for this discussion is, RPA is essentially saying, "Give me all this data. Let me do tools and actions for you, so the human doesn't have to do it." Imagine putting in data, the same thing every time in the same field. This does it for you, plus artificial intelligence, which says, "Give me all your AI, your unstructured and structured data. Let's pull it together and then create an output using all this information." When they come together, RPA and AI, you have a cognitive automation. You're doing human skills with the use of a computer.
Obviously, this is the beginning of that. Supply chain is definitely at the forefront of it but it's going to change a lot over the next three to five years. There are things like quantum computing and serious science, that doesn't matter for this call or for this talk, that's going to influence that change.
Michael Krigsman: This investment in technology, investment in data, investment in new processes sounds like supply chain is so important to J&J that it makes it worth, obviously, this investment in processes, investment in data, investment in technology that you're making.
Neil Ackerman: I would say that this is about transformation and disruption. Let's be really clear. Not just J&J is it important to. It's literally important to every person on this earth. It's important to space.
We're talking about the idea. It's not just a movement of goods anymore. It's not just a truck. It's the use of the production, sourcing it, producing it, making it, shipping it, delivering it, all in this unbelievable background, the backdrop that you didn't even know how it got there. Because it's so important, it's always ripe for disruption. There's always a new way to transform it or to win to make it even better.
I would say that I would push the audience here and say, actually, supply chain is a new revenue generating machine. It used to be that sales and marketing generated revenue and supply chain was a cost center. I want to be really clear that supply chain is no longer a cost center. Supply chain, for those companies that know how to work it, can become your revenue generating machine.
Michael Krigsman: How? How is that possible?
Neil Ackerman: Because when you exceed customer expectations and you deliver something that they want faster than everybody else, you innovate within the supply chain via 3D printing, let's say, or sensors on a device to give you information using Internet of Technology, Internet of Things, IoT, this is all part of the supply chain. You don't have to look any further than the major e-commerce companies to know that supply chain actually is a winner. It's an advantage.
You need traditional tools, but you also need to understand that you can't win without it. Wars are won and lost on supply chain. Soldiers have to be fed.
The whole global economic structure really depends on trade and the ability to create these magic, what I call, supply chain magic. I literally call it that because it's no longer a village trading with one in each other. It's a global world and we're creating magic.
There are Merlins all over the place, not just one. These Merlins that are everywhere are a team and they're trying to get it to you faster, cheaper, better, more efficient, make something different.
Let's just do one example. Let's say you need a medical device for a knee or anything artificial. In the past, you'd have to get it. They measure it. It'd have to come to you.
What if you could 3D print it right on the spot? That's what we're talking about. We're talking about the world is going to change so fast.
What's 3D printing? 3D printing is a form of supply chain. It's creating something. It's getting the plastics, the ceramics, the metals, whatever you need where it needs to be. Boom.
In the past, hospitals were these big, big buildings and they were sort of not everywhere. Now you have ambulatory centers everywhere.
How do you control the inventory? How do you know who is going to be sick where next? You need models, prediction, transformation, disruption because you have to predict the future. If not, you're always going to be behind.
We also have a responsibility to teach the world that supply chain is core to what is happening here, and so that's why we're so excited to talk about it.
Michael Krigsman: Neil Ackerman, a very passionate look at supply chain. Thank you so much for talking with us today.
Neil Ackerman: Well, thanks for having me. I really appreciate it. Let's keep moving.
Published Date: May 16, 2019
Author: Michael Krigsman
Episode ID: 597