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The Age of Enterprise AI
C3 AI senior director of products Lila Fridley and supply chain management & technology expert Scott Barrett discuss today’s most complex supply chain challenges and how AI has the power to solve them.
Recent years have revealed just how fragile supply chains are, as shifts in supply and demand, worker shortages, and poor visibility led to large-scale disturbances still felt today. In this environment, supply chain leaders need the right tools to avoid significant disruptions to their operations and, ultimately, their bottom lines.
We sat down with C3 AI senior director of products Lila Fridley and supply chain management expert Scott Barrett to discuss the obstacles facing supply chain executives today; why predictive AI–enabled supply chains are the key to gaining real-time visibility, planning for uncertainties, and managing risks; and how the C3 AI Supply Chain Suite can help leaders transform their supply chain strategies to drive their businesses forward.
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Reshaping the supply chain with enterprise AI
How has the supply chain landscape evolved in recent years and what has been the impact on organizations?
Barrett: There have been a trifecta of shocks—
the pandemic, geopolitical tensions, and the Great Resignation—that have largely affected the procurement and supply chain sectors. They have driven a new level of complexity for organizations.
There is a heightened focus on post-Pandemic resiliency and changing sourcing strategy to avoid geopolitical risks. With the Great Resignation, there has been high workforce turnover. New hires learned how to operate in “survival” mode. Now, organizations need to learn how to run a proactive supply chain as the economy shifts back to “normal.”
AI is changing the equation for sustainability teams in pretty much everything they do. AI is now being used to automate a lot of the burdensome work of ensuring data validity and mapping it to the proper frameworks and standards for reporting. And it is helping teams proactively address the shifting concerns and expectations across the stakeholder universe.
Lastly, for teams that don’t have enough time to think about their roadmaps to achieve their ESG goals, AI can be a tool to create specific, detailed plans and generate hundreds or thousands of different scenarios—the direction of energy prices, for example, or whether an organization is likely to hit the targets it’s been publicly announcing.
How is AI changing the ways organizations can address these challenges?
AI can serve as a hugely important value driver within the company by understanding the needs of customers and investors, suppliers, local communities, and our planet. Our C3 AI ESG solution helps give strategic direction and risk mitigation insights to large and complex organizations that really want to create value out of ESG.
How does C3 AI’s ESG solution use AI to help companies optimize their ESG strategies?
ESGBit™ is a term that C3 AI coined to describe a new concept that we introduced in our C3 AI ESG application. An ESGBit is an elemental unit of ESG information. Think a kilowatt-hour of electricity consumed, at a specific facility, in a specific region, and at a specific point in time. Or a single health
What are ESGBits™ and how does unifying disparate data into ESGBits™ enable more accurate and reliable ESG reporting?
Barrett: Advanced planning systems have been in the market and popular since about 2000. But many still use a rules-based heuristics approach. This falls short when you add nodes into your supply chain, like new suppliers or new transportation routes. New nodes add decision points.
Heuristics don’t do a great job figuring out the implications of those decision points. In the past 5 or 10 years, it’s become apparent that AI/ML approaches outperform rules-based algorithms. This is what supply chain leaders should be shifting toward.
One complication is that every software vendor is starting to say they have AI. Those that do are mostly creating models in a bit of a vacuum. They’re saying, ‘We believe we have an answer. Here’s our answer. Apply it to your problem.’
A better approach for vendors would be saying, ‘We have a framework and a workbench that allows customers to train AI to their specific data and business model.’
Supply chain teams really need this kind of framework to tune off-the-shelf AI applications to their needs, but this approach is rare in the market today.
Fridley: Our products at C3 AI do just this. They are highly configurable. We train machine learning models on our customers’ data so they’re highly geared to their demands. If they don’t have data scientists, we have out-of-the-box SaaS applications with out-of-the-box AI models. Depending on the way a business is run and [how] its supply chain is configured, we can turn on different machine learning models that are best suited to it.
The other thing we commonly hear from customers is that they have in-house teams applying data science. They say, ‘I’ve built a demand forecast with 10% higher accuracy than our existing solution,’ or, ‘My model predicts lead times.’ But they don’t know how to manage or scale these models.
Managing models at scale is one of the hardest problems facing data science teams. In an enterprise business context, you have to worry about things that data scientists don’t usually consider—like data access controls and security. You have to make sure none of the systems fail. Our customers are gravitating toward C3 AI as the AI provider that can handle that complexity at scale in an enterprise business environment.
How are some tools currently on the market falling short of addressing these challenges?
A lot of the sustainability goals today are long-term commitments. Think net-zero emission by 2040, or even 2050. Mature organizations are creating plans to achieve that goal over the next 20 or 30 years, but even the most mature organizations are still facing all of the uncertainty that comes with 25 years of shifting macroeconomic conditions, stakeholder expectations, and issue materiality. Not to mention uncertainties like how healthy your
What are the biggest challenges for supply chain organizations in AI adoption?
Barrett: Predictive response is by far the biggest value. You can never have 100% perfectly accurate plan or supplier performance. An AI system can tell you what is likely to happen in the future. Then it can tell you when you’re on the low end or high end—everything from the amount of physical product in your line to the financial impact—so you can adapt your management plans accordingly.
Fridley: We’ve developed a flexible approach to model network effects across a company’s supply chain. That network model allows us to simulate impact of changes, like losing a transportation lane or a late supplier delivery. Not only do we apply AI to help customers shift from a reactive to a predictive response, but we also use this network model to help them become more resilient by running scenarios and optimizing their response strategy.
Where do you see the biggest value impact of AI adoption in managing supply chains?
Fridley: First, we have end-to-end, very granular AI-powered supply chain management. We are modeling every part, every stock keeping unit (SKU), every customer, every order, every supplier, every node in the supply chain, and every transportation lane. We keep track of how it changes over time, so you can replay that history and see how things changed, where they got delayed, and what it impacted. Then you can use that data to build predictive and optimization models.
Our customers are seeing 10% to 15% improvement in demand forecast accuracy, 10% to 20% improvement in inventory reduction and service level, and an improvement in order fill rate by 1% to 2%—which may sound small but is a direct tie to revenue and customer satisfaction.
Second, the C3 AI Supply Chain Suite tackles AI adoption challenges in two ways: one, a simple search and chat interface; two, rich evidence packages.
Customers quickly onboard to our simple C3 Generative AI interface. You can simply ask a question, and you’re directly routed to the information you need. The application synthesizes all your enterprise data so you can pinpoint what matters—without needing weeks of onboarding and software training.
To the point on evidence packages: this is really important to us. Most AI systems are black box—I ask a question to one of those large language models (LLMs), and I don’t know where the answer is coming from. They also hallucinate. At C3 AI, we want to build trust with our users. All of our applications expose detailed evidence packages that explain exactly why the AI model came up with the answer it did. They provide the source of truth for the underlying data that is powering that prediction so end users can route through to the underlying data to say, ‘Okay, yes, I agree with this.’
How does the C3 AI Supply Chain Suite enable companies to create efficient, agile, and resilient supply chains?
Listen to Scott Barrett on
the importance of accessible,
user-friendly applications.
Barrett: Data quality is huge. Most companies’ data are riddled with inaccuracies, poorly governed, and scattered across disparate systems.
Getting it into a structured, or at least accessible, format and building AI models based on such a data landscape is a big challenge.
And again, there are workforce challenges. Moving away from manual steps and toward automation takes a different kind of worker and a completely different mindset. Employees need to be upskilled and retooled to embrace exception-based planning where the system does the heavy lifting.
Generative AI, a new set of artificial intelligence and machine learning algorithms that allows for the creation of new content from existing data, is taking the consumer and business worlds by storm. And its adoption is anticipated to rapidly ramp up, with the generative AI market projected to reach $110.8 billion by 2030. For individuals, the use cases for the technology span from creative to recreational to practical, while for enterprises, properly and responsibly deployed generative AI has the potential to increase efficiencies, reduce costs, and boost profitability.
Tom Siebel, chairman and CEO of leading enterprise AI software provider C3 AI, believes generative AI is a technological breakthrough on par with the internet and the smartphone—and since 2020, his team has been building on the company’s 14 years of enterprise AI research and development (R&D) and industry-leading AI platform to capitalize on this breakthrough and bring it directly to customers.
We sat down with C3 AI’s president and chief technology officer (CTO) Ed Abbo and Nikhil Krishnan, the firm’s CTO of products, who have been instrumental in developing the C3 Generative AI Suite, which was released to the public this spring. We spoke to Abbo and Krishnan about the challenges that come with enterprise data operations and how the C3 Generative AI Suite is designed to not only address these problems head-on, but to also revolutionize the ability for users to access and interact with information across an organization.
C3 AI senior director of products Lila Fridley and supply chain management & technology expert Scott Barrett discuss today’s most complex supply chain challenges and how AI has the power to solve them.
Reshaping the supply chain with enterprise AI
The Age of Enterprise AI
Back to home
©2023 Fortune Media IP Limited. All Rights Reserved. Use of this site constitutes acceptance of our Terms of Use and Privacy Policy (Your California Privacy Rights) | CCPA Do Not Sell My Information Fortune may receive compensation for some links to products and services on this website. Offers may be subject to change without notice. Quotes delayed at least 15 minutes. Market data provided by Interactive Data. ETF and Mutual Fund data provided by Morningstar, Inc. Dow Jones Terms & Conditions: S&P Index data is the property of Chicago Mercantile Exchange Inc. and its licensors. All rights reserved. Terms & Conditions. Powered and implemented by Interactive Data Managed Solutions. | EU Data Subject Requests
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What factors have contributed to the increasing complexity of supply chain management?
Barrett: I see three factors contributing to more complex supply chains.
First, geopolitical instability; second, a new focus on resiliency; and third, environmental, health, and safety (EHS) elements. As the U.S. shifts away from trade relations with China, the supply chain has had to shift south to India, Vietnam, Indonesia, and Malaysia. There is new sourcing competition in those regions. It’s become harder for companies to qualify, onboard, and manage so many new suppliers.
The new focus on resiliency was born out of the shock of the pandemic. For the first time, C-suite executives began to understand what it means to be single-sourced. What might have been seen as the best, lowest-cost sourcing options no longer are. Now leaders are asking ‘What is our resiliency score? Where are our single points of failure? How do we conduct automated, comprehensive analysis, and then how do we mitigate risks?’
Finally, there’s EHS. Companies that didn’t previously prioritize EHS now have to, with new government regulations. Organizations need to collect more data from suppliers and look at the carbon footprint of their supply network. That data has to become accessible and actionable.
Barrett: One of the greatest challenges facing business leaders today is related to the workforce. As I mentioned earlier, many organizations are considering shifting manufacturing and sourcing to improve resiliency. This requires building or retooling infrastructure and training new workforces.
To overcome challenges associated with these changes and deliver on the promise of a more resilient supply chain, companies need to introduce automation and intelligence. This means new software, support, and systems— and a workforce that knows how to thrive in an automation-heavy environment. Companies today are struggling with this shift.
What are the greatest challenges that business leaders face today when it comes to effectively managing their supply chains?
How do workforce challenges and disparate IT systems contribute to supply chain disruptions and risks?
Fridley: We observe that many enterprises have implemented various planning and ERP systems over time. This leads to disparate data and tools that supply chain planners must manually review to make decisions. In some cases, companies have tried to implement analytics-based systems to automate some of those decisions. But these analytics tools are still isolated, leading to even more uncertainty and noise. In short, garbage in equals garbage out.
There is promise in virtually “pooling” all your inventory and demand to smooth out some of that uncertainty. If you look at all the
Senior Director of Products, C3 AI
Lila Fridley
information together, you end up with a lot less variability, and you can lower your costs, manage supply, and manage inventory in a much more cohesive manner.
But because data lives in disparate systems, no one can do this very well. They’re suffering from the proliferation of individual, granular management decisions, unable to make holistic and accurate decisions based on what’s currently happening globally.
Barrett: AI is becoming a table stakes expectation for supply chain leaders. It is necessary to overcome all the complexities and challenges we’ve talked about, since manual decisions can’t keep up with the pace and magnitude of supply network complexity. Companies that adopt AI in a way that helps them make decisions more quickly—by clearly presenting the accurate and precise predictions with evidence and impact of those decisions—are the companies that are going to thrive.
How do you see the future of AI and supply chain management evolving?
Barrett: When supply chain leaders look for best-in-class supply chain software, the No. 1 question they ask is, ‘Am I buying something that solves today’s biggest supply chain problems?’ Some offerings may have very advanced data science, but are inflexible or can’t adapt to future business needs. Others are so complex to use that they companies don’t have the staff to run it. Leaders need solutions that come preassembled, giving companies something to start with out-of-the-box. But they also need solutions that can be retuned and trained by internal data science teams. Software that can do both is game-changing. That’s where C3 AI has a big advantage.
Fridley: Looking to the future, as leaders start to embark on bigger and more comprehensive AI projects for their supply chains, they need to be laser focused on value. That might be driving revenue improvement, reducing inventory, or mitigating impact of delays. But the goal is not about implementing fancy AI models so that you can write a press release on them. It’s about driving business outcomes.
What should organizations do to keep up with the changing supply chain landscape to stay ahead of the curve?
Listen to Scott Barrett on lorem ipsum dolor sit amet, consectetur adipiscing elit.
Lila Fridley
Fridley: First, we have end-to-end, very granular AI-powered supply chain management. We are modeling every part, every stock keeping unit (SKU), every customer, every order, every supplier, every node in the supply chain, and every transportation lane. We keep track of how it changes over time, so you can replay that history and see how things changed, where they got delayed, and what it impacted. Then you can use that data to build predictive and optimization models.
Our customers are seeing 10% to 15% improvement in demand forecast accuracy, 10% to 20% improvement in inventory reduction and service level, and an improvement in order fill rate by 1% to 2%—which may sound small but is a direct tie to revenue and customer satisfaction.
Second, the C3 AI Supply Chain Suite tackles AI adoption challenges in two ways: one, a simple search and chat interface; two, rich evidence packages.
Customers quickly onboard to our simple C3 Generative AI interface. You can simply ask a question, and you’re directly routed to the information you need. The application synthesizes all your enterprise data so you can pinpoint what matters—without needing weeks of onboarding and software training.
To the point on evidence packages: this is really important to us. Most AI systems are black box—I ask a question to one of those large language models (LLMs), and I don’t know where the answer is coming from. They also hallucinate. At C3 AI, we want to build trust with our users. All of our applications expose detailed evidence packages that explain exactly why the AI model came up with the answer it did. They provide the source of truth for the underlying data that is powering that prediction so end users can route through to the underlying data to say, ‘Okay, yes, I agree with this.’
How does the C3 AI Supply Chain Suite enable companies to create efficient, agile, and resilient supply chains?
Barrett: AI is becoming a table stakes expectation for supply chain leaders. It is necessary to overcome all the complexities and challenges we’ve talked about, since manual decisions can’t keep up with the pace and magnitude of supply network complexity.
Companies that adopt AI in a way that helps them make decisions more quickly—by clearly presenting the accurate and precise predictions with evidence and impact of those decisions—are the companies that are going to thrive.
How do you see the future of AI and supply chain management evolving?