Turning ERP Data Into Action: Sanjay Brahmawar on AI, Frontline Workers, and Manufacturing Outcomes
Manufacturing Happy Hour
Recorded at mHUB in Chicago during IMTS 2026, this episode of Manufacturing Happy Hour features host Chris in conversation with Sanjay Brahmawar, CEO of QAD Redzone. The central question is how manufacturers can get real value from artificial intelligence now, without first replacing their existing systems. Brahmawar's position is consistent throughout. AI should be applied to the systems manufacturers already have, aimed at specific operational pain points, and measured by business outcomes such as OEE, on-time delivery, margins, and capacity. In his view, it is not something to adopt for its own sake, and its role is to make scarce manufacturing workers more capable rather than to replace them.
Setting: mHUB and the Case for Innovation Hubs
The host explains that guests usually pick a favorite bar or brewery for the show. This time the host chose mHUB because the recording took place in the middle of IMTS and the venue was close by. The host describes mHUB as a facility in Chicago's West Loop that provides infrastructure for hard tech development and connects young companies with entrepreneurs, advisors, and investment. The host hopes other places replicate the model as hard tech startups become more common.
Brahmawar says he had already talked to a few people there and understood there were around 300 startups on site. He connects hubs like this to national competitiveness. As software, and now AI, become more important in manufacturing, he argues that countries trying to compete need more of these innovation hubs, and he says he hopes to contribute to this one.
The Misconception: "We Have to Modernize Before We Can Use AI"
The host asks for a belief that many manufacturers accept but Brahmawar thinks is wrong. His answer concerns technical debt. In his view, manufacturing has not been the fastest industry to adopt technology, so many companies carry significant tech debt and modernization work. Manufacturers, he says, often feel weighed down by that work. They assume they must spend heavy capital and operating expense to clear the debt before they can benefit from AI.
Brahmawar calls this a misunderstanding. He argues that AI makes it possible to address modernization and drive outcomes such as productivity, efficiency, and capacity at the same time. He says he sees "trailblazers" already doing both in parallel. The host restates this as manufacturers believing there is a long list of prerequisites before starting with AI, and Brahmawar agrees.
AI as a Capacity Story, Not a Cost-Cutting Story
His second misconception concerns jobs. Brahmawar cites roughly half a million open manufacturing jobs in the US today and says the number is expected to reach about 2 million by 2030. On that basis, he rejects the idea that AI is causing job losses in manufacturing. The real problem, he argues, is attracting enough talent at all. The role of AI is therefore to make each person "3 to 4x more powerful." In his framing, the conversation in manufacturing is about creating capacity, not reducing cost.
The host notes that the show began as an automation-focused podcast and that its audience broadly accepts automation and AI as job creators rather than job destroyers.
Champion AI and "Amplifying Human Potential"
Asked to explain the difference between a system of record and a system of action, Brahmawar first describes QAD Redzone's background. He says the company has been in manufacturing for more than 40 years, with deep domain knowledge in regulated industries such as automotive, food and beverage, medtech, and industrial. He describes its mission as using technology and innovation "to make these manufacturers champions." That is why the company's annual event is called Champions of Manufacturing.
About ten months before the recording, QAD Redzone introduced its AI capabilities under the name Champion AI. Brahmawar ties the name back to his first point: in manufacturing, AI is about amplifying human potential. Champion AI includes agents, assist capabilities, orchestration layers, and intelligence layers. He describes its scope as running "from the top floor to the shop floor," covering procurement, planning, execution, and the running of production lines. He says the company's value lies in connecting those areas.
The host draws a parallel to the show's description of itself as "a manufacturing podcast disguised as a leadership podcast." In the host's view, a job title does not decide who is a manufacturing leader; attitude does, whether in the office or on the shop floor.
Domain Expertise: Manufacturing as a "Show Me" World
Brahmawar argues that this philosophy starts with the people a company hires. He says he started his own career assembling engines at a Honda factory and that manufacturing is his passion. He says most people at QAD Redzone also come from manufacturing backgrounds. The reason, he explains, is that manufacturing "is a show me world," not a "tell me world." Without understanding the domain and its regulatory processes, a vendor cannot have an effective conversation with a manufacturer.
From the Honda Line to the Line Lead Agent
The host asks what frontline manufacturing was like when Brahmawar started. He jokes that nobody else in his family became an engineer; they were all fighter pilots. He describes a time with no mobile phones and no dashboards. Work relied on physical reports, Kaizen methodology for continuous improvement, and quality circles. He remembers more emphasis on engagement. He also remembers seeing firsthand the problems a production line faces: lost minutes, lost productivity, and quality issues.
He contrasts this with what is possible today. A line lead running a line can now know not only what the plan is but also use AI to anticipate how the line is likely to run and what the top two issues will be. QAD Redzone has a Champion AI agent called the line lead agent. It works with the human line lead to think through those likely issues and how they will affect OEE. Instead of spending time gathering and preparing data, which he remembers from his own early days, the line lead can focus on resolving issues and preventing disruptions on the line.
What Doesn't Change: Constraints and Outcomes
When the host asks how this history shaped his view of the future, Brahmawar names two things he thinks will not change. First, manufacturing is fundamentally about managing constraints: inbound, material storage, production line, people, and outbound. The goal is to manage them as well as possible to produce and ship goods and hit the metrics.
Second, manufacturing is about outcomes, not technology. He says no customer has ever asked him which LLM to use. What customers ask about is the effect on OEE and on-time delivery, how to improve margins, and how to increase capacity. Every innovation, he argues, should start from a manufacturer's problem and work backwards: understand the pain and find "a painkiller for that pain." In his words, manufacturers "are not interested in vitamins. They are interested in painkillers." He describes QAD Redzone's approach as being on the shop floor, understanding the environment, and working back to the innovation that is needed.
Systems of Record vs. Systems of Action: The Five Orders Example
The host notes that many people treat the ERP system of record as the "holy grail" of plant information, and asks how that mindset is shifting. Brahmawar says systems of record will always matter. Manufacturing is a predictable process that must be managed in a structured way. Systems of record document those processes and hold the transactions and data, so they serve as the single source of truth.
The limitation, he argues, is that systems of record have traditionally captured data, recorded transactions, and produced reports, which makes them reactive. Manufacturing, by contrast, runs 24/7. The question is how to turn that data, domain knowledge, and process into something proactive. That means predicting problems before they happen and guiding people on the shop floor, in the office, or in procurement on how to handle exceptions when they arise.
His example: five orders are due to ship on a given day, and there is a supply delivery problem. Today, he says, everyone on the shop floor "runs very hard" and moves mountains to ship all five, because on-time-in-full (OTIF) is the key metric. With AI, the system can instead check the contracts quickly. Suppose two of the orders carry contractual penalties; everything goes into delivering those two. The AI, looking across the whole business, might find inventory elsewhere that can fulfill a third order. For the remaining two, the system might recommend going back to the customer to renegotiate delivery dates, because it knows that customer has enough safety stock and the inbound delivery dates are reliable.
According to Brahmawar, this balances profitability, customer satisfaction, and execution, and it is what QAD Redzone calls manufacturing intelligence. The system of record remains necessary, but with Champion AI it becomes a system of action. People spend their time on exceptions and disruptions and make better decisions about them. The host summarizes the example as three distinct actions: prioritize the penalty-bearing orders, use a plan B from existing inventory, and decide which customers need a renegotiation conversation.
The "Messy Inbox" Agent for Procurement
For a second real-world example, Brahmawar turns to the inbound side. Once a buyer has secured a supplier and set up contracts, much of their time goes to monitoring whether materials arrive on time. Late materials can cause stockouts and disruption on the production line. He says it is not unusual for 40 to 50 percent of a buyer's time to go into managing communication with suppliers. Much of this happens outside the system, through emails, conversations, and messages about new schedules and dates.
QAD Redzone built an agent that works on what he calls "the messy inbox." It interprets this unstructured communication, combines it with structured data in the procurement system, and, by his account, saves the buyer 40 percent of their time. The buyer can then onboard more suppliers, add inbound capacity, and handle the few exceptions that really need attention. He presents this as increasing the buyer's capacity by roughly 40 percent.
Two Barriers to Adoption
Asked what manufacturers must overcome, Brahmawar names two things. The first is the myth that extensive upgrades and modernization are required before using AI. He tells customers that his company's job is "to meet you where you are, bring AI to you where you are," rather than making them go through six months to a year of transformation first. He frames this mainly as a mindset that needs education to change.
The second is focus. He advises starting small, with a particular use case, and driving outcomes on it. If a company launches six or seven initiatives at once, he says, they often end up as proofs of concept with no visible ROI. Disillusionment and setbacks follow. Adopting AI requires being "maniacally focused": pick the one or two pain points to address, define the specific use cases, and determine whether there are clear agents or orchestration layers that solve them.
Where Manufacturers Actually Are Today
The host asks whether manufacturers are ready without knowing it or whether they have homework to do. Brahmawar describes a typically diverse landscape: an ERP, some form of MES, quality and maintenance systems, other execution systems, and the ever-present Excel spreadsheets. Legacy and modern technologies run side by side, and people who understand how the old and new systems fit together bridge them.
He argues that AI can bridge this gap well because it can integrate with the landscape as it exists. The result is more productivity from existing systems. Where the conversation used to be about extensive digital transformation, he now tells clients they can keep modernizing at a pace that suits them while taking advantage of AI immediately.
A Catalog of Shop Floor and End-to-End Use Cases
The host recaps the scheduling and procurement examples and asks for more. Brahmawar says the company looks across the end-to-end manufacturing process and focuses on specific use cases. On the shop floor he lists:
- Quality agents that look at the quality process to identify issues well before they become quality problems.
- Maintenance agents that monitor equipment and predict where problems may arise in order to avoid downtime.
- Line lead agents, as described earlier.
- Supervisor agents that look across multiple lines, show how the floor is performing and why lines differ, and help raise OEE across all of them.
Beyond the shop floor, he points to procurement, outbound deliveries, end-to-end process execution, delivery schedules, and OTIF. He describes these as areas of specific, outcome-driven value.
What the Shift Means for People
On the human side, Brahmawar names three parts. The first is change management through hands-on experience: "theory never cuts it." The company creates demo environments and sandboxes where people on the shop floor and in offices can experiment with agents and see what outcomes they can drive.
The second is improving the quality of work. Humans in the loop focus on decision-making, problem-solving, and innovation. Agents, which "don't need to sleep" and can work 24/7, handle collecting and analyzing information and surfacing the top two things that deserve attention. He says QAD Redzone maps personas in manufacturing, such as planner, line supervisor, and plant manager, and builds agents that act as "champions" for each persona. Each person can then hand mundane tasks to their agent and spend more time on interesting, decision-oriented work.
The third is attracting talent. Returning to the half-million open jobs, Brahmawar mentions his two young sons and the challenge of convincing a new generation that manufacturing is a great career and not only building software companies. Chat-style interfaces to AI, he argues, match how younger people are used to working. A richer, more engaging technology environment could help draw them into manufacturing.
Field Evidence and the Line Lead Who Wouldn't Give Up Her iPad
The host asks for a story that shows AI improving someone's job satisfaction. Brahmawar says he has been at the company for about 16 months. When he joined, he set out to visit as many customers as possible and estimates he visited about 70 in his first 100 days, walking shop floors and talking to line workers, line leads, supervisors, and plant managers.
He says QAD Redzone is in 6,000 plants globally and measures their production runs. He cites company statistics: plants achieving on average 26% productivity (as he phrases it), and plants using the technology and AI achieving 80% engagement on the shop floor and 35% better talent retention. He presents these figures as evidence that innovation and AI can improve both productivity and worker satisfaction.
For the practical example, he describes Redzone's core idea. Historically, iPads and computers went to the office, while people on the shop floor worked with paper and pencil. Redzone reversed that by putting technology at the point of work. Workers have an iPad with all the information about a production run, including OEE, disruptions, and documentation, so they do not need paper or a separate workstation.
On one visit, a line lead showed him how her plant used Redzone to know what was happening as it happened. TVs around the floor used colors to show each line's output, which created some friendly competition between lines. What stood out to Brahmawar by the end of the tour was her pride in how enabled and independent she was. She did not need a supervisor to tell her anything; she could tell the supervisor how things stood. At the end, she told him: "If you take this away from me, I'm going to kill you." He takes the story as evidence that the right technology on the production line changes a worker's demeanor, excitement, and passion, and says the industry needs much more of it.
The host connects this to decision-making power. Without a system of action, frontline workers wait for management to spot a large problem and tell them to fix it. Brahmawar agrees, contrasting the old pattern of reports, huddles, and instructions with a line lead or operator who knows the issue and proactively plans to avoid disruption.
Building Courage in US Manufacturing: The Supervisor Lesson
The host asks how leaders across US manufacturing can find the courage to adopt this technology. Brahmawar says it starts with leadership: help leaders experience the technology, see its power, and understand what it would do to the environment they manage.
He shares a lesson from QAD Redzone's own experience. When the company built the line lead agent, it talked to several customers and focused on helping line leads, who understood it and were enthusiastic. But the company did not spend enough time with supervisors to help them see the potential. The lesson, he says, was to start with leadership. Leaders need to understand that democratizing information is important for increasing their capacity and OEE and for reducing disruptions and quality issues. The key is for leaders to connect AI with outcomes. "AI for the sake of AI. Nobody is interested," he says, adding that nobody in manufacturing is waiting for someone to give them AI. Once leaders make that connection, the second step is to bring agents and solutions that address specific use cases rather than generic tools.
Three Messages From 30 Customers
Brahmawar describes a session in May of that year where he brought together around 30 clients to discuss AI. He says he heard three messages.
First, they had no extra money but were carving out 5 to 7 percent of their opex IT budgets to invest in AI. ROI and fast turnaround were therefore important.
Second, they were struggling to attract AI talent, since most of it is being recruited by companies like Anthropic and OpenAI. So they did not want to build everything themselves or be handed a blank platform. They wanted real solutions that drive outcomes.
Third, which he calls the most important, trust is critical. These manufacturers run 24/7 in regulated industries with requirements for governance, auditability, and traceability. They want to "protect our alpha": strong assurance that models are not learning from their proprietary data and that everything is protected. As a result, they are inclined to work first with long-standing partners who understand their environments. Brahmawar says innovators must stay conscious of all three points.
Rebuilding Trust: Operation Bug Kill and a Focus on Adoption
The host asks how Brahmawar has built trust as a leader, reasoning that trust within a culture makes trust in new tools possible. Brahmawar is candid about the situation he inherited. When he started, the company had significant issues with product quality and response times. Customers told him they loved QAD Redzone and its 40-year history but that some things were challenging.
At the end of his first three weeks, he brought the whole company together and announced a month of 24/7 bug fixing, called "Operation Bug Kill." He acknowledges it sounds funny and says they even made T-shirts. The point was to live up to customers' trust and deliver the service, support, and quality they expected.
The second change was shifting the organization's focus to adoption. In his view, everyone in technology is "maniacally focused" on selling. But if customers don't get value from the technology, they are unlikely to trust the vendor with the next problem. He says the company now does a lot of coaching on the shop floor, through implementation and training on AI, Redzone, and its adaptive ERP capabilities. The aim is to show clients that the focus is on adoption and outcomes, not just on selling technology.
Closing Message: The Cost of Not Acting
For his one takeaway, Brahmawar calls this "a watershed moment for manufacturing" and an opportunity to leapfrog. The long-standing assumption has been that tech debt must be cleared and digital transformation completed before anything else. He argues that manufacturers can now do both in parallel. Some transformation and tech debt work will always remain, but much leaping can be done with AI focused on outcomes.
His direct message is that "the cost of not acting is far more now than the cost of acting." Sitting on the fence and observing is not an option, he says, if US manufacturing wants to be competitive and lead. He pairs this with reassurance for those who feel behind: no one should think they have already missed the moment, because "nobody's won the race." Speed will determine success, he says, so manufacturers should take a step now, break the inertia, and seize the moment.
So you start from a problem that the manufacturer is having and then you work backwards. Make sure you understand that pain and you figure out a painkiller for that pain. Our mission is to bring technology and bring innovation to make these manufacturers champions.
Your title is not what dictates whether you're a manufacturing leader or not. It's your attitude in the office, on the shop floor, in the operation, wherever it is that really sets that apart. It's about amplifying the human potential.
All right, we're talking about manufacturing champions today and how artificial intelligence and agents are empowering everyone from procurement to the front line to operations. We're going to be sitting down with Sanjay Brahmawar, CEO of QAD Redzone. Let's dive into today's conversation.
Well, Sanjay, let's dive on in.
All right. Welcome to Manufacturing Happy Hour.
Thank you very much. It's awesome. We've been talking for quite some time, Chris, and it's such a pleasure finally to meet you face to face in this lovely location. We're in mHUB. What is this place all about? Do you want to say a few words?
Yeah, it's funny, a lot of times I'll have my guest pick their favorite bar or brewery for the episode. But to an extent, I picked this venue today because as we're recording this, we're right in the thick of IMTS, and I'm like, I know a good spot not too far where we can have this discussion.
And yeah, mHUB, it's an awesome facility here in Chicago's West Loop, but more than that, it's a platform that really brings together the infrastructure for enabling hard tech development and helping young companies thrive. And then it also aligns them with networks of entrepreneurs, advisors, as well as capital and investment that goes along with that. So I think it's a very cool model, especially as hard tech startups become more ubiquitous, more frequent in this space, that I hope other spots are able to replicate as well. What have your first impressions been since you've been here?
I loved it. I've just had a chance to talk to a few people, and there's almost like 300 startups out here. It seems like there are some great ideas and a lot of room for innovation.
And it's exciting for us also because when we think software and we think about manufacturing and where software can be used in manufacturing, particularly now when you think about AI, I think these kinds of hubs are amazing. Because when you think about nations becoming more competitive in manufacturing, you need a lot more of these kinds of innovation hubs. So I love it. I hope to be able to contribute here.
Oh, yeah. Well, for an innovator like yourself, I think it's a perfect spot for the conversation. So we're going to get into the ways that you and your organization are innovating, but I want to get a little background, some personal questions out of the way first.
So one of the things we like to explore on the show is people's unique perspectives on the manufacturing space, because there are a lot of common themes that come up on the show, and rightfully so, because there are a lot of issues that are front of mind for manufacturing leaders across the country. But I'm curious, from your perspective, before we start talking artificial intelligence and systems of record and action, I want to hear something that is maybe a truth that a lot of manufacturers seem to accept, but you feel is wrong. Let's start there.
Well, I think one of the things is manufacturing has been, I guess, an industry where it hasn't been the fastest on adoption. And so therefore there is a certain amount of tech debt and a certain amount of modernization that has to be done within manufacturing. And so one of the things, I think a lot of manufacturers feel burdened with that modernization and are concerned that they would have to invest a lot of capital, a lot of opex, to be able to wipe out that tech debt, to be able to avail the innovation that's possible now with AI.
And I think that's a wrong understanding because today, with the help of AI, it's possible to do both. It's possible to be able to address modernization as well as take AI to be able to drive outcomes: more productivity, more efficiency, more capacity. And so both can be done in parallel, and I think that we do see some trailblazers where manufacturers are doing that.
So, if I were to rephrase what you were saying, they feel there are a lot of prerequisites before they can start going down the path of artificial intelligence. But what you've seen is that you can do both in parallel.
Absolutely, absolutely. And I think the other thing, of course, is that in manufacturing, in the US, for example, there's half a million open jobs today. So one of the misunderstandings, I think, is that AI is leading to job losses in manufacturing. Well, that's not true. If you look at all the indicators, actually the open positions in manufacturing are just growing. In fact, this is going to be roughly about 2 million by 2030.
So in manufacturing, we are struggling to attract and bring more talent in, and therefore the role of AI is not to replace the human being. The role of AI is to make that human being, that talent, three to four times more powerful. How do we create more capacity? So it's a capacity conversation in manufacturing, not a cost reduction conversation.
And we're going to get into that very soon because I would say one thing the Manufacturing Happy Hour audience generally agrees with across the board is we've been talking about automation on the show for a very long time. In fact, this podcast really started as an automation-focused podcast. And automation, artificial intelligence, realizing that these things are job creators versus taking away jobs. As you mentioned, there are more jobs available than we can fill right now.
Sure. So maybe let's go into one of your unique areas, your focus around artificial intelligence, which, if I were to summarize it, and in the spirit of having a conversation as if we're having a drink with one another, I'd love for you to describe this as if you're having a beer with someone. Define what a traditional system of record is and how where you're focused is on a system of action.
First, I don't want to lose that point about the beer, okay? I thought it's happy hour. So I thought, I'm sure after this we probably will be, and we should celebrate. Congratulations on your 300. That's quite amazing.
To answer your question, I think when you think about manufacturing and our thought process there, QAD Redzone, we've been in the manufacturing space for the last 40-plus years. So we have a lot of heritage and a lot of deep understanding of the manufacturing domain, particularly when you think about regulated industries like automotive, food and beverage, medtech, industrial. So we understand that space very well.
And so when I think, what is the mission of QAD Redzone? Our mission is to bring technology and bring innovation to make these manufacturers champions. So that's why our annual event, we call it Champions of Manufacturing, because everybody in my company is fixated with how do we make these manufacturers champions.
Yeah.
Now the question is, how do you do that, right? And the way we have gone about bringing—so roughly about 10 months ago, we introduced our AI capabilities to the world, and we called it Champion AI.
And the reason we call it Champion AI is because of the first topic that I addressed, which is, in manufacturing, it's about amplifying the human potential.
So that's the whole principle of Champion AI, where we are building agents, we're building assist capabilities, we are building orchestration layers, intelligence layers, all with the objective of being able to amplify the human potential. And our intelligence layer, Champion AI, is spanning from the top floor to the shop floor. So when you think about all the operations, from procurement to planning to execution to then really running the lines on the shop floor, and connecting that, that's where we believe our value is.
Yeah, what I like about the way you describe it with Champion AI, as it's called, on this show we describe it as this is a manufacturing podcast disguised as a leadership podcast, right? So we're very much—I use the word champion, I use the term leader, because we're all focused on how do we amplify the people in this industry. And you talk about champions from the front line to the office. I say the same thing. Your title is not what dictates whether you're a manufacturing leader or not. It's your attitude in the office, on the shop floor, in the operation, wherever it is that really sets that apart.
And it starts from, I guess, the talent and the people that you have in your company. Like my own background, I started my career working at a Honda factory assembling engines. So manufacturing is my passion. I love manufacturing. That's what I love doing. And if you interact with the people in our company, most of them come from a manufacturing background, because when we interact with manufacturers, we do find manufacturing is a show-me world. It is not a tell-me world, right?
So when you don't understand the domain, you don't understand the regulatory processes, etc., you're not able to have an effective conversation with a manufacturer. So I think that's also very important in the talent that you bring into your company. To be able to really have that connection with a manufacturer, you need to understand the domain. That domain expertise is very important.
Yeah. You said something that I was going to ask you about that I want to go into a little more detail on, and that is your background on how you started your career assembling engines at Honda. Paint the picture of what that was like back in the day, what manufacturing on the front line looked like back then.
Well, first of all, nobody in my family ever became an engineer. So everybody is a fighter pilot. I'm the odd one out who went to engineering. But engineering at that time was very interesting, right? There were no mobile phones. There were no dashboards and fancy—everything was pretty much nuts and bolts, and literally reports that you used to be able to understand. You had Kaizen methodology to be able to really do continuous improvement, quality circles. And so I think there was a lot more emphasis on the engagement part about working in a manufacturing environment.
So in my early days, I noticed the challenges that you have on a production line when you lose minutes, when you lose productivity, when you have quality issues. All of those challenges, you see that firsthand when you're on the shop floor.
Today I think it's a different environment, right? We have such great opportunity today with AI and technology to be able to not just plan, but then to be able to be proactive in the execution. Like today, when there is a line lead who's standing on a line and running that line, they don't only know what the plan was, they're actually able to use AI to try to figure out how is that production line going to run? What are the top two issues I'm going to experience?
We have an agent called the line lead agent, Champion AI agent. And that agent is working with the human line lead to help that person think through what are the top two issues you're going to have? How is that going to impact your OEE? So instead of this individual spending a lot of time collecting data and preparing, etc., which is what I remember from my days, now that line lead can actually focus on the issues, focus on making sure that that production line has no disruption. And that's a big change from the time when I started my career.
Yeah. Do you reflect on that often, right, where you think of what you were focused on? Because you were highlighting, right, what happens when you lose production, when you lose quality, right? Has that ultimately shaped how you think about what the future of manufacturing should look like, where the individual on the front line is no longer firefighting necessarily in that same way, but really maybe proactively looking at how they can do what they're doing even better? Would you say that's fair, or how would you describe how it's shaped your perspectives moving forward?
Yeah. Well, look, I think some things will not change in manufacturing. If you think about manufacturing, what is manufacturing? Manufacturing is actually managing constraints, right, to the best possible. So you always have inbound constraints, material storage constraints, production line constraints, people constraints, outbound constraints. So it's all about managing those constraints in the best possible way to be able to produce your goods, ship your goods, and get your measures.
So that's one thing that doesn't change. The second thing is, in manufacturing, it's all about outcomes. It's not so much about the fancy technologies or what you're using. So I don't find any of my customers asking me, Sanjay, which LLM should I use? They're interested in how does it impact my OEE? How does it impact my OTD? How can I improve my margins? How do I increase my capacity? So the customers are focused on outcomes.
And I think everything and every innovation that we bring into manufacturing environments has to have that thought process. So you start from a problem that the manufacturer is having and then you work backwards. Make sure you understand that pain and you figure out a painkiller for that pain.
Yeah.
The manufacturers are not interested in vitamins. They are interested in painkillers. And so that's our philosophy. That's the way we work in QAD Redzone, really. Be on the shop floor, understand the manufacturing environment, and then work backwards into what is the innovation you need to bring in.
So we've been in a world of manufacturing for a while where, to one of the topics we talked about earlier, I think everyone looks at their system of record, ERP, if you will, they look at that as a holy grail of information inside of the facility. I'd be curious to hear what the manufacturing world was like maybe even just five years or so ago, where people were more in that mindset, and how you've been helping it move to more of an action-oriented mindset, a system of action. Paint the picture of what it was like, and then maybe, whether it's through an example or a story, paint a picture of what it can look like today.
Yeah, I think systems of record are always going to be important. Why? Because, as I said, manufacturing is a predictable process and it needs to be managed in a structured way. And that's how you get an output, and then you can manage your outcomes. So those processes are documented in your systems of record, and then you have your transactions and your data in your system of record. So that's your one source of truth, and you have your data in there.
But systems of record traditionally have been ones that are capturing data, capturing transactions, reporting. It's very reactive. Whereas manufacturing is not reactive, right? Manufacturing is 24/7, things that are happening on the shop floor. And so how do we take that data and that domain understanding, those processes, and how do we convert that into a proactive? So how do we predict? How do we…
Understand challenges before they happen? How do we also provide the leaders on the shop floor or in the offices or procurement, for example, what to do with the exceptions and the problems when they hit the problems?
So I can give you an example. Let's assume there are five orders to be shipped on a particular day. Okay. If you have a problem in supply delivery, do you know what happens on a manufacturing shop floor? Everybody runs very hard and they move mountains to ship those five orders because OTIF is very important—on time in full—and that's the metric, and everybody's trying to meet that.
I lived in that world in the automation space for a long time.
Now with the help of AI, what we can do is to use manufacturing intelligence to be able to figure out, okay, we have supplier issues on these five orders. We can check the contracts very quickly to understand which of those orders have contractual penalties. Two of the top two ones have contractual. So everything has to be done to make sure that those two are delivered.
It could be possible that there's inventory somewhere else that can be used for a third order that the system understands because AI is looking across the board. And then the balance two, the system says, well, we can go back to the customer and we can renegotiate the delivery date because we know that customer has enough safety stock and we are assured about the inbound delivery dates. And that way you can strike a balance between your profitability as well as your customer satisfaction and your execution.
That is what we call manufacturing intelligence. So that's the way it changes to become a system of action rather than a system of record.
Yeah, and that's a very concrete action I think a lot of people can visualize, right? I love the five orders example because you talk first, like, how do we prioritize, right? Where are we going to get penalized? Those two move to the top of the list, right? Where's one where there's a plan B that exists, right? A different part in inventory that can help complete that order and get that out. And then again, another action: who do we have to have conversations with to say we need to renegotiate when this is getting shipped out? So, perfect example of the different actions that can occur and when they can occur when you have artificial intelligence to help you prioritize and get visibility to that.
And so that is the shift, right? Moving from a system—not to say that the system of record is not needed. System of record is needed, but now with the help of Champion AI, we can actually convert that to a system of action where the human being is in fact spending their time on managing those exceptions, those challenges and those disruptions. And hence their capacity is utilized for those, and to do that in the most optimal way, make the best and the most optimal decisions.
So I have a couple questions based on that, then. I'm curious where we've seen this play out in real time so far, if there's another specific example you could share. But also I'm curious to hear what manufacturers need to overcome to be able to implement something like that as well. So, can you give me another example of where this has played out in the real world?
Yeah, let's take the inbound side, right? So if you think about it, all manufacturers have to procure either direct materials or indirect materials. Now a buyer spends a lot of time obviously securing a supplier, setting up the contracts, etc. But then after that, a big portion of their time is spent on trying to monitor and understand how the deliveries are actually executed. Are the materials coming in on time? Because if the materials don't come in on time, we're going to have delays, and if there's issues, you could have a stockout, you could have issues and disruption on the production line.
It's not unusual that almost 40 to 50% of the buyer time is actually in trying to manage that communication with the supplier. That doesn't happen through the system. It happens through emails and conversations and messages, etc., and then new schedules, new dates, etc. So the buyer has to keep on top of it. We built an agent that effectively works on what's called the messy inbox.
It in effect understands all of that unstructured communication, combines that with the structured data in your procurement system, and then literally saves the buyer 40% of their time. So now the buyer can actually spend their time on literally onboarding more suppliers, creating more capacity in terms of inbound, and also managing those very few exceptions that they really need to manage. So you suddenly increase the capacity of that buyer by 40% or so. And that's a great example of where adoption of an agent, adoption of Champion AI, is creating a significant difference to the way of working for that individual.
Now to your point about what's the barrier to adoption. So again, two things. One, there is a myth, or at least there's a misunderstanding, that you have to do a lot of upgrades and modernization to be able to use AI. Now that's not true. It is possible to use AI with the existing landscape that manufacturers have. I often tell my customers, our job is to meet you where you are, bring AI to you where you are, not to have you go through six months to a year of transformation for you to be able to use AI. So that's one thing, and that's basically a mindset, and we have to make sure that everybody gets educated around that and breaks that mindset or changes that mindset.
And then the second thing is about starting small, working on a particular use case and driving outcomes on that use case. And I think that's also really important because otherwise, if you start with six or seven initiatives, often they end up with just proofs of concept, and you're not able to really create outcomes. The ROI is not visible, and then there is disillusionment, and I guess in some ways it's a bit of a setback, right? So adopting AI, it's also very important to be maniacally focused and prioritize what is the one or two pain points that you want to go after. What are the specific use cases, and are there clear agents or orchestration layers that can really help you with those problems? So I think those are the two things that are really relevant for manufacturers today.
I have a general question in that case. You mentioned you meet manufacturers where they are and in turn help identify the use cases of where they can adopt. So generally, where are manufacturers? Where are you seeing, where are they today? Are they in a spot where they're actually ready, they just don't know it? Are you seeing they're in a spot where there's a little bit of homework to do? What do you typically see, Sanjay?
Look, most manufacturers have quite a, let's say, quite a diverse landscape of applications, right? They all have an ERP. They're all using some form of an MES. They use a QM CMS. They might have some other execution systems. So it is a diverse landscape, and it's very important that they're able to bring information together from all of these different applications or tools that they're using. And then there might be Excel spreadsheets and other kind of tools. They're always there, right?
So most manufacturers are in that kind of a situation. They have legacy, and they have some modern technologies, and these legacy and modern technologies are working together. And they have people that are spanning across that because they understand how older systems work with how newer systems need to work. And I think AI can actually bridge the gap quite well because it can allow them to have that integration with their landscape as it exists today.
So now you're using AI to get a lot more productivity out of your existing systems landscape, which I think is the big advantage because in the past there's always been the conversation, you have to do a lot of digital transformation. My conversation with my clients is, no, you don't need to do a lot of digital transformation. You have your current landscape. You can continue progressing, modernizing that at a pace that is relevant for you, but you can immediately, right now, take advantage of AI. So that gives you the benefit and gives you the productivity uplift.
Let's talk a little bit more about use cases as well, because the stories we've been talking about so far, first example was really around scheduling, right? When things aren't necessarily going to plan. You also talked about getting a 40% increase in capacity for someone in procurement when they're able to work on the things that are the least transactional and most important to them doing their job on a more strategic, prioritized basis. What are other use cases you're finding these days?
I think you have to think about the end-to-end manufacturing process, and we identify areas where we specifically focus on a use case. So, for example, on the shop floor, you have quality agents that are specifically looking at the quality process and being able to really help identify issues way before it becomes a quality issue. You have maintenance agents that are able to monitor and plot and predict areas where there might be issues within, for example, to avoid downtime, etc. There are, like I gave you an example, line lead agents that are working specifically on that.
There are agents that are for supervisors who can look across multiple lines and see how their production floor is operating. What are the differences between the lines? Why are those differences, and how can they make sure that all of them are stepping up in terms of their OEE? So those are areas and use cases on the shop floor. And then when you think around the entire manufacturing environment, the procurement, the outbound deliveries, the end-to-end process execution, looking at a balance between IITa and output, delivery schedules, OTIF, those kind of areas are very specific and obviously valuable to manufacturers from an outcome perspective.
Yeah. I want us to talk specifically about people for a while, right? And they've been a part of this entire conversation so far, but I'm curious, what does this shift mean for people on the front line and also in the back office, if you will? I'd love to understand that because if we are getting these 40% increases in capacity like we talked about from a procurement standpoint, what does this mean for them as folks that are powering this operation, and then what does this mean for the overall business?
Yeah, I think there is a certain amount of change management, of course, to be done, and I think part of it is education and hands-on experience, right? Theory never cuts it. It's about creating demo environments and creating sandboxes where people on the shop floor or within the office areas can actually experiment with agents and understand what outcomes they can drive. So that's one part of it.
But I think the second part of it is improving the quality of work for the individuals in manufacturing so that the humans in the loop can actually focus on the more value-add areas: the decision-making, the problem-solving, the innovation side, and leave the agents to do part of the automation around collecting the information or analyzing the information to bring it in, in terms of what are the top two things that we need to spend our time on. All of those kind of activities can be done by agents, right? Agents don't need to sleep. They can do that 24/7.
So what we do is we map all the personas in manufacturing, and we create these agents that become champions for those personas. So you could have a planner, you could have a line supervisor, you could have a plant manager. Each of these people have their own agents, and they can ask the agents to do some of the mundane tasks, which then allow them to focus on the more interesting and, let's say, the more value and decision-making jobs. So that's one aspect of more engagement, more excitement in the work that you do. And then I think the third part, which is very relevant, is attracting talent into manufacturing.
We talked about the deficit, half a million open jobs. How do we bring the kids? I have two young boys, and how do I excite them about, hey, manufacturing is a great career, join manufacturing. Don't all think about going and building software companies, but manufacturing is a great space. Well, when you create technology with AI and the engagement is quite a rich engagement, the interface is like a chat interface, it's like a claw interface. Those kind of excite the younger generation. That kind of ways of working are what they're used to. And so when we can create that kind of an environment, we're going to be able to excite some of the younger generation, bring them into manufacturing. So I think that's the third part that I also feel is quite important when you think about the people aspect.
Yeah. I was going to ask you about the next generation, but I think it ties into what we were just talking about with the different type of agents that exist that serve as champions for the people and roles and quality. You talked about your line lead agent, talked about procurement. We've covered quite a range in that regard. And I'm curious, can you share a story or an example that illustrates where, by having this type of artificial intelligence, these agents, where that has increased someone's job satisfaction, the type of work they're able to do that's more fulfilling? I'd love to hear an example about that because I do think that ties directly into what you're talking about. Hey, if we're going to get people excited about manufacturing, particularly the next generation, your young boys, that's what it's going to take to make it happen. So, do you have a story that illustrates that?
Well, look, I've been in the company now almost 16 months. And so one of the things when I joined QAD Redzone, I did was really get out and try to visit as many customers as I can. I think I visited 70-odd customers in my first 100 days.
Nice.
And the whole point was walking the shop floor. And that's where you learn, right? In manufacturing, when you walk the shop floor, you interact with the line leads or, let's say, the workers on the production line or the supervisor or the plant manager, and you start understanding what are the challenges that they have.
And so we are in 6,000 plants globally, and we measure the production runs of these plants. And so on an average, we have statistics that show plants are achieving on an average 26% productivity. The plants that are using the technology and AI are achieving 80% engagement on the shop floor and 35% better retention of talent. So there is enough evidence to show that if you use innovation and you use AI, you can improve your productivity as well as your worker satisfaction, let's say.
Now to your point about, give me a practical example. So you know that we have Redzone, which is basically software that really empowers the front line. Historically all the technology, the iPads, the computers, were all given to the top floor, right? To the people in the offices, and they were the ones working with technology where
The people on the shop floor really had paper and pencil.
Now we flipped that over. We said, no, let's take technology to the point of work. That worker has an iPad which has all the information about that production run, OEE, disruptions, everything right at their fingertips. All the documentation, everything. They don't have to go and spend any time on paper, pencil, or go to any other workstation. They have it there.
And I went on a shop floor, and this lady who was this line lead, she was taking me around and showing me how they run and how they use Redzone to know exactly what's happening when it's happening. They have these TVs all around showing, with colors, exactly what's the output of a particular line. And they have a bit of competition going on because several lines, and they can see all the production lines.
At the end of that entire tour, one thing that was so evident was the pride she had in how enabled and how independent she was. Right? She didn't need the supervisor to tell her anything. She knew, and she could tell the supervisor exactly how things stand. At the end of that, she told me, "If you take this away from me, I'm going to kill you."
It kind of showed me, when you bring the right technology and innovation to somebody who's working on the production line, it changes. It completely changes their demeanor, completely changes their excitement and their passion about what they're doing. And I think we need to do a lot more of that.
Yes, her blunt response is the exact type of candid story we're looking for at Manufacturing Happy Hour. One thing that sticks out about that is you're putting the decision-making power into the hands of the folks on the front line. Because in the past, if you don't have a system of action, you need to wait for someone, usually in upper management, to walk out on the plant floor to say, "Hey, there's this big macro problem we've seen, and you, Mr., Mrs. Individual on the front line, now you need to go solve it." Right? That's not a fun situation to be in because all of a sudden you're under—
You're being told, and so there's a report. You get there, you try to do a huddle, you're being told what to do, etc. Now suddenly you have a situation where that line lead, that operator, knows exactly what the issue is, and they are proactively planning the actions that are needed to avoid the disruption. How empowering is that? And I think that's what AI can do.
Yeah, that's the exact type of scenario, exact type of situation where artificial intelligence makes a huge difference. Like you were saying, you were just going through all the macro examples, the macro stats, I should say, of how this solution, how artificial intelligence has helped improve worker satisfaction, retention, etc. So, excellent story as we get to the end of our conversation.
But I do have a couple more questions, and one of those is around US manufacturing specifically. I'm very curious to hear your perspectives on this, and I'll probably add some of mine as well. How do we get folks to find the courage to take on this type of new technology, to bring it into their process? Right? You said much, much earlier on in the conversation that you don't necessarily need every single prerequisite to make it happen. Right? There are things you can be doing today. So where do, or how do, leaders find more of the courage to take on some of this across the US?
Yeah, I think, one, it starts with leadership and making sure that you can help the leaders experience the technology, see the power, and then help them understand what it would do to the overall environment that they manage. So one of our own lessons, for example, was we created an agent for the line lead, and we went about and talked to several customers, and we were trying to help the line leads. Now the line leads got it. They were really excited, but we didn't spend enough time talking to the supervisors and helping the supervisors understand the potential.
And so that was a lesson that we learned. We said you have to start with the leadership to help the leadership understand that this democratization of information, knowledge is so important for them to increase their capacity, for them to increase their OEE, for them to reduce their problems and disruptions or quality issues. And when they can make that association, when those leaders can make association between the innovation, the AI, and outcomes, which is what I think is very, very important in manufacturing. AI for the sake of AI, nobody is interested, right? Nobody in manufacturing is sitting there waiting for somebody to give them AI. Nobody. They only want it if it impacts their outcomes, right?
So therefore, I think there's a very important job to do, which is to go talk to the leaders so that they understand how AI can help them drive better outcomes. And then with that, the second part, of course, is to bring agents or solutions that really hit the use cases. They're not generic because, think about it, we have limited talent and capacity in manufacturing.
I brought something like 30 of our clients in May this year. We brought them together to do a session on AI, and I got three messages from them. Message number one was, "Hey, Sanjay, we don't have any extra money, okay? But we're carving out 5 to 7% of our OPEX IT budgets because we want to invest in AI, and we want to understand what we can benefit." So they are carving out some money to really drive, use innovation, but ROI is important. Fast turnaround is very important.
The second thing was, "We're struggling to attract AI talent." It's not easy to attract AI talent into manufacturing because most of the AI talent is being recruited by Anthropic, OpenAI, you name it. So that's the second thing. So that means that we don't want to build everything ourselves. Don't give us a blank platform, and then we have to start building. We need real solutions that drive outcomes. So that was the second message.
And then the third, I think, was the most important message, which was trust is very important in manufacturing. We run 24/7. We are in regulated industries. We have governance, auditability, traceability, and we want to protect our alpha. So we want a very high level of assurance that all kinds of models are not learning from our alpha and our data, and everything is protected. So trust, governance. Therefore, we are first and foremost inclined to work with our partners who we've been doing business with for quite some time and understand our environments. And so I think these are the kind of messages we got from our customers, and I think those are things that are very relevant in manufacturing. And as we bring innovation, we have to be very conscious about those aspects.
Yeah, one question I have for you as a leader as well, because I like how you highlighted carving out OPEX, struggling to attract AI talent, and then talking about trust as well. This is talking about trust in a general standpoint because I think that is one of the barriers people have around AI. As a leader in the industrial technology space, how have you been effective at establishing trust? Because I feel like when there's trust in general within a culture, that allows there to be trust in new technology and new tools. So I'm curious how you've managed that as a leader of your own organization, maybe how you've seen other leaders managing that.
Yeah. Well, I can tell you, when I started, one of the challenges we had is we had quite a lot of issues around product quality and response times. And so when I was listening to my customers, they told me, "Look, we love QAD Redzone. We love that you have 40 years, but some things are challenging." And so I brought the entire company together literally at the end of the first three weeks, and I said, "Hey, for the next one month, we're going to do 24/7 bug fix. We call it Operation Bug Kill."
Okay? And it obviously sounds really funny, and we made T-shirts, etc. But the point really was to make sure that we can stick up and deliver to the trust that our customers have in us, and make sure that our customers get the service, the support, the quality that they expect from us.
And then the second thing is, within the organization, shifting the focus on adoption, because in technology the biggest point is adoption. Selling technology, everybody is maniacally focused on that. But the point is, if the customer is not getting value out of that technology, the likelihood of you going and asking for their trust in solving another problem is very low.
So getting the organization very focused on adoption, whether it's through implementation, whether it's through coaching. We do a lot of coaching on the shop floor into how to use AI and our Redzone and our adaptive ERP capabilities. Through that, building that trust with your client that your focus is on adoption and outcomes and not just on selling the technology. And I think that is also, we have been working on that in our organization to make sure we stand up to that trust that our clients have with us.
Yeah. Excellent examples. Last question I have for you: what would be the one thing you want folks to take away from this conversation if you had to pick just one? Or maybe we've been talking about systems of action. What's maybe one action you'd love to see them take, or they can take, to help move the needle forward?
Yeah, I think, first of all, the one message I would give is that, look, this is a watershed moment for manufacturing. I think this is an absolutely awesome opportunity for manufacturing to leapfrog. And what I mean by that is, there's always been this challenge around, there's a lot of digital transformation to be done, and we need to wipe out all this tech debt before we can really... And I think this is a big opportunity now for manufacturing to do both things in parallel. There will always be some amount of transformation and some tech debt that has to be taken, but a lot of leaping can be done with the help of the AI, which really is focused on driving outcomes. So my message to all manufacturers is, look, the cost of not acting is far more now than the cost of acting.
So this is not a matter of sitting there and waiting and sitting on the fence and observing. If US manufacturing wants to be competitive, if US manufacturing wants to be the leaders, they have to act and take this opportunity. And that's my very direct message to all the manufacturers: don't miss this moment.
And the second thing I keep telling everybody is, don't think you've missed the moment also, right? Don't think you're so far behind that you're lost. No, nobody's won the race. Okay? Speed, of course, is going to determine your success. So take a step. You move forward now. Break that inertia and avail this moment. That effectively is my message.
Yeah. Great summary, great conversation from start to finish. Appreciate you taking us through specific examples, not only from your own career, but things you're seeing across the hundreds of manufacturers that you've been able to visit during this time. Sanjay, thanks so much for being on Manufacturing Happy Hour.
Thank you so much. I really, really enjoyed the conversation. I'm looking forward to the beer now.
Yes, absolutely. Next time I'm bringing beers to the interview.
Awesome.
Cheers. Pro.
Exactly.
Hey, thanks for tuning in. Thank you to Sanjay for jumping on this episode. If you would like to connect with Sanjay, learn more about QAD Redzone, head over to the show notes page at manufacturinghappyhour.com. Make sure you're subscribed to Manufacturing Happy Hour wherever you get your podcasts. And with that, stay innovative, stay thirsty, and we will catch you again next time.
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