Joining us this week on the podcast is CEO and Founder of Pyze, Inc. (@PyzeInc) Prabhjot Singh (@psinghSF.) Here with futurist Trond Undheim to talk all about business process intelligence, the workflows in manufacturing and logistics, and the future outlook for low-code in industrial applications, the episode is 76 and the topic is: "Low on Code, High on Process."
Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. Technology is changing rapidly. What’s next in the digital factory? Who is leading the change? What are the key skills to learn? How to stay up to date on manufacturing and industry 4.0? Augmented is a podcast for industrial leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (@trondau), and presented by Tulip, the frontline operations platform.
Trond's takeaway: Business process intelligence is the "why" of technology. Because smoother operations are where the value of technology is realized. The future outlook for low code in industrial operations is bright because it has the potential to streamline workflows in manufacturing and logistics. However, it is important to keep in mind that to leverage automation to do better decisions, and not just to squeeze out more with less--that starts with keeping in mind what the real problem is and steering with that in mind. If you don't know, figure out the problem and then invest in the process and if technology gets you there, invest.
Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. And if you liked this episode, you might also like episode 73, "The Challenge of Front Line Operations." Hopefully, you'll find something awesome in these or in other episodes.
And if so, do let us know by messaging us because we would love to share your thoughts with other listeners. The Augmented podcast is created in association with Tulip, the connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location. Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at Tulip.co. Please share this show with colleagues who care about where industrial tech is heading.
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See you next time. Augmented--industrial conversations that matter.
[00:00:00] Trond Arne Undheim: Welcome to another episode of the Augmented podcast. Augmented brings industrial conversations that matter to manufacturing. Our vision is a world where Augmented lean management of technology will restore the agility of frontline workers. We serve an audience of executives, industry leaders, investors, founders, educators, technologists, process engineers, and shop floor operators, a long list across the emerging field of frontline operations. In episode 76 of the podcast, the topic is low and code and high on process. And our guest is Prabhjot Singh, CEO, and founder of Pyze. In this conversation, we talk about business process intelligence and workflows in manufacturing and logistics and the future outlook for low-code in industrial applicant.
[00:00:48] Trond Arne Undheim: Augmented is a podcast with industrial conversations that matter hosted by futurists, thrown on a Undheim, and presented by Tila. Don't be fooled by industry [00:01:00] 4.0 hype, which overpromises and under delivers instead discover how the best leaders in manufacturing and life science really work to be lean, achieve efficiencies, and empower their workers.
[00:01:11] Trond Arne Undheim: In Augmented Lean, a human-centric framework for managing frontline operations, serial startup founder, Dr. Natan Linder, co-founder of Formlabs and CEO and co-founder of Tulip.co. And futurist, podcaster Trond Arne Undheim provide evidence of how the best digital operators make use of technology to augment workers and not simply to randomly automate because it seems like a good idea.
[00:01:38] Trond Arne Undheim: The book is available for pre-order in a bookstore near you, and is published by Wiley. Pre-order your copy now, and be ready for this new management paradigm. Augmented lean is a practical playbook for augmenting your workforce with the latest cyber-physical adaptations to digital technologies.
Hey, Prabhjot, how are you?
[00:01:58] Prabhjot Singh: I'm doing well.
[00:02:00] Trond Arne Undheim: I'm doing great. I thought we would have a bit of a conversation on code and business processes.
[00:02:07] Prabhjot Singh: I'm looking forward to it. Yeah.
[00:02:10] Trond Arne Undheim: So it turns out you and I actually, you almost know each other. That was a funny coincidence.
[00:02:16] Prabhjot Singh: Yeah, whenever I small world, I thought you knew some random guy in Boston.
[00:02:23] Trond Arne Undheim: The reason I was mentioning that is it ties in a little bit to your journey.
[00:02:26] Trond Arne Undheim: I was curious. So you spent a bunch of time, on the east coast here. You're a bachelor of computer science from BU in Boston. And then you spent some time in New York at city group. After that, I understand you, you did a bunch of marketing roles. But then you turn to founding companies and now you have Pyze.
[00:02:43] Trond Arne Undheim: Yes. So tell me a little bit about your journey, your entrepreneurial journey and what you've learned, and how you ended up on this track?
[00:02:52] Prabhjot Singh: Yeah, it's been a really fun journey, certainly with its ups and downs as all entrepreneurial journeys go. [00:03:00] But I've learned a ton along the way.
[00:03:01] Prabhjot Singh: And I was telling our engineering team maybe a few months ago, look, everything you ever want to do takes longer than you expect. So plan. It's one key takeaway, right? From my journey. But like I said I did computer systems engineering in college. I joined Citi group right out of school as part of their management associate program.
[00:03:23] Prabhjot Singh: So John Reed, who was them, the CSA group had come out of that program. So it was a darling program within the. And we got a ton of training on banking on management, and then they rotate to your different parts of the bank. And then you'd come out on our management track. So I did that for about a year and I got a call from this little startup in the bay area that had just raised their first round.
[00:03:50] Prabhjot Singh: I called wily technology. He was 12 people crying, almost no revenue. I'd never been to California before they offered to fly me out for an [00:04:00] interview. So I was like, sure, let's do it. And I never looked back. And Wiley was a wonderful experience because we actually created a new market that didn't exist.
[00:04:09] Prabhjot Singh: If you remember, in the early days of the internet, you'd be trying to pay a bill or sign up for a card or rent a car and the site would just go. But no one knew why, because there are these monolithic java.net applications running in the backend and no one could figure out what was wrong. And you'd have these massive conference calls with DBHs and developers and, the whole world really just pointing fingers at each other in terms of, it was the code, it's the database, it's whatever, it's the webserver. So wildly figured out how to look inside. These monolithic applications up the bits and bytes.
[00:04:45] Prabhjot Singh: So what was happening, so we could figure out they're going to, Hey, it's an EJB or some memory leak or whatever, and recreated that market. And it led to other large companies like new Relic captain dynamics, and this whole flurry of [00:05:00] the APM space coming into being. And I got to ride that journey from being employee number 14, to reductive about 60 million in revenue.
[00:05:11] Prabhjot Singh: And that we sold the company to computer associates, where I took over as VP of marketing. And then we grew that to about 250 million in revenue before I left. So it was a fantastic journey. I learned a lot and, we did a lot of things, a lot of things we could done better. And with that experience, I said, okay let's try the entrepreneurial thing.
[00:05:29] Prabhjot Singh: So you had guns.
[00:05:30] Trond Arne Undheim: It's interesting what you said about not knowing what's going on inside of an application because incidentally, I guess your business now is tied to this new idea about, I guess it's both, it's important to know what's behind there and that somebody knows it, but it may not be important that everybody knows everything about the applications, right?
[00:05:48] Trond Arne Undheim: So there's this idea, that you're building on about how to build a business process in a more advanced way and use it to do. But the question is, of course always, how skilled do you have to be in any [00:06:00] given role in order to make use of that technology? And I wanted to ask you just a little bit more broadly about that issue.
[00:06:06] Trond Arne Undheim: You've got a computer science degree, but from early on, from what you're telling me you got the clear sense of at least in finance, right? It has to match. To financial metrics. Otherwise, the technology is serving a military purpose here. So is that kind of the mindset that you were drilled on and is that why you then turn to business process intelligence as opposed to diving into technology for its own sake?
[00:06:31] Trond Arne Undheim: Which I think it seems a lot of startups and certainly a lot of engineers, it's very tempting to sell a lot on the technology itself, but you've made a fairly explicit choice to focus on the process of. That's right.
[00:06:44] Prabhjot Singh: Yeah. Even in the Wiley days I always imagined if the technology that we had could be leveraged, to look at the business process because people always execute initiate projects, technology projects. To achieve some [00:07:00] business goal. You would hope at least, right? Otherwise you've got technology in search of a problem, whether it's a startup or a project from enterprise, and that's a bad place to be.
[00:07:10] Prabhjot Singh: When you are initiating any type of technology project, you typically are trying to do something faster, better, cheaper. There's some business goals associated with w with the project. So you have to be able to tie what you're doing to the business impact. And with pies, what we do is essentially do a MRI of the business process.
[00:07:37] Prabhjot Singh: And then I'm borrowing that verbiage from my friend Habiba tech Mahindra, who said, Hey, in the olden days, we'd inspect the patient and try to figure out what to prescribe them work with ties. We can actually do. And figure out, okay, we need to operate on the left kidney.
[00:07:52] Trond Arne Undheim: It's pretty useful, right?
[00:07:53] Trond Arne Undheim: To have more of a scientific, overview of what's going on as opposed to just making random [00:08:00] recommendations. So how does that work? Exactly. So you work with mostly larger businesses. I'm assuming to automate their, some of their business functions. How does that come about? Is it typically a client that knows they have a problem and then you apply diagnostics to.
[00:08:14] Trond Arne Undheim: Some of their business lines or is it the opposite way around that? They hope that they can improve something, but they don't really realize that they have a problem anywhere in the system or is it a bit.
[00:08:25] Prabhjot Singh: It's oftentimes a bit of both, if you look at kind of big enterprises, they're used to engaging with the large consultant companies that maybe come in once every six months, once a year, and do some sort of a strategy planning exercise, or they might initiate some sort of a process improvement exercise. And those engagements are very expensive. They are time consuming and you can't really be that thorough because of the way that in which they are executed, typically there's qualitative interviews that happen, they're spot checking of some transactions [00:09:00] or process flows.
[00:09:01] Prabhjot Singh: But with technology today, we can literally look at a hundred percent of all transactions that flow through a system. So if I'm looking at, let's say order management. We can look at a hundred percent of all orders that flow through the system from order creation to delivery and understand how much time is spent at each step of the process.
[00:09:23] Prabhjot Singh: What are the different workflows that order goes through? Which workflows are more efficient, right? In terms of speed in terms of costs. And we can also look at outliers, right? Which orders maybe take the longest and why they take the longest. So we have with PI's the ability to use AI to not just continuously monitor a hundred percent of all transactions, but also look at the data to make predictions.
[00:09:51] Prabhjot Singh: Of, Hey, here's an anomaly. That's going to impact your business, a week down the line or a month.
[00:09:58] Trond Arne Undheim: Give me a concrete example. Like we were [00:10:00] talking about the U S air force before. They have a bunch of challenges, obviously keeping planes in the air, but you were talking about a very specific challenge having to do with who's going to fly these planes in the air.
[00:10:10] Trond Arne Undheim: Why is that a computer problem?
[00:10:13] Prabhjot Singh: Yeah. So USL, one of our largest clients. They've got a challenge that on any given day. You only have a couple of people that have enough context to be able to make decisions on what planes should fly, which missions. Because let's say I've got a pool of 50 mission capable planes that can fly a specific mission, right?
[00:10:35] Prabhjot Singh: There's context around maintenance history, there's context around. The type of mission that has to be flown there's context around weather. And there's a number of other data streams that go into that decision and process of, okay, I should use this plane versus this plane. And there's very few people that actually have that in their head.
[00:10:55] Prabhjot Singh: And it's mostly sort of art. And what we're trying to do is [00:11:00] make that science, essentially by looking at the maintenance history of those planes, looking at these different data. Our process intelligence platform can actually say, okay, these planes are the best fit to be able to fly and then eventually get to a place where we'll actually be able to say, this is the best plane, but that requires developing trust in the system and really augmenting the decisioning process. So that there's confidence that okay, the system actually can make the best decisions.
[00:11:29] Trond Arne Undheim: Augmenting obviously plays well with me. So I'm curious, is it just a decision? I don't know how much you could speak to the details, but is it just the decision on what's the best plane is, or, surely it depends on who the pilot is available as an input as well, but maybe that's a little bit more personalized than the system could be at this stage, but it seems to me that it's not just an availability of equipment at the end of the day, it also has to.
[00:11:53] Trond Arne Undheim: A fit for mission, but I could just imagine this example is obviously very rich and interesting because it's a complex, it's a [00:12:00] complex thing to fly a fly, a plane, and I understand airplanes, certainly military planes, debris, complicated pieces of machinery.
[00:12:08] Prabhjot Singh: Yeah the point you're making is a really good one, right?
[00:12:11] Prabhjot Singh: The people dimension is extremely important when you're thinking about any sort of process improvement exercise, because we're constantly hiring new people. People are leaving right. For any process that we might be thinking about. And what we typically do when we're examining any process is not just look at sort of the process data.
[00:12:33] Prabhjot Singh: But the, what happened. If all you need is what happened when for a particular transaction to be able to put together a workflow map. But who did what, when is extremely powerful, because when we know who we can now bring in a whole different data set in terms of who that person is, right.
[00:12:52] Prabhjot Singh: In terms of their title, their role seniority pay band, right? All of these things can now become another [00:13:00] vector that we can do. Yeah.
[00:13:01] Trond Arne Undheim: That's why I was asking, because I was curious, you call it the process mining industry, and I was just curious, that's a funny name in and of itself, what sort of processes particularly, and what kind of data do you actually have available?
[00:13:13] Trond Arne Undheim: I guess it depends. Each client has organized their data in different ways. And then there's obviously external data to layer on top of it. But what is it that you're typically working with when you're trying to streamline a business operation?
[00:13:25] Prabhjot Singh: So typically we start with the base process activity.
[00:13:30] Prabhjot Singh: So if we're, if we take a insurance company and they're interested in optimizing their claim, There's things that happen from claim being created or submitted to that claim, getting closed out, right? There's a bunch of things that happen that you have different actors that are executing specific actions.
[00:13:51] Prabhjot Singh: So they're just the base activity that's happening at each step of that process that we're ingesting into our system. And, we've [00:14:00] got plugins to various different platforms. To be able to capture the state automatically we can pull it through API APIs, log files, right? There's lots of different ways to get the data we do extracts from databases all the time.
[00:14:13] Prabhjot Singh: So it depends on what the customer has available and that's really the basis of. Once we have that in the system, we then try to augment our understanding of the process with the employee data, like I mentioned earlier, right? So it might be active directory. Eldap HR system, right? Pulling in data about who that employee is.
[00:14:34] Prabhjot Singh: We'll even bring in payback information. Now we know if it takes Bob 30 minutes to do something, what does that cost the company? And now we can put a workflow together and look at the cost per word. Then we might bring in customer data, cRM information, because now we can do analysis based on who the customer is and look to see if there's, some sort of patterns related to whether it's a particular region [00:15:00] or a size of customer or industry.
[00:15:03] Prabhjot Singh: So the more data we have the smarter the system becomes.
[00:15:06] Trond Arne Undheim: Yeah. Can you give me some more examples on what kinds of specific workflows that you are enabling when it comes to perhaps manufacturing tasks and logistics tasks? We spoke earlier, you and I just about a couple of other use cases. So the air, in real estate, you had some work done there and then you'll see us mail.
[00:15:25] Trond Arne Undheim: It was a very visible when everybody can imagine that billions of pieces of mail, they have to be organized in a streamlined way to actually get where it needs to go. So that's a lot of opportunities for operational failure, for sure. And I could imagine they do have some data about what needs to go, where
[00:15:43] Prabhjot Singh: absolutely. Oftentimes organizations have data, they don't have information, this is the big problem is too much. Data is sometimes a bad thing. If you don't know how to make it actionable,
[00:15:52] Trond Arne Undheim: it's actually worse because now you're okay,
[00:15:56] Prabhjot Singh: that's right. It doesn't worse in many ways.
[00:15:58] Prabhjot Singh: Yeah. So Colliers [00:16:00] international is been a client of ours for about three years. They're one of the largest commercial brokerages in the. And we work with them to help streamline processes across a number of applications. But, we started with them to help them optimize their deal with. So their dealers nation system, that's used by brokers to originate deals and then process deals and close deals, right?
[00:16:24] Prabhjot Singh: That's really the heart of their business, right? To identify where there's opportunities to potentially improve the speed at which these deals flow or the orchestration of workflows. And, as we've started working with Colliers and other customers like Colliers has organizations go more towards digital.
[00:16:42] Prabhjot Singh: They want an understanding of their end to end digital. So with Colliers, we're now starting to think about, okay how do listings become deals? So you can now start to map to different process flows and understand how they connect. So what types of listings have a [00:17:00] better conversion to deals?
[00:17:01] Prabhjot Singh: And what can we learn from how those listings are creating? So that you can improve the conversion. And I expect to see more and more of that with a number of our other customers, rather than in the financial services and manufacturing, where you've got disparate processes and manufacturing, you might have upstream supply chain.
[00:17:21] Prabhjot Singh: You've got the actual manufacturing process and then you've knocked the delivery. And all of these things are interrelated because when an order gets created, you actually are executing all of these different flows. So to have that enter and visibility and identify okay exactly where there's a particular issue or a bottleneck is really important so that you can optimize that into.
[00:17:46] Trond Arne Undheim: But with the U S mail more than with Colliers, I'm assuming, here, we're talking about a cyber physical system. There's actually mail and bins and physical locations to take into account. How does your system [00:18:00] or other systems that you're aware of? How do you take all of that into account? Because the type of data and the sources of data, they might come from sensors and devices and edge systems and things.
[00:18:10] Trond Arne Undheim: It's not just coming from a clean software database that you may have to clean further and, join up with something else. It may not even come from a database at all is my point. It's coming from a physical process that they're capturing. Of imperfect information about something that's moving around.
[00:18:27] Trond Arne Undheim: That's right.
[00:18:28] Prabhjot Singh: In that kind of a context, there's a couple of key engagement points, right? One is looking at let's say scans data, for a container or for a trailer and identifying where there's mismatch in terms of what the process ideally is and how it's being used. Because you want to get clean data before you can optimize.
[00:18:50] Prabhjot Singh: Otherwise, you're never going to be able to optimize the end to end process if you have gaps in the data. So even being able to identify, okay, there's gaps in terms of assignment [00:19:00] of containers or closure of containers, for any kind of logistics organization, whether it's the air force or ups or FedEx or any of these types of.
[00:19:10] Prabhjot Singh: And then being able to identify when is that happening? And, putting together a plan to resolve that is really step one. So that now you have good data around, okay. I can match my assignments for a container with closures and then loading that container then onto a trailer and then unloading it.
[00:19:30] Prabhjot Singh: And you get that end to end flow. Once you have that, now you can start to say, okay okay, how do we actually improve that?
[00:19:36] Trond Arne Undheim: This brings me to the question that we really wanted to get to, which is the role of the user in these systems, because one of the challenges, is software or use, especially software.
[00:19:47] Trond Arne Undheim: Does handle big data. The idea was okay, let's get these data scientists in there and whether it's a consultant or a system or maybe your own employees, but now you're dealing with specialists that [00:20:00] are expensive and you don't have too many of them, but this idea of low code, which you seem to be pretty passionate about, the idea is different.
[00:20:07] Trond Arne Undheim: It's more of. Having a bigger group of managers, able to look into the system and make decisions and perhaps even adapt the application. How does that work?
[00:20:18] Prabhjot Singh: Yeah. So any given organization that we walk into where they're focused on process improvement and look, typically people are always working on ways to improve their existing processes, right?
[00:20:29] Prabhjot Singh: No one likes to see a process that isn't suboptimal remain suboptimal. They're generally spending as much as 50% of their time, just munching. In terms of data, extraction, data, transformation, data cleaning, and that's a really bad use of those expensive resources, right? Whether it's industrial engineers or business analysts or management, they want visibility into sort of what that process entails and what the execution that processes.
[00:20:58] Prabhjot Singh: So what we do at [00:21:00] pies, Really implement this low code process intelligence layer that sits alongside or sits on top of the process. So you automate the data collection. Has people are interacting with applications to push things from one step to another step. We extract data from BPM platforms that might be.
[00:21:20] Prabhjot Singh: Used for orchestrating that data flow, right? Whether it's like a Pega or an OutSystems or Mendix type of a system, and then we'll pull data via API from backend systems, whether it's SAP, Oracle, Salesforce, whatever that system of record is. And once we have those pipelines hooked up into our platform, well done, it's magic, right?
[00:21:41] Prabhjot Singh: Where you get real-time. Into that process flow
[00:21:46] Trond Arne Undheim: well. So now you have real time visibility. It's interesting how much work is needed before you get there, because these monolithic sort of legacy systems, or, some of them aren't, necessarily legacy systems, but it's just that they're not all [00:22:00] interoperable, are they?
[00:22:01] Trond Arne Undheim: So there's a significant amount of work to get to the point, even if your system is low code. So you're saying the data preparation here is not.
[00:22:10] Prabhjot Singh: Yeah it's not trivial because well, if the data does exist, there's nothing we can do about it. So the data has to exist, but the transformation of the data and kind of normalization data, all of that happens in our system.
[00:22:26] Prabhjot Singh: So there's very little prep work that's required. And then what we're continuing to do is build more and more of these automated integrations. So like we've got this deep integration with Pega systems, for instance, which is used, whether it's warranty or financial services or manufacturing verticals, where our system is automatically aware of process definitions and stages and steps.
[00:22:52] Prabhjot Singh: Of a given process, and we can understand how long people are spending at each step of that process. So now you actually never have [00:23:00] to go to a database or go to log files. We automatically collect that in real time. And that's really what I mean by low code. Whereas typically you might be spending six weeks gathering data from these different systems and get an access to that data.
[00:23:15] Prabhjot Singh: But now you can just drop in the pies component and capture that in.
[00:23:22] Trond Arne Undheim: Yeah. So there's much talk in these low code companies about changing the way that not just software is developed, changing the way that business processes and operations really work. And it just clarify for us what that actually means in your world.
[00:23:36] Trond Arne Undheim: So this idea of the old way of doing things, being, a sort of a waterfall metaphor where things are just flowing like water, Come to this stage, then you come to the next and then the idea of agile being more iterative and sprinting is the metaphor there where you were like joining up and solving a problem, but not really waiting for the next one.
[00:23:57] Trond Arne Undheim: And it's certainly not a wise [00:24:00] process, always. How does that actually work? And why is it important? Do you think, in, in terms of how a non-technical business or a business that doesn't necessarily consider them? Delivering technical things like that could be in finance or manufacturing, they're delivering real things.
[00:24:14] Trond Arne Undheim: Why do they have to care about these sort of operational steps that derive actually from the way software is done?
[00:24:21] Prabhjot Singh: So in the olden days, right? There's this waterfall development methodology where you have business analysts that would focus, let's say for three months or six months in understanding the problem that needed to be solved.
[00:24:37] Prabhjot Singh: Compile a holistic set of requirements. And recreate these BRDs and PRDs oh my God. I'm having like flashbacks to places where I don't want to go. And you'd have this 30, 50, a hundred page document of, okay. These are the requirements. People would code those right for the next three months or six months, and then [00:25:00] you'd eventually get that product to market.
[00:25:02] Prabhjot Singh: And then they would no longer be relevant because it's a year later, I don't actually have the market. And of course that has some flaws from a methodology perspective. And agile is, as you said all about sprinting, right? So we develop things in quick sprints. One week two week, three weeks, right? Maybe four weeks at maximum in which you have smaller set of features that you can push out to production, so that you're being more relevant and then you get feedback and then you develop those features more. That
[00:25:35] Trond Arne Undheim: ties into something, which is a, a really big issue. A lot of people think that automation is necessarily a good thing because it's, more efficiency, but there are some ways that automation, isn't always a silver bullet.
[00:25:48] Trond Arne Undheim: And I guess it ties in a little bit to this idea of, if you have predetermined that you think you have this issue and you designed this waterfall methodology to fix that problem, what if you're [00:26:00] fixing the wrong problem? Have you had that happen?
[00:26:02] Prabhjot Singh: Oh, absolutely. Like we, we see this all the time.
[00:26:05] Prabhjot Singh: Yeah. There's a financial institution, major financial institution in the U S that we work with. They implemented RPA, or robotic process automation as a potential solution to solve a bottleneck that they were seeing in one of their kind of critical banking processes. And they spent six months deploying.
[00:26:28] Prabhjot Singh: And then they spent the next three months ripping it out because it actually didn't solve their problem. And you can imagine like that didn't go well at a management level, for customers, for employees. And this is really where PIs comes into the picture, where we're providing that MRI of what is the current process and not just identifying that there's a ship at the Y there.
[00:26:52] Prabhjot Singh: So you've got. Root cause analysis of bottlenecks of issues before you figure out what the solution [00:27:00] should be. And you have to be able to do that at a pretty deep level. So you have confidence that the solution is actually going to fix your problem.
[00:27:08] Trond Arne Undheim: What about the future outlook here?
[00:27:09] Trond Arne Undheim: So low code as it stands now. Like you said smaller startups have started really implementing low code. And to some extent, no, no code would. And they're becoming more of these interfaces that are plug and play and you don't really need an enormous amount of training to at least to use the system perhaps differently when you're setting it up.
[00:27:28] Trond Arne Undheim: How do you see the future for low code in industrial applications, more widely, or specifically to your field? And what's going to really happen to this field? Is it. Is it always going to be this tinkering where you have to do this sort of analysis of the business problem, or are there any big sort of improvements waiting in the pipeline here that are gonna really transform the landscape either in a way, the power of the data, the power of the optimization of a, of an operational process that you can [00:28:00] execute using, a low code.
[00:28:02] Prabhjot Singh: Yeah. So I think we're going to see a lot more low code applications coming out in the market right. In terms of different verticals. And. Basically signifying simplicity, right? Things become simpler to execute simpler, to build some sort of deploy, some sort of maintain, we're already seeing that in everything from IP operations to development and in terms of kind of a future, this idea of a self healing system that automatically understanding.
[00:28:34] Prabhjot Singh: What the problem is and fixes itself and deploys that fixed to production. And we're in the state of Nirvana is very far.
[00:28:43] Trond Arne Undheim: And maybe that's good because one of the issues I would have with, with such a future for example, is that it's all well and good that you don't need to see the code.
[00:28:54] Trond Arne Undheim: So that I could appreciate that, that you don't necessarily need to be a software developer, but what you do want to see is you want to [00:29:00] see the, or understand the algorithms so that. One, you understand how the system is supposed to work, right? You want to know what priorities are being put into this system because something that's being spit out of the system isn't necessarily the truth.
[00:29:14] Trond Arne Undheim: It is, of course only what you have programmed the system to do, hopefully unless the system takes on its own properties. So that, I guess is my question with many of these low-code systems where the whole point is. You don't have to look behind the curtain. Isn't there a little bit of a danger that people then get lazy and don't even look beyond the curtain and they trust either a vendor like yourself or they trust, again, start trusting their it department because basically there is no need to look behind the curtain and things, apparently at the surface look like they are working, but there's murky stuff going on and you don't really understand how your own business operation works anymore because.
[00:29:55] Trond Arne Undheim: I guess in this example, because it is self healing, right? Or would that [00:30:00] be a good problem to have one once it shows up, like
[00:30:03] Prabhjot Singh: I said, we're far away from that actually happening. And look, our focus is on empowering people to do better. Because even in the world that you're talking about, you'd want to have an understanding of how those decisions were being made and what impact those decisions were having.
[00:30:20] Prabhjot Singh: Cause if you have visibility into a change that's made and the impact that change has while now you can decide whether that change is good, right?
[00:30:29] Trond Arne Undheim: Yeah. You're calling our example just for one, approvals for mortgages, for example, I imagined there was like some systematic bias in the system.
[00:30:37] Trond Arne Undheim: So it was a perfect system. People will approve for a mortgage five minutes, except some people who are not approved right. Also five minutes. And then you need to figure out why that's the case, any physical.
[00:30:47] Prabhjot Singh: That's right. And even rewinding back to today, in the agile world.
[00:30:51] Prabhjot Singh: When we do, let's say a release and we put out a new feature or a new product. The question is, [00:31:00] did that release make things better? If my business goal was to reduce the cost of processing a transaction or speed up your ability to execute that transaction, you want to be able to measure that with each release of the app.
[00:31:13] Prabhjot Singh: And that's what we're enabling is that don't just release code and then move on to the next thing, understand what the impact of that releases on the process on the business itself so that you can make decisions like, do we need to rejigger our backlog for instance, reprioritize things, because we're not getting to where we want to get to right from the business.
[00:31:37] Trond Arne Undheim: Got it. So I guess this is the unifying thread here in our discussion is that applications can only drive business results. If you know what business results you're looking for.
[00:31:47] Prabhjot Singh: That's right. If you don't go, you shouldn't be building applications.
[00:31:50] Trond Arne Undheim: That's great.
[00:31:51] Trond Arne Undheim: Any last thoughts? What's exciting with pies. What are you looking at in the next few months? We're
[00:31:55] Prabhjot Singh: in the middle of this international expansion, which is really exciting. [00:32:00] We've got. Customers and in Japan and Australia, which has been very cool to see how business gets done and in these areas, as we're wrapping things up from the ground.
[00:32:13] Prabhjot Singh: And so that's taking a lot of our time. And then from a product development perspective, we're continuing to invest more and more. Predictive analysis, right? To be able to help forecast what the future could be based on what we're seeing so that people can take more informed.
[00:32:30] Trond Arne Undheim: That was exciting to get a little taste of what you're up to. And I feel like process intelligence is instrumental for many businesses, certainly on the manufacturing side, in order to understand, how your process can be optimized, but maybe also understand what it is that you are doing or should be doing.
[00:32:47] Trond Arne Undheim: Maybe change your priorities as well and see opportunities coming from that data. So thanks a lot for contributing to the.
[00:32:54] Prabhjot Singh: Yeah, my pleasure. Thanks for inviting me on today.
[00:32:58] Trond Arne Undheim: You have just listened to [00:33:00] episode 76 of the Augmented podcast with host thrown on a Undheim. The topic was low on code and high on process.
[00:33:06] Trond Arne Undheim: Our guests was probably old Singh CEO and founder of pies. In this conversation, we talked about business process intelligence and workflows in manufacturing and logistics and the future outlook for low-code in industrial applicant. My takeaway is that business process intelligence is the why of technology because smoother operations is where the value of technology is realized.
[00:33:29] Trond Arne Undheim: The future outlook for low code in industrial operations is bright because it has the potential to streamline workflows in manufacturing and logistics. However, it is important to keep in mind that to leverage automation, to do better decisioning and not just to squeeze out more. That starts with keeping in mind what the real problem is and steering with that in mind, if you don't know, figure out the problem, then invest in process.
[00:33:57] Trond Arne Undheim: And if technology gets you there, invest. [00:34:00] Thanks for listening. If you liked the show, subscribe at Augmented podcast.co or in your preferred podcast player and rate us with five stars. And if you liked this episode, you might also like episode 73, the challenge of front line operations. Hopefully you'll find something awesome in these or in other episodes.
[00:34:19] Trond Arne Undheim: And if so, do let us know by messaging us because we would love to share your thoughts with other listeners. The Augmented podcast is created in association with. A connected frontline operations platform that connects the people, machines, devices, and the systems used in a production or logistics process in a physical location.
[00:34:42] Trond Arne Undheim: Tulip is democratizing technology and empowering those closest to operations to solve problems. Tulip.co is also hiring. You can find tulip at Tulip.co dot co priests. Share this show with colleagues who care about web industry and especially industrial tech is. To [00:35:00] find us on social media is easy. We are Augmented pod on LinkedIn and Twitter and Augmented podcast on Facebook and YouTube, Augmented, industrial conversations that matter.
[00:35:12] Trond Arne Undheim: See you next time.
President and CEO
Prabhjot Singh is a serial entrepreneur who has started multiple for-profit, social enterprise, and non-profit ventures. He serves as President and CEO of Pyze, the most recent company he founded to enable the world’s largest enterprises to improve business operations through AI-driven Process Intelligence and Analytics. He has over 20 years of experience in sales, marketing, and product management. He previously co-founded Pixatel Systems, a social enterprise that utilizes mobile computing to deploy apps and e-Learning solutions to millions of users.