(upbeat music) - Hello and welcome to "insight.tech Talk", formerly known as IoT Chat but with the same high quality conversations around IoT technology trends and the latest innovations. I'm your host, Christina Cardoza, Editorial Director of insight.tech and today we're going to explore digitizing the manufacturing supply chain with experts from Relimetrics and iProd but as always, before we get started, let's get to know our guests. We'll start with Kemal from Relimetrics first.
Please tell us about yourself and your company. - Hi, I am Kemal Levi. Founder and CEO for Relimetrics. We enable customers with a proven industrial-grade product suite they can easily use to control and automate quality assurance processes across use cases with no code and using our product, our customers are able to build, deploy and maintain mission critical AI applications on their own in conjunction with any hardware.
This can be done both on prem or in the cloud and a key industry challenge that our product repeatedly succeeds in tackling is our ability to adapt to high production variability which is commonly experienced in today's manufacturing. - Great, looking forward to getting into that and how that is going to impact the supply chain or bring benefits to the supply chain but before we get there, Stefano Linari from iProd. Please tell us about yourself and the company.
- Hello, I am Stefano, Stefano Linari. I am the Founder and CEO of iProd. iProd is an Italian startup founded in 2019 to create the first holistic tool designed for manufacturing companies of each size, accessible for free and as a software as a service. Our user can leave tons of purely integrated software like ERP, Amira, CRREM, IoT platform and use just one modern cloud platform, our platform. - Awesome, so I wanted to start off the conversation just getting the state of things right now.
Obviously a couple of years ago, the supply chain was headlining in the news, almost every day for weeks on end.
Just the challenges and the obstacles, but I feel like there's been a lot of integration and advancements in the technology space that those pain points we were feeling a couple of years ago, we have been able to get over a little bit but I'm curious, what challenges still remain or where are the pain points today Stefano if you want to talk a little bit about what's going on at the manufacturing and supply chain level. - Yeah, this supply chain unfortunately is still purely integrated.
Especially for its more than medium enterprises where digital tools are not updated and easy to be integrated because they are legacy technology. We are far away from the concept of this so-called manufacturing as a service where the manufacturing capabilities are accessible in a fluid way. This part of the, ask for a highly integrated, multi-tier supply chain able to digitally orchestrate and provide a custom made piece, optimizing cost, impact and user resources.
Unfortunately, even on the other side of this supply chain, if you look at the OEM, we face other issues. And the companies are not able to serve the new part of this for their industry that is the machine customer. Where a product, digital product is able to purchase autonomously spare parts, accessories and accessory from the OEM itself and even from third parties. For example, a turning machine that after digitalization can work a belt or a gear after several number of working hour.
This is still far away from the reality. - Yeah, you make some great points there Stefano and one thing I want to discuss a little further is you mentioned a lot of problems is that there's still legacy systems in place and I'm sure that's creating a lot of silos that these machines can't talk to each other. Data is not end to end.
So Kemal, I'm curious from your perspective, where are some areas that manufacturers can start digitizing aspects of the supply chain and how that's going to help address some of the pain points Stefano just mentioned? - First of all, digitizing aspects of manufacturing helps to trace quality across the supply chain. As parts move along the supply chain, quality automation helps to identify anomalies before they get to the customer and risk downtime.
So for the entire supply chain and particularly for the OEMs, it is really important to trace the quality status of parts or products from a multitude of suppliers and also run data analytics to see which one is actually performing better and read out those vendors who are not performing well. Now digitizing aspects of manufacturing also helps to improve the bottom line. So as manufacturers ship products to their customers, they must identify issues with outbound transportation and logistics.
So a magnifying lens, looking at different points of the supply chain gives better visibility to improve margins and in the case of the sectors that we typically serve to, margins are often razor thin. So maximizing the number of items, getting to the end of the manufacturing line that meet the required quality standards has a direct impact on the bottom line.
Another example is that digitizing aspects of manufacturing, helping to make better supply chain decisions and correlation of acquired data across the product life cycle and this can be all the way from manufacturing to sales to service, enables continuous business intelligence and a company that can trace quality in real time and do a better assessment on where quality issues originate can ultimately boost profitability. - Yeah, absolutely.
I'm glad you mentioned the quality automation aspect of the supply chain. I feel like sometimes when we talk about supply chain challenges, we are often thinking about deliveries and shipments and getting manufacturing production out the door but it also starts, it's an end to end issue. It starts on the factory floor, it starts as you are developing these products, making sure that everything is high quality, that it can go out the door and can be delivered on time.
So that's a great point that you made and then looking at the different points of the supply chain so that it's really an end to end experience. Stefano, I'm curious, you know, as we look at quality automation and all of the different parts manufacturers need to be on top of in order to have this end to end digitized supply chains, what are the technologies that are being used or how can we start enhancing and optimizing supply chain efficiency?
From our side, all these things can start from the demand side. If we start to build intelligent machines that can be transformed in a machine customer, we can create a more predictable demand. We can avoid to rush, to produce spare parts and install it in a non-planned way. Creating a simple condition to optimize the supply chain. So from our side in these months, in the last year, we are pushing this new part upgrade inside OEMs.
What we have created to support the OEM to handle new generation of machines that we call machine customer it's to create a free and self-service interface in the cloud while each OEM can create their rules and their identity, the digital twin of every machine that's built in few minutes. Gartner in their last books name it "When Machines Become Customer" recognize our platform as the first machine customer-enabling platform in the world.
We are then creating the condition to digitalize the supply chain. Because when you speak about potential saving, entrepreneurs are interested. But they are engaged when you tell them about increasing revenue and with our technology, embedding new intelligence on board of the machine, we are transforming our production tool in point of sales. And this is a remarkable shift in the mindset of the OEM that can be easily understandable.
- So I'm curious, because we were talking about the legacy systems earlier in the conversation. Is this a software approach that we can take to digitizing the supply chain or does there have to be investments in new hardware? Or can we leverage existing infrastructure?
- We have to combine both because for sure, software platform can make the interface and user experience simple but we can't forget that manufacturing tools and equipment, automatic warehouse and production machines are not yet intelligent enough to analyze their needs and try to simply find the life to the end user and to the OEM. So we need a combined approach at the moment.
- Great and of course, when we are talking about adding intelligence and doing things like quality automation, AI comes to mind. AI seems to be everywhere these days. Kemal, you mentioned you were you know, you have an AI approach to being able to provide that quality automation and look at different parts of the supply chain. So I'm curious from your perspective, what is the role that AI should be playing in these you know, supply chain processes?
- Well, AI in supply chains can deliver powerful optimization capabilities, required for more accurate supply chain inventory management. Can also help to improve demand forecasting, reduce supply chain costs and this can all happen all while fostering safer working conditions across the entire supply chain. Traditionally, the supply chain has relied on manual inspections and sorting. So I would like to give an example that centers around smart inventory management.
So this, this process, the inventory management process can be labor-intensive and prone to error, adding costs to the loss. So today, AI-driven quality automation tools like ReliVision can be deployed without requiring any programming skills or prior machine learning knowledge and they can offer access to real-time information that can improve efficiency and visibility.
Now similarly, AI can also be used in conjunction with computer vision and surveyance cameras to monitor work efficiency and safety objectively and provide data-driven insights for businesses to optimize workflows and improve their productivity. - So do you have any customer examples?
I know you just provided the inventory use case, but I'm curious if you have any customer examples that you can share with us, how, what problems they were facing and how Relimetrics came in and was able to help them and what the results were. - A good example is renewable energy leaders which engaged with us to help them inspect their wind turbine blades before they're released to customers.
So using our AI-based quality automation and non-destructive inspection digitization platform, our customer is today able to automate the inspection of phased array ultrasonic data and assess the condition of blades before they are placed in the field. And the main challenge that our typical customer has is to digitize inspections which is time-consuming and prone to errors and improve traceability across their supply chains.
And with our product, our customers can rapidly implement AI-based machine vision algorithms on their shop floor and they don't need to write a single line of code while doing this and they can share, train the models across inspection points and leverage existing camera hardware, irrespective of image modality. Whether it's infrared, X-ray or PAUT.
- I love the no-code approach that you guys are taking 'cause I know a lot of manufacturers, they see these benefits, they want to achieve them but there's obviously labor shortages happening in the area, in their space and can't always have the skills or be able to deploy these as fast but they'd like to get these benefits. So love seeing how we can make it more accessible. When you have these no-code solutions, who are the type of users that are able to implement some of these in practice?
Do you need those engineers or is it really an operator or a manufacturing manager that's able to take part in this as well? - Well we would like to enable process engineers to be able to build AI solutions and not only build but also deploy these AI solutions and then maintain them. So what we see is that maintenance of AI solutions can also be quite costly. So we are making it possible for non-AI engineers to be able to maintain AI solutions.
Now we can of course also serve AI engineers as well, we can help them just prototype their AI solutions faster and deploy them to the field. The maintenance piece again is typically an important aspect that AI engineers typically would like to transition to operators after they are successful in the field. And this is exactly what we do. We make it possible for maintenance of AI models and training of new AI algorithms for new products, new configurations to be done by non-AI folks.
- Yeah, it's amazing to see how far technology has come and how non-AI folks can be involved. Especially since these people are the ones on the factory floor with the domain intelligence. So they can spot the quality issues or you know, be able to train some of these models better than an AI engineer probably would if they don't have that deep manufacturing experience. Stefano, I'm curious from iProd's side.
What are the solutions and products that you guys have on the market that you're helping your customers in these different areas and if you had any customer examples that you could share with us as well. - Yeah, we have several use cases of machine customers spreading from concrete industry, industrial filtration and manufacturing. But I want to present you the most significant case that was done with Bilia.
Bilia SPA is the third largest turning center builder and their machines are sold to automotive companies and manufacturers of consumer goods and a lot of industry where metal parts are needed. Most of those machines, you can figure out to be installed in a shop floor, even in small and medium enterprises. You know that in Italy but in Europe in general, most of the company are under nine employees. So you can imagine that no expertise in IT can be found in the customer side especially.
So we have enriched, equipped this turning machine with external brain so we can go, it's in a panel PC, technically speaking but we like to describe it as an IoT tablet to make them more friendly for the end user and with this tablet we have two connection at the same time. One with the CNC of the machines and then we can acquire real-time data about usage and consumption of resources and on the other connections, usually Wi-Fi or forward dealing, we are connected to the iProd cloud.
This solution, it's a bundled solution because we hand to provide security and trust of the end user that no sensible data about their process and their secret sauce to create the perfect piece are not exfiltrated. Then in the cloud, Bilia, the manufacturer, with their process engineer, a maintenance engineers using a visual approach as Kemal defined before.
So even in this case, no programmer, no coder is needed but you have a wizard in the cloud where you can simply drag and drop spare parts and services from the Bilia catalog to conditions that can be simple rules, every 1000 hours, please change the filters or fill the oil or forward looking AI and ML that can predict more accurately what must be changed.
The main point when we start this project is okay, but why the end customer have to accept that the turning machine will ask him to buy something? I have still spent €200,000 for this turn and every day he ask more money, why? I have to pay. And so it was a bit scary but the customer not only accept the recommendation, but they ask machine more. They allocate a dedicated budget to the machine itself. Usually in the order of €200 per months, no big budget.
But in the most efficient area, because under this level, machine can automatically place the order and you receive a notification on your mobile, "Hey Stefano, "in a couple of days you will receive the new filter." Or new belt and so on. For €50, €60 because most of the spare parts are cheap but we try to estimate the cost to placing the order and processing the order and this is never lower than €50 for each side.
So the end user know that if the machine never stopped and by autonomously, they need the spare parts, consumable, periodic service, he is saving money and probably the same items, purchased in an autonomous way is even cheaper because on the other side, I happen to spend time to answer an email, answer phone, send a contract and blah blah blah. So what was something that at the beginning sounds very difficult to do because the no skill, no very digital guys it's a real market success.
- Yeah and I'm sure that is a common scenario in the industry. Not knowing where to start, being worrisome of getting started, how much it's going to cost, how complicated it's going to be. If it's going to be wasted effort. So it's great to see how manufacturers can partner with companies like iProd and Relimetrics to be able to integrate some of this and really make improvements in the supply chain.
One thing that comes to mind and I should mention, insight.tech, we are sponsored by Intel but we're talking about artificial intelligence and the cloud and real-time capabilities and insights into some of these things that you know, I'm sure that you guys are working with other partners to make this all happen end to end, much like your customers. Sometimes we need to rely on expertise from other areas.
So curious about how you're working with partners like Intel and what the value of that and their technology is. Kemal, I can start with you on that one. - In our implementations, we are taking advantage of Intel processors and Intel hardware such as Intel® Movidius™ vision processing units and we are also often relying on Intel software such as OpenVINO™ to optimize deep learning models for real-time inferencing at the edge.
Now in the case of quality automation or digitizing visual inspections, customers are very sensitive about computing hardware costs and they really do care quite a bit about smart utilization of CPU. So we use the Intel OpenVINO toolkit to minimize the burden and also as an Intel market ready solution provider. We have access to a large community of potential buyers of our product. - Great, we always love hearing about OpenVINO. That is a big toolkit in the AI space.
You know like you mentioned, taking some of the burden off of you know, engineers and just being able to easily run it once and deploy it on many different hardware. So it's great to hear. Stefano, I'm curious from iProd's end. How are you guys working with partners like Intel and you know, what are the areas that their technology really helps the iProd solution be able to benefit customers?
- At the moment we use widely Intel-embedded mobile processor because even if we haven't done a heavy workload on AI and ML, what our customer want is for sure, to reduce energy consumption at the edge, you have to consider that each IoT tablet is installed on top of each production machines and in a harsh environment. So we need a fanless processor with high computing power and low consumption for standby.
We also used Intel connectivity for Wi-Fi because we need connectivity that can be reliable in EMC. Difficult space where you have welding machine and robots with high power and this is what we have using now. OpenVINO and new processor with the Ultra core, Ultra is also in our ladder. We are starting to experiment these features to accelerate, especially ML and AI models. To predict the usage because we combine in the tablet, I don't tell before.
Not only IoT data that came in from the CMCs but from the cloud, we receive even a schedule of the next batch to produce and what we are trying to do is to forecast the production because you have to combine how many working hours this model will do if I will win this deal. Most of the calculation have to be done on the edge because customer don't want to move outside their company sensitive information.
For the manufacturer, for example that produce piece for aerospace industry or high end machines, super car, like Ferrari, just to name a brand. Their technology that is inside your software of the CMT machines, it's all, half of the value of your company and you don't want to transit even to iProd this information. You want to process all the information on the edge. - Yeah, absolutely.
One thing I love about these processors and toolkits is that this, you know, technology, it seems to be advancing super fast every day. Some things that a month ago that we were interested in is now becoming reality and manufacturers, sometimes they have trouble keeping up with all of the advances and getting all of the benefits. But with partners like Intel and these processors, they're really making new changes every day to ensure that we can continue to keep up with the pace of innovation.
I'm curious Stefano, how else do you think this space is going to change? What do we have to look forward to for the future of the supply chain? - I agree with even Kemal told before. What we see, it's a digital continuum. From the machines to the OEM to the supplier of the OEM to create a continuum of information. Because we don't want to spend time in the order process. This is the piece that is considered a loss of time and Amazon and other online store, is driving the user experience.
Because now B2B request inspired and drove by B2C experience in the day by day life. The second main points that is pushing the digitalization and will became mandatory in the next few years, at least in Europe will be the ESG regulation and the so-called Supply Chain Act. So a company in 2026 has to present the ESG report.
So they have to account the emission that each process in the company generate and the main focus is on the manufacturing side obviously and with the Supply Chain Act you have to provide this information, not only through the ESG report to the public but you have to share point and data to your customer in real time or near real time. This means that the supply chain must be heavily integrated in the next few years.
- Great point and you mentioned sustainability earlier where we were talking about how some of these things can help worker safety. There are so many different areas that we can talk about and we've only scratched the surface in this conversation. Unfortunately we are running out of time. So before we go, Kemal, I just want to throw it back to you one last time.
If there's any final thoughts or key takeaways you want to add, what we can expect from the future of the supply chain management or how else AI is going to continue to evolve in this space?
- Well I think, as I said before, there will be a lot of focus on real-time data analytics and correlate acquired data across the product lifecycle and this goes all the way from manufacturing to sales, to service, to overall enable continuous business intelligence and help to derive better supply chain decisions. And I think looking to the futures, looking to the future, companies will strengthen demand planning and inventory management in tandem with their suppliers.
There will be data visibility at all levels, whether it's from in-house manufacturing, suppliers and logistic partners or customers and distribution centers. The supply chain will no longer be driven by uncertainty in demand and execution capabilities and overall it will be characterized by continuous collaboration and flow of information.
- Well I can't wait to see how that all starts to shape out over the next couple of years and how Relimetrics and iProd, how the advancements and innovations you guys continue to make in this space.
So I invite all of our listeners to visit iProd and Relimetrics' websites, see how they can help you digitize the supply chain from end to end and really get that continuum of information in all aspects of your business and also visit insight.tech where we will continue to keep up with iProd and Relimetrics and highlight the innovations that are happening in this space. Until next time, this has been "insight.tech Talk". Thanks for joining us. (upbeat music)