<p id="isPasted">As resources become more visible — their value increases, which results in a crucial step towards achieving environmental, climatic, and financial benefits.</p><p>Studies show that people manage their spending more carefully when they pay by cash rather than card. Cash is visible and tangible, and wasting it feels harder to do.</p><p>Based on industrial sustainability work with countless manufacturers over many years, Professor Steve Evans in the<a href="https://www.ifm.eng.cam.ac.uk/research/industrial-sustainability/" rel="noopener ugc nofollow" target="_blank">&nbsp;IfM’s Centre for Industrial Sustainability (CIS)</a> has concluded that this principle applies to other resources too. He regularly observes that electricity, compressed air, water and surplus materials hide in plain sight in factories — in cables, tanks, pipes and skips — and are consequently wasted. However, when these resources are made more visible, their perceived value increases, resulting in more careful management. This is a positive development for the environment, the climate and the financial bottom line.</p><p>In the 1980s some researchers believed that we had two centuries to tackle the issue of climate change, but not everyone agreed with this prediction. Over the following decades, the tireless efforts of those who dissented against this view helped to shift perceptions. Unfortunately, climate change continued to worsen during this time due to a lack of consensus and inadequate action. We now face a crisis that we are not prepared for, which demands a large-scale transformation of our industrial systems. In 2011 Professor Steve Evans established the&nbsp;<a href="https://www.ifm.eng.cam.ac.uk/research/industrial-sustainability/" rel="noopener ugc nofollow" target="_blank">CIS at the IfM</a> to facilitate this change, and he has since observed some promising developments.</p><p>“In even the very best companies, what was considered normal 20 years ago is now seen as absurd,” says Steve. “For example, factories would leave machines on overnight or over the weekend just to ensure the next job would be high quality. The best companies don’t do that anymore. At Toyota, an employee comes in a few hours early to ensure that the machines are operational for the shift. This practice saves them a lot of energy and money,” he continues.</p><p>Examples like Toyota highlight that there is a growing focus on sustainability in industry, but many manufacturers still need convincing to change their practices, especially when they cannot see their waste.</p><h2><strong>The case for industrial frugality</strong></h2><p>Energy waste is a type of waste that often goes unnoticed, so it is an important area of focus for Steve and his CIS colleagues in their work with industry. With renewable energy powering more than half of the electricity sold in the UK, it is not self-evidently a sound priority, as Steve explains: “If a manufacturer exclusively relies on renewable energy, one might assume that there is little incentive for them to conserve energy, other than to save money. However, this is not entirely true. Paradoxically, reducing energy consumption can be an effective strategy to reduce real CO<sub>2</sub> emissions. When a manufacturer uses less energy, the overall energy demand in the industrial system decreases. In the UK, renewable energy has become cheaper to produce than fossil fuels, so it is more likely that fossil fuel providers will be impacted by lower demand than renewable energy providers will. Reducing the proportion of fossil fuels in the energy mix therefore makes sense from an economic perspective.”</p><p>With a view of the whole resource system, a frugal mindset is the only sensible option, according to Steve. Even on the materials side, there is work to be done to make that change: “Material waste should not be the biggest challenge for manufacturers because it is in their bins. You can physically see material waste if you bother, but most people don’t bother. Even in our homes, waste tends to become psychologically invisible once the bin lid goes down, so we need to be told to look for it,” he observes.</p><h2><strong>A material world</strong></h2><p>Manufacturing serves the purpose of producing material goods for consumers, and industry has the capacity to do so on a large scale. The sector’s history of pollution and industry’s connection to consumerism, however, may lead to the belief that manufacturing is incompatible with sustainability. This belief is incorrect and fails to recognise that food, clothing, medicines, building materials and other essential items are all manufactured goods that we rely on. A sustainable industrial system is necessary to provide these goods to a global population of over 8 billion people. Achieving sustainability cannot be delivered by closing down industry, which is good news, but we absolutely must increase our innovative thinking and implement solutions.</p><p>“It is hard to come up with general sustainability recommendations for manufacturers because they are so very different. We can’t say ‘Do this to this type of machine, do that to your schedule, do this to compressed air.’ But there is hope, because what we find works consistently is to show people that waste is happening. Each factory has its own toolkit for dealing with waste; we just need to show them that the waste is there. We find that we don’t have to teach them how to identify changes that will improve the situation. They’re already good at that, and that is really quite important. We must teach them how to see the waste so that they can tackle it,” says Steve.</p><p>Waste is just one part of the sustainability picture in industry, but, because there is so much of it, it is a very important part. As Steve notes, it is also an easy place to start when a company wants to improve its environmental impact, and making sustainability easy is crucial. With good and accessible data, it is even easier.</p><h2><strong>Drawing with data</strong></h2><p>Dr Duanyang Geng recently completed his PhD at the IfM, under Steve’s supervision. Duanyang has been working for years to understand how industry uses data to conceptualise energy waste — one of manufacturing’s most elusive and widespread forms of waste. For his thesis, he interviewed 30 manufacturers about their energy use, trying to establish what they perceive energy waste to be and what they do to avoid it.</p><p>“There is no such thing as energy waste in nature,” says Duanyang. “Energy waste is intimately linked to human industrial practices, so it is up to us to define what separates energy waste from energy use. My many interviews informed my working definition of energy waste as whatever energy is used on top of what is strictly necessary to produce something. The invisibility of energy adds complexity here — manufacturers can’t physically see the energy they use, so they don’t automatically know where their benchmarks are. With digital tracking software, the kilowatt-hours, £/$ and CO<sub>2</sub> equivalents can be made visible and translatable.”</p><p>As Duanyang observes, digital tracking software has real potential to help manufacturers, but it remains an underused resource, especially in small and medium-sized (SME) manufacturers. Tracking tools can provide figures on energy use, temperature, downtime and more, which can be useful in themselves and also in creative translations.</p><p>“Remember the hype around Google Glasses a few years ago?” Steve muses. “Imagine putting on a pair in a factory and getting visual access to the energy use of machines in real time, say, as a symbol that changes in size according to energy spend. Such instant visualisation of invisible waste could be a very powerful tool.”</p><h2><strong>Manufacturing a sustainable world</strong></h2><p>Industrial sustainability at the IfM goes beyond the work of the eponymous CIS. From using machine learning to tackle problems of wasted space on lorries to making sophisticated use of additive manufacturing (3D printing), and helping SMEs to benefit from digital tracking tools, different groups are investing time working to manufacture a more sustainable world. In various ways, they are bringing to light what industry and consumers overlook or ignore, providing simple solutions for improvement.</p><p>“Not long ago, I had a lovely email from someone who works for a company that made some significant CO<sub>2</sub> and financial savings thanks to watching videos on our research. We have not worked with this company, but they were able to make big changes based on the higher-level principles we have observed, which say that there is probably more waste energy, water and materials in your factory than you think. Go looking. This company went looking, they found areas for improvement, and they improved,” says Steve. “It can be that easy, but it still needs to be done.”</p><h2><strong>IfM Engage</strong></h2><p>IfM Engage is the the knowledge-transfer arm of the IfM, and offers membership of the Sustainability Association — a place where manufacturing leaders work with world-leading researchers to tackle sustainability challenges in their operations.</p><hr><h2 id="isPasted"><strong>IfM Insights on Medium</strong></h2><p>This article was originally published on <a href="https://medium.com/ifm-insights/sustainability-making-the-invisible-visible-86b9794aeda9" target="_blank">IfM Insights on Medium</a></p>

Members of the Institute for Manufacturing's (IfM) Centre for Industrial Sustainability, explore the crucial importance of making industrial resources more visible. 

Industrial sustainability: making the invisible visible

Harnessing cutting-edge technology to optimize energy efficiency, comfort, and security in modern buildings

Building Management: The Power of Building Automation Systems

<p id="isPasted">In the ever-evolving landscape of production and logistics, robotics &amp; automation play an increasingly vital role, driven by the wave of digitization. However, as with any technology, there are also hurdles which have to be overcome, especially when it comes to the production of customized and lightweight parts in a cost-efficient manner. This is where 3D printing steps in. Join us in this blog article as we explore the benefits and diverse applications of 3D printing for robotic parts and learn about the connection of those two technologies.</p><h2>Understanding 3D Printing</h2><p>Before exploring their interconnection, let’s understand 3D printing, also known as additive manufacturing. Additive manufacturing enables the conversion of digital designs into tangible objects by building up layers of material. You just need the right design and production can start without any huge production ramp-up. This freedom enables intricate designs, rapid prototyping, and on-demand production of customized parts, offering a level of flexibility and innovation that has redefined the manufacturing landscape.</p><figure><span class="fr-img-caption fr-fic fr-dii" style="width: 678px;"><span class="fr-img-wrap"><img width="1024" height="299" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMjc0NzI1NjAzLTE3MTMyNzQ3MjU2MDMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="3D-Printing-How-it-Works" srcset="https://replique.io/wp-content/uploads/2024/03/3D-Printing-How-it-Works-1024x299.jpg 1024w, https://replique.io/wp-content/uploads/2024/03/3D-Printing-How-it-Works-300x88.jpg 300w, https://replique.io/wp-content/uploads/2024/03/3D-Printing-How-it-Works-768x224.jpg 768w, https://replique.io/wp-content/uploads/2024/03/3D-Printing-How-it-Works-1536x449.jpg 1536w, https://replique.io/wp-content/uploads/2024/03/3D-Printing-How-it-Works.jpg 1900w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii"><span class="fr-inner">From digital file to printed parts in hours.</span></span></span></figure><h2>Custom Parts with 3D Printing</h2><p>Robotics is one of the industries, where these benefits of 3D printing can play a crucial role. And there are many reasons for it. One is the possibility to create custom parts of robotics and automation machinery using 3D printing. Traditional manufacturing methods often fall short when it comes to creating parts, such as grippers, tailored to specific tasks without incurring exorbitant costs due to the creation of specialized tools and molds. Thanks to 3D printing, even small players (SMEs), can use 3D printing to create budget-friendly robot parts, as there are no minimum order quantity, meaning they can produce parts starting from lot-size one.</p><p><br id="isPasted"><span class="fr-img-caption fr-fic fr-dii" style="width: 758px;"><span class="fr-img-wrap"><img width="1024" height="768" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMjc1MDQ2OTM1LTE3MTMyNzUwNDY5MzUucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" srcset="https://replique.io/wp-content/uploads/2024/03/3D-printed-caps-robot-automation-1024x768.jpg 1024w, https://replique.io/wp-content/uploads/2024/03/3D-printed-caps-robot-automation-300x225.jpg 300w, https://replique.io/wp-content/uploads/2024/03/3D-printed-caps-robot-automation-768x576.jpg 768w, https://replique.io/wp-content/uploads/2024/03/3D-printed-caps-robot-automation-1536x1152.jpg 1536w, https://replique.io/wp-content/uploads/2024/03/3D-printed-caps-robot-automation.jpg 1900w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii" style="width: 758px;"><span class="fr-inner">Robot caps can be cost efficiently produced in small volumes via 3D printing.</span></span></span></p><h2>Lightweight Parts Thanks to Intricate Designs</h2><p>Weight is a critical factor in optimizing robotic performance. Traditional manufacturing often results in excess weight due to subtractive processes. 3D printing revolutionizes this by allowing engineers to design intricate, hollow structures, optimizing weight without compromising strength. This newfound agility finds applications in drones, exoskeletons, and various robotic systems.</p><h2>Part Consolidation and Modulation</h2><p>3D printing’s technical benefit extends to the consolidation of multiple parts into a single, complex structure. This not only reduces overall weight but also minimizes assembly challenges and inventory complexity. On the other side, 3D printing allows the separation of a single part into distinct components, simplifying modifications if needed.</p><h2>Quick Prototyping and Design Changes</h2><p>Compared to traditional methods, 3D printing offers unparalleled agility in prototyping and design alterations. Engineers and designers can swiftly translate conceptual ideas into tangible prototypes. This means they can test a prototype out almost immediately to see if it’s a good fit. And if they want to tweak or change the design, they can easily do that thanks to the flexibility of 3D printing, empowering innovators to adapt and optimize robotic components with unprecedented speed. This not only accelerates the product development cycle and reduces time-to-market considerably, but also fosters a culture of continuous improvement in the ever-evolving field of robotics.</p><p><span class="fr-img-caption fr-fic fr-dii" style="width: 678px;"><span class="fr-img-wrap"><img width="1024" height="768" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMjc0ODUwNjU0LTE3MTMyNzQ4NTA2NTQucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="" srcset="https://replique.io/wp-content/uploads/2024/03/3D-printing-prototyping-robotics-1024x768.jpg 1024w, https://replique.io/wp-content/uploads/2024/03/3D-printing-prototyping-robotics-300x225.jpg 300w, https://replique.io/wp-content/uploads/2024/03/3D-printing-prototyping-robotics-768x576.jpg 768w, https://replique.io/wp-content/uploads/2024/03/3D-printing-prototyping-robotics-1536x1152.jpg 1536w, https://replique.io/wp-content/uploads/2024/03/3D-printing-prototyping-robotics.jpg 1900w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii"><span class="fr-inner">3D printing can be used for prototyping and visualizing products and ideas.</span></span></span></p><h2>Spare Parts On-Demand</h2><p>The wear and tear faced by robotics and automation machinery can lead to costly downtime. Traditional methods of obtaining spare parts involve a time-consuming process of ordering, shipping, and, in some cases, custom manufacturing.<br>3D printing provides a rapid and cost-effective solution for local or even on-site production of spare parts, just on-demand. Instead of waiting for shipments or relying on extensive inventories, robots can be quickly restored to operational status with 3D printed replacements. This capability significantly minimizes downtime, ensuring continuous robot functionality. At an automation fair for example, Replique was able to get needed spare parts of an exhibitor within a day.</p><p>Learn more about the benefits of <a href="https://replique.io/3d-printed-spare-parts/" target="_blank">3D printing for the production of spare parts.</a></p><h2>Application examples</h2><h3>Grippers</h3><p>Especially, grippers, are undergoing a revolution thanks to additive manufacturing. It enables the creation of grippers in small volumes that are not only cost-efficient but also remarkably lightweight and optimized. When the need arises to change grippers, 3D printing facilitates a swift process with short development and production times. This capability translates into faster movements, reduced cycle times, and an overall more agile manufacturing process.</p><figure><span class="fr-img-caption fr-fic fr-dii" style="width: 678px;"><span class="fr-img-wrap"><img width="1024" height="768" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMjc0NzI2Mzc4LTE3MTMyNzQ3MjYzNzgucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="3D printing for robotic parts_gripper" srcset="https://replique.io/wp-content/uploads/2024/03/3D-printed-grippers-1024x768.jpg 1024w, https://replique.io/wp-content/uploads/2024/03/3D-printed-grippers-300x225.jpg 300w, https://replique.io/wp-content/uploads/2024/03/3D-printed-grippers-768x576.jpg 768w, https://replique.io/wp-content/uploads/2024/03/3D-printed-grippers-1536x1152.jpg 1536w, https://replique.io/wp-content/uploads/2024/03/3D-printed-grippers.jpg 1900w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii"><span class="fr-inner">Cobot gripper designed for 3D printing.</span></span></span></figure><p>In a recent cooperation with struktur.form.design Engineering GmbH for example, we were able to reduce weight of a cobot gripper by 78%, part count by 84% and overall production cost savings by 30%, just via redesigning and producing via additive manufacturing.</p><p>And the benefits extend beyond industrial automation, making a significant impact on healthcare robotics as well. In surgical robots, for instance, 3D printed grippers can handle specialized instruments and execute delicate tasks.</p><h3>Embedded sensors and electronic components</h3><p>Beyond these core connections, the collaboration between 3D printing and robotics unfolds into an expansive canvas of possibilities. For example, embedding sensors and electronic components directly into robotic structures using 3D printing enhances their functionality and streamlines assembly processes.</p><h3>Soft robotics</h3><p>For soft robotics – those dealing with flexible and deformable structures – 3D printing opens avenues for the creation of parts that move and adapt like real muscles. Researchers from the Delft University of Technology, for instance, have pioneered the use of <a href="https://pure.tudelft.nl/ws/portalfiles/portal/142950401/s41563_022_01429_5.pdf" target="_blank" data-lf-fd-inspected-kn9eq4roawkarlvp="true">3D printing in manufacturing soft robots with intricate viscoelastic hydrogels</a>. These materials withstand high pressures and open new possibilities in fields like medical prosthetics.</p><h3>Innovation and Education</h3><p>Moreover, 3D printing serves as a catalyst for innovation and education in robotics. It fosters creativity among students and researchers, accelerating the learning curve and driving advancements in the development of robotic systems.</p><h2>Robots as 3D Printers</h2><p>The marriage of robotics and 3D printing can even extend beyond conventional roles. Here, robots themselves become integral 3D printers, amplifying the scope and scale of additive manufacturing. Especially noteworthy in large-scale 3D printing, robotic arms deposit materials layer by layer to create objects. This facilitates the creation of parts on a scale previously unattainable in 3D printing.</p><figure><span class="fr-img-caption fr-fic fr-dii" style="width: 678px;"><span class="fr-img-wrap"><img width="1024" height="576" src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEzMjc0NzI2MDYxLTE3MTMyNzQ3MjYwNjEucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" alt="robots-as-3D-printers" srcset="https://replique.io/wp-content/uploads/2024/03/robots-as-3D-printers-1024x576.jpg 1024w, https://replique.io/wp-content/uploads/2024/03/robots-as-3D-printers-300x169.jpg 300w, https://replique.io/wp-content/uploads/2024/03/robots-as-3D-printers-768x432.jpg 768w, https://replique.io/wp-content/uploads/2024/03/robots-as-3D-printers-1536x864.jpg 1536w, https://replique.io/wp-content/uploads/2024/03/robots-as-3D-printers-1200x675.jpg 1200w, https://replique.io/wp-content/uploads/2024/03/robots-as-3D-printers.jpg 1920w" sizes="(max-width: 1024px) 100vw, 1024px" class="fr-fic fr-dii"><span class="fr-inner">Thanks to the use of robots, even houses can now be 3D printed.</span></span></span></figure><h2>Conclusion</h2><p>The intersection of 3D printing and robotics reshapes how we conceive, design, and build robotic systems. From lightweight components to customized grippers and on-demand spare parts, this collaboration represents a paradigm shift, offering tailored solutions that enhance the efficiency and adaptability of robotic systems.</p>

Explore the benefits of 3D printing for light-weight, customized and on-demand parts.

How to Leverage 3D Printing in Robotics and Automation

<blockquote><p dir="ltr" id="isPasted">“For a new product to be successful, it is not enough for it to work—almost everything around us works quite well. The design matters.”</p></blockquote><p dir="ltr">This is what Iván Arakistain, a technologist and researcher at Tecnalia said. After winning the Connect for Good challenge, we interviewed Arakistain to discuss his award-winning project on sustainable logistics infrastructure and his insights into the logistics industry, particularly that of perishable goods.</p><p dir="ltr">Logistics is a dynamic field fraught with challenges, and sustainability is a growing concern, particularly in transporting perishable goods. Iván Arakistain, representing Tecnalia, won first place in the Connect for Good challenge with an innovative concept that leverages a low-power IoT solution to monitor and enable eco-friendly transport of perishable goods.</p><p dir="ltr">The&nbsp;<a href="https://www.wevolver.com/article/connect-for-good-low-power-wireless-sustainability-challenge" target="_blank">Connect for Good Challenge</a> is a joint initiative by Nordic Semiconductor, Mouser Electronics, Soracom, and Crowd Supply aimed at fostering innovation for social and environmental good. It brought together a worldwide network of engineers, entrepreneurs, and developers around one mission: “to devise low-power wireless solutions that contribute towards achieving the United Nations Sustainable Development Goals.”&nbsp;</p><p dir="ltr">Arakistain's winning project is a game-changer in sustainable logistics. Through advanced monitoring and anomaly detection, this innovative solution optimizes the transport of perishable goods while eliminating the need for energy-intensive refrigerated cargo trailers. Thus, it significantly reduces carbon emissions and operational costs while ensuring the integrity of the cold chain. Through the integration of IoT technology, including low-energy sensors and GPS and coupled with mini solar panels for autonomy, the project sets a new standard for eco-friendly transport in the logistics industry, with broad implications for global sustainability efforts. The key component of this project is the integration of the&nbsp;<a href="https://www.wevolver.com/specs/nrf9160-development-kit" target="_blank">nRF9160 Development Kit</a>, chosen specifically for its low energy consumption and reliable cellular connectivity.</p><p dir="ltr">In this exclusive interview, Arakistain sheds light on his journey, the inspiration behind his project, and the impact he envisions on the logistics industry's future.</p><h2 dir="ltr">Interview with Iván Arakistain of Tecnalia</h2><h3 dir="ltr">Can you briefly describe your winning project and the main problem it addresses in the logistics industry?</h3><p dir="ltr">One of the main challenges of the logistics industry is the sustainable transport transition. This focuses on decarbonizing the transport industry, especially because today, at least in Spain, it’s heavily based on trucks; most of the logistics happen on the road, and trucks consume a lot of fuel. So, we have to shift from that to a cleaner way of transport.</p><p dir="ltr">Some years ago, we had a customer who sold precooked food.&nbsp;They mentioned something to us that sparked this idea process: If they could deliver the food in one day from their factory to the end customer, they would be able to avoid refrigerated transportation, especially since it’s relatively expensive, requires a lot of fuel, and is very complex.</p><p dir="ltr">So, we started exploring that. At that time, the technology was not quite ready for the application, mainly for areas like the south of Spain, where it's very hot in the summer. The logistics were not as fast as today, but as things have evolved, we are now able to transport food—or perishable goods—in proper condition to the end customer in a short a time as needed so it arrives safely.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713160590293-0_Ivan+Arakistain.jpg" style="width: 100%px;" class="fr-fic fr-dib"></p><h3 dir="ltr">What inspired you to focus on developing a more sustainable method for transporting perishable goods?</h3><p dir="ltr">Well, the main goal is to deliver goods fast and with as low carbon emissions as possible. For that, we must change from only using transport by trucks to combining truck and train, which is obviously much cleaner environmentally. By doing so we can ensure more availability, lower cost per kilometer, and a smaller cargo footprint.&nbsp;</p><p dir="ltr">Let’s look at how your project aligns with the Sustainability Development Goals. Could you tell us how your solution targets these goals?</p><p dir="ltr">Our solution’s practical implications impact goals 7 (Affordable and Clean Energy), 11 (Sustainable Cities and Communities), and 13 (Climate Action). We are very focused on energy efficiency and sustainability goals for cities and districts. Our application can help reduce the cargo footprint and accelerate the transition from the current fuel-based transport to a more electrified way of transport.&nbsp;</p><p dir="ltr">Of course, we cannot transition directly from 0 to 100%, so we must ensure the transition is smooth. In our area of focus, the technology background is in artificial intelligence for microcontrollers and small devices. We embed this intelligence into the embedded device so that we can know via our electronic sensors if the transported food is in good condition. We can also sense whether any volatile compounds can impact the condition of the food.</p><p dir="ltr">By making those changes, we estimate that up to 10% of carbon reduction is possible. And that also applies to energy consumption, even though I’m not sure if it’s a linear relationship. However, the estimation stands.</p><h3 dir="ltr">Let's move onto the technical side. How does your solution work? Is embedded AI the differentiator from traditional energy-intensive refrigeration?</h3><p dir="ltr">As I said, we are focused on AI for microcontrollers. So, first, we develop the AI models in servers and powerful computers. Then, we reduce those models so that they can run in a small controller with a few kilobytes or a small RAM. For this application, we are focused on two types of inference.</p><p dir="ltr">The first type of inference is air quality to determine the state of the product. The second inference type is AI for vibration analysis, which is something we have implemented here and in other projects. In this case, we can analyze if the vibration of products in the truck matches the orientation to make sure everything is good. In other cases, we also do this AI analysis of vibration for structural health and many other applications.</p><p dir="ltr"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1713160621737-1_Manufactured+PCB+Top.jpg" style="width: 100%px;" class="fr-fic fr-dib"></p><h3 dir="ltr">How accurate and precise are the AI model results in a real-world application?&nbsp;</h3><p dir="ltr">There are two processes that we do. First, we collect all the data, and then we perform model training and model testing on our computers. We split the data set into two parts: about 80% of the data is used for training the model, and the remaining 20% or so is used for testing the model.</p><p dir="ltr">In our computer simulation, the results were very good; you can get up to 96% accuracy. But we both know that’s not the reality. Getting a very high score virtually means the model is quite good. However, when you go into the real world and flash the firmware into the microcontroller you’re using in the actual sensor, the accuracy decreases a bit. Still, we can get good enough accuracy with this model.</p><p dir="ltr">Were there any particular challenges you faced in the development process, and how did you overcome them?</p><p dir="ltr">As I said earlier, the original idea came some years ago with this customer we were working with. They produced precooked food, and we did some real testing with them. We sent the food with a trucker and other things to actual customers. At that moment, we realized that the technology was not quite ready, not because of the electronics but because of the isolation and insulation challenges. For that, you’d need excellent isolating materials so that temperatures are kept in the correct range.&nbsp;</p><p dir="ltr">This was about eight to nine years ago. At that point, we did develop a complete solution, and we knew the idea was good, but it was not quite ready because of the isolation challenge. Since then, we have kept thinking and working on it, and now we know that it is a feasible application, especially since there are much more efficient containers for food transport than years ago. There are better isolation materials, which helped this improve.</p><h3 dir="ltr">Looking into the next decade, what are the biggest hurdles in scaling and implementing your solution across different regions and logistics networks?</h3><p dir="ltr">The main difficulty is the limited time for delivery: It has to be done within the first 24 to 48 hours and the products need a low temperature range.</p><p dir="ltr">Of course, I think it's scalable. Still, one of the biggest challenges is the container. We need to use special containers with good insulation for this. Basically, once we are able to prove to customers that they are going to save so much on each transportation, then it makes sense.</p><p dir="ltr">So far, we have tested with first-class precooked food, which is already prepared and ready for eating upon reheating in a restaurant or somewhere else. We are not so focused on uncooked food.</p><h3 dir="ltr">Let’s dive a bit into the economic side of things. What are the economic benefits of your solution for logistics companies and end users?</h3><p dir="ltr">The economic benefits mainly rely on the use of nonrefrigerated transportation. As a result, you can save about 10% of the transportation cost. It consumes less fuel and energy. It's simply more efficient. We are also very interested in combining ways of transportation: using not only trucks but also trains. Here in Spain, we have quite a robust train infrastructure, but nowadays, it is more focused on passengers than goods. So, I think we must leverage the train infrastructure and combine it with trucks in the future.</p><p dir="ltr">Also, for international shipments, combining the truck with the train is important so that the driver does not need to drive so far. The truck driver would simply mount the truck with the goods from a train carriage, move onto another carriage to collect goods from there, and then deliver them locally.</p><p dir="ltr">This is a standard logistics process. We are simply using the existing infrastructure. The only additional requirement would be the special containers for the food that I mentioned earlier.</p><h3 dir="ltr">You mentioned one customer already.&nbsp;Have you discussed this with other members of your target market? How have your target customers responded to your solution so far?</h3><p dir="ltr">Yes. 15 days ago, I was at a logistics and transportation fair in Bilbao. I gave a speech there and talked to potential customers. Clearly, there is some potential, but of course, we need to keep the focus on the end customer.</p><h3 dir="ltr">By focusing on the end customer, what strategies are you employing to encourage the adoption of your technology within the market?&nbsp;</h3><p dir="ltr">In Tecnalia, we work with different approaches to ensure the technology adoption. We identify and manage deep-tech business opportunities, through a venture building process to accelerate their industrial commercialization. We create new startups or commercialize licenses, accelerating the incubation of technological assets until they are turned into investment opportunities capable of generating economic and social value.&nbsp;</p><h3 dir="ltr">How do you see your solution evolving in the next five years?</h3><p dir="ltr">I think that it has quite a lot of potential. We are still in a transition stage, where not everything is well-defined. The path is clear: we have to shift to the electrification of transport. But the speed at which this is going to happen is yet to be clear. Yet, I think that it has significant potential for the future because logistics is a fast-growing sector.</p><h3 dir="ltr">Is there close competition to your technology from bigger companies already established in the logistics market?</h3><p dir="ltr">Yes, there is. For example, some years ago, big companies like Amazon tried to sell perishable food to end customers in a very short time. However, they were not very successful because it’s not easy to deliver that fast in a cost-efficient way.</p><p dir="ltr">Our case is a little different. It focuses on precooked food for industrial partners. The customer I mentioned earlier is not selling to end customers but rather to restaurants and other businesses. So yes, there are big players, but they are not entirely in the same market niche as ours.</p><h3 dir="ltr">Are there other applications or industries where your technology could be applied beyond the niche cold chain logistics?</h3><p dir="ltr">Yes, we use embedded AI for structural health monitoring in various cases. Here, you would need to know the condition of a structure. In Tecnalia, for example, we have some dedicated lab equipment, such as sensitive accelerometers. But the cost of such devices is typically quite high. They are not devices you can leave on a structure but rather devices you use within a specific timeframe.</p><p dir="ltr">So, we are developing small, affordable devices that can use MEMS accelerometers, inexpensive microcontrollers, and embedded AI to provide early warnings if there is an anomaly, failure, or any other event on which the model is trained.</p><p dir="ltr">The new devices we are developing would cost as much as an order of magnitude less than the typical measuring and monitoring equipment. They would only provide an early warning, after which the experts could go and perform more extensive measurement and testing.</p><p dir="ltr">Another example of our embedded AI would be audio detection. Consider a large dataset of street sounds that can take over six gigabytes – that is a lot of information. For this, it's possible to train an AI model that takes no more than kilobytes – which is significantly less – and embed it into a microcontroller to classify the sounds. We tested this, and the model was able to accurately identify and classify the sounds of a car horn, for example, a dog barking or children playing. This is a solution to tackle problems with lots of data using a compressed model that can accurately classify the data without requiring so much power.</p><h3 dir="ltr">Going back to the project experience, what have you learned from working on this project that you think is crucial for innovation in sustainable technologies?</h3><p dir="ltr">At Tecnalia, we are experts in many different fields. The most interesting thing about working in an applied research and technological development centre like Tecnalia is that I can collaborate with experts in multiple fields, such as electronics, logistics, structures, and energy efficiency. For such projects to work, I have to work with lots of people to solve the problem.&nbsp;</p><p dir="ltr">Also, experts in AI are not all the same. Some of them are more focused on big processors like NVidia devices, and we need to collaborate with them to understand the models and be able to quantize and reduce the models down to the microcontroller level.</p><p dir="ltr">What advice would you give to other entrepreneurs or innovators looking to make a difference in the green technology space?&nbsp;</p><p dir="ltr">My advice would be to prototype as much as they can. In electronics, we are quite used to prototyping fast. That's a good thing for our sector. But in other areas, it might be more difficult to prototype. I would also encourage people to collaborate with other experts and learn from them.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">Iván Arakistain's winning project exemplifies the potential for low-power IoT solutions to revolutionize the transportation of perishable goods. By leveraging advanced monitoring and anomaly detection, Arakistain's project offers a promising avenue for reducing carbon emissions and operational costs while maintaining the integrity of the cold chain.&nbsp;</p><p>As we look to the future, the scalability and adoption of such solutions present ongoing challenges. Yet the momentum generated by initiatives like the Connect for Good Challenge signals a promising trajectory towards more innovation and sustainable solutions and systems. For more information about the challenge and its winners, check out our dedicated article:&nbsp;<a href="https://www.wevolver.com/article/connect-for-good-challenge-winners-logistics-infrastructure-monitoring-smart-bees-and-bioreactors" target="_blank">Connect for Good Challenge Winners: Logistics Infrastructure Monitoring, Smart Bees and Bioreactors</a>.</p>

Introducing the Winner of the Connect for Good Challenge 2023


Towards Sustainable Logistics Infrastructure: An Interview with Tecnalia

<p dir="ltr" id="isPasted">The integration of robotics into manufacturing has radically boosted efficiency and quality and lowered costs. However, large industrial robots also pose a threat to human safety due to their size, weight, and ‘on/off’ programming. In an effort to solve this problem, collaborative robots, or cobots, have been introduced to manufacturing facilities. These lightweight robots are designed to work alongside human workers. However, cobots cannot do the same grunt work as their more industrial cousins, leaving a gap between the small save robots and the caged larger machines.&nbsp;</p><p dir="ltr">Blue Danube Robotics, the company behind <a href="https://www.airskin.io/" target="_blank" rel="noopener noreferrer">AIRSKIN®</a>, aims to solve this industrial automation problem. Their innovative product, AIRSKIN®, is a breakthrough technology designed as a soft, tactile skin for industrial robots, enabling them to work alongside humans safely. This patented solution allows robots to perform a broader range of tasks in close proximity to human workers, revolutionizing how robotic automation integrates into human-centric work environments.&nbsp;</p><h1 dir="ltr">AIRSKIN® enables an open workplace with robots</h1><p dir="ltr">The integration of robots into the industrial workforce presents a unique set of safety challenges. The limitations of traditional safety measures and even the most advanced cobots have made it clear that a new approach is needed to harmonize human-robot collaboration. This is where AIRSKIN® enters the scene, offering a solution that addresses these challenges.</p><p dir="ltr">AIRSKIN® seamlessly integrates with industrial robots and transforms traditional manufacturing setups into collaborative environments. This reduces hazardous collision areas and ensures maximum safety with PLe / Cat. 3 certification.</p><h3 dir="ltr">Upgrade to Collaborative Robots</h3><p dir="ltr">AIRSKIN® transforms standard industrial robots into fenceless, collaborative &nbsp;robots. This transformation allows robots to operate in close proximity to humans and other machinery, enhancing productivity and efficiency while maintaining a safe working environment. By eliminating the need for physical barriers, AIRSKIN® enables seamless collaboration between humans and robots, optimizing workflow and reducing downtime. AIRSKIN® works with all robots, already off-the-shelves products exist for KUKA (Agilus 2, Cybertech 2, Iontec, Quantec 2), Stäubli (TX2touch) and Epson (Scaraflex).&nbsp;</p><p dir="ltr"><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyNzM5Mzg3MDk5LXNlY29uZC5qcGciLCJlZGl0cyI6eyJyZXNpemUiOnsid2lkdGgiOjk1MCwiZml0IjoiY292ZXIifX19" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">AIRSKIN® transforms standard industrial robots into fenceless, collaborative robots. Credit: AIRSKIN.</span></span></span></p><h3 dir="ltr" id="isPasted">Innovative Design and Functionality</h3><p dir="ltr">AIRSKIN® comprises soft and robust air-filled pads mounted on the robot, with embedded safety electronics that monitor internal air pressure and instantly react to contact anywhere on the robot. Dual-channel pressure sensors detect pressure changes, ensuring precise and rapid response to potential collisions in a maximum of 8ms.</p><p dir="ltr">Equipped with intelligent sensors and real-time monitoring capabilities, AIRSKIN® detects and responds to potential hazards with precision and speed, mitigating the risk of accidents and injuries. Certified according to ISO 13849 with the highest safety performance level PLe / Cat.3, AIRSKIN® meets all industrial requirements, including high durability and IP54 rating.</p><p dir="ltr">AIRSKIN® offers adaptability to various industrial environments and applications, seamlessly integrating into existing workflows. Its space-saving design eliminates the need for physical barriers, optimizing floor space by up to 90% compared to traditional fencing systems, thereby unlocking new possibilities for layout optimization and production line flexibility.</p><h1 dir="ltr">How&nbsp;AIRSKIN® is used</h1><p dir="ltr">AIRSKIN® can be added to industrial robot applications to enable them to have ‘collaborative’ status by effectively covering all hazardous collision areas. The maximum speed for the robot is based on a risk assessment and can reach up to 2m/s in collaborative mode.</p><p dir="ltr">AIRSKIN® can help large payload robots achieve movement speed of beyond 1 m/s, while still maintaining operator safety. The AIRSKIN® Modules help absorb energy and distribute the contact forces associated, helping them avoid serious injury.</p><h3 dir="ltr">Clamping/shearing collisions</h3><p dir="ltr">For clamping/shearing collisions the robot’s stopping distance must be shorter than the thickness of the soft AIRSKIN®. In order to accommodate these stringent application areas, a risk assessment needs to be done to determine the permissible speed. It must be obtained by measuring contact forces occurring in the collision using an ISO/TS 15066 compliant measurement device.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEyNzM5NDIyODQyLXRoaXJkLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Safer workplaces enable more agile and customized workflows. Credit: AIRKSIN.</span></span></span></p><h1 dir="ltr" id="isPasted">Advancing Industry 4.0</h1><p dir="ltr">Robotics is a pivotal component of Industry 4.0. AIRSKIN® enables companies to convert existing robotic setups to collaborative, human-safe zones. This can radically increase efficiency by reducing downtime usually created by safety cage protocols. It also enables more agile and customized workflows, which are integral to meeting industry 4.0 goals.&nbsp;</p><p dir="ltr">Industry 4.0 benefits With AIRSKIN®</p><ul><li dir="ltr"><p dir="ltr">Speed:&nbsp;Up to 6 times faster than applications with cobots, achieving speeds of up to 2 m/s for collaboration.</p></li><li dir="ltr"><p dir="ltr">Flexibility:&nbsp;Safety system mounted on the robot offers flexibility in location and task execution.</p></li><li dir="ltr"><p dir="ltr">Space Optimization:&nbsp;Optimizing installation footprint by up to 90%, maximizing operational efficiency.</p></li><li dir="ltr"><p dir="ltr">Payload &amp; Reach: Supporting a wide range of payloads and reaches for various robotic applications. – like AIRSKIN for KUKA Quantec – up to 300 kg payload&nbsp;</p></li><li dir="ltr"><p dir="ltr">Precision:&nbsp;Offering highest accuracy, repeatability, and path consistency for precise operations.</p></li><li dir="ltr"><p dir="ltr">Risk Assessment: Prepared safety concept simplifies application assessments, ensuring compliance with safety standards.</p></li></ul><h3 dir="ltr">AIRSKIN Safetyflange: Advancing Safety and Efficiency</h3><p dir="ltr">AIRSKIN Safetyflange, a compliant robotic collision sensor, extends the safety benefits of AIRSKIN® technology to protect tools, workpieces, and individuals from potential hazards. Designed for easy mounting with any robot and end-of-arm tool, AIRSKIN Safetyflange ensures compliance with safety standards while maximizing workplace safety without compromising productivity.</p><p dir="ltr">Key Features:</p><ul><li dir="ltr"><p dir="ltr">Finger-Safe Applications:&nbsp;Eliminates crushing and clamping risks, ensuring compliance with safety standards.</p></li><li dir="ltr"><p dir="ltr">Adjustable Break-Away Forces:&nbsp;Absorbs energy and reduces pressure and force during collisions in collaborative applications.</p></li><li dir="ltr"><p dir="ltr">Compliance in All Directions:&nbsp;Adjustable compliance minimizes contact forces, maximizing workplace safety.</p></li><li dir="ltr"><p dir="ltr">Easy Mounting Compatibility:&nbsp;Simplifies integration with any robot and tool using exchangeable ISO 9409-1 adapters.</p></li><li dir="ltr"><p dir="ltr">Safety Certification: Complies with the PLe / Cat. 3 ISO 13849 safety certification, setting a new standard for safe and efficient operations.</p></li></ul><p dir="ltr"><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/5ljMxWZMLvc?&amp;feature=youtu.be&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span><span class="fr-mk" style="display: none;">&nbsp;</span></iframe></span></p><h1 dir="ltr">Versatile Applications</h1><p dir="ltr">AIRSKIN Safetyflange is suitable for a wide range of applications, including palletizing, machine tending, visual inspection, gluing, and more, making it a versatile solution for enhancing safety and efficiency in industrial environments.</p><p dir="ltr">AIRSKIN® marks a pivotal advancement in the sphere of industrial automation, fundamentally transforming how safety and efficiency are perceived and implemented in collaborative workspaces. By integrating AIRSKIN® into your operations, your business can set new benchmarks for what is possible to achieve with human-robot collaboration. There is immense potential for AIRSKIN®-equipped robots to redefine and enrich collaborative workspaces, allowing a future where robots and humans can work side by side with unprecedented efficiency and safety.</p><p dir="ltr">As we move further into the Industry 4.0 era, the impact of <a href="https://www.airskin.io/" target="_blank" rel="noopener noreferrer">AIRSKIN®</a> represents the endless possibilities at the intersection of innovation, safety, and collaboration. It fosters a new period of industrial productivity and human-centric automation.</p>

AIRSKIN® revolutionizes industrial automation by adding air-powered tactile skin for to robots to create safe, collaborative environments, ensuring maximum safety with PLe / Cat. 3 certification.

Bringing Freedom into Factories: How Air-Powered Tech Paves the Way for Human-Robot Harmony

<p dir="ltr" id="isPasted">Machine vision represents a fascinating fusion of hardware and software designed to empower machines to 'see' and interpret the world around them. This technology, critical in manufacturing, automates tasks that require visual understanding, enhancing efficiency, accuracy, and safety on production lines. From guiding robots to inspecting products for defects, machine vision systems translate visual inputs into actionable information, fundamentally transforming industrial operations.</p><p dir="ltr">The evolution of machine vision is closely linked with advancements in artificial intelligence (AI). As AI technologies have progressed, they have significantly enhanced the capabilities of machine vision systems, enabling more complex and nuanced interpretation of visual data. This integration has been pivotal in driving Industry 4.0, the ongoing automation of traditional manufacturing and industrial practices using smart technology.&nbsp;</p><p dir="ltr">Augmented with AI, machine vision is an enabler of automation and a key player in creating intelligent, adaptable, and efficient manufacturing environments. The convergence of machine vision and AI pushes the boundaries of what's possible in industrial automation, leading to unprecedented productivity and innovation.</p><h2 dir="ltr">The Core Components of Machine Vision Systems</h2><p dir="ltr">Machine vision systems are intricate setups that combine hardware and software to enable machines to 'see' and make decisions based on visual inputs. These systems play a pivotal role in various applications, including automated inspection, process control, and guiding robots, significantly contributing to industrial automation and robotics advancements.</p><h3 dir="ltr">Hardware: The Observational Components</h3><p dir="ltr">The foundational hardware components of machine vision systems include cameras and sensors, which serve as the 'eyes' of the system. Cameras equipped with image sensors capture visual data from the environment. These cameras range from simple, compact smart cameras for specific applications to more complex multi-camera systems offering greater versatility and data collection capabilities.&nbsp;</p><p dir="ltr">Sensors are crucial for determining the system's specifications, such as resolution, directly impacting the amount of data collected and the system's ability to discern environmental changes. The resolution, defined by the total number of pixels in rows and columns, affects not just image clarity but also processing speed, with monochrome cameras often facilitating faster processing than colour counterparts due to simpler data.</p><h3 dir="ltr">Software: The Analytical Brain</h3><p dir="ltr">On the software side, machine vision systems are powered by image processing algorithms and, increasingly, by deep learning and AI technologies. These software components are tasked with analysing and interpreting the captured visual data. The traditional approach to image processing involves predefined rules for analysing images, which can be rigid and limited in scope.</p><h3 dir="ltr">The Synergy of Hardware and Software</h3><p dir="ltr">The interplay between the hardware's data collection capabilities and the software's processing power defines the effectiveness of a machine vision system. Adequate lighting and lens quality are also essential, ensuring the captured images are clear and detailed enough for accurate analysis. For example, the choice between backlighting and front-lighting setups can significantly affect the system's ability to capture usable data in real-world applications.</p><h2 dir="ltr">Implementing Machine Vision in Manufacturing</h2><p dir="ltr">Implementing machine vision in manufacturing significantly enhances quality control, defect detection, and production efficiency. This technology integration facilitates a transition from manual to intelligent manufacturing, providing high detection accuracy, speed, and improved coordination.</p><p dir="ltr">Machine vision technology is pivotal in collecting information, detecting quality, positioning, automatic sorting, and intelligent monitoring within manufacturing. It addresses the challenges of poor quality, low precision, low efficiency, and high labour intensity traditionally associated with manual operations. Adopting machine vision in manufacturing accelerates production, elevates detection accuracy, and minimises the risk of accidents.</p><h3 dir="ltr">Enabling Algorithms for Object Detection, Identification, and Classification</h3><p dir="ltr">The backbone of machine vision systems in industrial settings is a suite of sophisticated algorithms that enable object detection, identification, and classification. These algorithms leverage optical tools and computer technology to collect and process data, simulating human visual functions.&nbsp;</p><p dir="ltr">With the advancement of AI, machine learning, and deep learning technologies, machine vision systems have achieved higher accuracy and broader application scopes. These technologies encourage traditional manufacturing processes to become more intelligent, requiring expertise across various fields, including optical imaging, signal processing, image processing, and deep learning.</p><h2 dir="ltr">Current Trends and Future Directions</h2><p dir="ltr">The evolution of machine vision technology is rapidly transforming the landscape of industrial automation, robotics, and beyond. At the forefront of this transformation is integrating 3D imaging and point cloud data, offering enhanced spatial analysis and enabling more complex applications. Let's explore the cutting-edge research and predictions for future advancements in this exciting field.</p><h3 dir="ltr">Integrating 3D Imaging and Point Cloud Data</h3><p dir="ltr">In recent years, significant advancements have been made in deep learning techniques for processing 3D point cloud data. This has revolutionised applications in fields such as robotics and autonomous vehicles by allowing for detailed spatial analysis of environments. Techniques exist for various tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. Transformative methodologies like Pointr and SnowflakeNet have emerged, highlighting the potential for these technologies to evolve further and impact various domains.</p><h3 dir="ltr">Predictions for Future Advancements</h3><ul><li dir="ltr"><p dir="ltr">Augmented Reality (AR) Applications: AR's immersive experience can benefit greatly from advancements in machine vision by providing more accurate and real-time interaction with the physical world. This could revolutionise medical imaging, retail, and education by offering more intuitive and interactive ways to engage with information and environments.</p></li><li dir="ltr"><p dir="ltr">Further Integration with Robotic Process Automation (RPA): Machine vision technology allows robots to perform more complex tasks more efficiently and accurately. Combining machine vision and RPA could lead to significant productivity boosts from manufacturing to logistics by automating tasks that require understanding and interpreting visual data.</p></li><li dir="ltr"><p dir="ltr">Ease of Use and Accessibility: There's a trend towards making machine vision systems more accessible and easier to use. Efforts are being made to develop "ease-of-use" and "no-code" solutions to democratise the technology, making it more accessible to non-experts.</p></li><li dir="ltr"><p dir="ltr">Growth in 3D Imaging for Vision Guided Robotics: 3D imaging is no longer emerging but becoming a mature automated imaging offering. From bin picking to product palletizing, these solutions promise to expand the use of VGR by making it more user-friendly and applicable to a broader range of use cases.</p></li><li dir="ltr"><p dir="ltr">AI and Deep Learning as Automated Inspection Tools: Deep learning has become a valuable tool for segmentation and classification in automated inspection. It enables the identification of images or features within images more intuitively, akin to human perception.</p></li></ul><h2 dir="ltr">Practical Applications of Machine Vision in Industry</h2><p dir="ltr">Machine vision technology transforms operations across various sectors, particularly in the automotive and electronics industries, by enhancing product quality and operational efficiency.</p><h3 dir="ltr">Automotive Industry</h3><p dir="ltr">Machine vision is essential in inspections and robotic guidance in the automotive sector. Companies like Ford and General Motors (GM) use machine vision to ensure the safety and efficiency of manufacturing processes. Machine vision systems are employed for various applications, including flexible automation, model verification, and quality improvement. The technology offers significant advantages such as adaptable tooling, model verification on flexible lines, and in-station process control for immediate feedback. This enables manufacturers to uphold stringent quality standards and eliminate waste, ultimately contributing to producing a quality product.</p><h3 dir="ltr">Oil and Gas Industry</h3><p dir="ltr">Machine vision is critical in inspections and robotic guidance in the oil and gas sector. Companies like Wipro are leveraging machine vision in several cutting-edge ways, including visual inspections of oil rigs, automated quality inspections, and remote monitoring of safety-critical assets. For instance, machine vision enables magnetic crawlers with cameras to inspect difficult-to-reach areas, such as great heights and remote locations within industrial plants. Robots equipped with machine vision can proactively detect internal cracks in gas pipelines, potentially saving millions of dollars by preventing failures. Moreover, these robots can grade the corrosion of pipes based on drone-captured images and videos, facilitating more preventive and cost-effective operations.</p><h3 dir="ltr">Manufacturing Industry</h3><p dir="ltr">Machine vision systems are employed for various applications, including flexible automation, model verification, and quality improvement. The technology offers significant advantages such as adaptable tooling, model verification on flexible lines, and in-station process control for immediate feedback. This enables manufacturers to uphold stringent quality standards and eliminate waste, ultimately contributing to producing a quality product.</p><h3 dir="ltr">Electronics Industry</h3><p dir="ltr">While the specifics on electronics were not directly covered in the sources, it's evident that the benefits and applications of machine vision in the automotive industry, such as inspection, quality control, and robot guidance, are also pertinent to the electronics sector. High precision and accuracy are essential in electronics manufacturing, where machine vision can identify defects, verify component placement, and ensure product integrity, much like in automotive manufacturing.</p><h3 dir="ltr">Key Benefits</h3><ul><li dir="ltr"><p dir="ltr">Increased Efficiency: Automation of inspection processes and real-time decision-making capabilities speed up production lines and reduce manual intervention.&nbsp;</p></li><li dir="ltr"><p dir="ltr">Reduced Error Rates: High-resolution cameras and sophisticated software in modern machine vision systems enable precise defect detection, minimising the risk of errors.</p></li><li dir="ltr"><p dir="ltr">Enhanced Safety: In the automotive industry, ensuring the proper assembly of over 30,000 parts per vehicle is crucial for driver safety. Machine vision facilitates the flawless fabrication of each component.</p></li><li dir="ltr"><p dir="ltr">Flexibility and Adaptability:&nbsp;Companies highlight the demand for more flexible automation as a driver for increased use of machine vision. The technology's adaptability allows easy integration into existing production lines, supporting model verification and quality improvements without needing hard-tooled custom fixtures.</p></li><li dir="ltr"><p dir="ltr">Improved Quality: The ability of machine vision systems to provide feedback for in-station process control plays a vital role in quality assurance. By enabling the detection of defects at the source, these systems help maintain high-quality standards and reduce the cost and complexity of end-of-line repairs.</p></li></ul><p dir="ltr">Machine vision technology continues to evolve, offering scalable and versatile solutions that cater to the growing demands of industrial automation.</p><h2 dir="ltr">Challenges and Considerations</h2><p dir="ltr">Adopting machine vision technologies has several challenges and considerations that organisations must navigate to leverage their full potential effectively. These challenges include high initial costs, integration complexities, and the need for specialised expertise. By addressing these factors, organisations can better position themselves to take advantage of the numerous benefits that machine vision technologies offer.</p><h3 dir="ltr">High Initial Costs</h3><p dir="ltr">Implementing machine vision technologies often requires a significant initial investment. This includes the cost of hardware such as cameras and sensors, software for image processing and analysis, and the infrastructure needed to support these technologies. While the costs of components and engineering have decreased, making the technology more accessible, the initial setup still represents a substantial financial commitment.</p><h3 dir="ltr">Integration Complexities</h3><p dir="ltr">Integration involves ensuring compatibility between new and existing systems, configuring machine vision technologies to meet specific operational needs, and optimising performance within the current workflow. This complexity is compounded by the rapid pace of technological advancement, which introduces new standards and capabilities that systems must adapt to.</p><h3 dir="ltr">Need for Specialized Expertise</h3><p dir="ltr">Organisations often need professionals skilled in both the technical aspects of machine vision systems and the specific applications within their industry. This expertise is necessary for designing effective solutions, troubleshooting issues, and updating systems to incorporate the latest advancements in the field. The challenge here is finding and retaining talent with the necessary skill set.</p><p dir="ltr">By proactively addressing these challenges, organisations can harness the power of machine vision technologies to drive innovation and competitive advantage in their operations.</p><h2 dir="ltr">Conclusion</h2><p dir="ltr">The transformative impact of machine vision on manufacturing is undeniable. Machine vision has become a cornerstone of modern industrial automation through its ability to automate complex inspection tasks, enhance quality control, and drive efficiencies. Its application spans various sectors, providing unparalleled accuracy and speed in tasks once deemed challenging for automated systems. Machine vision systems minimise downtime, reduce waste, and ensure product quality by enabling more reliable and faster inspections, significantly improving manufacturing.</p><p dir="ltr">The journey of machine vision from a supportive technology to a pivotal component of industrial automation underscores its transformative impact on manufacturing. As we look to the future, continued innovation, coupled with strong partnerships between technology providers and manufacturers, will be key to unlocking the full potential of machine vision. Embracing these advancements will ensure that industries keep pace with modern production demands and set new standards for efficiency, quality, and innovation.</p><h2 dir="ltr">References</h2><ol><li dir="ltr"><p dir="ltr"><a href="https://www.automate.org/vision/industry-insights/trends-and-lessons-from-the-2021-machine-vision-market" target="_blank">https://www.automate.org/vision/industry-insights/trends-and-lessons-from-the-2021-machine-vision-market</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.frontiersin.org/articles/10.3389/frobt.2021.627280/full" target="_blank">https://www.frontiersin.org/articles/10.3389/frobt.2021.627280/full</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.cognex.com/what-is/deep-learning/challenges-to-machine-vision" target="_blank">https://www.cognex.com/what-is/deep-learning/challenges-to-machine-vision</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.automate.org/vision/industry-insights/machine-vision-in-the-automotive-industry" target="_blank">https://www.automate.org/vision/industry-insights/machine-vision-in-the-automotive-industry</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.zebra.com/us/en/blog/posts/2021/why-auto-manufacturers-should-take-a-second-look-at-new-machine-vision-systems.html" target="_blank">https://www.zebra.com/us/en/blog/posts/2021/why-auto-manufacturers-should-take-a-second-look-at-new-machine-vision-systems.html</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.automate.org/vision/industry-insights/machine-vision-in-the-automotive-industry" target="_blank">https://www.automate.org/vision/industry-insights/machine-vision-in-the-automotive-industry</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://www.vision-systems.com/factory/article/14300295/machine-vision-and-imaging-trends-in-automation-a-view-from-the-trenches-for-2024" target="_blank">https://www.vision-systems.com/factory/article/14300295/machine-vision-and-imaging-trends-in-automation-a-view-from-the-trenches-for-2024</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://news.mit.edu/2019/deep-learning-point-clouds-1021" target="_blank">https://news.mit.edu/2019/deep-learning-point-clouds-1021</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://pubs.aip.org/aip/acp/article-abstract/2788/1/110005/2903916/An-overview-of-machine-vision-based-quality?redirectedFrom=fulltext" target="_blank">https://pubs.aip.org/aip/acp/article-abstract/2788/1/110005/2903916/An-overview-of-machine-vision-based-quality?redirectedFrom=fulltext</a></p></li><li dir="ltr"><p dir="ltr"><a href="https://aimagazine.com/articles/machine-vision-enabling-the-factories-of-the-future" target="_blank">https://aimagazine.com/articles/machine-vision-enabling-the-factories-of-the-future</a></p></li></ol>

Discover the revolutionary impact of machine vision in manufacturing. Learn how integrating AI and machine vision enhances efficiency, quality, and safety in industrial automation, and explore the challenges and future advancements in this cutting-edge technology.

Transforming Manufacturing with Machine Vision Technology

<p dir="ltr" id="isPasted"><a href="https://r2-labs.io/" target="_blank">R2 Labs</a>’ goal is to make modern automation equipment systems more accessible by providing today’s software engineers with a tool that makes interaction and control easier, regardless of the specific application.&nbsp;</p><p dir="ltr">Successful projects – such as the development of an AI-powered agricultural robot for Padma AgRobotics with Arduino products – encouraged the team to pursue a combination of versatility and customization, making the range of applications they can tackle almost limitless.&nbsp;</p><p dir="ltr"><br></p><p><strong>Clear Requirements to Lower Barriers</strong></p><p dir="ltr">Wanting to create a “general-purpose controller”, R2 Labs knew it had to be programmable with modern languages, extensible, and have the computational power to manage motor control, I/O control, machine vision, and more. They decided to keep exploring with Arduino Pro hardware to find an innovative solution that would be <strong>open-source, extensible without vendor lock-in, and capable of providing a host of common functionalities customers need in almost any autonomy application</strong>.</p><p dir="ltr">With these requirements in mind and previous positive experience using Arduino microcontrollers, R2 Labs decided to develop their project with the <a href="https://www.arduino.cc/pro/portenta-machine-control" target="_blank">Portenta Machine Control</a>. This allowed them to cut down on time-to-market and quickly integrate robust, industrial-grade features – from the dual-core processor that allows for the segmentation of critical sections, to CAN bus support.</p><p><br></p><p dir="ltr"><strong>Unlocking Opportunities for All, with Open Source</strong></p><p dir="ltr">Instead of spending huge internal resources on developing their own control unit or going through complex sales processes, by partnering with Arduino R2 Labs immediately gained access to <strong>support, documentation, and high-quality products tested by a community of millions</strong>.</p><p dir="ltr">“We achieved exactly what we were aiming for: a solution well-suited for both modern robotics and industrial automation applications. Thanks to the Portenta Machine Control’s advanced features and Arduino’s prompt support, we can now enable any software engineer to tackle a broad range of applications and solve challenges that are common to most control systems,” says CEO Roby Lynn, who co-founded R2 Labs with Sulisay Phonekeo and Rebecca Peterson.</p><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1711267873603-controller+1.png" style="width: 100%px;" class="fr-fic fr-dib"></p><p dir="ltr"><strong>Meet RAC: A Paradigm Shift in Control Systems</strong></p><p dir="ltr">R2 Labs’ vision led to the launch of the RAC, a fully integrated ROS 2-native autonomy control solution that supports multi-axis motion control and is able to easily interface with a wide range of sensors and actuators. The device simplifies development with an array of digital and analog I/O, advanced vision processing, and standard connectivity (HDMI, Ethernet, USB). It allows users to effortlessly program with Python® and C++ and control/manage the project with the included interactive web HMI.&nbsp;</p><p dir="ltr"><strong>All in all, the RAC is a game-changing product for control and monitoring – both in robotics and industrial automation applications</strong>. The Arduino Portenta Machine Control is the brain of this brilliant solution to make Industry 4.0 and 5.0 accessible to software engineers, ready to tackle any specific application they may need.</p><p><br></p><p dir="ltr"><strong>Creating New Solutions for Modern Robotics and Control</strong></p><p dir="ltr">With a clear vision of the challenges in their field, R2 Labs is committed to creating modern robotics and control system solutions that champion flexibility and ease of use.</p><p dir="ltr">Pairing professional expertise with outside-the-box thinking, the company is now ready to tackle bigger deployments, new customers, and different industries. Choosing <strong>open source is key to this growth path, allowing them to expand their portfolio by focusing on the work they do without constraints</strong>.</p>

R2 Labs: democratizing robotics and industrial automation with Arduino

Manufacturing, agriculture, healthcare, and more: each industry needs specific robots and specific solutions to run and control them. But “Robotics for the Real World” provider R2 Labs, based in the United States, has set out to change that.

R2 Labs Presents RAC: An Industry-Agnostic Controller

<h2 id="isPasted">What Is a Smart Factory?</h2><p>A smart factory is a factory that achieves operational process improvements, higher quality, and lower costs based on digital data by connecting processes from design to development, manufacturing, and maintenance in a network. In the manufacturing industry, it refers to automating various processes in a factory to realize product quality and productivity improvements, a reduction in manufacturing costs, a shortening of the commercialization, mass production and delivery periods, and other areas of improvement. In other words, a smart factory is a factory working on digital transformation (DX) that aims to transform the entire business process from design to manufacturing, maintenance, and logistics in addition to improving productivity by automating factory equipment.</p><p>We introduce here the results of a questionnaire survey* we conducted with people involved in promoting the conversion of regular factories into smart factories in the manufacturing industry in Japan.</p><p>*About the Questionnaire Survey<br>Organization conducting the survey: Murata Manufacturing Co., Ltd.<br>Survey targets: 11,084 workers in the manufacturing industry in Japan (screening survey) and 500 people working in the manufacturing industry who are involved in converting regular factories into smart factories in their companies (main survey)<br>Survey method: Internet survey<br>Survey period: Three days from January 25 (Wed.) to 27 (Fri.), 2023<br>*Total figures may not match due to rounding.</p><h3>State of Progress in Converting Regular Factories into Smart Factories</h3><p>We learned that over 38% of factories are in the process of converting into smart factories or considering converting into a smart factory in the questionnaire we conducted this time (Fig. 1). We can see a trend for even more companies to convert their regular factories into smart factories in the future.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5NTg1NzUzLWltZzAwMDFfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 1: Situation concerning the conversion of regular factories into smart factories</span></span></span></p><p>Moreover, the most common reason for converting regular factories into smart factories is to improve productivity. The next most common reason is to reduce costs (Fig. 2).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5NjU1OTgxLWltZzAwMDJfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 2: Reasons for converting regular factories into smart factories</span></span></span></p><h3 id="isPasted">Main Factors Behind Inability to Promote the Conversion of Regular Factories into Smart Factories</h3><p>On the other hand, the following are given as the main factors behind the inability to promote conversion of regular factories into smart factories: following on from the understanding of top management, the establishment of specialist teams, the acquisition and training of specialist human resources, cooperation with external partners and other human resources and know-how shortage issues (Fig. 3).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5Njg1NDc4LWltZzAwMDNfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 3: Main factors behind inability to promote the conversion of regular factories into smart factories</span></span></span></p><p>In addition, we learned that even factories working to convert into smart factories are suffering setbacks in terms of data analysis and prediction and data collection and accumulation (Fig. 4).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5NzA2MzM1LWltZzAwMDRfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 4: Steps where setbacks are being suffered in the conversion of regular factories into smart factories</span></span></span></p><p id="isPasted">It is thought that how to overcome these steps will be the key to promoting the conversion of regular factories into smart factories. We will consider these issues using the example of short stoppages, which are one type of equipment loss that are a major factor adversely affecting the productivity of smart factories, from here onward.</p><h2>Short Stoppages: Blind Spot in the Conversion of Regular Factories into Smart Factories</h2><p>Equipment loss arising in each process is a major issue that must be resolved for the conversion of regular factories into smart factories that greatly contributes to improving productivity in factories.</p><p>The types of equipment loss include stoppage losses resulting from failures, setup changes, jig replacements and startup, defective product losses, speed reduction losses, and short stoppage losses. In particular, short stoppages are called idling losses and are losses included in the time when, in principle, the equipment could be demonstrating its performance (net operating time) (Fig. 5).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5NzI4Nzg2LWltZzAwMDVfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 5: Equipment loss times and types</span></span></span></p><h3 id="isPasted">Short Stoppages: Unseen Equipment Loss Lurking in Manufacturing Lines</h3><p>Setup change, jig replacement, and startup losses are planned. In addition, as it takes a long time to recover from failure losses, we keep a record showing the operational status. However, short stoppages occur unexpectedly on the line and workers at the worksite recover from it and resume work in a short time. The shortness of this time until recovery means that a record of these short stoppages is not kept in most cases. Furthermore, even if attempts are made to keep records of short stoppages, there is currently no time to do so because a speedy recovery is sought.</p><p>If we do not record the time of a short stoppage, the operating rate is calculated without including that time. Accordingly, a difference arises with the actual operating rate. Furthermore, the cause of long equipment stoppages such as failure losses may be lurking in short stoppages. It is possible to see cases in which problems in the operating rate due to such short stoppages are overlooked even in factories that have been converted into smart factories.</p><h3>Does Converting Regular Factories into Smart Factories Produce Short Stoppages?</h3><p>Many industrial robots and automatic machines are installed in the manufacturing lines of smart factories. These comprise precision components. It is not possible to avoid a decline in their precision due to the usage burden and degradation over time. If the precision of these components declines, malfunctions may occur. For example, an industrial robot may fail to grip work or a deviation may arise in the trajectory of a robot arm and it may not be possible to move the part equivalent to the fingers of the robot called the end effector to the correct position. Such malfunctions may cause short stoppages.</p><h2>Reasons Why Short Stoppages Are Not Eliminated and Countermeasures</h2><p>Why are short stoppages, which cause various problems in factories in addition to obstructing improvement in productivity, not eliminated? The reason is that in the midst of frequent short stoppages on a daily basis, there is an atmosphere in which manufacturing worksites do not take the situation seriously.</p><p>The most effective way to prevent such a situation is to calculate and visualize the losses from short stoppages. However, we would need to enter the process, cause, and equipment stoppage time on a worksheet each time such a short stoppage occurs and then aggregate that data to calculate it, which would interfere with production. Moreover, it is difficult to identify the cause of short stoppages by collecting data once a week or once a month.</p><p>We obtained results from the questionnaire showing that standardization and visualization using digital technologies and optimization of equipment maintenance with sensors and AI are effective ways of resolving such problems in factories which have been successful in promoting their conversion into smart factories (Fig. 6).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5NzUwNzg4LWltZzAwMDZfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 6: Initiatives in promoting the conversion of regular factories into smart factories</span></span></span></p><h2 id="isPasted">Clues to Reducing Short Stoppages</h2><p>We need data on the operating status of equipment to reduce short stoppages. For that, we must be able to accurately and quickly record the equipment stoppage times due to the frequent short stoppages and their causes. We introduce here the m-FLIP solution tool that helps to resolve such issues.</p><h3>What Is m-FLIP?</h3><p>m-FLIP is a solution that combines Murata Manufacturing’s worksite improvement know-how and IT technologies to maximize the operating rate of manufacturing devices. This solution makes it possible to quantify factors such as operating rate, actual working rate, and equipment stoppages to increase production and achieve optimal maintenance. This greatly contributes to the visualization of problems that are difficult to reveal such as short stoppages.</p><p>Furthermore, m-FLIP calculates and outputs the operating rate and actual working rate based on data obtained from PLCs made by multiple manufacturers. In addition, it collects workers’ work information through touch-panel PCs. As a result, we can clarify the duration and number of equipment stoppages, which was previously done by guesswork. We are also able to clearly see the main causes of equipment stoppages and the issues that must be resolved (Fig. 7).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5Nzc2NTU0LWltZzAwMDdfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 7: Services provided by m-FLIP</span></span></span></p><h3 id="isPasted">m-FLIP Utilization Examples</h3><p>m-FLIP can support the resolution of various issues at manufacturing worksites. We introduce here some typical utilization examples.</p><h4>Equipment maintenance:</h4><p>With checks by humans, it is only possible to monitor the operating status temporarily. However, m-FLIP can continuously monitor the operating status of equipment. It is possible to continuously collect data over several months or several years. This allows us to grasp the aging and changes over time of equipment. As a result, it enables maintenance at the optimal timing while minimizing the impact on the operating rate to the greatest possible extent.</p><h4>Quality control:</h4><p>For example, it is possible to monitor static electricity generation status based on data from temperature sensors and humidity sensors around the equipment and to output an alert when the set threshold is exceeded. We can utilize this in quality control such as to prevent the outflow of defective products. Moreover, we can also collect data including information on vibrations, current, and voltage. We can then integrate the data we have obtained from multiple sensors and output it as a single piece of information.</p><h4>Work procedure review, equipment improvement, and personnel allocation optimization:</h4><p>We can grasp the movements of workers and the operating status of equipment as quantified information. Therefore, we can also utilize it to consider work procedure reviews, equipment improvements, and personnel allocation optimization.</p><h4>Preparation of reports:</h4><p>m-FLIP comes with a function to prepare reports that can be used for reporting (Fig. 8). You can display information in charts and tables. It is also possible to input and save comments. You can share the reports you have prepared.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5ODAwNTEwLWltZzAwMDguanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 8: Monthly report screen</span></span></span></p><h4 id="isPasted">After-sales support by professionals:</h4><p>After introducing m-FLIP, we can identify issues from the data we have collected and receive suggestions for improvements and other advice from professionals familiar with production technologies, factory technologies, and other technologies.</p><h2>m-FLIP Introduction Case Study (Example of Ogaki Murata Manufacturing)</h2><p>We now introduce a case study from Ogaki Murata Manufacturing in which they resolved frequently occurring problems including short stoppages due to tray replacement in the product-washing process and short stoppages due to component adhesion errors with the m-FLIP operating rate improvement solution tool.</p><h3>Pursuit of the Causes</h3><p>We intuitively knew that short stoppages were occurring in the tray/rack refilling machine at the manufacturing worksite. However, we were not able to quantify the stoppage time or the number of stops. Accordingly, we collected data on the tray/rack refilling machine and the component adhesion device with m-FLIP. We uncovered the following problems as a result.<br>・Non-operation due to waiting for tray replacement: 12%<br>・Non-operation due to adhesion errors: 8%</p><h3>Improvement 1: Reduction in the Tray Replacement Time</h3><p>We now display on ANDON the number of trays remaining until the completion of the tray replacement (Fig. 9). We display in ANDON the timing of the tray replacement before the work of refilling the product into the tray is complete. This has meant we have been able to greatly reduce the time spent waiting for tray replacement.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5ODIxMDM5LWltZzAwMDkuanBnIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 9: Countdown ANDON that predicts the equipment work completion</span></span></span></p><h3 id="isPasted">Improvement 2: Resolution of Short Stoppages</h3><p>We analyzed in detail the contents of the short stoppages. As a result, we ascertained that 80% or more of the short stoppages were component adhesion errors (Fig. 10 and Fig. 11).</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5ODQyMjMyLWltZzAwMTBfZW4ucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 10: Breakdown of the equipment operating rate and non-operating rate items</span></span></span></p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 768px;"><span class="fr-img-wrap"><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzEwMTI5ODU2NjE4LWltZzAwMTBhLmpwZyIsImVkaXRzIjp7InJlc2l6ZSI6eyJ3aWR0aCI6OTUwLCJmaXQiOiJjb3ZlciJ9fX0=" style="width: 100%px;" class="fr-fic fr-dib"><span class="fr-inner">Fig. 11: Operating status Gantt chart</span></span></span></p><p id="isPasted">We found that the cause of the component adhesion errors was changes in the clearance between the product adhesion nozzle and product due to changes over time in the washing rack jig. As a countermeasure, we shortened the gap between the adhesion nozzle and the product. This allowed us to significantly reduce short stoppages due to adhesion errors.</p><p>In this way, it is first necessary to digitize the operating status of equipment and the behavior of workers to improve short stoppages. We can pursue the causes of short stoppages by analyzing and parsing the data we collect.</p><h2>Summary</h2><p>This questionnaire revealed issues in the conversion of regular factories into smart factories and challenges in promoting that in numerical data. We can see that quantifying and visualizing the various losses lurking in production lines is essential for improving productivity from these results. We focused on the theme of short stoppages this time. However, quantified data is also indispensable to resolve other issues in the conversion of regular factories into smart factories. The questionnaire results show that this requires high density and quality of the data to be collected as well as analytical skills.</p><hr id="isPasted"><p><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1711016095493-1711016095493.png" class="fr-fic fr-dii"></p><p>If you have any questions based on this article that you would like to discuss with an expert at Murata, don't hesitate to reach out. There's an entire team ready to support you with your question.</p><p><a href="https://www.murata.com/en-eu/contactform?&Detail=Wevolver%20request" target="_blank" rel="nofollow" class="button-main-action">GET IN TOUCH</a></p>

A smart factory is a factory that achieves operational process improvements, higher quality, and lower costs based on digital data by connecting processes from design to development, manufacturing, and maintenance in a network.

Pitfalls in the Conversion of Regular Factories into Smart Factories Seen in Data

<p id="isPasted"><em>Wageningen University &amp; Research developed MARVIN technology for high-speed plant phenotyping, seedling sorting, and other bulk quality assessment and sorting applications for the agricultural sector. For fast and high-quality scanning and 3D model creation of plants, they use Photoneo 3D camera MotionCam-3D Color and 3D Instant Meshing software. The combination of these two technologies enables fast and high-detail 3D model creation in motion, providing 3D data, color, and texture of plants at an exceptional speed.</em></p><p><strong>Partner:</strong></p><p><a href="https://www.wur.nl/en.htm" target="_blank">Wageningen University &amp; Research</a></p><p><strong>Challenge:</strong></p><p>Wageningen University &amp; Research has a fully automated greenhouse where they screen large amounts of plants for shape and color characteristics. For this, they developed MARVIN Gen2 – a high-throughput plant phenotyping device that generates 3D models of plants and accurately determines the size and shape of their leaves and stems plus the overall architecture in milliseconds.&nbsp;</p><p>Until recently, they used a machine vision solution that could only scan certain types of plants. To solve this challenge, they decided to use Photoneo MotionCam-3D Color, which can provide 3D models in higher resolution and with color information.</p><p><strong>Solution:</strong></p><p><a href="https://www.photoneo.com/motioncam-3d/" target="_blank">MotionCam-3D Color</a> + <a href="https://www.photoneo.com/3d-meshing/" target="_blank">3D Instant Meshing</a></p><p>The solution uses multiple MotionCam-3Ds attached to a robotic arm, which can rotate 360 degrees around a plant. The cameras can also move up/down and be angled in different directions. This enables our partner to scan both small plants, such as young cabbage plants, as well as taller, flowering orchid plants.</p><p><img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNzA3NDAxMDA4OTU5LU9yY2hpZHMucG5nIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" style="width: 766px;" class="fr-fic fr-dib"></p><p>The solution uses MotionCam-3D model S to capture as many details of small plants as possible. In contrast to a 3D scanner, which constantly needs to stop to take 3D scans, MotionCam-3D can slowly rotate around the plant at a constant speed, which saves time.</p><p>The key part of the solution is the construction of 3D models based on multi-angle scans from multiple MotionCam-3Ds. Photoneo 3D Instant Meshing software enables fast, high-detail 3D model creation in motion with texture and color.</p><p>3D Instant Meshing is part of Photoneo 3D Meshing solution. It brings a big shift to 3D model&nbsp;creation as it enables users to create 3D models from a continuous stream of 3D scans taken&nbsp;by MotionCam-3D Color.</p><p>The flexibility of the MARVIN system makes it easy to set it up and recalibrate. All plant species are different, which requires different camera angles to create a good 3D model. The setup makes it possible to scan both small as well as big plants.</p><p><strong>Discussion:</strong></p><p>Higher quality 3D models enable users to extract more accurate information from the models. This enables the tracking of the growth of plants over time. It provides deeper insights and thorough understanding of plant development over time, which will result in a boost in data-driven plant science.</p><p>Note: This application has been developed for greenhouse use, where the effects of wind and sun are minimal.</p><p>Would you like to discuss your use case with Photoneo? <a href="https://www.photoneo.com/contact-us/?utm_source=Wevolver&utm_medium=Case%20study&utm_campaign=Unique%203D%20vision%20technology%20for%20high-throughput%20plant%20phenotyping" target="_blank" rel="noopener noreferrer">Contact them here</a>.</p>

High-throughput plant phenotyping with novel 3D vision

Plant phenotyping has reached a new level! Wageningen University & Research developed a novel technology to determine the size, shape, color, and overall architecture of plants in milliseconds, using Photoneo MotionCam-3D & 3D Instant Meshing SW.

Unique 3D vision technology for high-throughput plant phenotyping

<p id="isPasted"><span data-contrast="auto">S</span><span data-contrast="none">mart retail — those retail outlets that use data and automation for better decision-making and store operations — can provide retail managers with insights and help them unlock efficiencies that enable better customer service and improve revenue potential.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="auto">Smart&nbsp;</span><span data-contrast="none">retail uses data and technology to upgrade customer service and achieve operational excellence in supplier and logistics, assortment and category management, store layout and workforce optimization, customer loyalty and marketing, and selling.&nbsp;</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><h2><span data-contrast="none">What’s the Difference Between Smart Retail and Traditional Retail?&nbsp;</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h2><p><span data-contrast="none">Retailing is still a labor-intensive industry ripe with opportunities for optimizing the path from manufacturers of a product to that product being purchased by a consumer from a retailer. Worldwide, retailers lose nearly $450 billion in revenue annually because of out-of-stock items, empty shelves, and other in-store inconsistencies.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">Brick-and-mortar stores have always struggled to find, train, motivate</span><span data-contrast="none">,</span><span data-contrast="none">&nbsp;and schedule personnel to perform a variety of labor-intensive, repetitive tasks of keeping the shelves stocked and forecasting customer demand to keep inventory orders smoothly flowing. This challenge has been made worse by the negative effects of COVID-19 on the labor market and the supply chain.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">Stocking shelves on time (to avoid out-of-stock situations and sales losses) and keeping price labels accurate is not easy. With around 50,000 stock-keeping unit (SKU) labels in an average-sized&nbsp;</span><strong><span data-contrast="none">hypermarket</span></strong><span data-contrast="none">, the task of efficient inventory availability in traditional&nbsp;</span><strong><span data-contrast="none">retail&nbsp;</span></strong><span data-contrast="none">is typically tracked by cash register checkouts. Price accuracy and inventory solutions mostly rely on manual price audits by store staff.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">Retailers</span><span data-contrast="none">&nbsp;have been experimenting with some degree of automation and digitization for some time. Some have implemented electronic shelf labels (ESLs) and cameras that monitor store on-shelf inventory, with various success rates, as functionality issues (battery lifespan, tedious device replacement) and vendor lock situations persist.&nbsp;</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">New advances in AI, neural networks, and edge computing made available recently are powering experimentation with autonomous and semi-autonomous robots that promise to make retail even smarter. By helping employees be more efficient and reducing the time for inventory and price accuracy checks by 2/3, retailers can collect digitized data, analyze it with AI with unprecedented accuracy, and apply data analytics for improved insights.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><h2><span data-contrast="none">What Are Some Benefits of Smart&nbsp;</span><span data-contrast="none">Retail</span><span data-contrast="none">?</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h2><p><span data-contrast="none">Smart&nbsp;</span><span data-contrast="none">retailers</span><span data-contrast="none">&nbsp;don’t just replace manual processes with automation but implement electronic inventory files, digital price labels, advanced camera solutions, and inventory forecasting software to replace analog store management heuristics and tools. Digital tools are poised to help shift from a r</span><span data-contrast="none">egular store to a smart store</span><span data-contrast="none">&nbsp;— one that not only collects siloed data but also analyzes it in near real-time to provide valuable, timely insights.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">Smart retail technology benefits not only store owners but customers, and potentially even suppliers in the following ways:&nbsp;</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><h3><span data-contrast="none">Smart&nbsp;</span><span data-contrast="none">Retail</span><span data-contrast="none">&nbsp;Benefits for Store Owners and Management</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h3><p><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1705482602189-ERIS-v2-1-backless-1-e1678797861855-486x1024.png" style="width: 300px;" class="fr-fic fr-dib"></span><br></p><p id="isPasted"><span data-contrast="none">The biggest three operational efficiency wins for the store owners, and management are:</span></p><p><span data-contrast="none">1. real-time actionable&nbsp;</span><strong><span data-contrast="none">inventory</span></strong><span data-contrast="none">&nbsp;data;</span></p><p><span data-contrast="none">2. significantly improved&nbsp;</span><strong><span data-contrast="none">price</span></strong><span data-contrast="none">&nbsp;label accuracy; and</span></p><p><span data-contrast="none">3. better&nbsp;</span><strong><span data-contrast="none">planogram</span></strong><span data-contrast="none">&nbsp;compliance.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="auto">While most&nbsp;</span><span data-contrast="auto">retail</span><span data-contrast="auto">&nbsp;inventory forecasting software relies on product checkout data provided by the cash registers, such data will not be available if the product is not on the shelf. Store cameras can help monitor inventory, but only the advanced camera systems combined with powerful AI that can identify store items with 99+% accuracy will be useful. Thankfully, solutions like that have been recently introduced on the market.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="auto">With up to 200 price labels per square meter on a typical</span><span data-contrast="auto">&nbsp;</span><span data-contrast="auto">shelf, checking and verifying price labels for accuracy is tedious and error-prone. Luckily, this is just the perfect task for smart automation, powered by the same advanced cameras and AI that help with inventory detection.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="auto">Even more so, that same hardware can detect SKU shelf placement inconsistencies and direct human operators to make the needed corrections on the spot.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">To be fair, training and reimagining business processes around newly possible operational tasks do require an upfront investment. Still, the benefits are felt immediately, yet most smart automation projects deliver a positive ROI in twelve months or less. An additional benefit is a reduced likelihood of missed scheduled price and inventory checks, an important consideration given that most retailers can increase revenue by up to 10% with optimal operations efficiency, assuming suppliers can resolve inventory bottlenecks. &nbsp;</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p>&nbsp;</p><h3><span data-contrast="none">Smart&nbsp;</span><span data-contrast="none">Retail</span><span data-contrast="none">&nbsp;Benefits for Suppliers&nbsp;</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h3><p><span data-contrast="none">Retail</span><span data-contrast="none">&nbsp;suppliers potentially have much to benefit from more accurate data on promotion-related sales figures and promotional items being placed appropriately, as they usually have to send representatives to gather some of this data in-store. Automating planogram and inventory status updates have potential, but so far, data sharing is still a contentious issue.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">Increasing operational efficiency at the&nbsp;</span><span data-contrast="none">store</span><span data-contrast="none">&nbsp;level can also help optimize deliveries and share quantity, frequency, and forecasted demand based on real-time inventory data thanks to decision-making powered by AI algorithms that provide more accurate forecasting and actionable insights.</span><span data-contrast="auto">&nbsp;</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><h3><span data-contrast="none">Smart&nbsp;</span><span data-contrast="none">Retail</span><span data-contrast="none">&nbsp;Benefits for Store Staff&nbsp;</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h3><p><span data-contrast="none">Not to be overlooked are the benefits to the employees at retail stores</span> <span data-contrast="none">that deploy AI and robotics to automate previously tedious, time-consuming tasks. Using smart detection of price, inventory, and planogram issues allows store employees to refocus on higher-value tasks that require knowledge and talent. By taking care of drudge work with automation, performing required operational tasks faster, and removing error-prone business process steps such as converting paper notes into digital reports, smart retail stores can allow staff to focus on interacting with customers for a better in-store experience.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><h3><span data-contrast="none">Smart Retail Benefits for Customers&nbsp;</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h3><p><span data-contrast="none">While operational improvements (on-shelf inventory availability, price accuracy, planogram compliance) mostly benefit the store owners and managers, bringing retail closer to the goal of consistent, high-quality service is also more feasible with automation. While AI-enabled robotics assist staff on the shop floor, more time and resources will now be available for customers needing attention than without intelligent automation. Robots are better at tedious tasks, and varied, unpredictable customer service work is still better done by human staff.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><h2><span data-contrast="none">Smart Retail Operations with ERIS</span></h2><p>Supporting efficient, intelligent retail operations practices, ERIS – the semi-autonomous retail robot - delivers more accurate and faster&nbsp;in-store inventory and price accuracy reviews.</p><p>ERIS is powered by an advanced AI platform, allowing edge computing for intelligent image processing, image recognition, store staff messages, and parallel processing. The semi-autonomous robot can deliver high AI computed performance right when needed to achieve real-time processing and aggregation of large volumes of data.</p><p>The core computation tool in ERIS allows the semi-autonomous retail robot to process high-resolution images captured on high-performance, industrial-grade camera modules on the device and run image recognition algorithms over neural networks. This edge computing tool, consisting essentially of a series of mini-computers with CPUs and GPUs, enables parallel processing that’s crucial to run efficiently.</p><p>ERIS runs four depth (3D) cameras and four RGB cameras. With the six modules on board, it can run models for multiple concurrent tasks like image inference, displaying actionable information, for the operator, on the touchscreen, and operating the price label printer. This ultimately provides accurate, real-time snapshots of the store shelves no trained human operator could ever hope to achieve at the speed and the level of detail ERIS can.</p><p><span class="fr-img-caption fr-fic fr-dib" style="width: 302px;"><span class="fr-img-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1705482619530-Panorama-Shelf-x-edit-768x413.png" style="width: 300px;" class="fr-fic fr-dib"><span class="fr-inner">Real-time panorama shelf scan generated by ERIS</span></span></span></p><h2 id="isPasted"><span data-contrast="none">How Can I Make My&nbsp;</span><span data-contrast="none">Brink-and-Mortar Store</span><span data-contrast="none">&nbsp;Smart?&nbsp;</span><span data-ccp-props='{"134245418":true,"134245529":true,"201341983":0,"335559738":40,"335559739":0,"335559740":259}'>&nbsp;</span></h2><p><span data-contrast="auto">What are the steps supermarkets need to take to start using ERIS? Preparation, time to install, training, best practices?</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">Running a smart store (for example, a hypermarket) requires an entire ecosystem of hardware and software solutions working harmoniously with existing store technology infrastructure and operating processes. To accelerate and improve performance, every application, device, sensor, and AI model in the system must share data and insights across the store and with its network partners.</span><span data-ccp-props='{"201341983":0,"335559739":160,"335559740":259}'>&nbsp;</span></p><p><span data-contrast="none">That said, integrating a solution like ERIS takes from one to three months. Shifting the approach to price and inventory management will surely make some people reluctant, but when performance and operational excellence are a priority, those will be the environments that will welcome the coming generation of smart supermarkets.</span></p>

Smart retail — those retail outlets that use data and automation for better decision-making and store operations — can provide retail managers with insights and help them unlock efficiencies that enable better customer service and improve revenue potential.

What Is Smart Retail?

Programmable Logic Controllers (PLCs) are the backbone of industrial automation, and PLC programming is the tool that brings these systems to life. In this article, we’ll take a look into the PLC basics, its programming process, programming language (with best practices), and tools to achieve this.

PLC Programming: A Comprehensive Guide to Mastering the Art of Automation

Unlocking the Potential of Zener Diodes: A Deep Dive into Principles and Practical Applications

Zener Diode: A Comprehensive Guide to Its Principles and Applications

<h2 dir="ltr" id="isPasted">What Is ESG?&nbsp;</h2><p dir="ltr">ESG stands for Environmental, Social and Governance – three key factors used to measure the societal impact and sustainability of a business or organisation. ESG metrics are gaining in importance, with companies facing mounting social pressure to comply with evolving regulations. Regulators in America and the European Union are increasing their oversight in this area, so there’s a strong focus on investment in ESG reporting.</p><p dir="ltr">The challenge facing companies today is to address these key factors of the ESG framework. You’ll be looking at:&nbsp;</p><ul><li dir="ltr"><p dir="ltr">Environmental issues, including energy usage and climate change impact&nbsp;</p></li><li dir="ltr"><p dir="ltr">Social issues, including equity and inclusion</p></li><li dir="ltr"><p dir="ltr">Governance issues, including transparency and accountability</p></li></ul><p dir="ltr">Most organisations are still using a time-consuming and error-prone approach to comply with ESG regulations. This means a manual process of data collection, analysis and reporting, with all its attendant risks. High volumes of raw data are often still processed by dispersed team members, using outdated and unspecialised tools like spreadsheets and calculators. Without a centralised intake point and streamlined digital processing, there’s no single source of truth, resulting in a risky and inefficient workflow.&nbsp;</p><p dir="ltr">However, the tech-driven advances of Industry 4.0 are introducing&nbsp;<a href="https://www.rowse.co.uk/blog/post/the-role-of-ai-in-sustainable-manufacturing" target="_blank">new methods</a> of keeping up with ESG compliance. Intelligent automation (IA) makes compliance not only possible but will transform your whole operation. Adopting appropriate technology like digital processing solutions is therefore crucial to the ESG agenda.&nbsp;</p><h2 dir="ltr">What Technologies Support ESG Compliance?</h2><p dir="ltr">As the pressure rises on ESG considerations, many companies will be considering IA technologies to improve their performance. These include process intelligence (PI), IA cloud services and robotic process automation (RPA) strategies that can help businesses become greener and more sustainable.&nbsp;</p><p dir="ltr">It’s important to distinguish IA from AI (artificial intelligence), which simulates human thinking. AI is only one of the factors contributing to IA,&nbsp;<a href="https://www.rowse.co.uk/blog/post/how-to-use-ai-ml-to-optimise-manufacturing-costs" target="_blank">along with machine learning</a> (ML), RPA, etc. Taken all together, this cognitive technology is able to expand factory automation capabilities.&nbsp;</p><p dir="ltr">Process intelligence is another way to describe big data and its analysis. It takes data collected from various business software systems such as Enterprise Resource Planning and Customer Relationship Management. It then uses this data to analyse your organisational processes, to find areas for potential improvement. PI detects process irregularities and identifies their root cause, which is often human error. It then works with RPA to deliver an automated solution that drastically reduces human intervention – even removing that factor altogether.&nbsp;</p><h2 dir="ltr">Technology’s Role In ESG Compliance&nbsp;</h2><h3 dir="ltr">Environmental</h3><p dir="ltr">To address climate threats like greenhouse gas emissions and become carbon neutral, you’ll have to take into account your current energy consumption. You will also need to assess the impact of ESG compliance on existing employees and how compliance aligns with your overall business strategy. Data collection and analysis tools like PI can help you understand your process flows and how they can fit into the ESG compliance framework, making environmental targets easier to achieve. For instance, you can identify process bottlenecks like sluggish or blocked supply chains. In such ways, you can streamline your operations and cut down your carbon emissions.</p><h3 dir="ltr">Social&nbsp;</h3><p dir="ltr">PI also helps identify instances where staff and/or customers are not treated fairly, equally or inclusively. It can locate potential conflicts of interest and suggest solutions that will help you address these issues promptly. This is a step towards the long-term development of trust between all members of a company and its customers. It will also improve your organisation’s business and social reputation, thus having a positive impact on its ESG performance.</p><h3 dir="ltr">Governance&nbsp;</h3><p dir="ltr">Transparency in regulatory reporting is founded on increasing data quality and data governance, automating processes and streamlining ESG data. ESG compliance includes following data privacy protection regulations, tracking the transparency of supply chains and enforcing inclusive hiring practices. PI tools help identify aspects of these areas where greater automation is an advantage to ESG reporting. These are primarily those where full transparency demands real-time frequent monitoring, as well as audit trails and demonstrable compliance.&nbsp;</p><h2 dir="ltr">Increased Efficiency</h2><p dir="ltr">Automating repetitive, time-consuming tasks with RPA liberates employees to concentrate on higher-level tasks. This also reduces these tasks’ carbon footprint.</p><p dir="ltr">Cost savings derived from increased efficiency might include labour costs on those tasks now automated, and less time spent on customer-related inquiries and service.</p><p dir="ltr">RPA performs automated tasks with greater accuracy, improving data quality and reducing errors. It also works 24/7, collecting data constantly in real-time and can be scaled up or down in response to changing market conditions. Since RPA can work in almost any system with a user interface, it has access to many more data points.</p><h2 dir="ltr">Reducing Your Carbon Footprint With RPA</h2><p dir="ltr">Digital workers are generally located on servers that don’t move from place to place. They create no carbon footprint – unlike human workers, who travel to and from the workplace. Neither do they produce waste from items consumed during working hours, such as disposable cups and food packaging.</p><p dir="ltr">Automation with RPA digital workers can help to reduce your carbon footprint, as they’re more energy-efficient than human beings. They run on electricity rather than food and drink, so usually require less energy to carry out the same tasks.&nbsp;</p><p dir="ltr">Modern computing equipment is also much more energy-efficient, especially when using cloud services, which further reduces their carbon footprint. Digital workers can operate continuously, 24/7, so they’re more efficient over longer periods. They’re also consistently far more accurate. They complete more work in the same amount of time as humans, with fewer errors or do-overs.</p><p dir="ltr">These factors all contribute to digital workers creating a lower carbon footprint than human workers.</p><h2 dir="ltr">Factory Automation And ESG Compliance&nbsp;</h2><p dir="ltr">The combination of RPA and PI can significantly improve your ESG performance. Once production processes are automated and optimised, you’ll start seeing reductions in waste and environmental threats. Your carbon footprint will be smaller, while your energy efficiency will improve. Your social impact will be enhanced by freeing human employees for higher-level work, thus encouraging a more engaged and motivated workforce.&nbsp;</p><p dir="ltr">All these factors can be collected as data and assessed by off-the-shelf software packages that make ESG compliance much easier.</p>

The Role Of Factory Automation In ESG Compliance

Discover the role of factory automation in ESG compliance.

<p id="isPasted">The work in the greenhouse at Beerstecher AG in Hinwil is demanding. With humidity levels of 80 percent and temperatures of up to 35 degrees Celsius, those working in it very soon find themselves exhausted. The family business in Hinwil is consequently faced with a challenge when finding and retaining suitable labour for its vegetable harvests.</p><p><span contenteditable="false" draggable="true" class="fr-video fr-deletable fr-fvc fr-dvb fr-draggable"><iframe width="640" height="360" src="https://www.youtube.com/embed/M_W38gdWgiI?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe></span><em>Video: ETH Zurich / Nicole Davidson</em></p><p>The picking robot from ETH spin-off Floating Robotics addresses these challenges. It automates crucial tasks such as defoliating, harvesting and boxing vegetables. “The robot primarily takes over strenuous and repetitive activities, allowing our employees to focus on more demanding tasks that require a creative and critical mindset," explains Bianca Curcio, who is responsible for production management in the Beerstecher greenhouse and is an ETH Zurich alumna.</p><p>Although the robot is still in the pilot phase, Curcio anticipates it will play a permanent role in Beerstecher AG’s production process.</p><p>The robot was developed by engineers and students from the Robotic Systems Lab (RSL) at ETH Zurich. Under the leadership of Salman Faraji, they established the spin-off Floating Robotics in 2023 with the aim of bringing the technology behind the picking robot to the market.</p><p>Equipped with an integrated camera, the robot monitors the crops, and with the assistance of its built-in computer can recognise different plants and objects. Its robotic arm is activated to perform specific tasks, such as de-leafing or harvesting.</p>

Working in a greenhouse is both strenuous and time-​consuming. The picking robot from ETH spin-​off Floating Robotics takes on particularly repetitive tasks, thereby alleviating the strain on human pickers. It is currently undergoing testing at Beerstecher AG in Hinwil.

A picking robot for the greenhouse

In this episode, we talk about how researchers from the Technical University of Denmark are tackling the issue of monoculture farming by utilizing robotics and next-gen image analysis.

Podcast: Robot Farmers Bring Back Biodiversity

Festo Corporation-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

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Festo Corporation-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

Festo Corporation-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

READY Robotics-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

READY Robotics-Mon Jul 31 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

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READY Robotics-Thu Aug 31 2023 23:59:59 GMT+0200 (Central European Summer Time)

READY Robotics-Sat Sep 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

Remix Robotics

Autoblocks

Matroid

Rowse-Fri Jun 30 2023 23:59:59 GMT+0200 (Central European Summer Time)

Rowse-Fri Jun 30 2023 23:59:59 GMT+0300 (Eastern European Summer Time)

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