Aircraft manufacturing demands the highest-quality materials, highly trained experts, rigorous quality control, and precision processes that remain largely manual. These demands make it an ideal candidate for a digital reinvention.
By combining major leaps in operating technology (OT) with integrated IT for a fully optimized environment, aircraft manufacturers can take advantage of recent advances in automation and analytics to improve manufacturing speed and accuracy and reduce waste, making manufacturing cells both more efficient and sustainable.
A single-aisle commercial aircraft contains tens of thousands of nutplates. The manual installation of each one takes three to four minutes.
This process is repeated thousands of times over the life of the craft as plates are removed and reinstalled for routine maintenance. Until now, nutplate installation has been done manually, exposing manufacturers to risks including:
- Human error: Over the course of tens of thousands of manual actions, mistakes are inevitable. The potential for error is compounded by the small size of nutplate components, which make them difficult to handle and increase the potential for mistakes.
- Rising costs: Beyond the sheer labor cost of time on the job, the associated errors of manual installation lead to increased costs through scrapped materials, poor inventory management and time-intensive quality control.
- Inefficiency: Manual installation often requires down time for changing tools and changing shifts. It also limits visibility across the manufacturing environment, which means that resources are sometimes unavailable or poorly optimized.
To meet today’s demands to become more sustainable and socially accountable, manufacturers must also find ways to reduce the waste and inefficiencies inherent in their legacy processes, and the aerospace industry is no exception.
To address these multiple challenges, automated manufacturing and robotics technology provider, JR Automation, has deployed Hitachi Vantara’s Lumada Manufacturing Insights in its SmartAttach™ manufacturing cell as a way to merge the huge advances in automation at the operational level with superior insights at the informational level to help drive a comprehensive shift toward smarter aerospace manufacturing for the future.
The semiconductor industry has been growing at a rapid pace for the past few years. Market research firm IDC estimates that the semiconductor industry grew at a rate of 10.8% in 2020 and will grow at 12.5% this year, resulting in a $522 billion market sector. IDC attributes much of this growth to the impact of COVID-19.
The increased demand for semiconductor chips is due to new generations of smartphones, tabs, laptops, and desktop computers used in industries such as healthcare for telehealth services; in the education sector for online teaching and instruction; and as more people worked remotely. At the same time, the automotive industry, a heavy user of semiconductors, is packing more and more chips into vehicles as it attempts to offer all the creature comforts consumers want as they embrace the connected car experience.
In the manufacturing sector, too, the pandemic has driven home the values and virtues of setting up connected factories that enable contactless manufacturing and uninterrupted operations in the face of a crisis. All these trends indicate that the demand for semiconductor chips will rise steadily in the future. Despite the rosy growth projections, the semiconductor industry still faces challenges, chief among which is continuing to innovate even as it delivers expected price/performance improvements.
Therefore, it is imperative that the industry invest more in research and development to drive innovation while at the same time optimizing costs by leveraging technology such as cloud computing.
If one examines the key attributes and requirements of the semiconductor industry – skilled resources, high competition, complex automation tools, data and IP, differences in industry supply chains, and the brief shelf-life of designed chips, it is apparent that these factors are highly expensive and difficult to manage. Given the level of investments and expertise required, there are very few players in this industry. The race for excellence is fierce, and a considerable effort and investment is dedicated to driving R&D to identify areas and avenues for innovation.
Faster time to market through the acceleration of design cycles, performance enhancements of chips through upgrades and updates, and IP protection through foolproof and flawless security systems are the top three business priorities of this industry. The chip companies invest most of their time, energy, and capital in fulfilling these priorities. However, operational priorities are equally important, such as driving efficiencies in the manufacturing process through data analytics; optimizing operations, processes, and costs; and driving productivity through collaboration.
Cloud computing provides a reliable and seamless infrastructure to address both the business and operational priorities of the semiconductor industry.
The ever-increasing demand from consumers for products with higher compute powers and processing abilities has resulted in shorter product lifecycles, requiring semiconductor manufacturing companies to bring products to market faster.
To this end, applying cloud computing in the semiconductor industry offers scalable storage, big data analytics capabilities, and enhanced productivity with collaboration tools for reviews and feedback that enable quick product launches.
Cloud also provides a flexible, scalable, elastic, and secure infrastructure for chip designing by providing on-demand compute for EDA tools. It enables semiconductor manufacturers to set up and access high-performance computing (HPC) power with virtual machines (VM) images, enabling quicker design and development cycles.
Cloud offers a data lake or repository that enables storing, processing, analyzing, and inferring the foundry’s generated data. Manufacturers can use data insights for predictive performance and analytics as well as the management of resources in their supply chains, thereby improving production uptime and yield. It also allows for specific artificial intelligence and machine leaning use cases for fault detection in the production line using imaging techniques and smart analytics tools.
Chip designs evolve with each release, and the chip design companies have families of chips in incremental progression/evolution cycles. The chip lifecycle data must be logged, analyzed, and processed for value generation. Cloud Service Providers (CSPs) like Amazon Web Services offer storage and analytics capabilities to chip design companies to apply AI and ML models for systematic data processing. They also provide the necessary infrastructure to integrate IoT and implement Industry 4.0 solutions for smart and connected manufacturing .
The semiconductor manufacturing industry is highly competitive, and the success or failure of a chip manufacturer entirely depends on the ability of the manufacturer to collaborate effectively with an eco-system that includes suppliers, OEMs, and internal teams for design reviews, feedback, and testing. Cloud infrastructure provides a centralized system to track the productivity of the different stakeholders, enabling transparency and boosting efficiency, especially in the current times of COVID 19 using collaboration tools such as MS Teams, Google Workspace, and Google Meet.
Unlike on-premise data centers managed by internal IT teams with constraints on skill, availability, and resources, cloud infrastructure is managed by specialists such as GCP, AWS, and Microsoft. These service providers have made huge investments in R&D, infrastructure, and resources, and provide service-level agreements which ensure uninterrupted operations for semiconductor foundries.
One of the primary reasons for the semiconductor industry to not adopt or scale cloud has been the business criticality of its operations. However, modern-day CSPs provide SLAs that comply with industry requirements and, in some cases, go beyond to ensure reliability. For example, GCP provides a robust architecture with high-bandwidth connectivity across 25 regions and 76 availability zones to deliver global services.
The use cases for sensors, chips, computing, IoT, and Industry 4.0 are ever-increasing. It is thus imperative for the semiconductor industry to be extremely agile and offer unmatched on-demand scalability and flexibility to ramp up/down its compute infrastructure to accommodate R&D, design, testing, and validation of GTM activities. Analytical capabilities to draw insights and make quick decisions must also be in place in order for the industry to deliver on its reputation of being agile. Cloud offers all these capabilities to the industry and at the same time drives home the cost benefits, security, and efficiency.
There are several aspects of cloud infrastructure that can drive innovation for the semiconductor industry. To begin with, it can provide a leeway for the industry to squeeze in cost efficiency to a perceived rigid cost structure. The possibilities of leveraging IoT, AI, ML, big data analytics for gaining visibility, and driving efficiencies throughout the chip manufacturing value chain are tremendous. It can provide EDA support, high-performance design, HPC, and High Volume Manufacturing (HVM) capabilities that will enable better outcomes at lower costs.
Cloud offers instant scale and capabilities to perform and execute operations across the semiconductor value chain from design to yield without investing in physical on-premise data centers, reducing infrastructure development costs. It provides a collaborative infrastructure for value chain stakeholders to review and test the designs and offer feedback irrespective of the location of the stakeholders. Chip manufacturers can also drive the cost efficiencies on account of improved uptime owing to predictive maintenance capabilities and the security that cloud infrastructure offers.
The semiconductor industry powers a host of other industries, and several of these industries manage data categorized as highly sensitive, IP, business-critical, or compliance-driven. The dedicated investments by the CSPs in ensuring the security of their cloud infrastructure is an added advantage for the semiconductor industry to ensure data and IP protection for its clients. These CSPs provide more secure and reliable infrastructure at lower costs than the on-premise setup. For example, Google’s global-scale infrastructure protects billions of users with world-class security.
The semiconductor industry has been a pioneer in enabling digitalization across industries. With Industry 4.0 and IoT gaining prominence, the use-cases of semiconductor chips have evolved rapidly from device-specific applications to sensorization, integration, and communication areas.
However, the irony of this industry is that despite being the transformation catalyst for all the other sectors to adopt digitalization, the industry on its own has been lagging when it comes to the adoption of technologies such as cloud computing for cost optimization, innovation, and streamlining operations. According to KPMG, even when most other technology industries have been adopting digital transformation at a rapid pace of 89%, the adoption rate of the semiconductor industry remains at a paltry 50%.
Considering the outlook for the semiconductor industry, utilizing the cloud for digital transformation is the only way the industry can scale and position itself to meet consumer demands for speed, accountability, security, innovation, and reliability.
Intertape Polymer Group (IPG) is undergoing a digital transformation, and it’s doing it without an army of data scientists and data engineers, revealed IPG’s Vice President of Business Transformation, Jai Sundararaman, during the MLC’s 2021 Rethink Summit this week. Instead, the company is rolling out its digital analytics platform by doubling down on process engineers and its operational excellence team.
When IPG, a producer of packaging and protective solutions with 27 manufacturing plants around the globe, began its digital transformation journey about three years ago, it quickly became apparent that, despite a plethora of articles on the topic of M4.0, there is no proven industry playbook companies can take off the shelf and implement, noted Sundararaman. “Success is hard-earned and based on experimentation,” he added.
The first thing the company did was to make a capital investment in its digital transformation, based on broad-based support from its operations leader, CEO, CFO, and board, “Which is critical,” stressed Sundararaman. IPG investigated more than 15 analytics platforms, with help and input from data from the MLC as well as advice from other MLC members. While no one platform is going to work for everyone, he noted that the MLC network had helped IPG shape its thought processes in these initial stages and determine which platforms to take on a pilot run.
The next investment was in talent. “It’s about rescaling and upskilling the talent, as well as establishing what we call the Digital Process Center of Excellence,” he added. “Our mantra is, ‘We believe in empowering employees with technology’…people are going to learn and grow as part of the process.” This means that the company didn’t double down on analytics per se. “We doubled down on our continuous improvement and process engineering. My team are the subject matter experts on the process side, and they work along with IT and OT specialists and the Continuous Improvement team, all of whom have a dotted line relationship with the plant teams. So when they go into a plant, the process is streamlined. This enabled us to get commitment for the resources as we scale across the plant.”
While IPG is still in the early phases of scaling analytics around multiple plants and executing pilot programs, it is already seeing success. “We have noticed the ROI is less than two years, which we’re very proud of,” said Sundararaman. Its work in centralizing management systems has also proven to deliver successful ROI. Still in the early phases are projects around augmented reality, virtual reality, and 3-D printing, though those also look promising.
One key to success thus far has been how IPG is leveraging analytics through process excellence. “It starts with a mindset, and commitment, and cultural enablers,” he said. “It’s not going to happen in one shot.”
Because success is not guaranteed, you must also be willing and able to embrace failure along the way, he advised, and learn from those failures to move forward. Don’t be too quick to come to a standardization mindset either, he noted. The technology continues to evolve, so the journey will be ongoing.
“The most prudent thing is to focus on what will create business value,” emphasized Sundararaman. “The value is critical. It’s not Step 5. It’s Step 1 through 5. We have been very meticulous in terms of our approach to where value creation will be, how we engage the different partners, and how to capture that value as we go on through the process.”
And you have to be willing to take risks, he added. There are tools, methodologies, and some pointers and guideposts along the way, “but you have to figure it out and then take a leap of faith.”
“We are in the midst of great change,” declared David R. Brousell, Co-Founder, Vice President, and Executive Director of the Manufacturing Leadership Council (MLC) in his opening speech at the MLC’s 2021 Virtual Rethink Summit today.
“It requires us all to reimagine the art of the possible – to expand our visions, to adapt, to devise new strategies, and to orchestrate change,” he said. “How well you transition to the digital model of doing business will be key to the competitive posture of your company and, as a result, our industry as a whole.”
Reflecting on the massive global disruption of the past year, Brousell noted that the COVID-19 pandemic’s impact on manufacturing has not only been profound but, in many areas of activity, has also led to permanent changes.
“Who could have imagined that as a direct result of a worldwide pandemic, Manufacturing 4.0 would suddenly arrive at an inflection point in its history,” he added. “Spurred by the crisis and the consequent need for greater flexibility and agility, manufacturing companies began accelerating their investments in digital technologies and the changes necessary to fully exploit them.”
Brousell cited the latest MLC survey research which confirms that 54.8% of manufacturing companies believe COVID-19 has increased management’s focus on digital transformation. Powerful majorities also report that many of the COVID-driven changes will now become permanent elements of their leadership approach. For example, 68.2% say that new disaster preparedness plans, resiliency strategies, and response teams will become permanent features in their companies. Likewise, 57.3% say that more collaborative, cross-functional organizational structures will take root. And 62.2% expect remote working by both leadership teams and employees to continue to be part of everyday life.
“More and more,” he predicted, “the digital model of doing business will sweep through other functions of the manufacturing enterprise – sales, marketing, HR, service. With manufacturing operations leading the way, the rest of the manufacturing enterprise will digitize.”
“So, open your minds over the next three days of Rethink and imagine a better future for manufacturing,” Brousell advised the hundreds of Rethink Summit virtual attendees. “Now is the time to think big about manufacturing.”
Schneider Electric’s Lexington, KY, plant is one of only a handful of manufacturing facilities in the U.S. that enjoys the coveted status of being a World Economic Forum-designated “Lighthouse” factory. To achieve that distinction, the Lexington plant, built in 1957, had to address some very basic operational issues.
In a presentation yesterday at Rethink, the Manufacturing Leadership Council Summit conference, Kenneth Labhart, North America Innovation Leader at Schneider, said that one of the issues that had to be addressed was the plant’s inability to share data from the plant floor that could be used to improve operational performance.
By adopting M4.0 technologies and approaches, including IoT, cloud, mobility, and analytics, the Lexington team focused on transforming the facility’s connectivity and integration platforms to break down silos and allow its teams to share data more easily. Those transformation projects have already resulted in a 26% reduction in energy spend, mean-time-to-repair reductions of 20%, the elimination of paper processes, and a five percent reduction in downtime.
The initiative was part of a smart factory program that began in 2017. Today, 80 Schneider factories have deployed a digital transformation roadmap.
But Labhart was clear that the company’s digital transformation had its challenges, chief among which were employee pushback, executive leadership buy-in, and expertise in digital technologies.
Schneider was able to overcome these challenges using a careful approach. “It is very important to take small steps,” Labhart said.
What draws the next generation of leaders to a career in manufacturing?
While the specifics may vary from person to person, it’s the challenge, and the satisfaction, of seeing a product through from design to being in the hands of satisfied customers in the best, fastest way possible while continuously learning all along the way, according to members of the Next-Generation Panel session at the MLC’s 2021 Rethink Summit this week.
The three 2021 Manufacturing Leadership Award Winners on the panel — Kat Duggan, Coatings Business Learning Leader at Dow; Kasia Karimee Garcia Bracho, Supply Chain Lead at IBM; and Katia Valenzuela, Communications and Design Association, MxD — had so much enthusiasm for their chosen career path that the audience at the virtual event couldn’t help but cheer them on.
Duggan said her experience so far has “really been a joy,” while Valenzuela added, “It’s such a wonderful, innovative world…it was like [I discovered] a treasure trove of opportunities I didn’t even know existed within manufacturing.”
To thrive in that continuously innovating world as it moves into the future, next generation leaders will need some hard and soft skills that perhaps their predecessors did not, such as how to quickly analyze data to make critical decisions, let go of past traditions, and have the flexibility to be open to new ideas, new tools, new technologies, and the new M4.0 culture. Future leaders, they said, must be open and empathetic, able to listen and contribute back to their communities, and able to collaborate with all the people on their teams.
To attract the next generation of leaders, manufacturers also need to ensure that their workforce, especially at the upper echelons, are diverse. As Valenzuela said, “It’s very important to see people who look and act and think like you do in positions of leadership, not just because they bring their own diverse set of perspectives, experiences, and knowledge, but also because they can give someone like me a role model to look up to.”
Bracho also emphasized the importance of fostering a culture where everyone feels welcomed and respected. “Let’s be active and proactive” when it comes to hiring from the outside and promoting from within, added Duggan.
Sustainability is also important to the next generation, they added. New technologies such as blockchain can increase transparency and trust in a company’s ability to maintain standards at every stage of manufacturing. While local or global regulation may drive some sustainability efforts, consistent consumer pressure is ultimately what’s going to change business attitudes and how money is spent.
Bracho cited an IBM survey of more than 414,000 people in nine countries that found environmental responsibility to be a key factor for consumers. “The trend toward sustainability is growing, and it’s something that companies should focus on,” she said.
As to what we’ve learned from the pandemic? Digital is going to be the biggest winner for manufacturing, they said. And with that increasing focus on digital technology comes adaptability, flexibility, a culture of continuous improvement, and the need for an increased focus on cybersecurity.
Manufacturers are generating more data, faster, and from more aspects of their operations than ever before. The key to harnessing all that data to produce higher quality products more quickly and efficiently, and to speed up the decision-making process, is having an intelligent platform and good data governance strategies, agreed Sid Verma, General Manager of the Manufacturing/IIoT Division at Hitachi Vantara, and Mike Lashbrook, Vice President of the Esys Division and Digital Solutions at robotics automation company, JR Automation, during an Executive Dialogue at the MLC’s Rethink 2021 Summit this week.
An M4.0-ready intelligent platform should have several key components, they said. It must enable operators to understand the data being generated and present it in a form that enables users to create analytics or decision models. It must also be able to provide intelligent output based on those models.
To make the system work enterprise-wide, it must also be able to talk to both the OT and enterprise sides, said Verma. And it must be scalable, fault-tolerant so it can self-correct, and the user interface must be consistent with the way people naturally work and think. It should also be flexible and easy to maintain.
This all sounds good, but how does it work for manufacturers who are still in the process of automating their legacy systems? The increasing need to improve data quality across all the key performance indicators is driving the push toward intelligent platforms, said Lashbrook. “We have an enormous amount of data and intelligence being collected from connected equipment and smart sensors…the system needs to be able to ensure that, if there are small changes in the system, you can adapt on the fly and still get good quality.”
The future, he added, will be a completely integrated digital twin system that can quickly enable the operator to come up with the production model they need to move forward without disrupting the system.
There are challenges to scaling these platforms to where they need to be to achieve a full M4.0 operation, they acknowledged. “It’s a journey for us as a platform company to learn and adjust to be able to provide that value that the OT world needs,” said Verma. IT companies like Google make it look easy, because everything was IT-enabled and the protocols are clean. When you get to the OT side, however, it gets more complicated, depending on the age of the assets and the volume of data involved. While internet companies can just collect all that data and scale it, it gets too costly and difficult to follow their example on the plant floor.
The main challenge is the explosion of data being generated, which session moderator, MLC Co-Founder, Vice President, and Executive Director David R. Brousell called “the 400-pound gorilla in the room.” According to MLC survey data, manufacturers expect up to a 500% increase in data volumes over the next two years as they become more connected.
“Just collecting data on the OT side does not work for us” in the same way it works for a Google, said Verma. “We have seen horror stories where people spent their entire IT budget just collecting data because they didn’t know where to start.”
“Step one has to be stepping back and working with the operational focus. What are those KPIs whose operational efficiencies you want to improve? Then we have to make sure that we collect data around those, not just collect everything,” Lashbrook said. The approach is to focus on the value you’re hoping to create, then collect data associated with that value and figure out which aspects of the legacy systems need adjusting. “In the future, we can look at creating connected systems from day one. But for now, we have to work through these challenges.”
Verma agreed that companies should work upfront to develop a business priority, and a business use case that has an associated ROI, then bring in the engineering expertise. “If you are looking for predictive maintenance, let’s target the most critical failure that can happen. Then let’s try to collect the data to address just that particular failure mode. That way we limit the cost of the solution and the value goes back to the business.”
Many companies don’t have data scientists on staff to help analyze the data, but that shouldn’t stop them, said Verma and Lashbrook. “The first phase of deployment for industry 4.0 is to get that expert knowledge from people in quality and maintenance using older systems and automate that information,” said Verma.
For example, if a technician hears a noise when a motor fails, put in an acoustic sensor that can be alerted when the tech hears that noise. Once your models start showing more accuracy, then companies can begin to layer in data science on the areas that are most business-critical. “That has been our recipe for giving incremental value and industry 4.0 at a much lower cost profile,” he said.
Lashbrook added that, if you don’t have the expertise in house, bring in partners that can fill the gaps. For longer term solutions, look to hire people who have the new skillsets you will need.
“Augmented Reality is definitely cool, it’s relatively easy to use, and it can have a big impact on productivity and quality,” argued Jim Heppelmann, Chief Executive Officer of PTC in an exclusive Executive Dialogue session during the Manufacturing Leadership Council’s 2021 Rethink Summit this week.
Speaking with MLC Co-Founder David R. Brousell, Heppelmann noted that most people have tended to think of digital benefits as always going to knowledge workers, or as part of connected machines and automation. The people who have not really benefited from digital transformation so far, are the frontline workers who stand and work next to those machines.
What Augmented Reality (AR) can do, he added, is to bring digital information directly into that work environment so that front-line workers can easily access and visually perceive information as they are actually doing their job.
It’s a technology that allows “bits and bytes to become sounds and sights”, explained Heppelmann.
Lots of companies have already seen the benefits of AR during the pandemic, he added, citing the example of auto company workers around the world who were able to be rapidly and remotely trained to make ventilators – a vital product which they had never made before.
Those kinds of primary use cases, involving work instructions, or training and mentoring, or remote support, are where many companies are already getting value. And Heppelmann believes there’s still lots of room for improvement. Despite all the digital investments manufacturers have made so far, he estimates that around 50% of front-line work is still not automated.
What’s more, he says that for every knowledge worker in a manufacturing organization there are around three front line workers on the plant floor or in customer facing and service roles. That’s where much of the skills gap exists in the industry today. And as experienced people continue to retire, they will continue take a lot of their domain knowledge with them, so the skills gap is likely to get even worse.
That’s why trying to digitize the knowledge of those retiring workers is also often a primary use case of AR. As companies use it, they are accumulating a large set of digital expertise that can help new workers learn their trade. And by harnessing AI and analytics with AR systems, companies can also ensure every step in a production or other process has been taken correctly and so verify the quality of the work. Over time, that helps all front-line employees to become more productive and more efficient.
“That’s why I call it a revolution,” he concluded. “We are bringing the power of the digital cloud to the front-line workforce for the first time. And that’s a big, powerful idea.”
Since its founding in 2018, the World Economic Forum’s Lighthouse Factory Network has served as a collection of role models for what is possible in advanced manufacturing. With 69 Lighthouse locations designated worldwide, the factories that have earned this distinction are at the forefront of digital transformation and have achieved significant financial and operational improvement as the result of their efforts.
During his session at Rethink: The Manufacturing Leadership Council Summit, Francisco Betti, Head of Advanced Manufacturing and Production at the World Economic Forum, said that when developing its Lighthouse Network, the WEF saw that there was momentum around digital transformation in manufacturing, but that companies were struggling to invest in shop floor use cases that could generate value. Using independent third-party evaluators, the WEF developed a process to identify companies that had overcome that challenge and achieved significant financial and operational improvement as a result of their efforts.
When asked about the common thread for members of the Lighthouse network, Betti pointed to four main elements:
- The realization that digital transformation is not just to help improve operations, but also to enable new business models.
- Some things that became important during the pandemic will be here to stay – agility and a focus on the customer; a balance between automation and employee engagement; a new concept of resilience.
- Sustainability does not come at the expense of efficiency, and there are new ways to reuse, recycle and re-manufacture – and these will become essential for companies to stay in business.
- C-level management and corporate boards have made digital transformation a significant priority, and they invest in technology and the workforce accordingly.
In the future Betti says the WEF will pay special attention to companies that take their digital transformation beyond the shop floor and move it out to other functions, such as procurement, customer service, and for meeting substantial benchmarks for sustainability.
More information about the Global Lighthouse Network is available from the WEF’s white paper, Global Lighthouse Network: Insights from the Forefront of the Fourth Industrial Revolution.
“The next 60 years will usher in an era where robots will become useful team-mates for people, helping them in both physical and cognitive tasks,” predicted the MIT’s Dr. Daniela Rus during her keynote session on the final day of the MLC’s 2021 Rethink Summit this week. “They will have a wide range of capabilities and will come in a variety of forms and materials, inspired by nature, by our built environment, and by our imagination.”
Rus, who is Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science, and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, continued: “Today we are surrounded by a world of digital opportunities. These possibilities only get larger when we start to imagine what we can do with advances in AI & robotics.”
Those advances will not only fundamentally transform the human/machine relationship, she believes, but will also lead to completely new kinds of production strategies and manufacturing business models.
“We assume that robots and AI will lead to fewer manufacturing jobs,” noted Rus, “but what if they really bring better jobs that allow workers to control production lines more finely and configure them rapidly for customized production? This could meet the rising demand for customization and personalization in almost everything we buy, and at an affordable price point. It would be a world where product templates get designed by specialists, customized by people at home, and fabricated locally. This means a whole new approach to production and jobs.”
Looking back, Rus identified three waves of robotics development so far: a first wave of large, constrained, and potentially dangerous industrial robots; a second wave of more flexible and autonomous robotics systems; and the current wave of automation where we are “building machines that can perform increasingly more complex physical and cognitive tasks in human-centred environments.”
This progress is being enabled by advancements in three interconnected fields, she noted, robotics, AI, and machine learning. She also acknowledged that there are tasks that people do better, and tasks where machines are better at the job. “The sweet spot today”, she said, “is to consider teams of humans and machines working together – to view machines as “Super-tools”, or as autonomous interns running errands or pouring over data for humans to act on.“
But while the last 60 years has been marked by robots mostly inspired by the human form, the next stage, Rus believes, will be more adaptive soft robots inspired by the animal kingdom and form diversity, by our built environment, and with far broader application potential. The future of AI-enabled robotics, she says, “will be inspired by nature – with machines becoming soft like materials, and materials becoming more intelligent like machines.”
To support her point, Rus showcased multiple examples of innovative lab prototypes based on computational design and leading-edge fabrication ideas already under development, from under-water robotic fish for sub-aqua applications, to origami-inspired grippers, to micro-bots that can choose different wraps depending on the tasks they need to perform, to robots that can interpret and mirror human muscle movements, to robots that harness deep learning systems to interact with human language and even respond to some instructions via human brainwaves.
“These are a good starting point for reimagining robots for production,” she added. “Imagine a world where if you can think it, you can make it. A world where anybody can create custom tools, custom robots, and custom products – on demand.”
So, as companies continue to embrace the use of autonomy and automation in manufacturing, Rus believes they need to be prepared for a constantly evolving manufacturing landscape in the years ahead that incorporates AI, robotics, and machine learning tools, and they should strive to better understand how these tools can impact all the processes in the factory, how to take advantage of those processes, and how to use computation and data in order to improve operations.
This, she stressed, requires developing both the right infrastructure and a workforce that is re-skilled to understand how to use the new tools, “because human/machine collaboration requires both better machines and humans who know how to leverage those machines.”
And companies need to start that process now. “It is not enough just to train the workforce of tomorrow,” Rus concluded. “We need to get serious about reskilling the workforce of today and cultivate a culture of agility and lifelong learning in every organization.”