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Enterprise Artificial Intelligence Revenue Will Reach $107.3 Billion Worldwide By 2025

Enterprise Artificial Intelligence Revenue Will Reach $107.3 Billion Worldwide By 2025

Artificial intelligence (AI) is rapidly entering a new phase within the enterprise market, with an increasing number of businesses leveraging AI to turn the massive amounts of process, operational, and transactional data being collected into actionable insights that can improve the way they run their businesses as well as improve customer interactions, according to a new report from Tractica.

Based on the number and variety of pilot programs, proof-of-concept (PoC) demonstrations, and commercial deployments of AI technology already being publicised by enterprise customers around the globe, it is clear that AI is not a fad, but a key part of the technology landscape of today and tomorrow. Tractica forecasts that annual revenue for enterprise applications of AI will increase from $7.6 billion worldwide in 2018 to $107.3 billion in 2025.

“No longer is the discussion of AI limited to science fiction, autonomous vehicles, or Siri; AI is being deployed across a multitude of industries and use cases with enterprises leading the way,” says principal analyst Keith Kirkpatrick. “Thanks to the use of template-driven AI platforms, even a small pilot program can demonstrate real-world benefits. As enterprises are realising, the benefits of AI are even greater when the technology is scaled across the entire organisation.”

Tractica’s report, “Artificial Intelligence for Enterprise Applications”, examines the practical application of AI within commercial enterprises, providing a comprehensive analysis of use cases, business models, market drivers and barriers, technology issues, and the evolving market ecosystem.

 

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Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

According to Tractica, global revenue for digital twins will increase to $9.4 billion in 2025, up from $2.4 billion in 2018. A digital twin is a digital representation that provides the elements and dynamics of how a device or ecosystem operates and lives throughout its life cycle. Digital twins are useful for simulating the capabilities of machine tools in a safe and cost-effective way, as well as identifying the root causes of problems occurring in physical tools or infrastructure.

The digitisation of nearly every industry type is helping to fuel the demand for twinning platforms, as is the desire to monitor, control, and model the future behaviour of real-world equipment, systems, and environments. Manufacturing, aerospace, connected vehicles, smart cities, retail, healthcare, and industrial IoT are key sectors for digital twins market adoption. Asia Pacific is one of the largest geographic regions for digital twins, forecasted to generate $11.2 billion in cumulative revenue.

“Like any technology, digital twins must be understood and accepted by several different stakeholders, from the operations workers up to the C-suite,” said Principal Analyst Keith Kirkpatrick.

“Vendors are highlighting their expertise in analytics and demonstrating domain expertise with specific industry verticals. Some are also spotlighting their experience with incorporating artificial intelligence (AI) and machine learning (ML) technologies, which can provide the ability to model future behaviour via digital twins. These technologies are anticipated to drive the functionality of digital twins beyond simply being enhanced analytics tools,” he added.

 

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Gripping And Clamping Solutions For Process Automation

Gripping and Clamping Solutions for Process Automation

In this interview with Asia Pacific Metalworking Equipment News (APMEN), Vincent Teo, general manager of Schunk, talks about the gripping and clamping challenges that their customers are facing, and how they are helping them address these issues. Article by Stephen Las Marias.

Schunk is one of the leading providers of clamping technology and gripping systems worldwide. Founded in 1945 by Friedrich Schunk as a mechanical workshop, the company has grown to become what it is today under the leadership of his son, Heinz-Dieter Schunk. The company is now under the leadership of siblings Henrik A. Schunk and Kristina I. Schunk, the company founder’s grandchildren.

Schunk has more than 3,500 employees in nine production facilities and 34 subsidiaries as well as distribution partners in more than 50 countries. With more than 11,000 standard components, the company offers the world’s largest range of clamping technology and gripping systems from a single source. In particular, Schunk has 2,550 grippers—the broadest range of standard gripper components on the market—and its portfolio comprises more than 4,000 components.

Based in Singapore, Vincent Teo is the general manager of Schunk, where he is responsible for the Southeast Asia market, including Singapore, Indonesia, Thailand, Malaysia, Philippines, and Vietnam. In an interview with Asia Pacific Metalworking Equipment News (APMEN), Teo talks about the challenges that their customers are facing, and how they are helping them address these issues. He also talks about the trends shaping the clamping and gripping market, and his outlook for the industry.

APMEN: What is your company’s ‘sweet spot’?

Vincent Teo: Schunk understands the needs of manufacturing companies, which have assembly, handling and machining processes. Our products can apply in multiple manufacturing sectors.

APMEN: What sort of challenges are your customers facing?

Teo: Today, businesses face the challenge of getting skilled workers—and staff retention for many industries is becoming a struggle. This is even more severe for countries such as Singapore, which depends on foreign workers. If automation can help reduce these problems and improve work conditions, then more high-value jobs can be created.

APMEN: How is your company helping your customers address their problems?

Teo: We work together closely with our partners such as robot manufacturers and system integrators, and we aim to reach out to more customers to help them see the benefits of automation.

APMEN: What forces do you see driving the industry?

Teo: Collaborative robots, or cobots, have revolutionized many applications that were impossible to think of over a century ago. Less complicated programming equates to less man-hour training, making it cheaper for businesses to adopt robotics. This is game changer, and Schunk is working with the major players in this new era of robotics.

APMEN: What opportunities you are seeing in the Asia market for robotic clamping industry?

Teo: The trend towards automated loading on machining by robots is picking up in recent years. The company is well-positioned to support this growing demand with immediate solutions.

APMEN: What about the challenges in the region? How do you see the trade war between China and the US affecting the manufacturing industry?

Teo: There has been increased investments towards Asia. This is a good problem, where we see customers valuing more our solutions to help them to increase their productivity and capture more businesses.

APMEN: What are the latest developments in robotic clamping/gripping?

Teo: We constantly develop new products in anticipation of the needs of our customers. One example is our latest product, the VERO S NSE3 clamping module, which improves set-up time and has a repeatability accuracy of <0.005mm.

APMEN: How do you position yourself in this industry? What sets you and your solutions apart from the competition?

Teo: Schunk is a unique company, having clamping technology (CT) and gripping systems (GS) solutions. With more than 11,000 standard products, no other company has a comparable scale and size across the range of products. With integrated solutions for both, we provide our customers the best opportunity to automate their processes.

APMEN: What advice would you give your customers when it comes to choosing the correct robot clamping/gripping solution?

Teo: For the machining industry, some customers often invested in clamping solutions and realized later that they need to automate their processes. When they started to review, they will realize that their investments may not be future proof. This may further discourage them towards the automation idea. Our comprehensive CT products allow our customers to later upgrade with our GS products, as both offers seamless integration.

APMEN: The trend is toward smarter factories now, with the advent of Internet of Things (IoT), data analytics, etc. Where does Schunk come in in this environment?

Teo: Schunk sees the need to embrace new technologies. iTENDO, our intelligent hydraulic expansion toolholder for real-time process control, records the process directly on the tool, and transmits the data wirelessly to a receiving unit in the machine room for constant evaluation within the closed control loop. With iTENDO—the first intelligent toolholder on the market—Schunk is setting a milestone when it comes to digitalization in the metal cutting industry.

APMEN: What is your outlook for the robotic clamping/gripping industry in the next 12 to 18 months?

Teo: We understands our partners’ and customers’ needs. For gripping, we have come out recently with new products to address the growing demand for collaborative robot (cobots). For clamping, our latest NSE-A3 138 is specifically designed for automated machine loading. It has a pull down force up to 28kN with integrated bluff off function and media transfer units.

 

 

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Schunk Investing €85 Million In Expansion Of Production Facilities

Schunk Investing €85 Million in Expansion of Production Facilities

Gripping systems and clamping technology provider Schunk is investing around €85 million in expanding its production facilities in Brackenheim-Hausen, Mengen, and St. Georgen in Germany, and in Morrisville, North Carolina, in the United States.

Around 42,000sqm of total production and administration space is being created, starting with the US plant, where the new buildings were officially handed over recently. In addition to the production area expansion, Schunk Intec USA created a 4,000sqm administrative building, which features a Customer Centre, where users can experience Schunk’s components live and receive additional know-how in technology forums and workshops. The new building was inaugurated in early May with an official ceremony followed by a Family Day. Schunk has invested a total of almost €10 million in the expansion of the site.

Meanwhile, €40 million are being put into the Competence Centre for Gripping Systems in Brackenheim-Hausen, Germany. The extension covers an area of 22,000sqm and represents a doubling of the existing production area.

Schunk is investing another €30 million in the Competence Centre for Lathe Chuck Technology and Stationary Clamping Systems in Mengen, in the district of Sigmaringen, Germany. Here, 12,000sqm are to be added for production and R&D.

Around €5 million were invested at the St. Georgen site in Black Forest, where the production area was doubled with an increase of 4,200sqm.

“In the coming years, we will experience a boom in automation and digitisation worldwide, and we’ll only be able to handle this by having the right capacities,” said CEO Henrik A. Schunk.

For several years, the company has been successfully focusing on these two trends and concentrating its resources and know-how. Schunk expects high growth rates, especially for mechatronic and increasingly intelligent clamping devices and gripping systems.

The company also recently announced its cooperation with AnotherBrain, one of the world’s leading specialists in artificial intelligence (AI).

 

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Embedded Motion Control

Embedded Motion Control

Embedded motion control is a major emerging trend that’s being driven by the interconnectedness of many different systems, such as new edge device applications in the Internet of Things and the industrial IoT, as well as other trends such as increasing integration and miniaturisation of systems, and the spread of mobile/wearable consumer electronics – and artificial intelligence. Article by Trinamic.

Several different trends, both application related and user (engineering) related, are working together to spur the increase in embedded motion control. Even before the recent emergence of IoT and IIoT edge devices, many of these trends were already occurring.

Simultaneous increasing miniaturisation/integration and automation: One of the most important trends, and one that influences so many others, is the increasing miniaturisation and integration of systems, components, and assemblies, at the same time they are also being automated. This is also true in new miniature motor types with very small form-factors. Demand for stepper motors overall continues to rise, due in part to a rise in demand for miniature motors, according to a report by P&M Market Research reports. Although industrial machinery has been the largest market segment for stepper motors, said this report, their rising use in medical equipment, desktop manufacturing, or home automation will drive market growth by 2023.

Other applications being enabled by this trend include 3D printing, and IoT-connected devices for consumers. This latter group includes connected home devices such as window shades, blinds, and cameras for smart home systems; environmental controls such as connected thermostats; appliances; robots; drones; automotive; and consumer devices that require stepper motors. For wearables, some examples are small portable insulin pumps containing small stepper motors, which also need a wired or wireless interface and are battery driven, and virtual reality goggles.

Fostered By Industrial IoT

Growing interconnectedness fostered by the IIoT: Networks are growing. Bandwidth is growing. The amount of information exchanged over all networks, including over the Internet, is growing. Global semiconductor and technology companies are placing their highest focus on solutions for networking, for data centres, and high-bandwidth communication technologies – in global telecommunication and media, in industrial control applications, as well as in automotive and home networks.

To keep pace with this development requires more intelligent systems, including motion control and drive solutions at the network edge with standardised APIs and standard interfaces so these systems can understand and communicate with each other.

AI: Artificial intelligence is a trend on the algorithm side, in software and dedicated hardware, and it is a radical change. AI allows for intelligent and autonomous machines, it allows for systems that make decisions based on their available “information” without human control, it allows for learning/adaptive machines, and it allows for interactive machines. Because of AI, new application areas are emerging which will become commodities in a few years, such as advanced robotics in factories and in medical applications, the transportation & delivery industry, or toys. Nevertheless, to actually interact with the real, physical world – transforming digital information into physical motion and vice versa – AI-based systems require smart actuators. Such smart actuators are examples of embedded motion control systems.

Embedded motion control not only means using an embedded system for motion control tasks or implementing the motor and motion control functions in highly integrated microchips. Embedded motion control means more than just motor control. It means the whole motion control system in miniature.

Examples Of Embedded Motion Control

The design of motion control is no longer difficult or complicated: instead, it has become a set of mainstream functions, or building blocks, which can help designers reduce their development overhead. We can now embed functions and sub-blocks physically (motor, sensors, housing, physical interface) and logically (algorithms, communication stacks, dedicated hardware accelerators), combined according to an engineer’s specific application needs.

Examples of increasing integration and miniaturization can be found in Trinamic’s smart stepper controller + driver IC family, such as the TMC5130 / TMC5160 integrated motor driver and motion controller IC. The TMC5072 can even drive two motors directly out of the IC. The TMC8670 dedicated EtherCAT motion controller IC is an example of the highest levels of integration. It’s an SoC with a field-programmable gate array (FPGA) and a real MCU inside, and includes EtherCAT real-time bus interfaces, protocol stacks, plus servo motor control in a single device.

If you think about all of these trends like AI, IoT, and IIoT, it becomes clear that they are typically located more on the processing and communication side. Nevertheless, many systems need a bridge to the real world. When people think about the IoT, they think sensors and data (the cloud). However, it’s the actuators that give meaning to the IoT and make life comfortable by enabling the physical cloud, which consists of all the physical devices connected to the Internet. Embedded motion control is this bridge that connects the digital to the physical.

 

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Artificial Intelligence Software Market To Reach US$118.6 Billion By 2025

Artificial Intelligence Software Market To Reach US$118.6 Billion By 2025

According to a report by Tractica, titled “Artificial Intelligence Market Forecast”, the global artificial intelligence (AI) software market revenue is expected to increase from US$9.5 billion in 2018 to US$118.6 billion by 2025. The study includes market sizing, segmentation, and forecasts for 315 AI use cases distributed across 30 industries. The steady growth of the AI market in the consumer, enterprise, government and defence sectors can be observed as applications of AI technologies and solutions are becoming a reality.

“While the market is still a few years away from an inflection point for real growth, it is critical for both end users and solutions providers to identify the technologies and use cases where they want to invest in AI,” commented Aditya Kaul, research director at Tractica.

AI use cases covered by this report includes three main categories: vision, language and analytics. Vision and language represent the perceptive brain which aims to enhance speech and sight capabilities. While analytics represent the analytical brain which deals with extracting and processing raw data, using traditional machine learning techniques for example. Although analytics and big data are huge drivers of the AI market, pure analytics only represent 35 percent of revenue from AI use cases. In fact, the main driver of the market is actually language and vision use cases in combination with analytics, representing 65 percent of the revenue. New AI use cases in the manufacturing sector includes supply chain optimisation, human-robot collaboration, digital twins and robotic and machine vision enhancements.

 

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Manufacturers With Artificial Intelligence To Nearly Double Competitiveness

Manufacturers With Artificial Intelligence To Nearly Double Competitiveness

Microsoft Asia and IDC Asia Pacific released findings specific to the manufacturing sector for the study, Future Ready Business: Assessing Asia Pacific’s Growth with AI.

The manufacturing sector, which contributes to a significant proportion of Asia Pacific’s GDP, continues to face rising competitive pressure due to growing costs and lower margins. Manufacturers are increasingly turning to emerging technologies to stay ahead of the competition. Those organisations that have started to adopt Artificial Intelligence (AI) believe it will nearly double their competitiveness (1.8 times) in the next three years.

“Manufacturers in Asia Pacific are slowly, but surely, seeing the importance of adopting a digital strategy and latest technologies. The study found that 76 percent of manufacturing business leaders agree that AI is instrumental to their organisation’s competitiveness in the next three years,” said Scott Hunter, Regional Business Lead, Manufacturing, Microsoft Asia. “To achieve supply chain excellence, and even develop new business models to address changing customers’ needs, integrating AI for their business is a must. Organisations which fail to adopt an AI-first strategy risk being left behind in today’s competitive market landscape.”

“However, 59 percent of manufacturers have not adopted AI as part of their business today. This is a worrying sign for the industry that needs to thrive on innovation,” added Hunter.

For manufacturers that have started their AI journeys, the top three business drivers to adopt AI include higher margins, higher competitiveness and business agility, as well as better customer relationships and outcomes.

They are already seeing business improvements in the range of 17 percent to 24 percent today, and further improvements are anticipated in three years by at least 1.7 times. The biggest jumps are expected in driving accelerate innovation (2.0 times), and higher margins (1.9 times).

One example is Piramal Glass, a leading glass packaging manufacturer in India, which has turned to AI, Internet of Things and advanced data analytics on the cloud to drive operational efficiency, enhance customer experience and generate new revenue models. Their in-house solution, RTMI, offers advanced insights in real-time that led to five percent reduction in defects, 40 percent reduction in manual data gathering and 25 percent improvement in employee productivity.

“The identified business drivers are a clear sign of how technology such as AI can create improved value by helping organisations gain insights, and better manage their operations in a highly complex environment,” said Stephanie Krishan, Research Director, IDC Manufacturing Insights. “In fact, according to IDC FutureScape for Manufacturing and Implications for Asia Pacific (excluding Japan), half of the top 10 predictions are driven by data and AI-centric solutions or use cases, such as creating new ecosystems for automation, or even to put data at the center of their processes to drive speed, agility and efficiencies. This only points towards the fact that the future of manufacturing will be built upon data in order to deliver scalable and accelerate growth for the industry.”

Asia Pacific’s Manufacturers Need To Focus On Its Culture, Strategy And Data Readiness

The Study also evaluated six dimensions contributing to the sector’s AI readiness. “The manufacturing sector is lagging behind in Culture, Data and Strategy, compared to Asia Pacific’s overall readiness. Business leaders must focus on those areas to stay competitive,” said Krishan.

  1. Strategy: Manufacturers need to have an AI strategy in place, and support a more distributed workforce

“By adopting AI industry players will accelerate their transformation and enjoy higher benefits. To succeed in an increasingly digital environment, Manufacturers need to have an AI- strategy in place, including workforce transformation,” said Hunter. Close to half of business leaders polled see a shift towards a more distributed and flexible workforce due to AI in the next three years.

  1. Data: Manufacturers need to work on availability, quality and governance of existing data

There is no surprise that manufacturers need to have a more robust data strategy in place in order to train task-based AI solutions. Today, manufacturers in the region are still dealing with a data structure where it can only be accessed by a centralised analytics team. The quality and timeliness of data are still major issues that are being addressed on an ad-hoc basis. There is also no extensive enterprise data governance program in place.

  1. Culture: Traits required for AI adoption lacking in manufacturing organisations

More than half of the manufacturing workers, and nearly half of the business leaders polled believe that cultural traits and behaviors are not pervasive in their organisation today. For example, 63 percent of workers and 57 percent of business leaders do not agree that employees are empowered to take risks, and act with speed and agility within the organisation.

“Manufacturers in the region must work on better integration of AI into their existing operations, including how data is used and processed. They need to build an AI-ready workforce that is agile and empowered to innovate,” said Krishan. “Only when manufacturers nail down its strategy and skill capabilities, they can fully harness the full power of AI for their organisation.”

Dairy enterprise ACM’s newly opened high-tech milk processing and manufacturing facility in Victoria, Australia is leveraging state-of-the-art intelligent technology to better manage costs via a rich data approach. By introducing machine learning capabilities, ACM is able to reduce human errors from contaminating organic milk with conventional milk, which also minimises wastage. In addition, by introducing automation for production planning, logging and quality assurance; as well as factory maintenance with the help of CRM and AI solutions, ACM has been able to rein in weekend overtime costs of AU$100,000 annually.

Skills For An AI-Ready Workforce

The good news is that majority of business leaders and workers in the sector believe that AI will have a positive impact on their jobs. 62 percent of business leaders and 77 percent of workers believing that AI will either help do their existing jobs better or reduce repetitive tasks.

However, according to business leaders, the skills required for an AI future are in shortage. Communication and negotiation skills, entrepreneurship and initiative-taking as well as adaptability and continuous learning are the top three skills identified in which demand will outstrip supply in the next three years. At the same time, business leaders believe that the demand for basic data processing, literacy & numeracy and general equipment operations and mechanical skills will decrease in three years. Those skills are broadly available today, and already now the supply is higher than the demand.

The disconnect comes with employers’ perception of their workers’ willingness to reskill. “Business leaders are aware of the massive reskilling efforts required to build an AI ready workforce. However, 22 percent of business leaders felt that workers have no interest to reskill, but only eight percent of workers feel the same. In addition, 48 percent of business leaders feel that workers do not have enough time to reskill, but only 34 percent feel the same way,” shared Hunter. “Business leaders in this space must prioritise reskilling and upskilling, dedicating employee’s time for this to address skills shortage. Even as it may result in short term productivity impact as building an AI-ready workforce will result in greater gains in the future.”

 

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Outlook Of Artificial Intelligence For Smart Manufacturing

Outlook Of Artificial Intelligence For Smart Manufacturing

The worldwide manufacturing sector investment in artificial intelligence (AI) services are expected to increase from US$2.9 billion in 2018 to US$13.2 billion by 2025, according to a report by Tractica.

AI technology includes machine learning, deep learning, natural language processing, computer vision and machine reasoning. Although manufacturing companies are wary of risks in implementing new technologies due to large amount of capital and time required, they are at a steady pace, increasing adoption of AI technology. AI technologies in smart manufacturing applications can increase operational efficiencies and reduce cost of production.

“As manufacturing becomes more cost-sensitive and customers demand quality, manufacturers are using AI to enhance the performance of equipment, reduce downtime, and improve the quantity and quality of products,” said Keith Kirkpatrick, principal analyst at Tractica. “The overarching driver of AI technology is the ability to find insights in large data sources that would be too unwieldy for humans to analyse quickly,” he added.

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NTU Singapore And AMD To Launch Data Science And Artificial Intelligence Lab

NTU Singapore And AMD To Launch Data Science And Artificial Intelligence Lab

Nanyang Technological University, Singapore (NTU Singapore) and AMD have joined forces to launch a Data Science and Artificial Intelligence Lab, which will nurture next-generation tech leaders with the latest industry-driven digital skills.

The S$4 million joint Data Science and AI Lab will leverage AMD’s deep-learning technologies and NTU’s global strengths in machine learning, artificial intelligence (AI) and data science, to complement the university’s Data Science and Artificial Intelligence undergraduate programme. NTU students will be exposed to real world applications such as developing software algorithms used in security fields like identification and motion detection. They will also work on big data and analytics which are now frequently used by leading organisations. For example, students will get opportunities to develop clinical support solutions using big data analysis to aid medical diagnosis. Students will also undergo training to participate in supercomputing competitions using AMD’s versatile open source software, Radeon Open Compute (ROCm) platform. This software facilitates ultrascale or hyperscale computing, a form of high-performance computing that can simulate complex systems within just a few days where it typically used to take years.

Potential research projects and real-world machine learning and deep learning applications that students are expected to work with in the lab will leverage AMD’s open software platform for accelerated computing. For example, with the ROCm open software platform, Tensorflow (open source machine learning framework) users will benefit from graphics processing unit (GPU) acceleration and a more robust open source machine learning ecosystem. The collaboration will also provide NTU students with local and overseas attachment opportunities with AMD. From the next academic year in 2019, NTU undergraduates will be able to intern at AMD’s Shanghai Research and Development Center (SRDC) and the Singapore Product Development Center (SPDC). NTU will also support AMD’s engineers who wish to pursue PhD programmes through EDB’s Industrial Post-graduate Programme (IPP). “The philosophy behind AMD’s open source deep-learning architecture is about flexibility and choice – for developers and academics to have a range of hardware vendor-agnostic tools to innovate at scale. NTU is the premier institution for artificial intelligence, and we’re very excited to bring our technologies to empower the participating students to freely explore the diversity of deep-learning applications,” said Allen Lee, Corporate Vice President for Asia Datacenter Group, General Manager for China R&D Center, AMD. NTU’s Dean for the College of Engineering, Professor Louis Phee, said, “This collaboration with AMD highlights NTU’s drive in nurturing strong relations with top industry partners to provide students with industry-relevant education. This will not only give them first-hand insights in solving real-world challenges, but also give them an edge when navigating today’s dynamic workplace environment.”

AMD will provide one instance of AMD’s current generation of server processor (EPYC) and Radeon Instinct MI25 accelerator that can be scaled to handle hyperscale workloads to start. AMD plans to provide the university with its latest server technologies, such as the Radeon Instinct MI60 accelerators in 2019 and beyond. AMD’s AI and machine learning experts will work closely with NTU professors to conduct joint training and workshop sessions for industry members.

The collaboration will also support Singapore’s digital initiatives such as the AI.SG programme and support the nation’s transition into industry 4.0.

“The establishment of the AI and Data Science Lab between AMD and NTU today is a great example of how industry and academia can leverage transformative technologies like AI to develop real-world solutions. This partnership is testament to the company’s confidence in Singapore and the innovation cluster we have here. Such an effort will also deepen Singapore’s talent pool for AI, and provide Singaporeans with the necessary skillset to participate in the opportunities that rapid technological adoption is bringing,” said Mr. Ling Yuan Chun, Deputy Director, Semiconductors, Singapore Economic Development Board.

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Insights From Omron: Trends In The Singapore Manufacturing Industry

Insights From Omron: Trends In The Singapore Manufacturing Industry

Through this article, Mr. Lieu Yew Fatt, Managing Director of Omron Electronics Singapore and Mr. Swaminathan Vangal-Ramamurthy, General Manager of Robotics Business Division, Omron Asia Pacific examine the future of manufacturing in Singapore and the relationship between local and global trends.

Manufacturing has been a key pillar of the economy in Singapore ever since we progressed to an innovation-intensive economy from a labour-intensive one in the early days of nation building. Now, manufacturing contributes close to 20 percent of our gross domestic product (GDP) and keeps more than 500,000 people employed.

Moving forward, the manufacturing sector here will face increasing external pressures in the coming years. In Southeast Asia, we have Thailand, which ranked well for high quality and low cost in a McKinsey analysis on which ASEAN country is most attractive for manufacturing investments. Singapore, not surprisingly, ranked high in high quality but performed badly in low cost.

Meanwhile, the rise of China as a manufacturing powerhouse in Asia has also brought a different level of competition to the landscape. Foreign direct investment (FDI) has been flowing in to China and this adds competitive pressure to the manufacturers in this region, especially since there are some significant overlaps in manufacturing capabilities between the manufacturers in China and here.

The Shift Towards Innovation And Research

Singapore’s manufacturing sector naturally leans towards advanced manufacturing in view of our knowledge-based economy. Manufacturers here are generally more open to leveraging innovation and technology to improve products and/or processes.

In 2016, the Singapore Government introduced the Research Innovation Enterprise 2020 (RIE2020), a plan that charts the course for harvesting an innovative and competitive economy as we progress towards 2020. As part of this plan, advanced manufacturing was identified as a key pillar among others to drive this forward. RIE2020 also identified four cross-cutting technology areas as essential enablers, which will undergird and support the verticals. These are: Robotics and Automation, Digital Manufacturing, Additive Manufacturing and Advanced Materials.

Additionally, the Government has also committed SGD$19 billion, the biggest allocation since 1995, as investment into innovation, research and driving enterprise growth under the RIE2020 Plan for 2016 to 2020.

Keeping Up With Technology Trends

Government support provides a much-needed boost for manufacturers here. However, manufacturing businesses must ensure that they are maximising cost efficiency and productivity in their operations to remain competitive. The good news is that technology can offer tremendous value in these areas.

There are two major trends to watch in advanced manufacturing:

1.Artificial Intelligence And Machine Controllers

Manufacturers can expect artificial intelligence (AI) to play an increasingly prominent role in manufacturing as factory floors become smarter and more collaborative robots (or ‘cobots’) work alongside humans to enhance productivity.

At OMRON, we recently took an innovative-automation approach. By this, we mean an integrating high-precision, high-speed manufacturing with more intelligent controls and data analysis and combining that with a more interactive and collaborative relationship between robots and people on the manufacturing floor.

For instance, we merged AI, machine learning and facial recognition technologies to develop Omron vestibulo-ocular reflex (VOR) technology. This is used in automobile manufacturing to create products that keep drivers safe. VOR technology uses a camera to capture and sense a driver’s eye movements to spot for early-stage drowsiness and determine his/her suitability for driving. This technology can also be applied to the factory floor to keep workers safe as well, such as when they are operating heavy machinery.

Separately, we have added learning capabilities to machine automation controllers by equipping them with machine learning AI algorithm. This allows the controllers to achieve real time integration between programmable logic controller and AI processing functions. The result is that these controllers can manage equipment changes on the factory floor in microseconds as they send collected data to the host IT system while maintaining control performance.

Additionally, these controllers can effectively keep track of equipment and production status when equipped with sensors set to monitor machines and production lines. They can look out for irregularities or unusual activities and built-in AIs can take action to fix issues or activate safety procedures depending on what they are programmed to learn.

2.Industrial Internet Of Things

The Industrial Internet of Things (IIoT) in manufacturing is currently already a primary trend affecting businesses in the industry. It transformed manufacturing in many parts of the world due to its ability to enable the gathering and analysis of data and then applying it in new and novel ways.

However, IIoT goes beyond machines to machine connectivity. It is also a movement that is uniting the people and systems on the factory floor with enterprise-level decision makers. The rise of IIoT platforms have also empowered employees as they now have better access to information. With improved collaboration a focus of these platforms, teams can now work across factory floors, or even remotely across wider geographies.

The mindset is also shifting towards that of consumers connected to the industry through customer interactions and social networks, and informed businesses are constantly adjusting their output and production based on consumer demand.

We readily see this in the automobile industry where manufacturers offer many customisable or optional choices. Now, car buyers are often spoilt for choice on things like exterior and interior colors, seat material and design, in-car stereo and GPS systems, sun roofs and so on. Manufacturers are embracing this connected customers and market-driven environment. To remain competitive, manufacturers have to be connected and nimble and the only way they can be successful is to leverage the power of data and newer technologies like IIoT.

Future-Ready Manufacturing

It will no doubt remain important for manufacturers here to continue to strive for the age-old goals of increasing speed to market, reducing overall costs and maintaining quality control. Nonetheless, they cannot ignore the fact that digitalisation and disruptive technologies are transforming the whole manufacturing landscape, and it is crucial that they take steps to modernise their operations and prepare for the business environment and the market of the future.

Advanced manufacturing methodologies that used to be mere concepts just a few years ago are now finding practical implementations. It is timely for manufacturers here to explore their actual feasibility and practicality as they modernise their own operations. They may also want to better incorporate automation, data analytics, IIoT, robotics and increased technology adoption into their business strategies and operational planning considerations.

To be future-ready, manufacturers will need to plan toward realising a more transparent supply chain that enhances product traceability by taking steps now to adopt newer and more intelligent production methods and processes.

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