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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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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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Industrial Robotics Market Outlook

Industrial Robotics Market Outlook

The industrial robotics market was valued at US$18.05 billion in 2018 and is expected to reach US$37.75 billion by 2024, at a compound annual growth rate (CAGR) of 12.15 percent over the forecast period (2019–2024), according to market analyst Mordor Intelligence. The market has been witnessing a huge demand over the past decade, owing to the rising adoption of smart factory systems, of which these robots play a vital part. The global smart factory market is expected to reach US$388.68 billion by 2024, which provides insights on the scope of the adoption of industrial robots for automation across end-user industries.

In particular, Industry 4.0, the newest industrial revolution, has fuelled the development of new technologies, like collaborative robots and AI-enabled robots, to name a few, and have enabled industries to use robots to streamline many processes, increase efficiency, and eliminate errors. Increased workplace safety and improved production capabilities have further driven industries to invest in robotic systems.

Rising Demand from Automotive Industry

The growing adoption of automation in the automotive manufacturing process and involvement of digitisation and AI are the primary factors driving the demand for industrial robots in the automotive sector.

In 2017, more than 170,000 robots took part in the production process in the European automotive industry. The growing presence of robots and automation in the European automotive industry is expected to fuel the market for industrial robots in the region.

Meanwhile, China has also become both the world’s largest car market and the world’s largest production site for cars, including electric cars, with much growth potential. In Malaysia, there are 27 automotive manufacturing and assembly plants. Overall, the growing automotive industry in Asia is also creating a massive opportunity for the global industrial robotics market.

 

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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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Large Manufacturing Companies In Asia Pacific Could Lose US$10.7 Million Due To A Cyberattack

Large Manufacturing Companies In Asia Pacific Could Lose US$10.7 million Due To A Cyberattack

A Frost & Sullivan study commissioned by Microsoft found that a cyberattack can cost a large manufacturing organisation in Asia Pacific an average of US$10.7 million in economic loss with customer churn being the largest economic consequence of a cyber breach, resulting in US$8.1 million of indirect cost. For mid-sized manufacturing organisation, the average economic loss was US$38,000. Furthermore, cybersecurity incidents have also led to job losses across different functions in more than three out of five (63 percent) manufacturing organisations.

While the impact of data vulnerabilities and breaches can be costly and damaging to the manufacturing organisations, its supply chain and consumers, the study uncovered that half (51 percent) of the manufacturing organisations in Asia Pacific had either experienced a security incident or were not sure if they had had a security incident as they had not performed proper forensics or data breach assessment.

The study further revealed that instead of accelerating digital transformation to bolster their cybersecurity strategy to defend against future cyberattacks, almost three in five (59 percent) manufacturing organisations across Asia Pacific had delayed the progress of digital transformation projects due to the fear of cyberattacks. Delaying digital transformation not only limits the capabilities of manufacturing organisations to defend against increasingly sophisticated cyberthreats but also prevents them from leveraging advanced technologies, such as artificial intelligence (AI), cloud, and the Internet of Things (IoT), to dramatically increase productivity, empower their workforce and deliver new service lines.

These findings are part of “Understanding the Cybersecurity Threat Landscape in Asia Pacific: Securing the Modern Enterprise in a Digital World” study launched in May 2018. The findings aim to provide business and IT decision makers in the manufacturing sector with insights on the economic cost of cyberattacks and to help to identify any gaps in their cybersecurity strategies.

The initial study surveyed a total of 1,300 business and IT decision makers ranging from mid-sized organisations (250 to 499 employees) to large-sized organisations (>than 500 employees), of which 18 percent belong to the manufacturing industry.

In calculating the cost of cyberattacks, Frost & Sullivan created an economic loss model based on the insights shared by the respondents. This model factors in two kinds of losses which could result from a cybersecurity breach:

  • Direct: Financial losses associated with a cybersecurity incident including loss of productivity, fines, remediation cost, etc; and
  • Indirect: The opportunity cost to the organisation such as customer churn due to reputational damage.

“The frequency and severity of cyberattacks targeting manufacturing organisations have increased significantly in recent years, underscoring the need to protect the ever-growing volume of data generated by and made available to manufacturing organisations,” said Kenny Yeo, Industry Principal, Cyber Security, Frost & Sullivan. “By integrating security into every digital process and physical devices, manufacturing organisations can not only mitigate the loss of intellectual property (IP) and customer data but also minimise downtime as well as remediation cost resulting from cyberattacks.”

 

Key Cyberthreats And Gaps In Manufacturing Organisations’ Cybersecurity Approaches

For manufacturing organisations that have encountered a security incident, data exfiltration, ransomware and remote code execution are the biggest concern as these threats have the highest impact and often result in the slowest recovery time:

  • Remote code execution is a unique threat that manufacturing organisations face, and it poses a grave threat to these companies as cybercriminals can remotely access and control their operations. This allows malicious actors to disrupt production and sabotage the business.
  • As manufacturing organisations need to adhere to tight schedules and strict deadlines, a ransomware attack – where cybercriminals encrypt files to restrict users’ access until a ransom is paid – can lead to production downtime and loss of customer confidence. Manufacturing organisations not only lose time and resources in dealing with the aftermath of the attack, but the entire supply chain will also be disrupted too.

Aside from external threats, the study also uncovered several key cybersecurity gaps in manufacturing organisations:

  • Complex security environment impeding recovery time: Contrary to the common notion that more security solutions will lead to greater efficiency, a large portfolio of cybersecurity solutions may not be a good approach to bolster cybersecurity. The complexity of managing a large portfolio of cybersecurity solutions may lead to longer recovery time from cyberattacks.

The study showed that nearly three in five (57 percent) manufacturing organisations with 26 to 50 cybersecurity solutions took more than a day to recover from cyberattacks. Conversely, only 26 percent of organisations with less than 10 solutions took more than a day to recover. In fact, 35 percent of them managed to recover from a security incident within an hour.

  • Traditional tactical viewpoint towards cybersecurity: Despite the growing sophistication and impact of cyberattacks, the study revealed that majority of the respondents (41 percent) hold a tactical view of cybersecurity – “only” to safeguard the organisation against cyberattacks. While only one in five (19 percent) viewed cybersecurity as a business differentiator and an enabler for digital transformation.
  • Security as an afterthought: If cybersecurity is not seen as an enabler for digital transformation, it will undermine manufacturing organisations’ ability to build a “secure-by-design” digital project, leading to increased vulnerabilities and risks.

The study revealed that only 26 percent of manufacturing organisations who had encountered cyberthreats considered a cybersecurity strategy prior to initiating a digital transformation project. The remaining respondents either thought about cybersecurity only after the commencement of their digital transformation projects or did not think about cybersecurity at all.

“Technology advances and innovations in intelligent manufacturing are delivering game-changing breakthroughs for leading businesses in every sector,” said Scott Hunter, Regional Business Lead, Manufacturing, Microsoft Asia. “As manufacturing organisations focus on increasing data-driven products and services to differentiate themselves in the global economy, building and maintaining trust within their ecosystem of partners and customers becomes an even bigger priority.”

“Cyber attackers are constantly looking for opportunities, so the more businesses know about their techniques and tradecraft, the better prepared they will be to build defenses and respond quickly. Building organisational resilience and reducing risk by adopting a security approach that includes prevention, detection and response can make a huge difference in the overall cybersecurity health of a manufacturing organisation,” he added.

 

Bolstering Cybersecurity Using Artifical Intelligence

AI plays a critical role in manufacturing organisations as they increasingly rely on machine learning automation to increase their efficiency and output by scale while reducing cost and downtime through predictive maintenance. AI is also a powerful tool that can enable manufacturing organisations to defend themselves against increasingly sophisticated cyberattacks. The study revealed that 67 percent of manufacturing organisations in Asia Pacific have either adopted or are considering an AI-based approach to improve their security posture.

Cybersecurity solutions that are augmented with AI and machine learning capabilities can autonomously learn what is normal behavior for connected devices on the organisation’s network, and swiftly identify cyberthreats at scale through the detection of behavioral anomalies. Cybersecurity teams can also put in place rules that block or quarantine devices that are not behaving as expected before they can potentially damage the environment. These AI-powered cybersecurity engines enable manufacturing organisations to address one of their largest and most complex security challenges as they integrate thousands or even millions of IoT devices into their information technology (IT) and operational technology (OT) environments.

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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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Updates On The Progress Of Thailand 4.0

Updates On The Progress Of Thailand 4.0

Industry 4.0 has transformed the way in which manufacturing is conducted and with buzzwords such as artificial intelligence (AI), analytics, cobots and cybersecurity dominating the industry. This has resulted in emerging markets such as Thailand developing innovative solutions in order to prosper. Article by Hazel Koh.

According to the Thailand Board of Investment (BOI), Thailand 4.0 is a result of the Thai government’s vision of a new economic model, aimed at pulling Thailand out of the “middle-income trap”. And through this vision, robotics and automation technology is expected to play an increasingly important role in manufacturing. This builds on Thailand’s progress in the last three decades whereby the country grew in global rankings in terms of its automotive, electronics and electrical appliance industries, which are also the main industries that drive global robotics and automation growth. Hence, as the world’s sixth-largest commercial vehicle producer, Thailand has been using robotics and automation technology at an increasing rate.

Growth In Industrial Machinery

Duangjai Asawachintachit, Secretary General of Thailand BOI, said at the end of 2017 that, “Advanced technologies are changing the business landscape, especially in the manufacturing sector.” And he further added that, “We now see many companies transitioning into Industry 4.0, making use of AI, big data management and the Internet of Things (IoT) to seamlessly work together to exponentially increase both production and productivity.”

Therefore, it can be observed that over the past few years, manufacturers in Thailand have increasingly automated manufacturing processes and adopted the use of machinery in order to remain competitive globally. In addition, 50 percent of Thai manufacturers are considering the adoption of automation systems within one to three years while medium-sized businesses will be ready in three to five years, followed by small companies in five years or more.

This has resulted in a dramatic expansion of the Thai industrial robots industry and between 2013 and 2018, Thailand’s exports of industrial robots has increased by 133 percent.

Infrastructural And Ecosystem Support

In order to facilitate the development of Thailand 4.0, Thailand has invested in numerous support networks. For example, educational institutions are playing a role in supporting research and development as well as human resource training and this can be observed in the case of the Institute of Field Robotics (FIBO) of King Mongkut’s University of Technology, Thonburi, which is currently offering undergraduate and graduate programmes in robotics and automation engineering.

To top this off, The BOI offers a consortium of tax and non-tax investment incentives for projects that meet national development objectives in automation and robotics. For example, machinery and import duty for raw materials that are meant for export production can attain up to eight years of corporate income tax exemption while for projects related to assembling robots or automation equipment and/or automation parts, investors will be exempted from corporate tax for five years. And investments relating to robotics and automation in the Eastern Economic Corridor (EEC) will also be given another 50 percent corporate income tax reduction for an additional five years.

Future Outlook And Challenges

As Thailand builds on its vision of advance manufacturing, the workforce has to be trained in order to meet the changing industry requirements. And it has been estimated by the ILO that 56 percent of Thai-based jobs are at high risk of being automated during the next two decades. Therefore,as the government continues to focus on the development of robotics, mechanics, AI and automation, Thailand has to invest on its workforce in order to remain competitive.

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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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