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Thailand BOI Accelerates Investment For Digital Adoption

Thailand BOI Accelerates Investment For Digital Adoption

The Thailand Board of Investment (BOI) has approved a series of measures to accelerate investments, particularly in target industries and to encourage business to adopt digital technologies.

“The package to promote large scale projects was designed to boost investment in the post-Covid-19 period,” Ms Duangjai Asawachintachit, Secretary General of the BOI, said after a board meeting chaired by Prime Minister Gen Prayut Chan-ocha.

“As for the digital technology adoption measure, they complement the sets of productivity improvement measures we have been implementing continuously to promote increased efficiency and productivity, and ensure companies are ready to seize the business opportunities arising from the upcoming economic recovery.”

Under the measures to accelerate investment in target industries, projects with realised investments of at least 1 billion baht (USD33 million) within 12 months from the promotion certificate issuance, will be eligible for an additional 50 percent corporate income tax (CIT) deduction for a period of 5 years, on top of the standard five to eight years CIT exemption, Ms Duangjai said. Qualified projects must submit applications from January 4, 2021 to the last working day of 2021.

Existing businesses of all sizes applying for investment under the digital technology adoption program in systems and activities such as software integration, artificial intelligence (AI), machine learning or big data analytics by the end of 2022, will, if approved, be granted a 50 percent corporate income tax exemption for three years on their existing businesses.

“We expect to see faster adoption of digital technologies including cloud computing through this incentive scheme,” Ms Duangjai said. To further promote investments in Thailand’s ten Special Economic Zones (SEZ), all located in border areas, the BOI approved the extension of the application period, to the end of 2022, for the special incentive scheme for SEZs that has been implemented over the past several years.

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Rolls-Royce Establishes Covid-19 Data Alliance To Kickstart Businesses And Economy Recovery

Rolls-Royce Establishes Covid-19 Data Alliance To Kickstart Businesses And Economy Recovery

Rolls-Royce has invited a group of leading companies to collaborate on Emer2gent, a new alliance of data analytics experts challenged with finding new, faster ways of supporting businesses and governments globally as they recover from the economic impacts of COVID-19.

Early alliance members are Leeds Institute for Data Analytics, IBM, Google Cloud, The Data City, Truata, Rolls-Royce and ODI Leeds. The alliance will be facilitated and co-ordinated by innovation specialists, Whitespace.

Together the initial wave of members brings all the key elements of open innovation; data publication, licensing, privacy, security; data analytics capability; and collaborative infrastructure, to kick off its early work and grow its membership.

Emer2gent will combine traditional economic, business, travel and retail data sets with behaviour and sentiment data, to provide new insights into – and practical applications to support – the global recovery from COVID-19. This work will be done with a sharp focus on privacy and security, using industry best practices for data sharing and robust governance.

Emer2gent models will help get people and businesses back to work as soon as possible by identifying lead indicators of economic recovery cycles. Businesses, both small and large, around the world, as well as governments, can use these insights to build the confidence they need to take early decisions, such as investments or policies, that could shorten or limit the recessionary impacts from the pandemic.

“We want the global economy to get better as soon as possible so people can get back to work. Our data innovation community can help do this and is at its best when it comes together for the common good,” said Caroline Gorski, Global Director, R2 Data Labs, the Rolls-Royce data innovation catalyst which started the alliance

“People, businesses and governments around the world have changed the way they spend, move, communicate and travel because of COVID-19 and we can use that insight, along with other data, to provide the basis for identifying what new insights and trends may emerge that signify the world’s adjustment to a ‘new normal’ after the pandemic, ” she continued.

 

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Powering Additive Manufacturing With Data Analytics

Powering Additive Manufacturing With Data Analytics

In an interview with Asia Pacific Metalworking News, Dr. Mohsen Seifi, Director of Global Additive Manufacturing Programs at ASTM International, discusses the benefits of additive manufacturing (AM) in manufacturing and the role of data analytics in AM.

Dr. Mohsen Seifi, Director of Global Additive Manufacturing Programs, ASTM International

  1. Tell us more about ASTM International, for those who may not be familiar with the organisation.

ASTM International is one of the world’s leading standards development organisations, founded in 1898.  We have 150 technical committees that oversee about 13,000 standards that are widely used around the world.  Several of those committees are in emerging industries, including one for additive manufacturing technology that now has nearly 1,000 members, known as F42.  For over a decade, this group of the world’s top additive manufacturing experts has been meeting and working through ASTM to develop groundbreaking standards that have begun to form the technical foundation for the future of additive manufacturing.  Furthermore, ASTM International has made a dramatic investment in front-end research to develop even more standards through our Additive Manufacturing Center of Excellence, a network of high-profile partners around the globe which includes Singapore’s National Additive Manufacturing Innovation Cluster (NAMIC).  Please visit our website for more detailed information.

  1. In the Industry 4.0 era, greater efficiency and product innovation are key priorities for manufacturers. How can they leverage additive manufacturing/3D printing to achieve both?

A big challenge for manufacturers is the lack of communication between stakeholders at different steps in the process chain. Smart, digital manufacturing could allow manufacturers to effectively transfer the most relevant information across all stages of product development, from designers to end-users. Additive Manufacturing is an integral part of Industry 4.0 and is an excellent technology for product innovation that could significantly reduce the time for product development through iterative design capabilities.

Also, Additive manufacturing can substantially improve the efficiency of the manufacturing process by parts consolidation. This will enhance the effectiveness of a system as a whole in terms of weight reduction, material optimisation, and reduction in fuel consumption.  For AM, digital manufacturing means integrating physical system-oriented manufacturing with digital system-oriented Industry 4.0 technologies (e.g., artificial intelligence (AI), big data, robotics, cybersecurity, and Internet of Things [IoT]). To fully unlock the potential of smart, digital manufacturing, there are still issues to address, which include cybersecurity concerns, data management challenges, and other critical gaps. ASTM uses various roadmaps to develop standards to address these gaps and to meet the industry needs.

  1. Which end-markets do you see increasing adoption of additive manufacturing?

AM has the potential to impact all manufacturing-related sectors—from aerospace, medical and automotive to oil/gas, maritime and other sectors—and we anticipate adoption will increase exponentially across the board in the next 10 years. In particular, AM holds great promise for aerospace/defense and medical applications. Both of these sectors require complex, specialised parts, which AM is capable of producing. More importantly, the demand for AM qualification and certification in these high-tech areas/end-markets is high. This is because successful qualification and certification provide end-market users with increased confidence (i.e., improvements in quality and reduced safety concerns). According to a recent survey, the three most significant challenges to adoption of AM for end-market users over the next ten years are: 1) the certification of finished parts and products, hindering its mainstream commercial uptake in the future; 2) the quality and standardisation of material inputs; and 3) unknown quality of printed components.

  1. What are the biggest challenges when it comes to additive manufacturing?

As an emerging field, the AM industry still needs a shared language and framework for addressing problems. Lack of standards is one of the biggest challenges for additive manufacturing in addition to other challenges such as lack of qualified workforce, limited availability of materials, and the lack of full-fledged certification programs. Standards provide a common reference point to help the industry avoid the time and expense of solving problems by trial and error. For example, there is an ongoing need for a better understanding of feedstock properties, methods for in-process monitoring and control, machine-to-machine variation, and rapid inspection methods for AM parts, among other topics. In addition, standards are a key enabler of the qualification and certification procedures that were mentioned above.

To accelerate the development of standards to address these challenges, we launched the AM Center of Excellence (CoE), a collaborative partnership among industry, academia, and government that integrates research and development (R&D) with standards development. By initiating R&D projects that target specific high-priority standards needs, I believe we can speed the overall advancement and adoption of AM technologies. Detailed information will be available in our upcoming external R&D roadmap, which will be released this spring. In the meantime, our annual report provides an overview of the AM CoE’s activities.

  1. Why is analytics a feasible solution?

One benefit of analytics is that it presents decision-makers with the key information required to make informed decisions. Manufacturers have access to a wealth of data about their products and processes but are not always able to use it. Analytics is a great tool to convert data into actionable knowledge that can be used to optimise product development. In the case of AM, solutions such as data-enabled material screening, build monitoring, and post-build characterisation ensure the product meets its specifications with as few iterations as possible, helping minimise production time and cost.

  1. How will data analytics make additive manufacturing more efficient?

AM generates more data than any other manufacturing field—this data has great value, but there are challenges to extracting useful information. Structuring data in a way that adheres to FAIR principles (findable, accessible, interoperable, and reusable) will be vital to the success of AM. Data analytics holds the key to processing and making sense of vast stores of data, which will ultimately accelerate the AM development timeline. Data analytics is a solution that cuts across all sectors and is already shaping the future of technology as we know it.

Through AI, which encompasses machine learning (ML) and deep learning (DL), the AM industry can quickly decode quantitative structure/process/property/performance relationships, which is a core challenge in the AM field. For example, it is possible to use AI to sift through potential AM materials to find those with optimal properties or functionalities. AI can also enable data-driven in-situ/real-time monitoring for identifying better processes. However, to enable these data-driven advances, the AM community needs an AM data ecosystem that enables the easy and secure generation, storage, analysis, and sharing of data. ASTM and America Makes recently convened a workshop on manufacturing data management and schema to identify and prioritise challenges and potential solutions for strengthening the AM data ecosystem.

  1. What is your outlook for additive manufacturing/3D printing this year?

It is very hard to predict the future of AM because technology is rapidly changing, but I would like to see 2020 as the year of standards. There is an exciting opportunity for more integration between AM and other elements of industry 4.0, in terms of automation, robotics, cybersecurity, and big data—creating these links is a great way to connect the physical world and digital world. I believe that the best way to create synergy between these critical technologies is through standardisation to add trust. The more we can focus on developing standards, the sooner we can see these advances.

 

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ABB: New Performance Optimisation Service For Long Product Rolling Mills

ABB: New Performance Optimisation Service For Long Product Rolling Mills

ABB has launched its Performance Optimisation Service for long product rolling mills, an advanced service powered by ABB Ability Data Analytics for long product rolling mills, a digital solution which applies process-specific analysis to large volumes of complex data. This helps metals producers to achieve unprecedented levels of yield, quality and productivity, with remote monitoring and support via ABB’s Collaborative Operations Centers.

ABB’s Performance Optimisation Service for long product rolling mills allows operations to be monitored around the clock from ABB’s Collaborative Operations Centers, where experts alert designated onsite staff to process deviations and disturbances and advise on corrective action, supporting faster, more data-driven decisions that will enhance process performance. To facilitate continuous improvement, ABB utilises process insights to generate regular reports identifying focus areas and recommended actions. Enterprise-level integration provides insights across multiple mills, enabling users to identify and analyse trends that could impact performance at several sites.

The solution at the core of this service – ABB Ability Data Analytics for long product rolling mills – integrates with ABB Ability Data Analytics Platform for metals and is able to collect high frequency data in real-time from data acquisition systems, such as Iba or over other industry standard communication protocols, and analyse the performance of the mill with process-specific algorithms. It uses the data to detect deviations, identify their root cause and determine trends, benchmarks and other performance factors including predicting and preventing faults before they affect production. Data is collected in real-time, stored securely onsite at the customer’s premises using the ABB Historian server, and visualised for designated users in intuitive, customisable dashboards.

 

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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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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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Industry 4.0: Are Businesses Stepping Up To Be Future Ready?

Industry 4.0: Are Businesses Stepping Up To Be Future Ready?

Vincent Chong, President and Chief Executive Officer of ST Engineering shares his views on the adoption of Industry 4.0 in Asia.

The inaugural Industrial Transformation Asia-Pacific (ITAP), a Hannover Messe event, concluded in Singapore recently. As business leaders, experts, government representatives and other stakeholders gathered to discuss Industry 4.0, what emerged clear to all was that technology adoption across Asia remained uneven.

Is this a case of change not happening? Far from so. Industry 4.0 is very much an evolution rather than a revolution. Even as we speak, industries are transforming. Today, it is not a question of whether businesses are future-ready; it is whether businesses realise the implications of not participating in the fourth industrial revolution when it will move on regardless of their actions.

Industry Evolution

Driven by the rising operational costs and a human resources crunch, the local industry in Singapore understands that it is imperative to adopt Industry 4.0.

Even for ST Engineering as a technology and engineering group, digitalisation of the workflow at the Aerospace business or the “Aerobook” occurred more than 10 years ago.

This began with the adoption of Augmented Reality/Virtual Reality (AR/VR) and robotics, with other advanced technologies progressing only when the business case became clearer. Other possibilities were also adopted to redefine the company’s value proposition such as customer participation and mobile interfaces in the digitised process, improved interaction via AR between engineers and mechanics to reduce the time taken for repairs; reducing turnaround time and minimising inventory stock-keeping of aircraft parts through additive manufacturing. These have all led to productivity improvements of up to 15 percent to date. Looking forward, ST Engineering will also be certifying the use of unmanned aerial vehicles for aircraft inspection, which, when implemented will help to improve efficiency and minimise workplace accidents.

Furthermore, with technological advances in the company’s aerospace business, the company is able to drive goals to improve productivity and capture efficiencies which are essential in order to operate in higher-cost locations like Singapore, Germany and the US. This augments the company’s competitive differentiators in quality and value.

Challenges Of Transformation

Government support is not lacking for Industry 4.0. In March this year, the Economic Development Board (EDB) announced that it would be funding 300 companies to undergo assessments using the Singapore Smart Industry Readiness Index, so as to accelerate the industry transformation of small and medium-sized enterprises (SMEs), large local enterprises (LLEs) and multinational corporations (MNCs) across various industries. This follows the launch of as many as 23 industry transformation maps, public-private partnerships like Tech Labs (ARTC and SimTech), Tech Access and Tech Depot to help SMEs test and experiment with advanced manufacturing technologies, translate research to applications and access technologies easily. There have also been numerous workforce transition programmes.

Even as the government invests time and resources to move the industry, business leaders remain pragmatic. The push to transform will happen only where there are strong drivers. Many will start on the digitalisation journey, but will invest only when they can see immediate value in doing so.

Indonesia’s Minister for Industry Airlangga Hartarto, has observed that millions of Indonesians in the workforce will require training to be digitally literate under the country’s Industry 4.0 rollout plans. Additionally, Dr. Gunther Kegel, CEO of Pepperl+Fuchs, Germany, has said that his company had spent hundreds of training hours to ready the workforce. He also added that even with buy-ins for change, it requires transforming processes from computer-assisted ones to computer-dominated ones, and changing the way people have been working for the past 20 years.

What tends to happen however, as Singapore’s Minister for Trade and Industry Chan Chun Sing pointed out at the panel discussion, is that many companies “often get stuck” at the application stage of technologies, and “they never really go to Stage 3, which is the re-engineering part”. He was referring to the four stages of the technology industry known as DART: Diffusion, Application, Re-engineering and real Transformation. His view is that the mere application of technologies will not lead to real transformation, as it was only “mechanising, robotising and digitising current processes”.

Transforming the organisation thus requires a mindset shift from leaders and staff alike. It is Worker 4.0 who would be critical in the success of Industry 4.0, as Senior Minister of State for Trade and Industry Koh Poh Koon, said at ITAP.

Firstly, from constantly thinking pragmatically on just which technologies are needed on hand, managers and employees need to think more strategically and with a future-oriented view to consider the opportunities that Industry 4.0 can bring, and how best the business can harness these. They need to build the business and economic case, and not pursue technology for technology’s sake.

With the production of more proven use cases, the adoption rate of technologies will grow. It will grow even more quickly if business cases are clearly in sight and it will require senior leaders to take a top-down approach to drive implementation and overcome barriers and resistance for transformation.

Readying The Workforce

Minister Chan additionally observed that Singapore will need to compress the learning cycle; the conventional model of using the school system to churn out workers is a bit too slow for tomorrow’s needs. He added that the frontiers of learning will need to be in companies where there is constant experimentation, even as we rely on conventional learning for building fundamentals.

Similarly, organisations will welcome the development of more industry 4.0-related talents through the institutes of higher learning (IHLs) in the future. In addition to degree courses, on-demand micro-learning modules in areas such as autonomous systems, robotics, data analytics and cyber security should also be offered. This is also an area where corporates, government agencies and IHLs can work together to co-develop.

ST Engineering’s approach to training and retraining of the workforce for Industry 4.0 is multi-pronged, with the company’s top 100 managers attending data analytics and cyber security executive workshops in order to ensure that a mindset shift occurs from the top. Additionally, engineers are also put through courses that are targeted at further enhancing domain expertise.

For instance, 70 of the company’s engineers have already been trained at ST Engineering’s Cybersecurity Academy, which is a professional cyber security training school. And 350 of the company’s engineers attended a technical course in robotics and digitalisation, made possible by ST Engineering’s strategic partnership with Singapore Polytechnic, to create a bespoke Digital Transformation & Robotic course. Moving forward, the another 1,000 employees will be trained in a customised data analytics programme over the next one and a half years at the National University of Singapore.

Strategic Technology Centres have also be established to develop deep capabilities in areas such as data analytics and cyber security, to provide group-wide support in further differentiating products and solutions. Lastly, extensive collaborations with external technology partners and IHLs through Corporate Labs, Corporate Venture and Open Innovation Labs have also been carried out.

Are Businesses Ready?

Industry 4.0 is a major shift for many organisations. Are business leaders prepared to redefine and re-engineer their business models and processes by drawing from technological advances for real transformation?

If having platforms and infrastructure in place at both the country and organisation levels are not good enough an impetus for change, perhaps the reality of being left behind by competitors is.

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Implementing IIOT: The Time Is Now

Implementing IIOT: The Time Is Now

Applying the Industrial Internet of Things (IIoT) to factories confers a host of operational benefits. By Advantech

Machinery and production automation systems need to be advanced enough to deliver high performance, and integrated enough to provide economical operation, yet must be based on mature products and methodologies offering sufficient reliability.

So why push for tightly integrated operational information and other advanced functionalities if individual pieces of machinery are running “good enough”?

The main reason is because harvesting, processing and analysing the correct data helps operational personnel make the best informed choices at their facilities, and enables management to optimise strategic plans throughout multiple locations. Simply put, advanced data analytics improves efficiency, reduces maintenance, and creates a safer work environment.

Convergent Evolution

Fortunately in recent years, a number of device, communication, and software capabilities have developed in an interrelated manner—making it easier to extract and analyse manufacturing data.

When combined effectively, they can elevate “business as usual” manufacturing to “smart” manufacturing. In fact, in many ways automated manufacturing is already smarter than one might expect.

Machinery and process plants commonly employ control systems with many types of sensors. While the highly-touted Internet of Things (IoT) concept promises that one day all devices will become networked information providers, it turns out that the Industrial IoT (IIoT) already has countless sensors and other devices reporting data to higher level automation systems. Where the IoT is directed toward consumer convenience, the IIoT takes a laser focus on efficiency and safety.

Manufacturers such as Advantech offer a spectrum of hardware and software to facilitate gathering information from the lowest level sensor, or any machine, and routing it over a network to higher level automation, visualisation, and information systems. Automation controllers pre-process and package the raw information from sensors and other field devices. These devices are the “things” in the IIoT.

Industrial wired and wireless networks, working in conjunction with the Internet and cloud services, are the superhighway for moving information. This information moves from field controllers to human machine interfaces (HMIs) located on the plant floor and in control rooms, and from the HMIs to front office PCs and out into the mobile world of smartphones and tablets.

Smart manufacturing is a powerful trend, building on readily available hardware and software to take production operations to the next level of performance.

The Time To Implement The IIoT Is Now

Manufacturing businesses worldwide want to implement the IIoT to gather more data and improve operations. While these objectives have been present for many decades, it’s now much more feasible to implement the IIoT because of the technology advancements as expounded upon below.

Why Implement The IIoT Now?

  • Most new devices offer smart connectivity
  • Methods exist to enable traditional devices to become smart
  • Controllers are proficient at handling smart data
  • Standardised wired and wireless Ethernet networks are economical, powerful, and pervasive
  • Specific industrial networking formats are common
  • Open interfaces and numerous drivers are available to facilitate economic integration
  • Communication methods are suitable for private and public clouds
  • Mobile visualisation offers new ways to bring data to users
  • Big data harvested from the IIoT can be more easily analysed
  • Smart manufacturing adoption can occur in steps, with benefits realised along the way

More often than not, connectivity is the “killer app”. Consumer devices such as phones, watches, appliances, and even sneakers are commonly able to connect and interact with each other.

Similarly, industrial devices have moved from awkward and proprietary communication interfaces to standardised networks and protocols, often Ethernet-based. In today’s market, industrial manufacturing demands connectivity from most devices purchased. Even if the functionality is not immediately needed, it helps to future-proof investments.

For legacy devices using basic analogue and digital signals, or maybe simple serial communications, there are modules that can boost this equipment up on to contemporary networks and protocols. In this way, end users can choose an upgrade path that preserves their existing system, yet provides value by making their “dumb” devices smart, leading to intelligent machinery.

Connecting Islands To The Mainland

Many production plants consist of “islands of automation”. Often, there are many automated skids or systems with minimal interaction among them, even though taken as a whole they form a production line. Sometimes these systems have been assembled and grown over a long period of time.

What they have in common, though, is that each island is operated by one or more controllers. Industrial controllers have more than enough power to perform some data processing, but may not share common communication protocols.

Fortunately, there are many flavours of “gateways” or “bridges” available. These can take the form of dedicated configurable devices, or PCs running various drivers and communication software. These gateways can translate pertinent information from existing systems into a suitable format for higher level integration.

When disparate controllers and the systems they control are capable of being connected, some huge informational advances can be achieved. Such systems can be interconnected to supervisory alarming and historian systems, consolidating key information from a whole production line into a few effective displays or reports.

For many operations, when subsystems are integrated in this way, it is possible to achieve a transfer of upstream and downstream information and improve the production flow. Or, when production goes down it is possible to use the integrated information to identify and eliminate the root cause, promoting overall equipment effectiveness (OEE) tracking.

These are just a few of the benefits of a connected factory. As Jamie Carter puts it, “In the wider economy, the IIoT is critical in reducing unplanned downtime of production facilities and plants.”

Moving Information To The Next Level

Assuming that technical and cost barriers are overcome for gathering information in a smart factory, what are the next steps? The first is typically to make the information visible to operators and managers so that they can make informed decisions.

This used to mean tabular lists or printouts of numbers, but information presented in this manner is difficult for people to process. That is why so many variants of graphical display software and HMI packages have been developed.

Earlier generation HMIs used to just reside locally to their associated factory processes. Today’s HMIs use networking, the Internet, and public or private cloud services to extend their reach to wherever users are. Instead of just a single machine, production line, or factory being coordinated—it is now possible to manage multiple factories across the world in a more organised manner.

The Internet and cloud services are ideal for publishing smart manufacturing information to laptops, tablets, and smartphones, putting the information directly in user’s hands. Many visualisation software packages have features specifically adapted to mobile device operation. It has become especially prevalent and useful for mobile devices to present a streamlined “dashboard” view which shows only the most important information in an easy-to-read format.

End user expectations from HMI packages have soared, due to consumer familiarity with high performance home computers, phones, and tablets. The graphics must be informative and must also look good and easy to use. HMIs that take advantage of multi-touch swipe and zoom gestures position themselves that much close to the everyday user.

Browser-based products like Advantech’s WebAccess are available that offer a familiar user experience, are easily extendable to all types of devices, and are able to publish the information conveniently over the Internet.

Harvesting Big Data

But the smart factory is about much more than just dishing out pretty graphics. At the factory level, the proper flow of status and command information is crucial for manufacturing execution systems (MES) that strive to track and record the production of finished goods. At an even higher level, data is required for enterprise resource planning (ERP) and business logistics systems to be effective.

A real opportunity exists when all of the big data can be harvested from many IIoT sources, and then effectively analysed to reveal inefficiencies that can be overcome or trends that can be intelligently re-vectored.

Gathering enough of the right information can enable users to make discoveries that would be otherwise impossible. Besides just improved throughput, benefits can be found in material costs, energy efficiencies, labour costs, maintenance costs, and the cost of adverse quality.

Keep in mind that implementing smart manufacturing is not an all-or-nothing proposition. If fact, adopting smart technologies and methods can (and often should be) carried out in steps. This reduces the initial cost, and allows an organisation to determine which pieces of the smart factory yield the most benefit for their situation.

The time to implement the IIoT is now, and here are the specific components which make up a typical IIoT implementation in a manufacturing plant.

IIoT Building Blocks

Data flowing through the smart factory can be imagined as a pyramid structure as shown graphically in Figure 1, and as detailed in Figure 2.

Another good reference is ISA-95, which defines industrial automation interface concepts from the lowest (Level 0) to the highest (Level 4) level in terms of both functionality and immediacy. If “Level 0” is considered to be the actual physical process, then the smart manufacturing foundation begins at “Level 1” and consists of the sensors and field devices.

Examples of IIoT building blocks:

  • Smart sensors
  • Network-capable I/O
  • Controllers–PLCs, PACs, DDCs, Proprietary
  • Network switches, media converters, routers, security
  • Visualisation, fixed location
  • Visualisation, mobile
  • Business strategy systems

Traditional sensors were historically hardwired and offered only a single basic process signal, but today’s smart sensors are networked and provide additional process signals and device diagnostics. They can maintain on-board calibration data, and technicians can interact with these sensors remotely. Think of a flow transmitter that also provides temperature and pressure information, and can alarm when the data readings are suspect.

More advanced analysers can simultaneously provide multiple-sensed variables for complex parameters. Barcode readers and RFID tags are key ways to establish material tracking. Many other types of smart sensors and field devices are available, all capable of providing data to higher level systems.

The Highest Levels Of Smart Manufacturing

HMIs are “Level 2” systems that facilitate detailed plant operations. They can be PC-based running software, or a more dedicated hardware type. Plant networks supply HMIs with the information they need, either directly from field devices, or more commonly through I/O and controllers.

These HMIs can be flexibly located in main control rooms, on machines, in maintenance and management locations, or elsewhere. More recently, it has become common to configure consumer-grade or industrial-grade tablets as HMIs and troubleshooting stations that can be carried around the factory.

One of the real game changers in HMI space over the past decade is the emergence of browser-based products. No longer are users tied to specialised hardware, or difficult software installations. Just as PCs and Ethernet successfully leveraged commercial technology into the industrial arena, browser-based products prospered by offering much of the same end user experience as traditional software, but at a lower price point and requiring near-zero configuration on the end user’s device.

These products are capable of providing an HMI interface anywhere within a facility, on all types of mobile devices, and throughout the world via the Internet. Not only that, but they can offer advanced features such as integration with Excel, Google Maps, and video streams.

Comprehensive Smart Manufacturing Solution

Residing above HMIs are “Level 3” MES and “Level 4” ERP systems. These software-based systems typically run on servers located at a given production plant, or even far away in a corporate office. Software systems at each progressively higher level are typically less “real-time” than at lower levels. While MES and ERP systems are a subject of their own, they both require close integration with lower level sensor and control systems in order to be effective.

A comprehensive smart manufacturing solution built on an IIoT foundation is necessary to power operations and business management. These IIoT building blocks can be combined to create real-word applications to deliver specific benefits, as shown in the following example.

Any time there are multiple steps in a process, it is critical to identify which steps are the limiting throughput factor. Similarly, if there is a failure, then operators need information to point them to the root cause. Smart manufacturing will harvest all of the production key performance indicators, and use them to identify bottlenecks that can be improved, and will also facilitate troubleshooting.

At the highest level, data provided via smart manufacturing allows business operators to track, direct and optimise their raw material usage and productive output. Uptime and downtime can be analysed, and inefficiencies identified and wiped out. Without the data provided by smart manufacturing systems, none of this is possible.

Putting Your Data To Work

For today’s factory, superficial good looks aren’t enough to prove that things are running at their best. Instead, additional improvement opportunities must be actively sought to create a smart factory. One way to do this revolves around obtaining more operational data and putting it to work. Any process of improvement is based on quantitative analysis of measurements, and fortunately the IIoT opens up a whole new world of quantifiable data.

Connectivity is no longer a unique luxury, as it has instead become a baseline requirement. Intelligent machinery leads to a connected factory, which in turn provides the platform for smart manufacturing. Businesses everywhere want to leverage the IIoT in the most expedient way possible, and fortunately the technology is available now to make this happen.

The building blocks are smart devices, methods for making legacy equipment smarter, robust networking, and a wide variety of software—all of which are readily available to build into new facilities or integrate into existing operations. The widespread availability and ease-of-use of these enabling technologies allows end users to focus less on how to harvest the data, and concentrate more on improving operations.

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Top 4 Industry 4.0 Trends In Manufacturing

Top 4 Industry 4.0 Trends In Manufacturing

There are new possibilities offered by big data, 3D printing, machine learning and augmented reality in the manufacturing industry. Leveraging on these into a new way of doing business is a key factor in Industry 4.0 to gain a competitive edge, and for companies to be more profitable and scalable. By Farah Nazurah 

The global Industrial Internet of Things (IIoT) market is expected to reach US$195 billion by 2022, growing from US$113 billion in 2015, at a compound annual growth rate of 7.89 percent between 2016 and 2022, according to a market research report by Markets and Markets. A key factor identified is the need to implement predictive maintenance techniques in industrial equipment to monitor their health and avoid unscheduled downtimes in the production cycle.

In the metrology segment, the view for Industry 4.0 in terms of part inspection is to increase quality and maximise throughput, whilst reducing costs right down the production line, making manufacturing processes faster and more accurate. Multi-sensor metrology alone is not enough to seize the maximum potential of the production line; integration of autonomous processes and hardware as well as complete connectivity is needed to fully embrace manufacturing of the future.

Four Industry 4.0 trends will be discussed in this article—from big data, predictive maintenance, augmented reality to cybersecurity. Industry players should be aware of these trends as they have already begun to affect many aspects of industrial automation going forward.

Industry 4.0 is the no longer the future of the industry, and the time is now for companies to implement intelligent manufacturing practices.

1. Big Data/Data Analytics

Big data describes the large volume of data, both structured and unstructured. Insights from big data can enable better decisions to be made—deepening customer engagement, optimising operations, preventing threats and fraud, and capitalising on new sources of revenue. The majority of data created between now and 2020 will not be produced by people, but by machines as they communicate with each other over data networks.

The insights gained from big data analytics and the IIoT to drive greater manufacturing intelligence and operations performance is considered essential by 68 percent of manufacturers, according to a recent survey by Honeywell. This highlights that manufacturers are increasingly aware of the importance in big data analytics and its potential in the industry.

Exponential Growth In Big Data

The global big data and business analytics market will grow to US$203 billion over the next few years, according to a report by International Data Corporation. The growth forecast for the global big data and business analytics market through 2020 is led by manufacturing and banking investments. The rate at which data is being generated is rapidly outpacing the ability to analyse it, according Dr Patrick Wolfe, a data scientist at the University College of London. The complex nature of the information created requires solutions capable of addressing data security, privacy and flexibility issues. Dr Wolfe added that it is key to turn these massive data streams from liability into strength.

Machine tool manufacturer Mazak has developed its own data collection and analysis system called SmartBox to connect machine tools securely and intelligently. The company uses this system at several of its own facilities worldwide. Most recently, the i-Smart Factory concept in their Singapore production facility is based on the company’s accumulated knowledge on factory management.

Tomohisa Yamazaki, president of Mazak, stated that rising personnel costs is a social problem not only in Japan but also in other countries as the labour force population declines. As such, one of the most crucial issues for the manufacturing industry is to keep increasing productivity by investing in advanced production technology.

Data Analytics To Reduce Machine Downtime

A survey of manufacturing executives in the US by Honeywell revealed 67 percent of respondents have plans to invest in data analytics. The executives viewed data analytics as a fundamental component of the IIoT, and as a solution to unplanned downtime and lost revenue.

The survey revealed companies are feeling pressure to continue working under threats of unscheduled downtime and equipment breakdowns, which was viewed as the most crucial factor in maximising revenue. Employing data analytics to ensure machines are kept running at optimum level could vastly reduce and even eliminate unplanned downtime.

2. Predictive Maintenance

Predictive maintenance foresees when equipment breakdowns might arise, and it prevents machine breakdowns by carrying out maintenance. When repairs and maintenance are planned, it could save manufacturing companies 12 percent in cost savings, whereas a loss as much as 30 percent could be incurred when unplanned repairs occur, according to research by the World Economic Forum and the consultancy Accenture.

With predictive maintenance, manufacturers can lessen maintenance and servicing costs, and boost reaction times within disruptive production processes.

The unchanging objective in metal cutting manufacturing is to further increase productivity, creating added value for the customer. Heller, a milling machines and systems manufacturer, has developed its own system to improve transparency of its current machine status, by evaluating data to allow purposeful diagnostics which yields higher productivity and reduces machine downtimes. The visualisation of specific information, including status displays of axes, spindles or other assemblies, enables users to determine wear and take preventive measures in order to avoid unscheduled downtimes.

Real-time Condition Monitoring

Machine and sensor data can be catalogued and displayed in real time using Industry 4.0 software, which provides support for condition monitoring. Data visualisation is not confined to the control station, and can be accessible on any platform everywhere—from tablets, smartphones, and bigger screens, both on the production floor and in the cloud.

When the software has determined an imminent maintenance task from the pre-set specifications, the information would be sent immediately to maintenance staff. After maintenance has been carried out, staff can note down tips to improve subsequent maintenance works.

3. Augmented Reality

Augmented reality (AR) is an enhancement of a real-time display using real images alongside computer generated information. AR is associated with Industry 4.0 practices relating to smart manufacturing, and has tremendous potential to influence manufacturing industries. With augmented reality, challenges which arise with conventional 3D measurement can be eliminated.

For example, Keyence’s XM Series handheld probe coordinate measuring machine allows for an operator to perform 3D measurements via an onscreen interactive visual guide and touch probe. Augmented-reality guidance images are created automatically, and the system overlays the measurement points along with their 3D elements.

Shared programmed work instructions and measurement results in consistent measurement regardless of the operator, environment or other circumstances.

Potential Usage Scenarios

AR has numerous uses, involving different types of operations that can be executed on the factory floor—manufacturing activities such as production, and support processes such as maintenance and training.

“Some companies are concerned or even hesitant to adopt industry 4.0 practices as they are not even at the Industry 3.0 stage. However, it is possible to jump straight from Industry 2.0 to 4.0. For example, to improve standard operating procedures among plant staff that are still using physical papers for instructions, they could instead make use of augmented reality to simplify and learn new procedures,” said Lim Yew Heng, partner and managing director, The Boston Consulting Group. Some potential usage scenarios of AR are as follows:

  • Operations: any kind of operation which requires some step by step procedure can benefit from the adoption of AR—installation, assembly and machinery tool change.
  • Maintenance and remote assistance: AR is efficient at reducing execution times, minimising human errors and sending the relevant performance analytics to maintenance staff.
  • Safety management: AR allows risk and safety of operators and equipment to be managed.
  • Design and visualisation: AR provides tools that improve design, prototyping and visualisation in the design phase.
  • Training: for companies where training is a critical process involving many field technicians, AR-guided training can be effective at training staff, especially in the beginning where there is a learning curve.
  • Quality control: AR support in quality control processes enables staff to determine if products meet manufacturing standards.

4. Cyber Security

The integrated nature of Industry 4.0-driven operations means that cyberattacks can have devastating effects, evident in the unprecedented “WannaCry” global cyberattack in May this year. Cyber security strategies should be secure and fully integrated into organisational and information technology. Picking the right cybersecurity provider is essential in ensuring data is protected.

“Some of our clients have come to us and said they do not think they will be able to put up their data on the cloud as they have very sensitive data. Within their operating database, there are certain data that are more sensitive, and there are those which contain less sensitive information,” said Mr Heng, partner and managing director, The Boston Consulting Group. “They could start out with putting less sensitive data on the cloud and understand how it works first, and understand how cybersecurity providers can help them. From there, they can move towards a more balanced approach.”

Data Sharing: Increased Access To Data

Companies should consider which data should be shared and how to protect the systems, and which data that is proprietary or have privacy risks. Companies should leverage tools such as encryption for data which are at rest or in transit, to safeguard communications should they be intercepted or if the systems are compromised.

It is important for manufacturing companies to perform risk assessments across their environment—including enterprise, DSN, industrial control systems, and connected products. Data evaluations should then be applied to update cyber risk strategies.

Protecting Data

Sensitive data are not limited to sensor and process information; it also includes a company’s intellectual property or even data related to privacy regulations.

As more IoT devices are connected to networks, the risk of potential attack increases, along with risk from compromised devices. The first step companies should take is to discover all assets, especially industrial controllers. Picking the right cybersecurity provider who understands what your company needs is essential in protecting your data against cyberattacks. Transparency is important for companies with highly sensitive data therefore, ensure that third-party cybersecurity providers inform you where the information goes.

The Right Strategy Is Important

“Companies have to ask themselves why they want to create a fully automated manufacturing factory, and what value it creates for the end users. Once the staff in the company knows why this is being done, it will change the company’s culture, and they will start focusing on value delivered to the customers,” said Scott Maguire, global engineering director, Dyson. “At the end of the day, these are big investments, and companies have to plan strategies for the long-term and be willing to change their company culture.”

Manufacturing companies should embrace the positive disruptive changes that Industry 4.0 practices can bring. A digitalisation strategy which is tailored to your company’s needs should be mapped out, and disseminated to staff so they understand it is a part of the company’s new culture.

Employing data analytics ensures machines are kept running at optimum level.

Employing data analytics ensures machines are kept running at optimum level.

 

OLYMPUS DIGITAL CAMERA

Augmented Reality holds vast potential and it is the future of manufacturing.

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