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C-Com Projects With MARPOSS And Oerlikon Balzers To Introduce Intelligent App For Tooling

c-Com Projects With MARPOSS And Oerlikon Balzers To Introduce Intelligent App For Tooling

Step-by-step networking for in-house manufacturing, involving suppliers and customers and efficiently using data together – the digital services provided by c-Com, a member of MAPAL Group, make it all possible. However, the start-up isn’t just developing its own applications. It’s also generating added value for customers by working closely with cooperation partners.

Cooperation with MARPOSS: reduced setup times and maximum tool service life

The optimal and longest-possible use of tools represents a vital cost factor for machining companies. But compromises are often necessary – particularly in series production and as part of automated processes. Tools with a defined tool life are replaced as soon as the specified tool life has come to an end. In many cases, though, the tool has not truly reached the end of its tool life and replacement is not yet necessary. However, companies play it safe to avoid quality issues and the risk of producing items that later need to be rejected.

READ: Marposs Optimistic of the Philippine Metalworking Industry

This is one of the elements addressed by the ARTIS GENIOR MODULAR module by MARPOSS. The fully automatic tool- and process-monitoring system has been an established feature of the market for many years. It works by recording various measurements and assessing them on the basis of several criteria.

MARPOSS recently launched a collaboration with c-Com GmbH and its c-Com open cloud platform to provide module users with additional value: the ARTIS GENIOR MODULAR module and c-Com are set to exchange data. Once the defined tool limits have been reached, the staff member responsible receives a notification on their mobile terminal – which is made possible by the cooperation with c-Com. As a result, operators can react more quickly and boost the efficiency of their manufacturing processes.

Cooperation with Oerlikon Balzers: transparency and sustainability thanks to digital processing for coating

Many tools are re-sharpened and re-coated to make production as cost-efficient as possible and to use raw materials sustainably. This procedure is very complex for everyone involved – from the machine operators to the staff members carrying out the re-sharpening and coating. If a staff member responsible for re-sharpening sends a tool for coating, this staff member is often not aware of corresponding order status. This results in frequent queries. In some cases, the number of re-sharpening processes is simply marked on the tool shank. Overall, the total benefit is reduced by the very high investment of time and effort required.

READ: Coating Processes with Increased Material and Energy Efficiency

In cooperation with Oerlikon Balzers, c-Com has developed an application that enables significantly more effective and transparent order processing. The prototype was showcased at EMO Hannover. The only prerequisite to benefitting from the advantages of digital processing for coating is identifying all tools with a unique ID.

The c-Com application exchanges data with the myBalzers customer portal run by Oerlikon Balzers. This way, the entire order process is digitalised, and all receipts are available online. It is easy to share documents such as delivery slips, invoices or order confirmations, and the status of each coating order can be viewed in real time. There is no longer a need to ask for order updates – a quick glance at the application provides the user with all the information they need. On top of this, machine operators have access to all the important information about their tool at all times. Thanks to the collaborative approach by c-Com, they can access all data via the cloud.

The c-Com wear detection app: a technical advisor in your pocket

c-Com has developed a wear detection application to provide answers to these questions. The prototype for the application was presented at EMO Hannover. The application is very simple to use: first, the worn blade is documented using a smartphone and a conventional auxiliary lens for zooming in. The app then identifies the type of wear and suggests corresponding recommended actions. This allows users to prevent this type of wear in future.

The application is based on machine learning, a sub-category of artificial intelligence. This means that the application uses datasets to learn. Together with tool specialists at MAPAL, c-Com has compiled and categorized hundreds of images. Effectively, the algorithm was trained by being shown what different types of wear look like, allowing it to assess whether or not a blade is in good order.

 

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Artificial Intelligence In Bending

Artificial Intelligence In Bending

Manufacturers are now adopting artificial intelligence (AI) to further create value for the customers. But how would AI be applied to sheet metal bending? In this article, Melvin Tham, Regional Technology Expert – Bending, for TRUMPF, explains.

Using conventional press brakes to achieve high accuracy for sheet metal is challenging due mainly to the property of the material, where its elasticity varies according to its composition and grain direction. Therefore, the process would usually take a longer time as it requires more knowledge and skill in order to achieve higher accuracy.

In today’s industrial environment, machines are loaded with functions to ensure that the manufactured parts are precise and consistent with minimal human/operator intervention, and manufacturers are now adopting artificial intelligence (AI) to further create value for the customers. But how would AI be applied to sheet metal bending?

Automatic Set Up

Given the current high-mix, low-volume market demand, the system must be easily set up within minutes to cater for a job change over. Therefore, a self-centring tooling system would be most ideal. With an automatic tool changer, there is no longer a need for alignment as the tools are automatically placed in position and integrated into the machine. It has three to four times more storage capacity than the machine’s bending length, all just to ensure a quick changeover and without the hassle of tool shortage.

Positioning and Angle Accuracy of Part

Since the bending process is now automatic, the quality of the parts has to be checked automatically as well. Such system would require high dynamic functions such as the backgauge. The backgauge with an axis tolerance of ±0.02 mm and the angle sensor tool with tolerance at ±0.5 deg are required to ensure that the part is placed accurately in position and angle tolerance is achieved by an angle checking device.

Sensors of the backagauge are necessary for the identification of the part in position. Without this, the part would not be able to achieve its desired flange length.

An automatic detection of the angle needs to be equipped to determine the correct angle to be achieved for each bend. With Automatic Controlled Bending (ACB), the total completion time to bend, calculate and adjust will take less than a second!

Identification of Parts and Positioning Compensation

The system must be able to detect the correct part to pick up and automatically determine the datum point to compensate positioning error. It is important to define the datum point so that all bending sequence and positioning accuracy can be referenced.

Although a structured stand that pre-fixed the part datum point can be achieved, the best possible solution will be with a high-resolution and precise camera profile detection that is flexible and automatic. This camera device could detect the sheet stack, height and fine profile of the part for single sheet without the need to specifically prepare sheet in a fixed position. With such function, a lot of time is saved from the preparation for defining, picking and loading of parts.

Gripper Technology

The grippers picking up the parts are of critical importance as well. Our grippers are designed with the concept of holding the parts as firmly as a human hand would. The gripper can be used for multiple parts and the suction cups can be pneumatically turned on or off to cater to different profiles and gripping area.

CAM-assisted Offline Programming

Software plays a very important role in automation. It should be able to strategically control all movement offline with intuitive graphical teaching.

In the past, robot movements are codings that are entered line by line in order to perfect a smooth travel path. With advanced software like TruTops Bend Automation, not only are we are able to graphically teach the movement from one point to another, we can also teach the robot to flip, load and unload the part. The software enables us to run a simulation prior to the actual process.

Robotic Movement and Payload

There are many robotic equipment in the market, with some having more than eight axis of movement and payload of more than 1,000 kg! So how do we know which is suitable?

In bending, it is always the working area within the press brake and robotic system. The bigger the working capacity means there is a better flexibility on the type of profile that can be bent.

The longer the trackway of the robot arm, the more parts can be prepared for loading and unloading. This is to ensure that the machine is always filled with part for continuous production and not idling or waiting for parts. There are also possibilities that the finish part can be stacked in cage or drop box.

The higher the payload means a bigger robot arm would be required. When the arm gets too big, there is a minimum distance of limitation due to the kinetic movement, therefore small parts cannot be picked up. Hence, it is important to define the size of the product before the selection of the automatic bending cell. This will make it easier to select the type of press brake and robotic arm for the job.

With all the necessary functions that are in place to ensure the output quality of the parts, the production is all ready for artificial intelligence bending!

 

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

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

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

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

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

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

 

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Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

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

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

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

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

 

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Gripping And Clamping Solutions For Process Automation

Gripping and Clamping Solutions for Process Automation

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

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

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

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

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

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

APMEN: What sort of challenges are your customers facing?

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

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

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

APMEN: What forces do you see driving the industry?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

 

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Schunk Investing €85 Million In Expansion Of Production Facilities

Schunk Investing €85 Million in Expansion of Production Facilities

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

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

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

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

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

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

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

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

 

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Embedded Motion Control

Embedded Motion Control

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

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

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

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

Fostered By Industrial IoT

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

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

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

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

Examples Of Embedded Motion Control

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

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

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

 

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

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

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

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

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

 

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Manufacturers With Artificial Intelligence To Nearly Double Competitiveness

Manufacturers With Artificial Intelligence To Nearly Double Competitiveness

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Skills For An AI-Ready Workforce

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

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

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

 

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Outlook Of Artificial Intelligence For Smart Manufacturing

Outlook Of Artificial Intelligence For Smart Manufacturing

The worldwide Outlook Of Artificial Intelligence For Smart 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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