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

 

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How Digitalisation Is Transforming The Aerospace Sector

How Digitalisation Is Transforming The Aerospace Sector

The aerospace industry is growing at an exponential rate. In fact, by 2028 it is predicted that upwards of 38,000 aircraft will be in service, a vast increase from the 26,000 being used today. As a result, digitalisation is increasing the reliability and efficiency of aerospace systems across the world. Here, John Young, APAC director at automation parts supplier, EU Automation, explains how digitalisation is transforming the aerospace sector in the Asia-Pacific region.

Like many other industries, digitalisation is transforming the aerospace sector. Currently, there is already an uninterrupted flow of real-time information coming from aircrafts updating ground operations and the pilots on the status of systems, equipment and weather conditions. However, this is simply the beginning of what is possible with the integration of digital technology across the sector.

Artificial intelligence

Across maintenance departments in the industry, data is being monitored and analysed by artificial intelligence (AI) and machine learning systems. In fact, airlines in Asia have already begun implementing AI tools for simulation and data modelling of aircraft.

This information can then be used to decide precisely when an aircraft’s components should be replaced or repaired and when other maintenance is required. This integration has helped to ensure that the lifespan and function of individual parts are fully optimised, and the overall aircraft systems are kept safe.

By using AI to monitor and predict requirements, it is possible to ensure that all required maintenance equipment and parts are ready for when the time is right.

Virtual reality

In recent years, Virtual Reality (VR) alongside big data has pushed the boundaries of predictive maintenance. Since 2016, the aerospace company Airbus has been making use of this technology to help boost Asia’s maintenance, repair and overhaul (MRO) sector inside its Hangar of the Future initiative in Singapore.

VR and augmented reality (AR) technologies are disrupting traditional techniques of aerospace maintenance by allowing engineers to see maintenance activities from new and unexplored angles. This means that new data can be captured, and advanced simulations can be created to train maintenance teams for future procedures, as well as allowing personnel and pilots to view and test virtual replicas of the aircraft equipment before physically handling them.

Cyber Security

One of the downfalls of rapid uptake in digitalisation is the risk of data security and breach of privacy. This uncertainty applies to the aerospace sector especially, where the increasing connectivity of systems is also putting aircraft at risk of hacking and attack from cybercriminals.

Countries in the Asia-Pacific region have been reported to be 80 percent more likely to be victims of cyber theft as a result of their lack of awareness. Leading suppliers, however, can offer cybersecurity services and build a safe environment of data security and trust, while also helping organisations to avoid and recover quickly from cyber-attacks.

There is no shortage of digital technologies being used in the aerospace sector. These new and rising innovations are disrupting traditional methods of maintenance, operations and repair by providing experts with more intel about vital parts and the mechanical needs of aircraft. However, much of the vast quantities of data that technology such as AR and VR are producing still need to be kept secure. Only then can the digitalisation of aerospace fully flourish and continue to grow.

 

Further reading:

Cutting The Cost Of Cutting: Latest Developments In Industrial Fabrication

Increasing Productivity And Quality Gains Through Digitalisation

Predictive Maintenance for the Metalworking Industry

ABB To Build Highly Advanced Robotics Factory In China

Paradigm Shifts In Auto Industry Sector: China Key Growth Sector

 

 

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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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Predictive Maintenance Solutions For Machine Tools Reduce Cost The Smart Way

Predictive Maintenance Solutions For Machine Tools Reduce Cost The Smart Way

Preventing unscheduled downtime is one of the most effective cost reduction measures in any production environment. Mitsubishi Electric is offering practical solutions for both machine tools and robots.

Mitsubishi Electric and its [email protected] Alliance partner Lenord + Bauer have developed an advanced condition monitoring system for machine tools. It utilises smart encoders and a direct communication interface within Mitsubishi Electric machine controllers, such as CNCs, for accurate status information that is easy to access.

Operating hours are recorded and monitored along with temperature, speed and position by the Lenord + Bauer MiniCODER range. These parameters are then used to help schedule maintenance activities by providing an early warning message when component servicing or replacement are required. The ferromagnetic measuring gear and scanning unit can record speeds of up to 100,000 revolutions per minute, making the system ideal for feedback on machine tool spindles and positioning systems.

The second solution on the stand uses AI to increase the effectiveness of predictive maintenance. The cloud-based solution using the AI platform within IBM Watson analyses operational data and can optimise maintenance regimes based on actual usage and wear characteristics. It can be applied to robots and other equipment such as machine tools. Both smart solutions demonstrate how predictive maintenance for machine tools and robots can reduce operational costs, increase asset productivity and improve process efficiency.

 

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Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

Growth Of The Digital Twins Market Is Driven By Industrial Digitalisation

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

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

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

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

 

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Schunk Investing €85 Million In Expansion Of Production Facilities

Schunk Investing €85 Million in Expansion of Production Facilities

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

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

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

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

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

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

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

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

 

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Industrial Robotics Market Outlook

Industrial Robotics Market Outlook

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

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

Rising Demand from Automotive Industry

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

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

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

 

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Embedded Motion Control

Embedded Motion Control

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

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

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

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

Fostered By Industrial IoT

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

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

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

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

Examples Of Embedded Motion Control

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

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

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

 

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

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

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

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

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

 

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

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

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

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

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

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

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

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

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

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

 

Key Cyberthreats And Gaps In Manufacturing Organisations’ Cybersecurity Approaches

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

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

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

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

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

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

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

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

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

 

Bolstering Cybersecurity Using Artifical Intelligence

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

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

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