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Faster transaction processing for manufacturers

ThinkSystem SR645 and SR665 two-socket servers feature enhanced performance and I/O connectivity for higher performance workloads. Many industries, such as manufacturing need faster transaction processing, improved data analytics, and greater grid-computing capacity, while still improving efficiency and total cost of ownership (TCO). To address these needs, Lenovo focuses on delivering open, reliable, and secure infrastructure solutions comprising industry-leading servers, storage, and software. With the addition of the new ThinkSystem SR645 and SR665 servers featuring more CPU cores and a larger memory footprint, Lenovo can help customers accelerate higher performance workloads and improve efficiency. ThinkSystem servers are designed for workloads such as in-memory databases, advanced analytics, virtualisation, and AI. They feature: Next-generation technologies, including PCIe 4 support, which doubles I/O bandwidth eliminating potential bottlenecks found in previous generation servers increase networking capabilities essential for I/O intensive applications Increased GPU support (up to 8x 75W NVidia T4s) allowing customers to efficiently implement video analytics and inference solutions for artificial intelligence Increased onboard storage up to 40 2.5” drives or up to 32 NVMe drives allowing for dense software defined storage solutions The high core counts allow customers to buy fewer servers, saving them rack space and power, in addition to obtaining results faster. These benefits are material in multiple industries: In the financial services industry, typically, servers are in a premium space located near the exchanges so saving rack space is paramount In retail environments, faster video analytics results can identify high-traffic areas in stores to strategically place products In manufacturing, processes rely on efficiency, therefore the ability to quickly process analytics data allows for better quality control and improved throughput. This results in higher yields and better forecasting of warranty needs In high performance computing (HPC) environments like scientific research, higher throughput means faster results, yielding earlier discoveries The Lenovo SR665 […]

IOT the future of manufacturing

–Nathan Knight, General Manager ANZ, Lenovo DCG With Australia’s 5G networks set to go live this year, we can expect a new wave of possibilities in the manufacturing industry to usher in a new era of economic growth and hyper-connectivity. 5G isn’t just about consuming more content faster, but also how high-speed connectivity supports new work from home arrangements, smart vehicles, devices and factories where plenty of data are exchanged in the network every second. This looks set to propel the adoption of the Internet of Things (IoT) and catalyse connectivity beyond recognition. IoT is the future of manufacturing IoT and 5G together are key for manufacturing companies to connect all stages of the planning-to-sales process, offering a holistic view of real-time benefits. They allow data to flow back and forth through multi-dimensional ecosystems, enabling suppliers, manufacturers and customers to conduct business more efficiently. IoT-enabled devices collect masses of data, allowing manufacturers to achieve higher product quality, better-informed decision making and greater foresight with predictive maintenance. At a supply chain level, IoT streamlines processes where various shipments coming in daily need to be synced into the production schedule and tracked until the final delivery stages. This entire process needs to be arranged with precision so entire factories are being IoT-enabled with smart devices that track the position of each employee and machine. While the benefits of the technology are vast, a quick summary of what enterprises can hope to achieve through the creation of IoT enabled networks includes: Identification of issues before they occur, for example, potential breakdowns Optimisation of supply and reduction of costs through closer observation of the production network Improved analytics and decision making through relevant, real-time data Enable efficiencies of scale in areas such as inventory, production, logistics, etc. Increased consumer loyalty through improved products Prediction of issues and optimisation of inventory management by closely monitoring materials and products throughout the manufacturing process Getting the most out of IoT with Cloud and Edge IoT generates mass amounts of data that manufacturers are required to store, secure, manage and analyse in order to gain business insights. Not only do cloud-based solutions perform these essential functions, but they also allow for better collaboration with developers and analysts who are able to store and access data remotely while managing workloads in response to operational cycle demands. Further to this, by incorporating IoT into the cloud, manufacturers can better connect different plants and storage clouds across a variety of locations, and the use of artificial intelligence in this network can help manufacturers capture new growth opportunities. It’s no wonder that manufacturers are now more interested than ever to leverage the cloud to intelligently […]

The next start-up cities to transform the global economy

During the past decade, much of the discussion about start-up ecosystems has been centred on the question of which city or region will become “the next Silicon Valley.” Although there are several places with promising growth trajectories, this view is short-sided. It implies there needs to be a new champion overshadowing the old one. In fact, there will be no “next Silicon Valley”. Instead, new research from Startup Genome’s 2019 Global Startup Ecosystem Report (GSER) points to there being 30 “next” hubs that will reach critical mass and reshape the state of the global economy. While none of them will be as big as Silicon Valley in the foreseeable future, each will thrive due to either regional dominance or start-up sub-sector leadership. Now, it’s not obvious which ecosystems will end up as the global change agents we predict, but we have some big clues. The first place to look to determine the next hotspots is at present start-up ecosystem rankings. 12 such ecosystems are: Greater Helsinki, Finland,Hangzhou, China,Jakarta, Indonesia,Lagos, Nigeria,Melbourne, Australia, Montreal, Canada, Moscow, Russia Mumbai, India, São Paulo, Brazil, Seoul, South Korea, Shenzhen, China, Tokyo, Japan. When it comes to specific start-up sub-sector leadership, Montreal emerge is one of the global hotspots for artificial intelligence (AI) start-ups. Since 2016, more than $1 billion has been invested in AI companies located there (including notable startup Element AI), and it has the largest concentration of AI academic researchers in the world. Montreal also hosts the NeurIPS conference, the largest AI event held annually in the world. Other “Challenger” ecosystems on the list have not created such a strong brand, or ecosystem identity, for themselves yet. But that is changing rapidly, partly due to aggressive government investment. In Asia-Pacific, for example, Seoul Metropolitan Government stands out with a recent pledge of $1.6 billion in funding for start-ups by […]

NVIDIA-Announces-Industry’s-First-Secure-SmartNIC-Optimized-for-25G

NVIDIA announces SmartNIC optimised for 25G

NVIDIA has launched the NVIDIA Mellanox ConnectX-6 Lx SmartNIC — a highly secure and efficient 25/50 gigabit per second (Gb/s) Ethernet smart network interface controller (SmartNIC) — to meet surging growth in enterprise and cloud scale-out workloads. ConnectX-6 Lx, the 11th generation product in the ConnectX family, is designed to meet the needs of modern data centers, where 25Gb/s connections are becoming standard for handling demanding workflows, such as enterprise applications, AI and real-time analytics. The new SmartNIC extends accelerated computing by leveraging software-defined, hardware-accelerated engines to offload more security and network processing from CPUs. Advanced Security Accelerated security features such as IPsec in-line cryptography and Hardware Root of Trust, and a 10x performance improvement for Connection Tracking, enable Zero Trust security throughout the data center. Best-in-class RDMA over converged Ethernet (RoCE), advanced virtualisation and containerisation, and NVMe over Fabrics storage offloads provide highly scalable and performant networking. The server industry is rapidly transitioning from 10GbE to 25GbE network speeds with very fast growth expected. With increasing requirements being placed on the network, more offloads need to be performed on SmartNICs. Key Features The Mellanox ConnectX-6 Lx provides: Two ports of 25Gb/s, or a single port of 50Gb/s, Ethernet connectivity with PCIe Gen 3.0/4.0 x8 host connectivity Security features including Hardware Root of Trust, Connection Tracking for stateful L4 firewalls, and in-line IPSec cryptography acceleration GPUDirect® RDMA acceleration for NVMe over Fabrics (NVMe-oF) storage, scale-out accelerated computing and high-speed video transfer applications Zero Touch RoCE (ZTR) for scalable, easy-to-deploy, best-in-class RoCE without switch configuration Accelerated switching and packet processing (ASAP2), with built-in SR-IOV and VirtIO hardware offloads for virtualization and containerization, to accelerate software-defined networking and connection tracking for next-generation firewall services ConnectX-6 Lx is sampling now, with general availability expected in Q3 2020. Like all products in the ConnectX […]

OnRobot takes robot compatibility to the next level

New Digital I/O Converter Kit and Quick Changer for OnRobot end-of-arm tooling (EoAT) dramatically simplifies automation with a single platform for a full range of collaborative and light industrial robotic systems. OnRobot, a global leader in end-of-arm tooling (EoAT) for collaborative robot applications, has introduced two new products – the Digital I/O Converter Kit and Quick Changer – which together with other OnRobot EoATs, creates a unique One-System Solution that dramatically simplifies automation. OnRobot’s Digital I/O Converter Kit allows OnRobot EoATs to work with a full range of leading collaborative and light industrial robot brands. Building on that initiative, all OnRobot products now have a unified mechanical and communications interface based on the OnRobot Quick Changer, which is now an integrated part of all OnRobot products. An additional Dual Quick Changer incorporates these same new capabilities while allowing the use of two tools in one cycle, mixing and matching to suit application needs and maximising robot utilisation. Now, with a single robotic system, single platform for programming and training, and single vendor relationship, manufacturers have access to a full range of tools, full robot compatibility, and virtually unlimited possibilities for collaborative applications. This new one-system solution speeds deployment so manufacturers save time and money and can quickly realise the benefits of automation. Manufacturers are demanding more from their robotic applications, and end-of-arm tooling has become the key to optimising their investments. This integration of the Quick Changer across the current product lineup makes it easier than ever to switch between tools and robot types, allowing flexibility and reducing downtime for more efficient production and faster automation ROI. This is another step closer to a unified OnRobot one-system solution that makes it easy, fast and cost-effective for manufacturers to build collaborative applications, no matter which robots they choose.” Unified platform across […]

Machines can’t dream

People are concerned about robots. Ever since a computer system defeated chess champion Gary Kasparov 20 years ago, public perceptions of progress in artificial intelligence (AI) research have been defined in terms of high-profile competitions pitting human against thinking machine. Anxiety is high about what the ultimate consequences could be. In the wake of Deep Blue’s triumph, other machines powered by AI have racked up momentous victories against human opponents in the game show Jeopardy and, most recently, against the world champion Go player. The latest version of Google’s AlphaGo software taught itself to play the strategy board game without any human help at all. The debate among leaders, technologists, futurists, employees of all stripes is on the profound impact AI will have on our workplace, our societies, our lives. The scale of this impact is hotly debated. Could machines replace us? Could they actually take over? There are smart minds on all sides of this issue. Some, like Professor Stephen Hawking, believe AI’s rise represents an existential threat. He told a BBC radio audience: “I think the development of full artificial intelligence could spell the end of the human race.” Others believe the rise of automation represents a veritable utopia made possible by smart machines. Optimism is a free stimulus in any country. In that spirit, this evolution could create massive potential for the human race. Any way you look at it, one thing is clear: there is nothing to be gained by hand-wringing about a dystopian future that we have the power to avoid. Let’s create the future we want to live in. Even in divisive times like these, we see human qualities such as ingenuity, kindness, innovation, and creativity rise up, take hold and gain unstoppable momentum. It is possible to create a world where AI benefits humanity, […]

Freedom to think

Top three applications for machine learning in manufacturing Machine learning means machines do not have to be programmed to perform exact tasks on a repetitive basis, they collect data and use it to make informed decisions about their next move. This allows them to correct any errors and improve their operational parameters. There are three key areas where manufacturers can benefit from this technology. Industrial maintenance According to McKinsey, artificial intelligence can generate a ten per cent reduction in maintenance costs, up to a 20 per cent reduction in downtime and a 25 per cent reduction in inspection costs. Machine learning is a significant player in this positive impact of artificial intelligence. In traditional predictive maintenance, engineers program the thresholds for a component’s normal operation into a supervisory control and data acquisition (SCADA) system. When the component deviates from normal operation, the system alerts an engineer of the developing fault. The problem with this approach is the lack of flexibility. It does not take into consideration variations in plant activity or the context of manufacturing processes. For example, a system may detect a sudden increase in a component’s operating temperature and interpret this as a developing fault, when in fact it is due to the machine being sterilised. Machine learning technology means predictive maintenance systems do not have to be programmed with normal operating thresholds. They use data from the factory floor and IT systems to monitor operational patterns and make informed decisions about what is normal and abnormal activity. Quality assurance There are two main ways machine learning can improve quality assurance (QA). Firstly, it enables assembly robots to continuously monitor and optimise their processes. Secondly, machine learning increases the capabilities of machine vision systems. Like with predictive maintenance, traditional machine vision systems for QA lack flexibility. For example, […]

The word globalisation is out of date. This is what should replace it

The word globalisation has lost its relevance and lustre with the emergence of the new global economy of the 21st century. In fact, it’s become an anachronism. Its deficiency is that it’s not a new concept which creates nuances of confusion. Globalisation describes the international outreach of countries for the purpose of economic, social, political and cultural liaisons. Global linkages between countries through military conquest, colonisation, multilateral free trade agreements and cultural exchange existed in an uninterrupted continuum in the evolving history of humankind. Historically, however, the process of globalisation has taken different forms, and its meaning has changed and mutated since it was first used in English in the 1930s. For lack of a better word, the term received an extension to its lifeline until the end of the 20th century despite the profound structural changes and technological advances that began to take place in the 1980s. Therefore, globalisation has come to be used to represent “modern” 20th century economics and social processes, including the instant exchange of money enabled by internet technologies. Yet because of its varied historical use, it does not truly reflect the electronic and digital empowerment that has been unleashed by the information technology revolution. Therefore, globalisation is not an accurate descriptor of the 21st century and the internet-driven transformational change sweeping the international economic landscape. And so I have coined the term internetisation which I believe should replace the concept of globalisation. Internetisation: Time, geography are irrelevant Internetisation is the contemporary face of globalisation. It includes the modern tools of electronic globalization and embraces the digital connectivity and empowerment of the internet and the World Wide Web. Although we could widen the term globalisation to include internetisation, I believe it is better to replace the word globalisation altogether because the long duration of its […]

Working across the supply chain

Industry 4.0 is changing the way we work across the supply chain. Using AI, sensors, and Internet of Things (IoT) technology, a smart and data-driven distribution center can be developed. For example, by cross-referencing enterprise resource planning (ERP) systems with consumer trends data, AI technology can automatically order the correct amount of raw materials to fulfil orders, reducing waste and increasing profit. As the complex web of distribution is opened to the benefits of AI, the supply chain could have a bigger economic benefit than any other application of AI in manufacturing. Using this technology, distributors will no longer need to predict demand for products through guesswork but will instead merge datasets to make accurate predictions about the future, enabling them to make well-informed business decisions. Inventory level efficiency With insight into future demand, AI can also help with forecasting the demand of your suppliers, based on previous orders. This means crucial decisions can be made to optimize stock levels. For example, if your AI software lets a distributor know that many other distributors will want the same equipment in 12 months’ time, you would be sure to jump the queue and get ahead by ordering it much sooner than this. Cost of goods sold Why does it matter if inventory levels aren’t optimised? Well, it’s related to inventory level efficiency. Your cost of goods sold (COGS) will reduce since you don’t incur costs of holding inventory beyond its use. In 2015, the cost of over-stocking was $470 billion, and of under-stocking was $630 billion worldwide, according to IHL Group. Freeing up cash and storage space creates the potential for savings. Lead times As Industry 4.0 empowers your supply chain to manage different orders faster, lead times for customers will shorten. However, this increases the pressure to deliver on time, […]

Nintex unveils major enhancements to Workflow Cloud

Nintex, the global standard for process management and automation, announced significant enhancements to its cloud-first process automation solution, Nintex Workflow Cloud, including a market first offering – the ability for customers to quickly and easily create workflows that include robotic process automation (RPA) bots. This new feature within Nintex Workflow Cloud called Nintex Gateway, provides two-way, drag-and-drop interaction between workflows and bots. In addition, Nintex subscription-based workflow customers now have access to RPA bots included with their current service. With Nintex Gateway, customers utilise bots to achieve end-to-end automation across a much broader set of process scenarios. Process workflows can also delegate tasks to Nintex RPA bots that mimic human keystrokes and mouse clicks to interact with apps, systems, services, and websites through their existing screens and interfaces. This results in less work being assigned back to the user and helps organisations leverage cloud workflow capabilities while protecting their on-premises investments. Nintex Workflow Cloud enhancements accelerate configuration, results and compliance Additional new features within Nintex Workflow Cloud include easy testing of workflows as they are built. This helps workflow designers get quick feedback to ensure their workflows are correctly configured before they are published. Workflow designers can also activate new “task authentication” features to require the person assigned to execute a task to first log into Nintex Workflow Cloud, ensuring that only authorised users may take that action. Existing Nintex Workflow Enterprise customers will now have full access to attended and unattended RPA bots within their current workflow subscriptions to enhance the value of their automation with Nintex RPA bots. Nintex customers can create and train RPA bots to execute specific automation tasks – called botflows – just like they create workflows today with Nintex. This means that Nintex customers can allocate existing workflows towards deploying botflows, providing enormous automation […]

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