Manufacturing Technology

Becoming a data-driven organisation to optimise performance

-Adam Sharmer, senior partner, Dsifer As the pace of digital innovation accelerates, and organisations look to exploit the financial and strategic advantages of digital technology, automation and AI, data is increasingly becoming the lifeblood of organisational decision ecosystems. Whilst the collection and analysis of data to inform operational decision-making is not a new concept, advances in connectivity of technology, AI and machine learning as well as ERP and MES solutions have driven exponential gains in the availability, quality, and integration of data in manufacturing organisations. Spend a few moments on LinkedIn and its easy to become overwhelmed by the level technology available and the visions of possible futures available through its adoption. However, whilst there is no doubt that Industry 4.0 technologies represent a potential step-change in manufacturing performance, the reality is that the CAPEX required for investments in, for example, lights-out warehousing or production automation, is beyond the reach of a lot of organisations. As a result, the journey from current state to the Industry 4.0 utopia can seem like an insurmountable one. The good news is that, whilst strategic adoption of technology should be on the business plan for all organisations if they are to remain competitive, there is a lot of value that can be unlocked with the current context or with low levels of investment. In our experience, most organisations have increased their maturity in the collection and storage of data in all or most organisational processes. However, the true value of this data is not yet being realised. A deliberate focus on process, system, analytics and cultural dimensions to become a data-driven organisation, can unlock the latent value in current systems and data and build the foundation from which to optimise I4 technology. From our experience, data-driven organisations share the following characteristics: They collect […]

Top 10 Emerging Technologies of 2023

1. Flexible Batteries Standard rigid batteries may soon be a thing of the past as thin, flexible batteries – made of lightweight materials that can be twisted, bent and stretched – reach the market. This new generation of battery technology – expected to hit a market value of $240 million by 2027 – has applications across medical wearables, biomedical sensors, flexible displays and smart watches. 2. Generative Artificial Intelligence This year’s list would not be complete without mentioning generative AI – a new type of AI capable of generating new and original content by learning from large datasets that was catapulted into public dialogue at the end of 2022 with the public release of ChatGPT. Evolving rapidly, generative AI is set to disrupt multiple industries, with applications in education, research and beyond. 3.Sustainable Aviation Fuel With 2%-3% of annual global CO2 emissions coming from aviation, and no sign of long-haul electric flights, sustainable aviation fuel produced from biological (e.g. biomass) and non-biological (e.g. CO2) sources could be the answer to decarbonize the aviation industry in the short to medium term. 4. Designer Phages Phages are viruses that selectively infect specific types of bacteria. Equipped with increasingly sophisticated genetic engineering tools, scientists can now reprogramme phages to infect the bacteria of their choosing, allowing them to target one type of bacteria in a complex community of co-existing types of bacteria such as in plant, animal and human microbiomes. Though many of the near-term applications will be in research, there are signs these “designer” phages could eventually be used to treat microbiome-associated diseases or eliminate dangerous bacteria in food supply chains. 5. Metaverse for Mental Health Responding to the growing mental health crisis, product developers are starting to build shared virtual spaces to improve mental health. Video games are already being used to treat […]

Increasing interest in ZLD driven by costs and environment

By Matt Hale, International Sales & Marketing Director, HRS Heat Exchangers In 2021 the global market for zero liquid discharge (ZLD) technology was estimated at US$1 billion and is forecast to grow at almost 12% over the next ten years. The rise is being driven in particular by an increase in adoption of the technology by the food and drink and textile industries as a growing world population puts greater pressure on fresh water supplies. What is ZLD? Zero liquid discharge (ZLD) is a liquid waste stream treatment which involves transforming liquid waste streams into clean water (which can be reused) and a minimum volume of solid residues. One of the advantages of ZLD over other treatment techniques is its theoretical ability to separate unwanted materials from water, whether they are benign, hazardous or toxic. The resulting solid residue is often more stable, making it suitable for recycling or landfill. However, poor management or handling of the remaining residue can result in unintended environmental consequences. For example, storage ponds may leak or affect local wildlife while there is the potential for toxic chemicals to leach into groundwater from landfill. It is therefore important that when implementing a ZLD system, full consideration is given to the entire process, including the ultimate fate of liquid and (semi-)solid waste streams. A well-designed ZLD system should minimise or even eliminate liquid waste streams, resulting in clean water for reuse or environmentally-friendly discharge, and a solid residue suitable for further processing (often to recover valuable components for use elsewhere) or for safe disposal. The factors driving ZLD uptake According to Transparency Market Research, ZLD is being implemented across a wide range of industries, including chemical and petrochemical production, food and drink production, textiles, energy and power, and pharmaceutical manufacturing. These industries are being driven to […]

AI helps the impossible become possible

When Sandvik Coromant engineers, Henrik Loikkanen and Jakob Pettersson, were tasked with creating an AI-generated, stainless steel synthesis of some of history’s most famous works of art, their metal cutting expertise was put to the ultimate test. In partnership with Sandvik Group, Sandvik Coromant has developed a statue for the history books. Made using AI modelling and cutting-edge manufacturing solutions, the sculpture combines the dynamic poses of Michelangelo’s work, the musculature craftmanship of Auguste Rodin, the somber tones of Käthe Kollwitz, Takamura Kotaro’s Japanese influence and Augusta Savage’s inspirational defiance to unite some of history’s most famous artists from a period spanning 500 years. Weighing 500 kilograms and standing at 150 cm tall, the Impossible Statue was officially inaugurated at Tekniska Museet, Sweden’s National Museum of Science and Technology, in April 2023. Producing a statue in such a way has never been done before. So how did Loikkanen, Pettersson and the team craft this blend of art and science, past and future? When AI meets art AI has been around for some time, with intelligent machines tasked with performing activities that normally require human intelligence, such as visual perception, speech recognition, decision-making, language translation and problem-solving. Its concept dates back decades with the earliest program written in 1951 by Christopher Strachey, later director of the Programming Research Group at the University of Oxford. But the emergence of new, conversational AI programs such as Google’s Bard and ChatGPT open up even more applications of the technology. Today, the achievements of AI seem almost limitless — it can even create art. After establishing a 2D design that brought together the styles of the five artists, Sandvik began translating the model into a complete 3D image. Using depth estimators to build the 3D model, human pose-estimators to refine the body, videogame algorithms to […]

Electronics manufacturers adopt generative AI and Omniverse to digitalise state-of-the-art factories

Electronics manufacturers worldwide are advancing their industrial digitalisation efforts using a new, comprehensive reference workflow that combines Nvidia technologies for generative AI, 3D collaboration, simulation and autonomous machines. Supported by an expansive partner network, the workflow helps manufacturers plan, build, operate and optimize their factories with an array of technologies. These include: Omniverse, which connects top computer-aided design apps, as well as APIs and cutting-edge frameworks for generative AI; the Isaac Sim application for simulating and testing robots; and the Metropolis vision AI framework, now enabled for automated optical inspection. At Computex, Nvidia founder and CEO, Jensen Huang, showcased a demo of an entirely digitalised smart factory — an industry first for electronics makers. “The world’s largest industries make physical things. Building them digitally first can save enormous costs,” said Huang. “We make it easy for electronics makers to build and operate virtual factories, digitalise their manufacturing and inspection workflows, and greatly improve quality and safety while reducing costly last-minute surprises and delays.” Foxconn Industrial Internet, a service arm of the world’s largest technology manufacturer, is working with Nvidia Metropolis ecosystem partners to automate significant portions of its circuit-board quality-assurance inspection points. Innodisk is deploying Nvidia Metropolis to automate optical inspection processes on its production lines, saving cost and improving production efficiency. Pegatron, a leading electronics manufacturer and service provider, is using the reference workflow to digitalise its circuit-board factories with simulation, robotics and automated production inspection. Quanta, a major manufacturer of laptops and other electronic hardware, is using AI robots from its subsidiary Techman Robot to inspect the quality of manufactured products. Techman is leveraging Isaac Sim to simulate, test and optimise its state-of-the-art collaborative robots while using NVIDIA AI and GPUs for inference on the robots themselves. Wistron, one of the world’s largest suppliers of information and communications products, is tapping Omniverse to build digital twins of its automated […]

How to keep AI developing without compromising safety and security

Christina Montgomery Francesca Rossi The introduction of generative AI systems into the public domain exposed people all over the world to new technological possibilities, implications, and even consequences many had yet to consider. Thanks to systems like ChatGPT, just about anyone can now use advanced AI models that are not only capable of detecting patterns, honing data, and making recommendations as earlier versions of AI would, but also moving beyond that to create new content, develop original chat responses, and more. A turning point for AI When ethically designed and responsibly brought to market, generative AI capabilities support unprecedented opportunities to benefit business and society. They can help create better customer service and improve healthcare systems and legal services. They also can support and augment human creativity, expedite scientific discoveries, and mobilize more effective ways to address climate challenges. We are at a critical inflection point in AI’s development and use and its potential to accelerate human progress. However, this huge potential comes with risks, such as the generation of fake content and harmful text, possible privacy leaks, amplification of bias, and a profound lack of transparency into how these systems operate. It is critical, therefore, that we question what AI could mean for the future of the workforce, democracy, creativity, and the overall well-being of humans and our planet. The need for new AI ethics standards Some tech leaders recently called for a six-month pause in the training of more powerful AI systems to allow for the creation of new ethics standards. While the intentions and motivations of the letter were undoubtedly good, it misses a fundamental point: these systems are within our control today, as are the solutions. Responsible training, together with an ethics by design approach over the whole AI pipeline, supported by a multi-stakeholder collaboration around AI, can make […]

Detecting faults: A stitch in time for manufacturing?

By Alessandro Chimera TIBCO’s director of digitalisation strategy The way we use data to monitor and control manufacturing systems has changed. TIBCO’s director of digitalisation strategy, Alessandro Chimera, explains the shape of the anomaly economy guiding operational performance management in manufacturing. “A stitch in time saves nine” is a well-known and well-worn adage. It neatly encapsulates the reality that early remedial action to deal with a minor fault detected can avoid greater repair costs caused by damage down the track. Never has this saying rang truer than in today’s high speed, high volume, near real-time manufacturing processes, often described as Industry 4.0, where rapid production can make fault detection difficult. Without appropriate anomaly detection and rapid intervention, it would be easy for large volumes of a flawed product to be distributed before a problem is discovered, or for expensive manufacturing machinery to be seriously damaged. Detecting anomalies does not only increase production it can produce considerable savings: machine downtime and possible damage can be avoided along with product wastage and, most importantly, flawed products can be detected before they are delivered to customers. However, this is not an easy task, even with the availability of massive amounts of real-time data, artificial intelligence (AI) and machine learning (ML). This data is likely to originate in multiple different formats from different systems and scattered across many different locations. For example, some valuable data might need to be gathered from products that have already been shipped that have exhibited abnormal behaviour or failed. To make this data usable for AI and ML techniques, it must first be harmonised and related to the components of the manufacturing process it represents so it can be meaningfully analysed. This requires the use of an autoencoder. It is not an easy concept to explain in-depth, but simply […]

Manufacturers need to leverage a data mesh for innovation and competitiveness

By Vinay Samuel, Founder and CEO, Zetaris The success of any manufacturing business depends on closely managing every element of the process. That starts with accessing raw materials, monitoring the actual process of manufacturing, the dispatch of finished goods and ensuring they are shipped to the customer. The key to understanding all those steps is data. That data is often spread across multiple systems and managing a manufacturing supply chain requires access to real-time information. Traditional approaches manufacturers leveraged to perform data analytics focused on looking back at what happened in the past. The old extract, transform and load (ELT) process relied on being able to extract data from various systems in order to load them into a centralised data warehouse or data lake for analysis and insights. This approach was piecemeal as the ETL process was limited to data sources the manufacturer could access easily. This put external systems and data sources, such as those operated by logistics companies and raw material suppliers, out of reach. The cost, time and complexity required to connect to those external systems, extract the required data in a timely way and then integrate it into the data warehouse was prohibitive and when it was done, already vastly out-of-date. As a result, manufacturers only had a limited view of what was happening across their entire manufacturing process and supply chain and little ability to detect and address changing customer preferences in real-time. A data fabric takes a very different approach. Rather than relying on the ETL process, data fabric technology leverages the data mesh principles to enable manufacturers to access information from internal and external locations across disparate sources without the need to extract it. Data from multiple systems can easily be integrated into a metadata-driven virtual data warehouse that can be accessed by […]

Decarbonise while you Digitalise

Nathan Knight, Hitachi Vantara’s Managing Director for Australia and New Zealand, makes the case for a coordinated approach to decarbonisation and digitalisation Imagine that manufacturers have a character on each shoulder, one with the word PROGRESS emblazoned across its chest, and the other, SUSTAINABILITY. When deciding where to invest precious resources like time, expertise and money, they can feel like two competing priorities. “Pick me,” says Progress. “More digital services, more automation, more cloud, more diversification, more data analytics, more!” The other voice pleads, “But what about me? You have to consider the planet, responsible resource management, diversification of your staff and your supply chain. You have to reduce your emissions.” Sustainability’s voice is getting louder and more insistent. In the pursuit of digitalisation in the name of progress and competitive edge, the two do not have to be at odds with one another. In fact digitalisation is a powerful tool that can help manufacturers achieve the critical sustainability goal of decarbonisation. Many Eyes on the Manufacturing Sector Australia strengthened emissions reduction targets with the goal of net zero by 2050. According to Grattan Institute, decarbonising Australian industry is an “industrial revolution against a deadline” and the scale and pace of change required is unprecedented. The think tank has called for government policy change, stricter emissions limits and a focus on decarbonising heavy manufacturing and mining, which reportedly produce a third of Australia’s carbon emissions. The heavy industry sector collaborated with decarbonisation experts to form the Australian Industry Energy Transitions Initiative (ETI), which says that regional Australia’s industrial economy could cut greenhouse gas emissions by more than 80% and become centres for multibillion-dollar investments in renewable energy. It would take an unprecedented transformation of the energy system, they say. Acknowledging that the agriculture, mining, manufacturing, data centre and other sectors […]

Industry 4.0 transforming manufacturing processes for increased efficiency

Frank Baldrighi, business development manager – ANZ, GETAC Thanks to digital transformation powered by artificial intelligence (AI), automation, and machine learning (ML), manufacturing processes are becoming smarter, more efficient, and safer. This transformation is often referred to as industry 4.0, or the fourth industrial revolution, and marks the beginning of a new era of digitalisation, data-driven decision-making, and smart automation. In the manufacturing industry, digitising certain crucial key performance indicators (KPIs) has long been a challenge. Among these persistent issues is the need to reduce downtime and enhance worker productivity. Industry 4.0 promises to change this for both process and discrete manufacturers. By leveraging data as its core resource, Industry 4.0 delivers a ‘smart’ factory that is not only efficient but also capable of self-correction and self-optimisation. Mobile devices on edge have a crucial role to play in this modern data-driven landscape. These devices serve as a central access point for all data-driven decision-making processes. Their sturdy build and portability also make them highly reliable tools on the plant floor of a smart factory. Here are some ways in which Industry 4.0, through edge devices, can deliver results: Improve worker productivity To enhance the productivity of workers, mobile devices can serve as a valuable tool for completing administrative tasks effectively. This includes clocking hour sheets and signing off on completed tasks, which can be easily accomplished through the convenience of a digital device compared to manual and paper-based processes that often lead to inaccuracies and lack real-time visibility into production processes. Through the use of a mobile device, workers can access the company’s manufacturing execution system (MES), which offers centralised information and real-time updates. This delivers a more collaborative approach to work, ensuring that every worker is on the same page and working towards common goals. And, the ability to […]

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