The Fundamentals of Smart Manufacturing
The Fundamentals of Smart Manufacturing
Manufacturing is changing at a very rapid pace. There is a new type of connected, data-driven, and architecturally open factory emerging in response to these demands, led by the Industrial Internet of Things (IIoT). Along with increased machine automation, other characteristics of this new smart factory include hyper-agility, autonomous production, and data utilization as a tool for business.
Research conducted by Accenture for the World Economic Forum showed that 73% of the C-level executives interviewed were convinced that the IIoT would fundamentally change their industry. But just 20% had a strategy for harnessing it. Companies that want to succeed in the future must master the radical digital transition headed our way by opening themselves to a journey that will change their organization models beyond recognition – the alternative being a catastrophic loss of market share and profitability.
Smart, connected and data-driven
Smart manufacturing is now seen as a natural progression of the “digital convergence” already underway between information technology (IT) and operational technology (OT). There are four essential characteristics that set it apart.
Firstly, the smart factory uses data and IIoT connectivity to easily control all aspects of operations in near real-time, with near full automation across all locations. IoT and digital investment is the foundation for proactive, self-aware factory operations, maintenance and innovation.
Sensor-equipped machines, inter-operable systems and reliable real-time computing are connecting machines across the smart factory. Product, raw materials, equipment, and control systems all have the potential to collect and share data. This data can be analysed in context and in real time to equip workers with actionable information.
Throughout the factory, end-to-end security at both hardware and software levels helps reduce vulnerabilities as more machines are connected. With more than 40 years of experience in both OT and IT solutions, Advantech is uniquely qualified to address the issues of OT-IT convergence which are fundamental to migration from yesterday’s ‘islands of automation’ to tomorrow’s smart factories.
A self-managing “Systems of Systems”
Secondly, the smart factory is based on multiple interconnected systems, each with a high level of flexibility, efficiency, and autonomy. Future factories will eventually become one large system comprising hundreds of smaller systems independently working toward the same goal. From production and maintenance to supply chain and security, each system and subsystem uses AI, machine vision, deep learning, and edge analytics to control everything on the factory floor.
This environment of machine-to-machine communication improves operational efficiencies and reduces unplanned downtimes. Production becomes so responsive to custom requests and material variations that the factory essentially operates at “economies of one” to compete with today’s economies of scale.
Self-monitoring equipment using sensors such as Advantech’s LoRaWAN smart vibration sensor can detect when quality could suffer due to degradation and then schedule its own service. Materials follow the most efficient path, and workloads are consolidated at all architecture layers to provide the flexibility to respond to fast-changing demands. Orchestration of applications and services across hardware enables data aggregation and control to provide new levels of performance.
Human-machine collaboration
A third aspect of the smart factory is its emphasis on machine-to-human collaborations, allowing employees to work more safely and empowering them to make faster, more educated, innovative responses to business needs. As smart factories reduce the number of humans on the floor, workers are helped by collaborative ‘co-bots’ on complex tasks, while repetitive, injurious work is handled by robots.
Workers use augmented reality and data visualisation to overlay information about production, maintenance and product status. A digital culture encourages the use of data for daily work, freeing employees to respond with greater creativity to resolve issues and support business success. A younger workforce is attracted through updated technology, safer work environments, and roles better suited to their generation.
Autonomous and self-adapting
Through autonomy and adaptability, the smart factory enables manufacturers to expand IIoT’s application and value to support changing business strategies.
The factory is becoming smarter and more autonomous over time, using data to optimise resource allocation and transform businesses. As more machines and systems are connected, manufacturing matures into an intelligent factory model in which OT and IT converge and strategically engage in business decisions.
AI and deep learning produce increasingly detailed, accurate and meaningful digital models of equipment and processes, enabling data-driven decision-making; and devices will grow more intelligent over time and respond to events more efficiently. Production controls become self-running, and new business approaches emerge.
Aided by the insights from data, the main manufacturing drivers have expanded from efficiency and product quality to also include production flexibility. Amid this continually evolving environment, the factory systems become increasingly intelligent and autonomous with systems beyond themselves.
Key trends shaping the future of manufacturing
In a smart factory environment, “data collection” refers to the process of gathering information from the manufacturing processes, equipment, and systems involved in production. Data collection in a smart factory is typically facilitated by various sensors, IoT devices, and automation systems that continuously monitor and record relevant information in real-time. This data is then aggregated, processed, and analysed using advanced analytics, machine learning algorithms, and other tools to gain insights, optimize processes, improve efficiency, and make data-driven decisions.
EdgeLink, a versatile IoT gateway software, is engineered to connect with over 200 edge devices and diverse platforms. It adeptly supports multiple protocols, unifying data sources, optimizing data processing, and publishing data to mainstream platforms or other automation systems through cellular, 4G, 5G, Wi-Fi networks, and VPN connections.
“Edge AI” and “Edge Computing” refer to the processing and analysis of data at or near the ‘edge’ of the source of data generation, rather than relying on centralized cloud computing resources. In the context of an intelligent or smart factory, edge computing and AI technologies are employed to perform data processing, analysis, and decision-making tasks directly within the factory environment. They play a crucial role in enabling the responsiveness required in modern manufacturing environments, contributing to the concept of Industry 4.0 and the evolution of smart factories.
MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) are software systems used to manage manufacturing operations and overall business processes, while OEE (Overall Equipment Effectiveness) is a performance metric used to assess the efficiency of equipment or processes. These concepts are all essential components of modern manufacturing management practices.
In the context of a smart factory, “Analytics AI” and “Machine Learning” (ML) are critical components that enable data-driven decision-making, optimization, and automation. They play crucial roles in leveraging the vast amount of data generated in a smart factory environment to drive insights, improve decision-making, optimize processes, and ultimately enhance productivity and profitability. They enable the transformation of data into actionable intelligence, empowering manufacturers to adapt quickly to changing conditions and stay competitive in today’s fast-paced business environment.
Industrial connectivity refers to the network infrastructure and technologies used to connect various devices, systems, and components within an industrial environment. It enables seamless communication and data exchange between machines and other equipment, facilitating industrial processes. Industrial connectivity is a foundational element of Industry 4.0 and the concept of the IIoT.
Overall, industrial connectivity is essential for enabling automation, data-driven decision-making, and optimization in industrial environments. It forms the foundation for Industry 4.0 initiatives aimed at improving efficiency, productivity, and competitiveness in industrial sectors.
Advantech’s Role in Driving Smart Manufacturing
Software tools play a crucial role in the optimization of smart factories. These tools encompass a wide range of applications and platforms designed to support manufacturing planning, execution, monitoring, analysis, and optimization. Advantech’s WebAccess and other IoT software products provide a complete solution for device and data management to enable edge intelligence.
Advantech has implemented Industry 4.0 in its manufacturing centres, including a ‘situation room’ as the factory’s central brain where data is collected, analysed and visualised for real-time management. The equipment connectivity solution – consisting of an edge data gateway and distributed digital I/O – facilitates machine connections without replacing existing equipment while also collecting data. The industrial computer with Advantech software enables data transfer between production and management systems.
The process visualisation solution enables production monitoring, data integration with MES, and visualisation on the situation room dashboards. This allows production optimization and data-driven decision making. The Advantech WebAccess app gives push notifications of unexpected downtime, allowing immediate action to be taken.
The fundamentals of smart manufacturing aren’t slowing down. When combined together, they usher a new Industrial Internet of Things (IIoT) era where machine automation, hyper-agility, autonomous production, and data utilization deliver transform business processes.
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Smart Cities And E-Health: The Convergence Of Urban Infrastructure And Digital Healthcare
Smart Cities And E-Health: The Convergence Of Urban Infrastructure And Digital Healthcare
In an era where digital transformation is reshaping industries, the convergence of smart cities and e-health is redefining urban living. Smart cities are no longer just about optimizing transportation, utilities and governance. They are evolving into intelligent ecosystems where digital healthcare is becoming a core element of urban planning. As cities grow and technology advances, integrating healthcare into urban infrastructure is reshaping how medical services are delivered, accessed and managed.
The convergence of smart cities and e-health is about reimagining healthcare delivery in a way that is more accessible, efficient and responsive. Cities are moving beyond traditional models of care by leveraging AI, IoT and real-time data analytics to improve patient outcomes and healthcare accessibility.
Specifically, the increasing adoption of agentic AI systems provides sophisticated and real-time monitoring and decision making for enhanced services. Agentic AIs are capable of taking autonomous actions to achieve specific goals or objectives, typically based on predefined rules, learned patterns or programmed behaviors. The shift toward technology-driven healthcare solutions is setting new standards for urban well-being and quality of life.
Smart Cities As Enablers Of Digital Healthcare
The United Nations Department of Economic and Social Affairs estimates that 68% of the global population will live in urban areas by 2050. Many of these cities will be built using smart infrastructure principles that rely on real-time data, AI and IoT-driven systems to improve efficiency and sustainability.
By integrating e-health solutions into urban infrastructure, cities can make healthcare services more connected, data-driven and patient-centric. Digital transformation is ensuring that healthcare is no longer confined to hospitals and clinics.
A study by Fardin Quazi (2024) on “eHealth Services in Comprehensive Smart Environments” highlights the role of urban infrastructure in supporting digital healthcare and the seamless interaction between them. The research emphasizes seamless interactions between smart environments in enhancing patient care and streamlining healthcare operations through advanced digital technologies.
How Smart City Infrastructure Supports E-Health
The global e-health market is currently valued at $274.35 billion and is expected to reach $576.73 billion by 2030. The U.S. remains at the forefront of this growth, driven by the increasing demand for smart, technology-enabled healthcare solutions. Urban infrastructure plays a crucial role in supporting this transformation in several ways.
Real-Time Health Monitoring And IoT Connectivity
In a smart city, healthcare is no longer limited to in-person visits. Wearable devices, home-based health monitoring systems and IoT-powered medical sensors provide real-time data on patients’ vital signs, such as heart rate, oxygen levels and glucose levels. Agentic AI complements this by analyzing the data in real time and triggering actions without waiting for manual input.
This data is transmitted securely to healthcare providers, enabling remote monitoring and timely medical intervention. By integrating real-time health tracking with urban data systems, cities can create more proactive healthcare models that focus on preventive care rather than reactive treatment. This shift reduces hospital overcrowding and enhances medical efficiency.
Emergency Response Optimization
Cities with AI-powered emergency response systems and real-time traffic monitoring are improving the efficiency of medical services. By leveraging real-time GPS data, ambulances can navigate faster routes, bypass congested areas and reduce response times in critical situations.
Additionally, AI-assisted surveillance systems can detect accidents, medical emergencies or sudden health incidents in public spaces, triggering automatic alerts to emergency responders. These innovations are particularly valuable in large urban centers where delays in emergency response can have life-threatening consequences.
The increasing use of agentic AI in smart traffic management is aiding in better monitoring and real-time response. For instance, if a pedestrian meets with an accident or faces a health emergency in a public space, agentic AI surveillance can identify the incident, alert emergency responders and analyze environmental factors such as air quality or crowd density to determine potential causes.
Telemedicine And Virtual Healthcare Services
Telemedicine is becoming a mainstream mode of healthcare delivery rather than an alternative to traditional consultations. With 5G connectivity, cloud-based healthcare platforms and AI-powered diagnostics, patients can now consult doctors remotely without visiting a hospital. This transformation is particularly beneficial for elderly residents, individuals with mobility challenges and underserved communities.
Smart city infrastructure is also facilitating the deployment of virtual health kiosks, allowing residents to access medical consultations and conduct basic health screenings conveniently. Beyond facilitating virtual consultations, agentic AI systems can autonomously schedule follow-ups, analyze symptoms during video calls and recommend diagnostic tests based on patient data.
Recognizing the growing impact of digital healthcare, the U.S. Department of Health and Human Services (HHS), through HRSA, allocated $55 million to 29 health centers to expand access to telehealth, remote patient monitoring and AI-driven health technologies. These investments are reinforcing the role of smart infrastructure in supporting accessible healthcare.
Challenges In Smart Healthcare Integration
Despite its potential, integrating e-health into smart cities presents significant challenges. Data privacy and security remain primary concerns, as healthcare data must be securely transmitted and protected from cyber threats. Additionally, ensuring interoperability between different healthcare platforms, IoT networks and urban systems is an ongoing challenge that requires industry-wide standardization.
Another critical issue is bridging the digital divide. While smart healthcare solutions are advancing, not all urban residents have equal access to digital health services. Investments in affordable digital literacy programs, healthcare technology accessibility and public-private collaborations will be necessary to ensure inclusivity.
Collaboration between healthcare providers, technology developers and policymakers is essential to overcoming challenges in digital healthcare integration.
The Future Of Smart Cities And Healthcare
As cities continue to evolve into data-driven, intelligent environments, healthcare will become an even more central component of urban planning. Future innovations in AI-driven personalized medicine, blockchain-secured health records and 5G-enabled smart hospitals will further revolutionize how cities manage healthcare services.
Public-private partnerships will play a key role in scaling digital healthcare initiatives, bringing together tech companies, government agencies and healthcare providers to create sustainable solutions.
Healthcare is no longer just a standalone service—it is an intrinsic part of modern urban infrastructure. The cities of the future will not only be smarter and more efficient but also healthier and more resilient.
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Prioritize play to help your city thrive in a post-pandemic world
Prioritize play to help your city thrive in a post-pandemic world
August 14, 2024
We are at a pivotal moment in urban development, facing a housing crisis that affects cities across North America. While addressing the housing shortage is unquestionably critical, we must also remember that cities, especially great cities, are more than shelters.
Cities are the birthplace of inventions, new forms of collaboration and vibrant social interactions. Over the years, much of the social infrastructure that fostered these interactions — such as corner stores, bowling alleys, clubs and bustling main streets — has been stripped away. Therefore, as we work to provide shelter and basic security, we must also rekindle the idea of cities as habitats for the human spirit, laying the foundations for a united, collaborative and flexible society capable of tackling the complex, interconnected issues of our age.
Often, traditional methods of city-building can obscure new opportunities. Perhaps we are now at a point where the erosion of old principles can allow us to leap forward with innovative ideas.
Historically, the relationship between a city and its residents was framed by the Live, Work, Play planning model. This model assumed that these three attributes, in that order, were what people looked for in a potential city. A core pillar of Live was housing and, in North America, home ownership. However, while cities are working diligently to catch up with the housing problem, the underlying causes and the attribute Live are often beyond a city’s control.
Another sign of a weakening city-resident relationship is the post-pandemic shift to flexible work models, especially in the innovation economy. Work is becoming less of a determining factor in where people live. Last year, 35% of workers did some or all of their work at home, according to a U.S. Bureau of Labor Statistics survey, meaning that Work is also an attribute not fully within a city’s control.
These changes suggest that cities are losing relevance in their relationship with residents, potentially leading to an era of mediocre cities. But mediocrity is not sufficient for social, economic and environmental reasons. Cities need to thrive. Anything less will accelerate social isolation and division.
If we move past the old, ineffective priorities, we can see a new opportunity in Play. Traditionally, Play was the afterthought attribute of city building — prioritized last, funded with leftover money and created on land that wasn’t useful for anything else. Given the tenuous state of Live and Work, how a city facilitates social interaction between residents (Play) is now the best way to differentiate its offering and directly improve social and economic prosperity. Moreover, Play is entirely within the control of cities.
Play, as a city attribute, means connecting residents and making them feel they belong. It means celebrating a city’s uniqueness and identity, putting inclusiveness into action, supporting an innovative entrepreneurial ecosystem, fostering trust and compassion, and offering vibrancy that helps a generation often cut out of homeownership feel like fully valued residents of a city.
Practically speaking, Play and the collective joy it creates can help address the housing crisis and other contemporary issues. Joyful cities redefine what it means to live in urban density in a “smaller” home by devoting public space to playful participation. In this model, neighborhoods become the best amenity for a home, and the city becomes everyone’s communal backyard.
Joyful cities are also more competitive in today’s innovation economy, which thrives on ideas, invention and the people who create them. A city’s ability to attract creative talent through vibrant living, collaborative spaces and a lifestyle that blends work and play will determine its economic future.
Despite our efforts to solve the housing problem, cities are unlikely to revert to what they once were. But we can move forward with a Play+Live+Work = Thriving joyful cities prioritization.
It starts with asking, “How do we want our city to play?”
Read the article here: Prioritize play to help your city thrive in a post-pandemic world
What Is a Smart Home?
What Is a Smart Home?
July 12, 2024
The term ‘smart home’ has become an increasingly popular buzzword in the world of home security. Every aspect of our home life seems to become increasingly digitized, with the realm of domotics —a contraction originating from the Latin word ‘domus’, meaning home, and the term ‘robotics’— being front and center throughout this process.
But what does having a smart home even mean and how can homeowners use this technology to increase the peace of mind in their home? Read on to learn more about the nuts and bolts surrounding this increasingly popular term.
What is a smart home?
A smart home is a living space with home automation devices that use an internet connection. Connected devices can communicate with each other and synchronize tasks through a common network. This differs from home automation in general, which can include devices connected through other means such as bluetooth and local networks.
Smart home devices are usually connected through Wi-Fi and are included in the broader term of the Internet of Things (IoT), which includes devices connected through local networks. A smart home can increase the energy efficiency in your home, improve your home security system and make your daily task easier to manage.
History of smart homes
Although we may not think of it this way now, technically, a washing machine is an example of home automation. A task that was once commonly done by hand and took a considerable amount of time and energy was now processed automatically by a machine. In this way, the rise of home appliances in the beginning of the 20th century was the first wave of home automation.
The first main communication protocol for electrical devices, X10, was invented in 1975. The protocol uses power line wiring for signaling and control between appliances and is still widely used today. Modern interest with home automation started in the late 1990s and kept growing as Wi-Fi access and new connecting technologies became more prevalent.
How does smart home technology work?
Smart appliances can synchronize tasks in a specific sequence, known as a routine. These appliances communicate through home automation connectivity standards —technical specifications that ensure devices from different manufacturers can communicate with each other. Some, such as Z-Wave and Zigbee, are available only for specific brands, while the recent advent of Matter as a common standard across companies has gained traction.
Smart home products can also be activated through voice commands, usually with the aid of a voice assistant. The most common of these assistants are Amazon Alexa, Google Assistant and Apple’s Siri. Whether operated through a smartphone or a smart home hub, voice controlled assistants help you control multiple appliances at once and start routines that facilitate your daily life.
Examples of smart home technologies
- Smart lighting (such as smart light bulbs)
- Smart thermostats
- Smart home security appliances (such as security cameras)
- Smart locks
- Smart plugs
- Refrigerators
- Dishwashers
- Smart speakers
- Video doorbells
- Washers and dryers
- Ovens
- Sprinklers
- Motion sensors
- Televisions
- Automated garage door openers
Reasons to invest in a smart home system
A smart home can make your house more energy efficient by automating turning off lights at a certain time or optimizing your energy consumption. It can also improve your home security by integrating your home automation system with security cameras and motion sensor technology. This can then be controlled through a central hub or your smartphone.
However, smart home systems also can expose you to security risks in terms of data privacy —some gadgets connected through the IoT lack reliable encryption. Smart homes also need a consistent and reliable internet connection, which is not available in every part of the US. Ultimately, your home’s particular situation and needs are the factors you should consider to determine if smart home automation is right for you.
Read the article here: What Is a Smart Home?
Rise in AI Adoption Prompts Global Push for Regulation
Rise in AI Adoption Prompts Global Push for Regulation
June 14, 2004
The rapid expansion and deployment of generative artificial intelligence (gen AI) and AI more broadly across organizations worldwide has resulted in a global push for regulation.
In the US, President Joe Biden signed an executive order on AI in October 2023, laying out AI standards that are set to be eventually codified by financial regulators. Over the past five years, 17 US states have enacted 29 bills focused on regulating the design, development and use of AI, according to the Council of State Governments.
In China, President Xi Jinping introduced last year the Global AI Governance Initiative, outlining a comprehensive plan focusing on AI development, safety and governance. Authorities have also issued “interim measures” to regulate the provision of gen AI services, imposing various obligations relating to risk assessment and mitigation, transparency and accountability, as well as user consent and authentication.
Recently, Japanese Prime Minister Fumio Kishida unveiled an international framework for the regulation and use of gen AI called the Hiroshima AI Process Friends Group. The group, which focuses on implementing principles and code of conduct to address gen AI risks, has already gained support from 49 countries and regions, the Associated Press reported on May 03.
Impact of EU’s AI Act on financial services firms
The European Union’s AI Act is perhaps the most impactful and groundbreaking regulation to date. Approved by the EU Parliament in March 2024, the regulatory framework represents the world’s first major law for regulating AI and is set to serve as a model for other jurisdictions.
According to Dataiku, an American AI and machine learning (ML) company, the EU AI Act will have considerable impact on the financial services industry and firms should prepare for compliance now.
Under the AI Act, financial firms will need to categorize AI systems into one of four risk levels and take specific mitigation steps for each category. They will need to explicitly record the “Intended Purpose” of each AI system before they start developing the model. While Dataiku says that there’s some uncertainty about how this will be interpreted and enforced, it notes that this indicates a stricter emphasis on maintaining proper timelines than current regulatory standards.
Additionally, the AI Act introduces “Post Market Monitoring (PMM)” obligations for AI models in production. This means that firms will be required to continually monitor and validate that their models remain in their original risk category and maintain their intended purpose. Otherwise, reclassification will be needed.
Dataiku recommends financial services companies to promptly familiarize themselves with the AI Act’s requirements and assess whether current practices meet these standards. Additionally, documentation should begin at the inception of any new model development, particularly when models are likely to reach production, it says.
Moreover, Dataiku warns that the EU’s proactive stance may encourage other regions to accelerate the development and implementation of AI regulations. By 2026, tech consulting firm Gartner predicts 50% of governments worldwide will enforce use of responsible AI through regulations, policies and the need for data privacy.
A groundbreaking regulatory framework
The EU’s AI Act is the world’s comprehensive regulatory framework specifically targeting AI. The legislation adopts a risk-based approach to products or services that use AI, and impose different levels of requirements depending on the perceived threats the AI applications pose to society.
In particularly, the law prohibits applications of AI that pose an “unacceptable risks” to the fundamental rights and values of the EU. These applications include social scoring systems and biometric categorization systems.
High-risk AI systems, such as remote biometric identification systems, AI used as a safety component in critical infrastructure, and AI used in education, employment and credit scoring, are forced to comply with stringent rules relating to risk management, data governance, documentation, transparency, human oversight, accuracy and cybersecurity, among others.
Gen AI systems are also subject to a set of obligations. In particular, these systems must be developed with advanced safeguards against violating EU laws, and providers must document their use of copyrighted training data and uphold transparency standards.
For foundation models, which include gen AI systems, additional obligations are imposed, such as demonstrating mitigation of potential risks, using unbiased datasets, ensuring performance and safety throughout the model’s lifecycle, minimizing energy and resource usage and providing technical documentation.
The AI Act was finalized and endorsed by all 27 EU member states on February 02, 2024, and by the EU Parliament on March 13, 2024. After final approval by the EU Council on May 21, 2024, the AI Act is now set to be published in the EU’s Official Journal.
Provisions will start taking effect in stages, with countries required to ban prohibited AI systems six months after publication. Rules for general purpose AI systems like chatbots will start applying a year after the law takes effect, and by mid-2026, the complete set of regulations will be in force.
Violations of the AI Act will draw fines of up to EUR 35 million (US$38 million), or 7% of a company’s global revenue.
AI adoption surges
Globally, jurisdictions are racing to regulate AI as adoption of the technology surges. A McKinsey survey found that adoption of AI has reached a remarkable 72% this year, up from 55% in 2023.
Gen AI is the number one type of AI solution adopted by businesses worldwide. A Gartner study conducted in Q4 2023 found that 29% of respondents from organizations in the US, Germany, and the UK are using gen AI, making it the most frequently deployed AI solution.
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https://fintechnews.ch/aifintech/rise-in-ai-adoption-prompts-global-push-for-regulation/71038/