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 Farming: 6 Emerging Trends for Agritech Startups to Watch
Smart Farming: 6 Emerging Trends for Agritech Startups to Watch
The agriculture sector is changing, and the revolution is as a result of technological development. The concept of smart farming involving the use of advanced technologies such as AI, IoT, and big data analytics is defining the future of agricultural practices and is currently generating a tidal wave of opportunities for agritech startups. These startups are well placed to meet the growing demand for food around the world while at the same time offering efficient, sustainable farming solutions.
Here are six emerging trends and the opportunities that they present for agritech startups:
- Precision Agriculture: Data-Driven Farming for Maximum Efficiency
Precision agriculture is an important pillar of smart farming, which includes the use of data in enhancing farming methods. Precision agriculture helps to make real-time assessments of factors like soil, water, and crop health. The use of satellite imaging, GPS, and sensory technology enables the farmer to apply water, fertilizers, and pesticides appropriately, increasing production and minimizing wastage.
For agritech startups, this presents the opportunity to create systems that collect data and present this to farmers so they can make better decisions. Such sensors, drones, or software solutions can be created by startups, and this real-time data can help farmers to work most efficiently, with the lowest operational expenses and with reduced emission of pollutants to the environment. The possibility of customization of these solutions depends on the type of crops and geographic location, which only adds value to these solutions.
2. AI-Driven Crop Management: Enhancing Productivity and Decision-Making
Artificial intelligence (AI) is rapidly transforming crop management, where it performs tasks which were previously handled manually and also carries out risk assessment. Automated crop management employs big data analytics by relying on machine learning (ML) algorithms to predict climate changes, disease risks, and the optimum time for harvesting. This makes it possible for the farmers to prevent anything that might hinder the growth of the crop and also improve the yield of the crop.
Agritech startups can leverage artificial intelligence to create apps that will assist farmers in managing the growth of crops, pinpointing the onset of diseases, and assisting in informed decision-making. Their products might also ensure optimal use of materials and manpower, reduce costs on labor, and boost the management of the supply chain.
If the startups manage to tackle such essential problems as crop losses and resource wastage, they will have a great potential for enhancing food safety and environmentally friendly technologies.
3. IoT Integration: Connected Devices for Smarter Farms
The arrival of the Internet of Things (IoT) in the agtech space is making everything more connected by using devices such as smart devices, automatic irrigation machines, and drones. These IoT devices help farmers monitor and manage their farms even from far away by providing real-time information about the soil, weather, and the performance of the equipment on the farm.
When it comes to agritech startups, IoT offers themselves a grand avenue to grow solutions that address the multiplicities of the farming value chain. Startups can develop software that optimizes irrigation based on forecasts or work on drones, which will help to control the state of huge fields and identify if crops are stressed.
4. Robotics and Automation: Redefining Labor and Productivity
Robotics and automation convergence in agriculture is mitigating the growing shortage of labor force and increasing efficiency. In terms of reliability, autonomous machines do relieve people of responsibilities in activities such as planting, weeding, and harvesting among other responsibilities.
From an innovation perspective, technologists in startups can identify the low-hanging fruit of using robots in the agricultural industry regarding repetitive duties that require human intervention in undertaking the task.
Automation technology offers scalability, enabling farmers to manage larger areas of land without compromising productivity. With advancements in AI and machine learning, these robots can adapt to different environments and crops, making them a versatile solution for diverse farming needs.
5. Blockchain for Traceability and Transparency
A trend that recent studies have identified is the application of that technology in the food supply chain which primarily guarantees the traceability of products. With the use of blockchain technology, all the information relating to farming, such as what seeds were planted, how the farm was cared for, and how the crops were transported, is stored in a secure, tamper-proof mode. This kind of transparency is not only appealing to the consumer who wishes to trace the sources of the delicacy but also helps the growers in defending their earnings and minimizing swindles.
Such agritech startups can easily leverage blockchain to create ecosystems that will guarantee proper quality assurance at every level of food production to combat and prevent any violations of the quality standards or environmental laws. It will increase the level of transparency, and assist in trusting farmers in terms of their ability to meet consumer needs and be competitive in the market.
6. Sustainability and Climate Resilience: Future-Ready Farming
However, because climate change is an ongoing process that impacts agriculture in a negative way, climate-friendly and sustainable farming practices are required more than ever before. There are increased uses of practices like regenerative agriculture farming and efficient water use, while innovations such as artificial intelligence and the internet of things are making farming more efficient.
The major opportunity that presents itself to agritech startups is to design solutions that ensure farmers make minimal negative impacts on the environment and are sustainable. This ranges from developing tools that help in the conservation of water, reducing the use of chemicals, and generally improving the ground. Climate-smart agriculture is one of the key areas that start-up companies will be able to address in a short time, hence becoming profitable ventures, especially due to the increasing demand for environmentally friendly farming practices.
Seizing the Opportunities: The Role of Agritech Startups
With the advent of artificial intelligence, IoT, robotics, and data analytics in the agricultural industry, there is a conducive environment for agritech startups to thrive. As those startups come up with solutions that address some of these major challenges in farming, such as food production, sustainability, and resources, they will also shape the future of farming while occupying a large niche in the global agritech market.
It will be startups that are innovative, scalable, and sustainable that will drive the change in traditional farming practices. Working hand in hand with farmers, scientists, and governments, agritech startups have the potential to widely implement smart farming tools and establish a better, more productive farming industry.
To summarize the above thoughts, the future of agriculture will belong to people creating better technological solutions for farming and optimizing farming for the benefit of both people and the environment. This is the moment for agritech startups to come up with solutions and capture the market. Agricultural startups have a unique opportunity to look for a solution for current problems and shape the future of agriculture with a global trend for food security and new sustainable agricultural practices.
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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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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/