Industry 4.0, also known as the Fourth Industrial Revolution, represents a significant transformation in the world of manufacturing and industry. It is characterized by the integration of digital technologies into industrial processes with the primary aim of improving manufacturing responsiveness, quality, and efficiency. This revolution is reshaping the landscape of manufacturing, enabling companies to achieve higher levels of productivity, flexibility, and self-managing production processes.

In this essay, we will explore the key principles, technologies, and advantages of Industry 4.0, as well as its applicability across various industrial segments.

Principles of Industry 4.0

At the core of Industry 4.0 are several key principles that define its approach to manufacturing and industrial processes. These principles serve as guiding philosophies for the implementation of digital technologies in the industrial sector:

• Interoperability: Interoperability emphasizes the seamless communication and integration of various components within a manufacturing ecosystem. In an Industry 4.0 environment, different machines, sensors, and systems can work together effectively, sharing data and information in real-time. This interconnectedness enables the efficient flow of data and decision-making.
• Virtualization: Virtualization involves the creation of virtual models or digital twins of physical assets and processes. These digital replicas provide a means to simulate and analyze real-world scenarios, allowing for optimization, testing, and troubleshooting without disrupting actual operations. Digital twins are instrumental in predictive maintenance and process improvement.
• Decentralization: Industry 4.0 promotes decentralization by empowering individual components and devices with decision-making capabilities. Rather than relying solely on centralized control, smart machines, and systems have the autonomy to make real-time decisions based on data and predefined rules. This decentralization leads to increased flexibility and adaptability in manufacturing.
• Real-Time Capability: Real-time capability is a fundamental aspect of Industry 4.0, enabling the immediate processing and utilization of data. In a manufacturing setting, real-time data analysis ensures rapid response to changing conditions, such as production anomalies or shifts in customer demand. It supports agile decision-making and optimization.
• Service Orientation: The service-oriented approach in Industry 4.0 extends beyond physical production to include value-added services. Manufacturers can offer customized services alongside their products, creating new revenue streams and enhancing customer experiences. This shift towards servitization is a hallmark of Industry 4.0.
• Modularity: Modularity refers to the design of systems and processes in a way that allows for easy integration, modification, and scalability. Modular systems facilitate the replacement or addition of components without extensive disruption, promoting efficiency and flexibility in manufacturing environments.

Technologies Driving Industry 4.0

Industry 4.0 leverages a range of advanced technologies to bring its principles to life. Some of the key technologies include:

• Cyber-Physical Systems (CPS): At the heart of Industry 4.0, CPS combines physical machinery with digital intelligence. These systems enable real-time monitoring, control, and coordination of physical processes. For instance, a smart factory may employ CPS to optimize production and maintenance processes.
• Internet of Things (IoT): IoT connects devices and sensors to the Internet, facilitating data collection and sharing. In manufacturing, IoT enables predictive maintenance, remote monitoring, and efficient resource utilization. Sensors placed on machinery can transmit data for analysis and decision-making.
• Big Data and Data Analytics: The vast amounts of data generated by IoT devices and other sources require advanced analytics to derive meaningful insights. Big data analytics identifies patterns, anomalies, and opportunities for improvement. Manufacturers can use these insights for quality control, demand forecasting, and process optimization.
• Cloud Computing: Cloud computing provides a scalable and flexible infrastructure for data storage and processing. It supports remote access and collaboration, making it possible for geographically dispersed teams to work together in real-time. Cloud platforms also facilitate the deployment of machine learning models and data sharing.
• Automation and Robotics: Automation in Industry 4.0 involves the use of robots and artificial intelligence (AI) to automate tasks and processes. Robots can handle repetitive and dangerous tasks, while AI algorithms can optimize production, inventory management, and logistics.
• Human-Machine Interaction (HMI): HMI focuses on improving the interaction between humans and machines within the manufacturing environment. Augmented reality (AR) and virtual reality (VR) interfaces enhance operator efficiency and decision-making.
• Additive Manufacturing (3D Printing): Additive manufacturing technologies allow for the creation of complex, customized parts and prototypes. This contributes to the concept of mass customization, where products are tailored to individual customer needs without sacrificing efficiency.
• Blockchain Technology: Blockchain provides a secure and transparent way to record and verify transactions. In supply chain management, it ensures traceability and authenticity of products, reducing the risk of counterfeit goods and enhancing trust among stakeholders.

Advantages of Industry 4.0

The adoption of Industry 4.0 technologies offers numerous advantages to industrial companies, especially amid the challenges presented by events like the COVID-19 pandemic. Here are some of the key benefits:

• Enhanced Productivity: One of the most significant advantages of Industry 4.0 is the substantial increase in productivity and operational efficiency it brings to manufacturing and industrial processes. Through the integration of advanced technologies such as automation, data analytics, and artificial intelligence, production processes become streamlined and optimized. Real-time monitoring, predictive maintenance, and autonomous systems lead to reduced downtime, higher throughput, and improved resource utilization. This enhanced productivity ultimately translates into cost savings and increased competitiveness for businesses.
• Improved Quality Control: Industry 4.0 technologies provide unprecedented capabilities for quality control and assurance. IoT sensors and real-time data analytics enable manufacturers to detect defects and anomalies in products or processes immediately. This allows for timely adjustments, reducing the production of faulty goods and enhancing overall product quality. As a result, companies can maintain higher customer satisfaction levels and reduce costs associated with rework or recalls.
• Flexibility and Adaptability: In a rapidly changing business landscape, flexibility and adaptability are crucial. Industry 4.0 promotes these attributes by decentralizing decision-making and enabling quick responses to market fluctuations and customer demands. Smart manufacturing systems can adjust production schedules, product configurations, and resource allocations in real time. This flexibility not only improves agility but also helps businesses remain competitive in dynamic markets.
• Predictive Maintenance: The implementation of Industry 4.0 allows for predictive maintenance strategies. By continuously monitoring the condition of machinery and equipment through IoT sensors and analyzing data with machine learning algorithms, companies can anticipate when maintenance is needed before equipment failure occurs. This proactive approach minimizes unplanned downtime, reduces maintenance costs, and extends the lifespan of assets.
• Mass Customization: Industry 4.0 enables a shift from mass production to mass customization. Through technologies like additive manufacturing (3D printing) and advanced robotics, companies can efficiently produce personalized products tailored to individual customer preferences. This not only meets the growing demand for personalized goods but also fosters stronger customer engagement and loyalty.
• Digital Operations: The ongoing digital transformation in Industry 4.0 has proven invaluable during unexpected disruptions, such as the COVID-19 pandemic. With remote monitoring and control capabilities, manufacturers can continue operations even when physical presence is limited. This resilience enhances business continuity and minimizes the impact of crises, ensuring that production can continue without compromising safety.
• Sustainability and Resource Efficiency: Industry 4.0 technologies contribute to sustainability efforts by optimizing resource utilization and reducing waste. Predictive analytics and process optimization lead to more energy-efficient operations, reduced material waste, and minimized environmental impact. This not only aligns with corporate social responsibility goals but also reduces operational costs in the long run.
• Competitive Advantage: By embracing Industry 4.0, companies gain a significant competitive advantage. They can deliver higher-quality products, respond faster to market changes, and offer personalized solutions that meet customer demands effectively. This enhanced competitiveness can lead to increased market share, revenue growth, and a stronger market position in their respective industries.

Applicability Across Industries

The transformation brought about by Industry 4.0 is not limited to a particular sector. It is applicable across various industrial segments, including manufacturing, aerospace, food, energy, mining, and healthcare. Let’s explore its applicability in a few key sectors:

• Oil and Gas Industry: The oil and gas sector has adopted Industry 4.0 to enhance exploration, drilling, and production processes. IoT sensors on offshore platforms monitor equipment health and environmental conditions, while predictive maintenance ensures the reliability of critical machinery.
• Mining Industry: Mining companies leverage Industry 4.0 to optimize resource extraction, reduce operational costs, and enhance worker safety. Autonomous mining equipment, equipped with sensors and AI, can operate in hazardous environments, making operations more efficient and less risky.
• Healthcare: In healthcare, Industry 4.0 technologies are used to improve patient care and streamline hospital operations. IoT devices and wearable sensors enable remote patient monitoring, while data analytics support disease diagnosis and treatment planning.
• Additive Manufacturing (3D Printing): Industry 4.0 technologies have revolutionized additive manufacturing processes, allowing for the creation of complex and customized products. 3D printing, supported by digital design and real-time monitoring, enables rapid prototyping, reduced material waste, and on-demand production of parts and products.
• Aerospace and Defense: The aerospace and defense sector uses Industry 4.0 to improve aircraft manufacturing, maintenance, and operations. IoT sensors and data analytics help optimize aircraft performance, reduce fuel consumption, and enhance safety.
• Food and Beverage: Industry 4.0 is used in the food and beverage industry to monitor and control production processes, ensuring food safety and quality. Automated systems and sensors help with inventory management, production scheduling, and traceability.
• Energy and Utilities: The energy and utilities sector employs Industry 4.0 technologies to manage power generation, distribution, and consumption more efficiently. Smart grids, sensors, and real-time data analysis enable better energy management and grid reliability.
• Pharmaceuticals: Pharmaceutical companies utilize Industry 4.0 to improve drug development, manufacturing, and quality control. Automated processes, robotics, and data analytics enhance the production of pharmaceuticals while ensuring compliance with regulatory standards.
• Retail and E-commerce: Retailers and e-commerce companies leverage Industry 4.0 for supply chain optimization, inventory management, and customer personalization. RFID technology, AI-driven demand forecasting, and automated warehouses are some examples of its application.
• Logistics and Transportation: The logistics and transportation industry utilizes Industry 4.0 to optimize routes, track shipments, and improve overall logistics efficiency. IoT-enabled tracking devices, autonomous vehicles, and predictive maintenance play significant roles in this sector.
• Agriculture: Precision agriculture employs Industry 4.0 technologies to enhance crop management, optimize resource usage, and monitor environmental conditions. Sensors, drones, and data analytics assist farmers in making informed decisions to increase yield and sustainability.
• Textiles and Apparel: Textile and apparel manufacturers benefit from Industry 4.0 by automating production processes, reducing waste, and enabling customization. IoT devices and digital twins help monitor and control textile production lines.
• Construction and Real Estate: In construction, Industry 4.0 aids in project management, building design, and maintenance. Building information modeling (BIM) and IoT sensors improve construction efficiency and building performance.
• Financial Services: The financial industry incorporates Industry 4.0 technologies for fraud detection, risk assessment, and customer service. Machine learning algorithms and data analytics are used to analyze financial data and make informed decisions.

Challenges of Industry 4.0 Adoption

While the promise of increased efficiency, productivity, and competitiveness is alluring, the adoption of Industry 4.0 technologies presents several challenges that must be addressed strategically. We will explore the key challenges associated with Industry 4.0 adoption.

• Lack of Internal Alignment: One of the foremost challenges faced by businesses when embracing Industry 4.0 is the lack of internal alignment regarding which strategies to pursue. With the advent of digital technologies, new business models are emerging, necessitating a shift in how companies operate. However, without a consensus on the business strategy, or the right people in place to drive it, internal challenges can impede progress.
• Cybersecurity and Data Privacy Concerns: As businesses become more interconnected through Industry 4.0, there is a heightened concern for cybersecurity and data privacy. The online integration of processes, systems, and people creates vulnerabilities that can be exploited by cyberattacks, potentially resulting in security breaches and data leaks. Companies must make substantial investments in advanced encryption, authentication protocols, and robust cybersecurity measures to safeguard critical information generated by connected devices and systems.
• Workforce Displacement: Automation, a key component of Industry 4.0, can lead to concerns about workforce displacement. As machines and algorithms take on more tasks, the nature of work may change, potentially displacing some workers. This challenge requires companies to address the impact on their employees through reskilling and upskilling initiatives to ensure a smooth transition to new roles and responsibilities.
• Technology Adoption Pathways: The path to Industry 4.0 adoption varies significantly based on the specific technologies being incorporated and the existing infrastructure and skills of organizations. For some, the transition may involve significant changes and investments, while others may find a more gradual approach suitable. Navigating these pathways can be complex and challenging.

Strategies to Overcome Industry 4.0 Challenges

To harness the power of this transformative era, companies must navigate these challenges effectively. Given below are strategies to overcome the adoption challenges.

• Comprehensive Understanding of Capabilities: To address the lack of internal alignment, businesses should start with a comprehensive understanding of their current capabilities. This involves assessing the skills, resources, and technologies already in place. Identifying the gaps that Industry 4.0 can fill is crucial. This assessment may reveal the need for reskilling or upskilling initiatives to ensure that the workforce is prepared for the technological shift.
• Addressing Cybersecurity Concerns: Prioritizing cybersecurity is non-negotiable in the age of Industry 4.0. To mitigate cybersecurity and data privacy concerns, companies must make substantial investments in advanced security measures. This includes implementing robust encryption, multi-factor authentication, intrusion detection systems, and regular security audits. Moreover, fostering a cybersecurity-aware culture within the organization is equally important to ensure that employees are vigilant and informed.
• Change Management Strategies: Effective change management is pivotal in overcoming resistance and driving acceptance of new technologies. Collaborative efforts to manage change within the organization can help address the challenges associated with Industry 4.0 adoption. This involves clear communication of the reasons for the changes, providing training and support to employees, and involving them in the decision-making process where possible. Engaging leadership and leading from the top can play an important role in bringing about the cultural change needed for digital transformation.
• Scalability and Flexibility: Industry 4.0 solutions must be scalable and flexible to adapt to changing demands and future growth. Companies should design solutions that are agile and can evolve with their business needs. It’s advisable to start with smaller, scalable pilot projects that can demonstrate the value of Industry 4.0 technologies before committing to larger-scale implementations. This allows businesses to learn, iterate, and scale gradually.
• End-to-End Approach: Successful implementation of Industry 4.0 technologies requires an end-to-end approach that incorporates people, processes, technologies, and data. Rather than viewing technology adoption in isolation, businesses should consider how it fits into their overall operations and strategy. This holistic approach ensures that technology is integrated seamlessly, and its benefits are maximized.

Conclusion

Industry 4.0 represents a pivotal transformation in the industrial landscape, driven by the seamless integration of digital technologies into manufacturing and industrial processes. Its foundational principles of interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity serve as guiding pillars for the adoption of cutting-edge technologies like Cyber-Physical Systems, the Internet of Things (IoT), extensive data analytics, and automation. Embracing Industry 4.0 yields numerous advantages, including heightened productivity, superior quality control, enhanced flexibility, and the agility to respond to dynamic market conditions.

Moreover, the COVID-19 pandemic has expedited the uptake of Industry 4.0 technologies, as they facilitate digital operations and contactless processes. This transformative shift extends beyond a specific industry; it has wide-ranging applicability across sectors, spanning from discrete manufacturing to healthcare.

In the ongoing progression of the Fourth Industrial Revolution, it becomes imperative for businesses to wholeheartedly adopt Industry 4.0 and leverage its capabilities to maintain competitiveness, efficiency, and adaptability within an ever-evolving global marketplace. The principles and technologies underpinning Industry 4.0 are shaping the future of industry, enabling a more interconnected, efficient, and sustainable approach to manufacturing and production.

Although the challenges associated with Industry 4.0 adoption are substantial, they are by no means insurmountable. Companies that strategically and proactively address these challenges can unlock the full potential of the Fourth Industrial Revolution. By gaining a deep understanding of their own capabilities, prioritizing cybersecurity measures, adeptly managing the process of change, and embracing scalable solutions through a comprehensive approach, organizations can successfully navigate the intricacies of Industry 4.0, positioning themselves for a future that is marked by efficiency, competitiveness, and digital transformation.

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IoT Analytics released a new analysis, based on the ’Industrial IoT & Industry 4.0 Case Study Report 2023’.

Key insights:

• Digitalization has become essential for industrial companies worldwide, as IoT Analytics expects the industrial IoT market to reach $145 billion in 2023.
• The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves into 22 case studies, exploring their project objectives, technologies deployed, lessons learned, challenges, and outcomes.
• In this article, we share five learnings from the report: 1) Upgrading the ERP is often the first step in digital transformation, 2) cloud-native data storage and streaming are coming up, 3) first successful implementations of private 5G use cases, 4) digitalization becoming a prerequisite to achieving sustainability, and 5) the continued journey toward predictive maintenance.

Key quotes:

Rajini Nair, Analyst at IoT Analytics, remarks: “Upon analyzing the case studies outlined in the report, it becomes evident that digitalization is a key catalyst driving technological progress in industrial settings. This includes the enhancement of ERP systems and the migration of data to cloud platforms equipped with real-time streaming capabilities. Furthermore, digitalization proves essential in harmonizing companies with their sustainability objectives. Strategies such as the implementation of predictive maintenance algorithms and the adoption of private industrial 5G use cases are leveraged to improve operational efficiency. Looking forward, the technological landscape is transforming with the rise of AI, poised to shape the future of manufacturing.”

Digitalization has become crucial to manufacturers globally

Digitalization has become crucial for manufacturers in their respective competitive landscapes. IoT Analytics estimates the industrial IoT market to reach $145B in 2023, with a CAGR of 17.9% until 2030, as more and more companies undertake digital transformation initiatives.

For many, digitalization has already become a game changer:

• Carmaker Mercedes has achieved 25% greater efficiency in its S-class assembly after optimizing the value chain and introducing innovative technologies at its Germany-based Factory 56 facility.
• Energy giant TotalEnergies aims to generate $1.5 billion annually in savings with its digital solutions by 2025.
• Chemical company Covestro increased efficiency and reduced unnecessary downtime by shifting from calendar-based to condition-based maintenance.

These are just some of the benefits of the digital transformation initiatives our research uncovered as part of the 255-page Industrial IoT and Industry 4.0 Case Studies Report 2023. The report delves into 22 recent industrial digital transformation case studies, looking at initiatives related to digital transformation, data architecture, predictive maintenance, AI, and industrial 5G.

Benefits of case studies for digitalization journeys

Case studies by peers or companies in other industries are a great way to learn about digitalization, identify common challenges, develop a view of best practices, and understand how companies manage to scale.

The 22 case studies in our report offer readers a diverse set of manufacturing examples of current IIoT and Industry 4.0 projects, along with project objectives and takeaways from each. Our analysis of these takeaways yielded many trends in these companies’ digital transformation journeys, five of which we will delve into in this article (including selected highlights from the report):

1. ERP: An upgraded ERP is often the first step to digital transformation.
2. Cloud: Cloud-native data storage and streaming are increasingly accepted.
3. 5G: First manufacturers have successfully implemented private 5G use cases.
4. Sustainability: Digitalization is becoming a prerequisite to achieving sustainability objectives.
5. Maintenance: The journey toward predictive maintenance and remote monitoring continues.

Learning 1: An upgraded ERP is often the first step to digital transformation

Our analysis found that many manufacturers elect to prioritize upgrading their ERP systems to ensure their various data sources are connected before prioritizing other digital transformation initiatives. Since ERP systems are often the central nervous system of a business, prioritizing an updated ERP allows different departments to share and operate on the same data, reducing errors and improving efficiency.

Selected highlight: Celanese

In recent years, US-based chemical manufacturer Celanese has acquired several businesses or divisions from other companies. Celanese had been operating on a legacy SAP ERP system, and the acquired assets had different legacy ERP environments, making cross-section integration difficult. Celanese’s global CIO, Sameer Purao, did not want the company to invest in integrating the new acquisitions into older technology. Given this situation and Celanese’s adoption of a strategic, long-term approach to a scalable digital transformation plan, an ERP upgrade became necessary.

“Our previous ERP system had been a backbone, but it was close to 20 years old. Given its age, we didn’t want to invest in transforming until we upgraded that piece first. The acquisitions underscored that it wouldn’t make sense to invest in integrating them into older technology, so we opted to upgrade.” – Sameer Purao, senior vice president and global CIO, Celanese Corporation

In May 2023, Celanese announced that it had completed its upgrade to SAP S/4HANA. Further, since Celanese acquired DuPont’s Mobility & Materials (M&M) business just six months prior, it quickly cutover to upgrading M&M’s legacy ERP system as well, which is expected to be completed in the first half of 2024.

While there are other major aspects of Celanese’s digital transformation journey detailed in our report, this upgrade to its ERP provided a stronger backbone from which other digitalization solutions could be built. Another benefit of this upgrade is improved visibility and collaboration, enhancing transparency and teamwork and allowing for efficient data access across the enterprise.

Learning 2: Cloud-native data storage and streaming are increasingly accepted

The cloud market nearly doubled between 2020 and 2022, growing from $109 billion to $206 billion, based on our analysis of global cloud projects. While the COVID-19 pandemic certainly played a major role, it was not the only growth factor. Our analysis found that large-scale enterprise digitalization efforts and strong SaaS adoption also helped fuel this growth.

Cloud storage and data streaming allow companies to centralize and share their data with a smaller footprint than running their own on-premises servers, which comes with footprint and maintenance costs. Moving these services to the cloud also allows companies to scale without the need for significant capital investment in physical hardware.

Selected highlight: Michelin

In 2019, tire manufacturer Michelin started using Apache’s Kafka event streaming platform on-premises in its data centers to gain real-time insights and process data as continuous streams. However, as its operational footprint expanded, so did the resources it had to dedicate to maintaining the solution. By Q4 2019, Michelin’s IT department initiated its migration to the cloud, with Microsoft Azure as the cloud partner.

“One of the challenges with [streaming technology] Kafka was its operational complexity, especially as the footprint expanded across our organization. It’s a complex, distributed system, so we had to allocate a lot of our valuable technical resources and expertise to babysit it and keep it running.” – – Olivier Jauze, now CTO of Experiences Business Line, Michelin

By 2021, Michelin migrated its services to Confluent Cloud for Azure, a Kafka-based platform, to support its multi-cloud environment. Soon after, the company began exploring use case projects and has since migrated one of its most critical projects, online order management, to the cloud—replacing its on-premises orchestrator. By 2023, Michelin expanded its cloud-based event streaming architecture into several departments, including supply chain management, customer services, manufacturing, and R&D.

Through its adoption of cloud-native data storage and streaming, Michelin achieved the following benefits (among other things):

• Cost savings: Estimated 35% in cost savings in the cloud compared to on-premises operations
• Improved uptime: 99.99% uptime

Learning 3: First manufacturers have successfully implemented private 5G use cases

As 5G continues its public rollout globally, some manufacturers have successfully deployed private 5G networks to enable new use cases within their facilities. While faster speeds and lower latency may seem like key adoption drivers, our analysis found that improved reliability over Wi-Fi, enhanced cybersecurity, and the ability to access data locally are the core motivating factors.

Our analysis also found that during the public rollout of 5G, some companies did not simply dive into integrating 5G-specific technology. Instead, many integrated robust LTE solutions that were upgradable to 5G with relative ease (or so-called 4.9G solutions) once the technology evolved or became approved for industrial use.

Selected highlight: Airbus

To increase aircraft production and validation efficiency, European multinational aerospace corporation Airbus partnered with Ericsson, a Swedish multinational telecommunications company, in 2021 to implement private industrial 5G networks at 11 aircraft assembly manufacturing sites in Europe. The approach began with implementing 4G networks that either already had 5G capabilities or could seamlessly upgrade to 5G.

However, Airbus is not limiting this deployment to its European facilities. During a Q&A at the 5G Manufacturing Forum in November 2022, Hakim Achouri, the 5G and IoT solutions expert for digital aviation at Airbus, noted, “Airbus is going way beyond 11 networks at 11 sites, expanding beyond its core European manufacturing bases in France and Germany, to also deploy private 5G in Canada, China, Spain, the UK, and the US.”

With its implementation of private 5G networks at its production and assembly facilities, Airbus has realized the following benefits:

• Ability to implement advanced use cases: This includes site surveillance, efficient flight-to-ground data offloads, quality inspections, and the operation of automated guided vehicles (AGVs).
• Enhanced user experience: With increased speed, bandwidth, and reliability, employees at the production sites have access to more data, making operations smoother, more efficient, and more secure.
• Scalability through reusability: By developing a pattern in its strategy, Airbus was able to roll out private 4G/5G networks across its many sites with consistent quality and performance.

Learning 4: Digitalization is becoming a prerequisite to achieving sustainability objectives

We recently noted a trend of companies deploying digital twins to help realize their sustainability goals. But it is not simply digital twins assisting companies on this front—digitalization projects overall are helping companies monitor energy consumption, optimize resource usage, and reduce their environmental footprint in the manufacturing process.
Backing this awareness and trend toward sustainability are data points from our latest What CEOs Talked About report, where “sustainability” and related terms remained among the most discussed topics in boardrooms.

Selected highlight: TotalEnergies

French multinational energy and petroleum company TotalEnergies has publicly declared its ambition to achieve carbon neutrality by 2050. To meet this goal, the energy company has leveraged digital solutions to advance the implementation of sustainability measures on its offshore platforms.

For instance, TotalEnergies retrofitted their pipes with LoRaWAN-connected temperature sensors to detect gas leaks along their flare networks. As hydrocarbons are released, the temperature of the pipes significantly changes. When this change is detected, operators are alerted via emails for immediate action. This not only helps limit the release of hydrocarbons but also saves TotalEnergies money by reducing the loss of product.

Learning 5: The journey toward predictive maintenance and remote monitoring continues

According to our Predictive Maintenance and Asset Performance Market Report 2023–2028 (published in November 2023), the predictive maintenance market reached $5.5 billion in 2022. While the report notes several tailwinds supporting this interest and market growth, such as skill shortages and interest in reducing energy usage and CO2 emissions, costs are a major driver, as noted in our case studies report as well.

Equipment failure, especially during core operational hours, reduces productivity and adds repair expenses. To avoid these costs, companies often use preventative maintenance procedures, such as time-based inspections and repairs or condition criteria from sensors or physical measurements to trigger preventative intervention. However, intervening based on time can be inefficient since the equipment may not be in need of repair at that time, and data collection/monitoring requires personnel to conduct these tasks.

By implementing digital solutions, companies can remotely monitor the condition of critical equipment and establish conditions in which intervention is actually needed well before failure occurs.

Selected highlight: Battalion Oil Corp

US-based Battalion Oil Corp partnered with Novity, a US-based predictive maintenance solutions company, to pilot a predictive maintenance solution to detect valve leaks within their compressors and reduce unexpected compressor downtime. Initially, Battalion would sporadically measure valve cap temperatures using handheld devices to identify potential gradual leaks that could lead to a failure. While the checks were intended to be conducted daily, varying daily maintenance tasks and priorities often disrupted these important checks.

“Predictive automation is a game-changer for the oil and gas industry. By analyzing data in real-time and making accurate predictions about future events, drilling companies can optimize their operations to maximize efficiency, reduce costs, and improve safety. This technology has the potential to transform the way we do business and stay competitive in today’s market.” – John Smith, CEO of Oil and Gas Exploration Company

An initial step in the solution was to use a crank angle sensor and pressure transducers. However, physical crank angle sensors are usually the most difficult and expensive sensors to install, so the engineers developed a virtual crank angle sensor based on physics-based and data-driven methods using data from the pressure sensors.

After validating that the rotational position calculated by the virtual sensors matched the position provided by the physical sensors, engineers applied prognostic methods to the data from the virtual crank angle sensor and physical pressure sensors. The result was predicted gradual valve failures several weeks in advance—five to seven days on average before temperature checks indicated a gradual leak.

The digital transformation journey carries on

The Industrial IoT and Industry 4.0 Case Studies Report 2023 delves further into the above-mentioned and 18 other case studies of ongoing digital transformation projections. While these companies and many others are advancing in their digital transformation journeys, there is still a long road ahead for many companies, some of which still rely on analog, pen-and-paper methods in their facilities. Even still, many companies are already experiencing real value, e.g., Mercedes’ achieving 25% greater efficiency and Battalion observing signs of gradual valve failures several weeks in advance.

Digitalization has become more than a nice-to-have for manufacturers today—it has become crucial for them in their respective competitive landscapes. The market reflects this assessment: according to our enterprise IoT market dashboard, the IIoT market size in 2023 is approximately $145 billion, with a forecasted CAGR of 17.9% between 2023 and 2030.

Looking ahead, AI continues to become a major theme in companies’ digital transformation initiatives. According to our continual series What CEOs Talked About, the topic and its related terms have already been of high and growing interest in boardrooms throughout 2023. We see a plethora of generative AI projects across the board, even in the industrial space (which we will report on soon). We will continue to monitor this space and highlight interesting case studies from adopters.

The post Industry 4.0 check-in: 5 learnings from ongoing digital transformation initiatives appeared first on IoT Business News.

According to IoT Analytics, a leading global provider of market insights and strategic business intelligence for the Internet of Things (IoT), the industrial IoT gateways market accelerated significantly from 2021 to 2022, growing ~14.7% to reach $860 million (38% of the overall IoT gateways market).

KEY QUOTES:

Knud Lasse Lueth, CEO at IoT Analytics, comments that:
“Industrial IoT gateways are critical for connecting legacy systems with modern technology. They also play an important role to support the migration of manufacturing applications to the cloud. In the future I also expect a number of smaller applications to sit directly on IIoT gateways, leveraging containerization technology, more powerful storage and computation and in some cases even AI chips for ML inference.”

Kalpesh Baviskar, Analyst at IoT Analytics, added that:

“IoT gateways have emerged as a highly cost-effective solution for deploying IoT systems with multiple sensors. They play a crucial role in connecting legacy equipment and devices that were previously unconnected.”

“In recent years, we have seen the integration of high-performance processors and AI chipsets into IoT gateways, transforming them into edge IoT gateways. These edge gateways can perform local data processing and analytics, significantly reducing the amount of data that needs to be sent to the cloud. This can lead to significant cost savings and performance improvements for IoT applications.”

KEY INSIGHTS:

• According to the IoT Gateway Market Report 2023–2027, the $0.9B industrial IoT (IIoT) gateway market experienced accelerated growth between 2021 and 2022, which is set to continue on the back of several favorable tailwinds.
• IIoT gateways are enabling IT and OT convergence by securely and efficiently sharing data between floor-level OT equipment and IT equipment or the cloud—with implementation typically as part of one of four broader IoT architectures.
• There are several IIoT gateway advancements e.g., in security, edge computing, and storage.

The vital role of industrial IoT gateways in bridging IT and OT

IIoT gateways: Market and role

The industrial IoT gateways market accelerated significantly from 2021 to 2022, growing ~14.7% to reach $860 million (38% of the overall IoT gateways market), and we forecast continued growth at least through 2027. The factors driving this growth include the following:

• Connecting the unconnected. Many companies are retrofitting their legacy equipment with sensors and controllers, using IIoT gateways to perform necessary protocol and data transformation and transfer the data to an IT endpoint.
• Software applications are migrating. Companies with connected equipment are moving some key applications to the cloud, with IIoT gateways emerging as the main nexus point for information flow in and out of industrial premises. Some applications are also now run locally on the gateway itself.
• More powerful hardware. New, enhanced gateways with embedded multi-core processors, AI chipsets, and secure elements are enabling faster and more secure data processing and transmission (to an IT endpoint or cloud).

These factors reflect our assessment that IIoT gateways are becoming the juncture for IT and OT convergence.

Note: When we refer to IIoT gateways, we refer to (ruggedized) hardware that connects sensors, IIoT devices, and industrial equipment to cloud or on-premises servers or PLCs/IPCs operating on distinct industrial networks. For an exact definition, refer to our report.

How IIoT gateways connect the IT and OT worlds

Many companies maintain legacy equipment that does not have sensors or control devices. Even if the legacy equipment has sensors or controllers that connect locally, such as to a human-machine interface or panel PC on the factory floor, it may not offer connectivity options or use messaging protocols that end equipment (like an IT server or cloud) uses. Meanwhile, companies that possess IoT-enabled equipment may desire to move data off-premises (e.g., to remote IT equipment or the cloud) or enhance local data computation for automated responses before transmitting the data.

In cases like these, IIoT gateways can connect with standalone or integrated sensors—either wirelessly or wired through I/O module masters—to transmit data to IT or cloud servers. As described below, they fit within many architectures found in industrial IoT solutions.

IIoT gateways within IoT architectures

Whether a company builds or buys an IoT solution, the solution will align with an IoT architecture to collect and transmit data to the endpoint. While a direct sensor-to-cloud architecture does not require the use of an IIoT gateway, IIoT gateways are commonly found in 4 general types of IoT architectures.

1. Sensors/devices to PLC/IPC to IIoT gateway to cloud

In industrial environments with existing automation hardware, the sensors/devices to PLC/IPC to IIoT gateway to cloud architecture is very common. Field sensors or actuators are connected to I/O module masters. These I/O module masters transmit data to the on-premises PLC or IPC. The PLC/IPC is then connected to the IIoT gateway, which serves as a bridge between the PLC/IPC and the cloud.

This architecture can be very powerful but also potentially dangerous. The IIoT gateway can technically be configured to remotely access the entire architecture that sits below the PLC/IPC. While this setup enables any data to flow between IT and OT and thus any imaginable use case, it also has the biggest potential attack surface (potentially the entire facility), e.g., in case of a misconfigured security architecture.

2. Sensor to I/O modules to IoT gateways to cloud

In the sensor to I/O module master to IoT gateways to cloud architecture, simple sensors connect to I/O module masters. The I/O module master then uses wired or wireless connectivity standards to transfer data to IIoT gateways, bypassing any PLC or IPC. This architecture proves to be highly effective in scenarios where multiple sensors are arranged into clusters—the I/O module master acts as the central node for each cluster of sensors, efficiently gathering and transmitting data to the cloud via an IIoT gateway.

3. Sensors in devices to IoT gateway to cloud

In the sensors in devices to IoT gateway to cloud architecture, devices equipped with single or multiple onboard sensors are connected directly to the IIoT gateway. This architecture is often deployed where non-standalone IoT devices are used (i.e. devices that cannot connect to the internet by themselves).

4. Sensors to IoT gateway to cloud

In the sensors to IoT gateway to cloud architecture, IIoT gateways enable connections between sensors and cloud servers directly. This architecture can for example be found when retrofitting specific sensors on an asset (e.g., for condition monitoring) with the desire to bypass all other existing networks (to not interfere with them and create a new security risk).

Advancements in the capabilities of IIoT gateways

As IIoT gateways have become more common in IIoT solutions, they have become capable of offering more for their users. In general, IIoT gateways typically offer 8 key functions:

• Protocol translation
• Data management
• Device management
• Computation
• Communication
• Resource management
• Security management
• Managing quality of service

As the IIoT gateway market has grown, these functions have advanced. The following are just some examples of advancements.

“A growing number of customers [are] requiring proof of security level from manufacturers for their industrial IoT equipment.”

Security management

As the number of connected devices continues to increase, the risk of cyberattacks and unauthorized access becomes more significant. This is especially true for companies looking to connect factory equipment to external IT or cloud servers. Fortunately, to address these risks, IIoT gateway vendors are proactively incorporating security features into their products and adhering to industry-specific regulations and standards, allowing OT monitoring and control to reside securely behind layers of policies and access controls.

A notable series of standards is IEC 62443, approved in 2021, which directs all IEC 62443-certified products to adhere to specific product development requirements from the early stages of design. This set of standards has become mandatory technical requirements in many countries, and according to Pascal LeRay, Head of Cyber Security at Bureau Veritas, “a growing number of customers [are] requiring proof of security level from manufacturers for their industrial IoT equipment.”

In our research, IIoT gateway companies noted the importance of incorporating security standards in their products. Teltronic’s CEO, Juan Ferro, stated that “the sudden irruption of cybersecurity in the industry has been interpreted by Teltronic as an opportunity to improve both [our] products and associated processes,” adding that the pre-emptive adoption of security standards placed them ahead of other companies in their sector.

Along with standards, IIoT gateways are increasingly incorporating hardware security, using embedded secure elements either within processors or on the PCB/modules as trusted platform modules (e.g., TPM 2.0). Ultratronik’s A1 IoT gateway integrates NXP’s EdgeLock SE051 secure element, and Eurotech’s RELIAGATE 10-14 series maintain IEC 62443-4-1, -4-2, and PSA Level 1 certifications and have a TPM 2.0 security chipset—OPTIGA TPM SLM 9670 from Infineon Technologies.

“The milliseconds of latency [between] an industrial robot and many real-time systems can be the difference between a safety hazard and a productive assembly line.”

Computation

IoT gateways in general have trended toward more processing power. In industrial solutions, this has helped companies move data processing and computation toward the solution’s edge—nearer to the data collection point—saving them bandwidth and communications power and freeing their IT and cloud servers to manage other tasks. Additionally, there has been a trend of integrating AI chipsets into some IoT gateways to facilitate edge computing. A noteworthy example is AAEON’s AIOT-AVID IoT Video Analysis Gateway, which incorporates Intel’s Myriad X vision processing unit (VPU).

The ability to process and automate data in real time can mean much for a company’s bottom line, as one senior VP stated, “The milliseconds of latency [between] an industrial robot and many real-time systems can be the difference between a safety hazard and a productive assembly line.”

Data and resource management

Local data storage helps enable data processing at the edge. Further, some industrial use cases may call for data sorting and analysis before being transmitted to an IT or cloud server, either due to limited network connectivity or the desire for more efficient use of IT equipment.

The need for local data storage has led to eMMC flash memory and SSD solutions on IoT gateways in general. In mid-2022, Robustel launched three ARM-based IIoT gateways with varying DDR and eMMC sizes to meet application needs. A few months later, Compulab announced its IOT-GATE-RPI4, a Raspberry Pi-based IIoT gateway that offers up to 128 GB of eMMC memory and mPCIe slots for SSD storage expansion up to 256 GB. Other examples include MOXA’s AIG-301 series IIoT gateway with 16 GB of eMMC and Belden’s Hirschmann OpEdge-8D with 64 GB of SSD flash memory.

Device management

With integrated storage comes the ability to containerize applications for deployment on IoT gateways, including device management software. Traditionally, deploying applications on IoT gateways involved installing them directly on the equipment’s operating system, which had limitations in terms of scalability, flexibility, and ease of management. However, companies are increasingly using containerization as a deployment strategy for applications on IIoT gateways, offering platforms like Kubernetes and runtimes like Docker. These technologies provide a way to create lightweight and isolated runtime environments, known as containers, where applications can run consistently across various platforms and environments.

Many gateway OEMs are also building app stores with hundreds of ready-made applications that end users can deploy to their gateways (and in the cloud), such Bosch Rexroth’s ctrlX store, Siemens’s Industrial Edge Marketplace, and Advantech’s WISE-Marketplace.

Key Benefits of IIoT Gateways

In our analysis of 65 case studies, we identified numerous benefits of IIoT gateway implementations based on various use cases. The following are 3 of the main benefits companies cited.

1. Better IT/OT integration

Indeed, this is a key goal of implementing IIoT gateways, and our analysis shows that many companies have achieved this goal. A common goal of IT/OT integration is remote monitoring and response. As an example, Vitesco Technologies Italy used Zerynth’s 4ZeroBox, an on-premises IIoT system for real-time monitoring and predictive maintenance. This solution enabled Vitesco to predict pneumatic valve malfunctions 24 hours in advance, which reduced assembly downtime and increased productivity.

2. Reduced labor costs

IIoT gateways are often deployed for automation purposes and as such can reduce labor costs, human effort, and human errors. While Vitesco saw a 50% reduction in its manual labor requirement with its 4ZeroBox application, Colombian steel manufacturer Corpacero cut costs associated with repair labor after partnering with Senzary to deploy RotaryIQ and InsightsIQ solutions for predictive maintenance and remote machine management.

3. Energy savings

Enterprise energy management analytics software provider Wattics partnered with Kontron to use Kontron’s KBox A-101 as a central ‘edge node’ for Wattics Sentinel software at the customer’s site. It connects to the local energy grid and the Sentinel grid, facilitating meter configuration, reliable data collection, pre-processing, compression, and secure communication.

IoT gateway market outlook

With many companies seeking to either retrofit their equipment or enhance their IoT solutions, we have seen solid growth in the IIoT gateway market (8.1% CAGR) between 2018 and 2022, with acceleration specifically from 2021 to 2022. We assess that this trend will continue since use cases in manufacturing and certain applications continue to demand real-time processing, low latency, and secure data handling. Further, the following 5 trends, which are discussed in more depth in the IoT Gateway Market Report 2023–2027, support this assessment:

1. IoT gateways are becoming more modular, allowing IIoT gateway vendors to offer a range of options and configurations to meet customer needs and enable easy scalability.

2. IoT gateways are supporting more wireless connectivity options, such as secure cellular solutions with eSIM/iSIM technology, enabling IIoT gateways to handle multiple connected devices in an expanded perimeter of operations.

3. IIoT gateway vendors are collaborating to combine hardware and software solutions, simplifying deployments and reducing costs.

4. OT hardware is starting to consolidate (e.g., I/O module masters shifting to IIoT gateways)

5. Virtualization of workloads (e.g., virtual PLCs) allows IPCs and IIoT gateways to perform tasks that were previously tightly coupled to other pieces of hardware.

IIoT gateways play a pivotal role in bridging the gap between legacy machinery and modern systems, facilitating retrofits and brownfield installations. As industries strive for global connectivity and centralized management of OT devices, IIoT gateways will continue to play a major role in integrating operations across various locations.

What it means for IoT gateway vendors

5 key questions IoT gateway vendors should ask themselves based on the findings of the report:

1. Do our IIoT gateways remain compliant with updated security standards?

2. Do our customers require AI edge capabilities as a general offering?

3. Have we explored local data storage options to increase computation while decreasing latency?

4. Should we containerize our edge IIoT applications? And if so, how?

5. Are our general solutions enabling seamless IT/OT integration for customers? If not, should we focus on tailored solutions for our customers?

What it means for IoT adopters

5 key questions IoT adopters should ask themselves based on the findings of the report:

1. Have we assessed the various available IIoT gateways and their potential impact on our overall IoT strategy?

2. Which IoT architecture(s) are we using? Can an IIoT gateway offer improvements?

3. Do our current IIoT gateways meet current security standards? If not, what updates do we require to meet these standards?

4. Have we assessed the possible benefits of edge computing (e.g., automating controls locally based on the data)?

5. Should we leverage local data storage and containerized applications for better device management and updates?

The post The vital role of industrial IoT gateways in bridging IT and OT appeared first on IoT Business News.

In today’s advanced industrial era, effective maintenance is paramount. Companies grapple with the repercussions of inadequate maintenance strategies, leading to unexpected downtimes with staggering costs of up to $260,000 per hour. These interruptions, frequently stemming from equipment malfunctions, underscore the pressing demand for proficient condition monitoring solutions. Enter the Industrial Internet of Things (IIoT); a game-changer that elevates condition monitoring by providing real-time insights and foresight, significantly mitigating these expensive operational halts.

What is Condition Based Monitoring?

Condition Based Monitoring (CBM) is an industrial maintenance strategy that focuses on monitoring the real-time conditions of machinery to predict potential failures. This involves equipping industrial machines with sensors that collect data on variables like temperature, vibration frequency, and pressure. Once gathered, this data is sent to the cloud where it’s aggregated and analysed, providing insights about the equipment’s health and operational parameters. However, traditional CBM has its limitations, especially in terms of scalability and real-time data processing, which is where IoT-powered technology comes into play, and in particular Industrial Internet of Things (IIoT) powered technologies.

Harnessing the Power of IIoT: A Deep Dive into Industrial Interconnectivity

The Industrial Internet of Things (IIoT) is a subset of IoT that focuses on the interconnections and communications between machines. It leverages technologies like cybersecurity, big data, cloud computing, and machine learning to enhance industrial operations.

In the realm of CBM, IIoT technologies enable real-time monitoring of machinery across vast industrial landscapes. For instance, in the electric power industry, IIoT-based CBM can track the health of turbines, electrical substations, and even nuclear power plants. By integrating IIoT with CBM, businesses can overcome the traditional limitations of CBM and achieve more efficient, scalable, and real-time monitoring solutions. Harnessing IIoT technologies for condition based monitoring also brings organisations a myriad of invaluable benefits:

1. Vast Data Storage

Traditional Condition Based Monitoring (CBM) systems, which rely heavily on on-premises data centres, often grapple with constraints in their data storage capabilities. These limitations can hinder the real-time analysis and insights that industries desperately need to optimise their operations.

IIoT transcends these limitations by harnessing the power of cloud computing. This not only facilitates the storage of colossal amounts of data but also ensures its accessibility and analysis in real-time. For perspective, consider the renewable energy sector. A single wind turbine, in its operational life cycle, can generate about 1 terabyte of data every week. This data encompasses various metrics, from vibration frequencies to temperature variations, all of which are vital for assessing the turbine’s health and efficiency. The use of IoT powered technology allows this data to be accessed instantly to analyse the condition of the turbine.

2. Enhanced Analytics

The digital transformation of industries is not just about collecting data but also about analysing it in ways that were previously unimaginable. The Industrial Internet of Things (IIoT) plays a pivotal role in this transformation, especially in the realm of Condition Based Monitoring (CBM). With the computational prowess of cloud platforms, IIoT-driven CBM can deploy advanced machine learning algorithms, which not only refine diagnostic precision but also amplify predictive capabilities.

One of the primary challenges industries face is the siloed nature of data. Data is often collected infrequently, is hard to access, and is not interconnected, making it challenging to derive actionable insights. However, with IIoT, data from various sources can be integrated, offering a holistic view of operations. For instance, in the realm of manufacturing, IIoT can bridge the gap between operational technologies and IT, allowing businesses to monitor their operations remotely and in real-time. This seamless integration ensures that data from different devices, manufacturers, and systems can be linked together, providing a comprehensive picture of the operational health.

Furthermore, AWS IoT services demonstrate how businesses can integrate this technology into their operations, enhancing their maintenance strategies. By adding smart devices to equipment such as motors and pumps, businesses can collect real-time data on equipment performance. Smart devices are also used in non-destructive testing (NDT) scenarios to identify hidden cracks and rust through vibration and infrared sensors. Advanced analytics then identifies potential issues before they culminate in equipment failure, leading to significant cost savings, increased productivity, and extended equipment lifespan.

3. Data from Multiple Critical Assets

Machine learning requires significant data for accuracy. By collecting data from multiple machines across different locations, IIoT enhances the accuracy and functionality of predictive models, allowing for a holistic understanding of the condition of these assets and enabling predictive capabilities to prevent issues from escalating.

The significance of this multi-machine data aggregation is manifold. Firstly, it provides a comprehensive view of operations, ensuring that no equipment anomaly goes unnoticed. Research indicates that 64% of unplanned downtime is attributed to equipment failures, often stemming from improper maintenance or a lack of efficient machine condition tracking. By aggregating data from multiple sources, IIoT ensures a holistic understanding of equipment health, thereby reducing the chances of such failures.

Moreover, the data fetched by a condition monitoring solution offers invaluable insights about the current state of equipment and can be applied to indirectly monitor the quality of goods in production. For instance, in paper manufacturing, condition monitoring solutions help monitor the quality of paper being produced by tracking the condition of all roll presses through IoT powered technology.

Furthermore, this aggregated data serves as a rich training ground for machine learning models, enhancing their predictive capabilities. With more data points to analyse, these models can identify intricate patterns and correlations that might be missed when analysing data from a single machine. This not only improves the accuracy of predictions but also aids in the early detection of potential equipment failures, allowing businesses to transition from reactive to proactive maintenance strategies.

4. Reduced Physical Intervention

In the digital age, the ability to remotely monitor machinery through IoT has emerged as a transformative solution, especially in sectors where physical access is challenging. The oil & gas industry, for instance, often operates in remote terrains, offshore platforms, or other inaccessible areas, making routine inspections and maintenance a logistical challenge. According to a study, unplanned equipment downtime in such industries can lead to losses amounting to millions of dollars daily, emphasising the need for timely interventions1.

IoT technology addresses this challenge head-on. By equipping machinery with sensors that transmit real-time data to centralised systems, industries can monitor equipment health, performance metrics, and potential anomalies without the need for on-site personnel. This not only reduces the costs associated with sending teams to remote locations but also ensures timely interventions, as potential issues can be detected and addressed before they escalate.

In essence, the integration of IoT in condition-based monitoring is not just about technological advancement; it’s about redefining operational efficiency, ensuring safety, and driving cost-effectiveness in industries where every minute of downtime can have significant financial implications.

Conclusion

In the intricate tapestry of today’s industrial landscape, the role of IoT in enhancing Condition Based Monitoring stands out as a beacon of innovation. As we’ve journeyed through the myriad facets of this integration—from the vast data storage capabilities to the power of enhanced analytics and the promise of reduced physical intervention—it’s evident that IoT-powered CBM is not just a fleeting trend but a transformative force for industries.

However, while the allure of these benefits is undeniable, it’s crucial for businesses to approach this technology with a holistic mindset. CBM, as potent as it is with IoT, should not be an isolated strategy. Instead, it should seamlessly integrate into a broader, comprehensive maintenance plan. Such an approach ensures that businesses don’t just react to the immediate insights provided by CBM but also strategize for long-term equipment health and operational efficiency.

By weaving IoT-powered CBM into the broader fabric of their maintenance strategies, businesses stand to not only mitigate the exorbitant costs associated with unplanned downtimes but also to usher in an era of unprecedented operational excellence. As we look to the future, where AI and machine learning further amplify the capabilities of CBM, the marriage of IoT and CBM promises a horizon brimming with possibilities, setting the stage for a more efficient, productive, and innovative industrial future.

The post Beyond Downtime: The $260,000 Per-Hour Case for IoT-Driven Condition Based Maintenance appeared first on IoT Business News.

The Sparkplug specification that enables a “Plug n Play” Industrial Internet of Things Poised to become an Official ISO/IEC Standard.

The Eclipse Foundation, one of the world’s largest open source software foundations, in collaboration with the Eclipse Sparkplug Working Group, today announced the release of the Sparkplug 3.0 specification.

Sparkplug is an open software specification that enables mission-critical operational technology (“OT”) clients to use industry standards, including MQTT, to seamlessly integrate data from their applications, sensors, devices, and gateways with most Industrial Internet Of Things (IIoT) Infrastructure. The Sparkplug 3.0 specification represents the first version of the specification managed under the Eclipse Foundation specification process. It also represents the proper formalization of the specification over the v2.2 release.

The goals for Sparkplug 3.0 are to leverage the Eclipse Foundation’s open and vendor-neutral specification process to clarify ambiguities in the v2.2 version and add explicit normative statements while maintaining backward compatibility. In addition, the Sparkplug specification has begun the process of transposition as an international standard at ISO/IEC, an independent, non-governmental international organization with a membership of 167 national standards bodies. To support this standardization effort, the Eclipse Foundation has obtained the status of Publicly Available Specification (PAS) submitter from ISO/IEC’s Joint Technology Committee (JTC) 1. The PAS process is a fast-track process enabling a specification to be approved as an ISO/IEC standard in less than a year, as opposed to a full-length process that can take up to four years. Already growing quickly, Sparkplug’s transition to approved industry standard specification should further speed its growth and acceptance throughout multiple industries.

“Today’s release of Sparkplug 3.0 represents a major milestone,” said Mike Milinkovich, executive director for the Eclipse Foundation.

“This march to Sparkplug’s transformation into an official industry standard has come from significant industry-wide collaboration that continues under the auspices of the Sparkplug Working Group. We look forward to continuing to foster new partnerships to advance the adoption of MQTT and Sparkplug in the industry.”

The Sparkplug Working Group is simultaneously launching a product compatibility program for Sparkplug implementers. The program will ensure that Sparkplug-compatible products and implementations demonstrate a high degree of compatibility and interoperability.

About Sparkplug & MQTT

Sparkplug provides an open and freely available specification for how Edge of Network (EoN) gateways or native MQTT-enabled end devices and MQTT Applications communicate bi-directionally within an MQTT Infrastructure. It is recognized that MQTT is used across a wide spectrum of application solution use cases and an almost indefinable variation of network topologies.

By design, the MQTT specification does not dictate a Topic Namespace or any payload encoding. However, as the IIoT and other architectures leveraging the publisher/subscriber model are adopted by device OEMs in the industrial sector, having different Topic Namespace and payload encoding can inhibit interoperability for the end customer. To that end, the Sparkplug specification addresses the following components within an MQTT infrastructure:

• Sparkplug defines an OT-centric Topic Namespace.
• Sparkplug defines an OT-centric Payload definition optimized for industrial process variables.
• Sparkplug defines MQTT Session State management required by real-time OT SCADA systems.

The post The Eclipse Foundation Announces The Release of Sparkplug 3.0 and Unveils it is Being Fast Tracked to Become an International Standard appeared first on IoT Business News.

• Powered by deviceWISE® EDGE, deviceWISE® CLOUD, Telit’s LE910 module family and connectivity, Lantech Intelligent Network Connection (LINC) addresses and resolves key smart factory and machine challenges including uptime, performance, load quality and cost
• LINC is Lantech’s first IoT powered SaaS offering which includes machine data, analysis, and alerts to allow users to monitor stretch wrapper performance remotely

Telit, a global leader in the Internet of Things (IoT), today announced that it is working with Lantech to change how companies package and protect their products for shipment from the factory floor to the retailers door.

Without proper stretch wrapping, loads can fail and shift during shipment causing costly damage to a company’s product or brand. With the Lantech Intelligent Network Connection (LINC) — powered by Telit’s deviceWISE Edge, deviceWISE Cloud, connectivity and LE910 cellular module — Lantech addresses key problems for smart factories that stretch wrap pallet loads, increasing uptime, performance and load quality, and decreasing film cost.

“In 1972 Lantech revolutionized how customers ship their products with the invention of the stretch wrapper,” said Alex Verret, director of Customer Care Services, Lantech. “Today, Lantech stretch wrappers help our customers produce millions of safe-to-ship loads every year, at the lowest possible cost. LINC, our latest innovation, allows our customers to easily connect to machine performance data and proactively address issues to improve wrapping operations.”

LINC is a data visibility solution that allows subscribers to monitor machine data and performance in real-time, from any place. It provides actionable intelligence to maintain and improve system uptime, productivity, load quality, and cost — allowing customers to react to issues before they become problems.

Performance feedback information is vital to anticipating — and proactively addressing — systemic issues. Previously, the only way to access important information was directly from the HMI for L-Series systems (legacy systems that did not even tabulate summary data). Lantech built LINC to harvest and transmit data to allow for wrapper status and performance data to be collected, displayed and analyzed without accessing a customer’s network. The goal of the system is to significantly reduce response times, downtime, quality problems and environmental impacts.

These products enable the LINC system:

• Telit deviceWISE Edge – Industrial IoT edge platform made by hundreds of industrial drivers. It interfaces directly with Programmable Logic Controllers (PLC) and other industrial control systems, enabling the edge application and supporting machine learning.
• Telit deviceWISE Cloud – Cloud-based IoT platform that aggregates data collection, device management, secure remote access, connectivity management and remote access all from one intuitive web interface (with the necessary tools and resources).
• Telit connectivity – Global visibility and control down to the individual SIM, reducing the costs of managing a connectivity ecosystem.
• LE910 module family – LTE Cat 4 industrial grade modules for IoT applications that need a combination of performance and affordability. The modules are ideal for remote asset monitoring and tracking use cases.

“Viewing critical machine analytics anytime — from anywhere — is critical for today’s smart factories and represents a competitive advantage,” said David DeLaRosa, VP deviceWISE Industry 4.0, IoT Platforms Business Unit, Telit. “Empowering the right people to view data to improve uptime and productivity is often an overly complex task. deviceWISE simplifies the process by providing industrial drivers and enabling advanced edge logic, cloud connectivity and secure remote access.”

DeLaRosa continued:

“Through our partnership with Lantech, its LINC system allows users to set and monitor performance targets and receive event-based notifications. This allows wrapping systems to become part of the team by notifying users when important events occur.”

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IoT Analytics published a 217-page adoption report containing statistics on the current and future status of Industry 4.0 and smart manufacturing.

This research is based on the feedback from 500 decision makers in manufacturing organizations.

Key insights:

• A look at five key statistics shows that Industry 4.0 activity has risen since 2011, with COVID-19 and the 2022 slowdown presenting recent bumps on the road.
• The analysis considered five indicators: Public search interest, academic papers, startup funding, M&A activity, and enterprise adoption.

Key quotes:

• Dimitris Paraskevopoulos, analyst at IoT Analytics, says: “The concept of Industry 4.0 was introduced 11 years ago and first started as a vague concept to revolutionize manufacturing through “cyber-physical systems”. It has since become a mandatory part of most manufacturers’ strategy. Today, search interest has increased by 140 times and all signs point to the fact that interest in Industry 4.0 is still going strong.”

• Knud Lasse Lueth, CEO of IoT Analytics, adds:
  

  “Our latest research shows that industrial companies have further advanced on their digitization journey. The fact that 72% of manufacturers have an Industry 4.0 strategy in place is a clear indicator that adoption is happening, and Industry 4.0 is here to stay.”

The rise of Industry 4.0 in 5 stats

More than 10 years after its creation, it seems that Industry 4.0 is in full swing: Public search interest has risen 140 times since then, 50,000 research papers were published on the topic in 2021 alone, start-ups are now receiving a total of $3 billion in funding each year (Q3 2021 to Q3 2022), and M&A activity has doubled.

The term “Industry 4.0” was first publicly introduced at Hannover Fair 2011 by German Chancellor Angela Merkel who (as rumor has it) picked up the term, sparking a number of interesting discussions as a result.

What first started as a vague concept to revolutionize manufacturing through “cyber-physical systems” has since taken shape.

Some of the key advances since 2011 include:

• Much more powerful chipsets
• Widespread adoption of cloud services
• Containerization of software
• Creation of much-improved software middleware/tools
• Open interfaces to edge computing hardware
• New and improved communication standards/protocols
• Availability of relevant AI models and libraries

Few of these were obvious 10 years ago.

| “Industry 4.0” Definition
The use of modern I4.0 tech stack elements, or specific supporting technologies that enable manufacturers to integrate various data sources, achieve higher OEE, reduce costs, or improve other KPIs relevant to a production setup (manufacturing of goods, mining, oil, and gas) mostly in conjunction with rolling out new or improved use cases.

Here is a look at five key stats that show how “Industry 4.0” activity has changed since 2011:

#1 PUBLIC SEARCH INTEREST: 140 TIMES HIGHER

Searches for Industry 4.0 on Google in 2022 are 140 times higher compared to the year it was first introduced and made public (2011). In addition, related terms such as Industrial IoT and Smart Manufacturing grew quite fast in the same time frame as well (32 times and 3.5 times more searches respectively).

Industry 4.0 is an umbrella term that describes several advances in the manufacturing industry. The below picture shows how four of the supporting technologies for Industry 4.0 have also risen in popularity in the same time frame:

Search interests for “Cobots” increased more than 10 times since 2011, “Additive Manufacturing” almost nine times, “Digital Twin” more than four times, and “Machine Vision” 1.3 times. These supporting technologies are also representative of the interest in several other Industry 4.0-related technologies.

#2 ACADEMIC PAPERS: 200,000+ PAPERS PUBLISHED

More than 50,000 academic papers on Industry 4.0 were released in 2020 and 2021, and more than 200,000 have been published in total within the last 10 years. It is safe to say that a lot of research is being done on Industry 4.0. One of these ideas can be the “next big thing” that will move the topic forward.

Based on Google Scholar, the three most cited papers relating to Industry 4.0 are:

#3 FUNDING: 2,513 DEALS SINCE 2011

The annual funding of start-ups that are active in Industry 4.0 has increased by +319% from 2011 to 2021. 2021 saw a total of $2.2 billion of funding spent on upcoming companies that develop technology related to Industry 4.0. A total of 2,513 deals were announced in the 11-year time frame. The total number of funding rounds decreased when COVID-19 hit in 2020 and has also taken a hit as of late with the inflation and war-related slowdown.

Notable investment rounds included a $179 million Series A for BrightMachines in 2018, a $100 million Series C for Tulip Interfaces in 2021, and a $75 million Series E in Xometry in 2020.

#4 M&A ACTIVITY: DOUBLED FROM 2011 TO 2021

The annual number of Industry 4.0-related acquisitions reached a peak of 132 in 2021. This marks a 116% increase in the past 10 years. In the first three quarters of 2020, there were less than half
acquisitions compared to the usual average in the rest of the years. COVID-19 had a clear impact on the M&A activity as well.

Notable acquisitions included AspenTech acquired by Emerson for $11 billion in 2021, Fetch Robotics acquired by Zebra Technologies for $290 million in 2021, and IQMS acquired by Dassault Systèmes for $425 million in 2018.

#5 ENTERPRISE ADOPTION: MOST ORGANIZATIONS NOW HAVE AN INDUSTRY 4.0 STRATEGY

A 2015 World Economic Forum survey of 250 market leaders found that 88% of the participants did not understand the underlying business models and long-term implications of the industrial IoT to their industries

A 2019 Industry 4.0 adoption survey by IoT Analytics showed that the situation had changed with 25% of Industry 4.0 use cases already fully or extensively rolled out with enterprises on a global level.

The latest 2022 Industry 4.0 adoption survey now shows that companies have even further advanced. Companies that are not executing against an Industry 4.0 strategy are in the minority. A staggering 72% of the survey respondents report that they are in the process of implementing their Industry 4.0/Smart Factory with many initiatives in progress and some already completed.

The post The rise of Industry 4.0 in 5 stats appeared first on IoT Business News.

The financial implications of the IIoT and, on a wider scale, Industry 4.0 are constantly changing. This is, in large part, because the underlying technology enabling the IIoT is itself evolving.

Change isn’t welcome in most areas of manufacturing. Any change represents risk, and even when that change brings with it almost guaranteed gains the risks remain. Many OEMs are now assessing the risk and reward of letting the internet onto their shopfloor.

Most of the time we don’t go looking for change, it is forced upon us. What really matters is how we manage that change. This is where many of those manufacturers now find themselves with their assessment of the IIoT.

It may feel like a herculean task. The scale can be overwhelming but as with most tasks it needs to be broken down into manageable efforts. What is it they say about Rome? The perceived problem is that, unlike Rome, manufacturers are not starting with a clean piece of paper. Neither do they have the luxury of letting style overtake substance.

But like any large structure, getting the foundations right is probably the most important step. It may involve some remodeling, but if those foundations are solid the rest should follow. This is happening in the IIoT. New technologies are being developed to provide the scalable, solid foundation needed to bring existing manufacturing plants into the IIoT.

Services bring the IIoT together

The practice of delivering ‘X as a Service’ is already strong, and not only in traditional service industries. One often cited example is the jet-engine as a service offered by Rolls-Royce. Turning ‘things’ into services makes them much more manageable because it creates uniformity and removes superfluous complexity at the interface.

The same concept is now being used in the deeply embedded domain. Turning functions into services makes those functions more accessible across domains. This is the subject of an Avnet article , which takes a look at the technologies being developed to support the move to services in the industrial IoT.

Making the IIoT simpler

The internet provides the critical infrastructure for connectivity. The IoT takes advantage of this infrastructure to allow any device, anywhere, to become part of that wider network. The Industrial IoT represents a slice of this wider network, one that puts safety and security above almost anything else.

As industrial connectivity predates the IoT there are many legacy networks that now need to be integrated. Doing that safely is part of the challenge, but so too is removing the complexity that already exists within these different networks.

Ethernet is the single most common protocol used in the information technology domain. Its use is now permeating other verticals, including the industrial sector. But Ethernet alone cannot meet all the requirements imposed by the industrial world.

To address this, the IEEE has developed standards for single pair Ethernet (SPE). This relatively simple development has huge implications across the IIoT. In the article , Avnet talks with George Zimmerman, the chair of the IEEE Ethernet task force and independent consultant on high-speed communications technology to find out why.

Taking control at the network edge

An important aspect to remember in this transition is that industrial control is often a real-time application. In the example above, SPE may only be viable in an industrial setting if time-sensitive networking (TSN) technology is also applied.

This real-time imperative propagates throughout the network, all the way to edge. This is where the control resides, typically in the form of a programmable logic controller, or PLC. These highly optimized embedded devices have also been evolving in response to the introduction of the IoT.

In the article , Avnet takes a closer look at this evolution and the technology behind it. Edge controllers are emerging that combine PLC and PC in a way we haven’t seen before.

An edge controller relies on combining real-time control with network connectivity. The key is to provide this functional combination in a way that doesn’t compromise either side. Industrial PCs and gateways represent a best-effort to achieve this but each has its own weaknesses. Can the edge controller bring balance to the IIoT?

Find the right approach

At its core, the IIoT relies on the same electromechanical, hardware and software components that enable all modern life. As a leading distributor working with a broad supplier base, Avnet also provides design services to help its industrial customers throughout their digital transformation journey.

Avnet has been doing business the right way since 1921. It has over 1,800 FAEs available to customers, support from its extensive engineering communities, subject matter expertise and access to the latest development kits. These resources will help you move from proof of concept to production faster. Avnet supports you through rapid prototyping and small volume production.

Customers have access to over a century of experience in supply chain management, to ensure your production scales. We provide assurance of supply and flexible inventory programs through a global distribution and logistics infrastructure.

Avnet’s capabilities cover technologies and solutions, design services and expertise, and supply chain and logistics. Accelerate your digital transformation to the IIoT with Avnet.

The post The IIoT is change in a changing landscape appeared first on IoT Business News.

There is a lot to gain from diving deeper into the Industrial IoT. This huge topic requires some navigation. Avnet provides resources to help engineers find the right solution for their current and future design objectives.

The Industrial IoT is one, important, element of Industry 4.0. On a wider scale, Industry 4.0 impacts all vertical markets. It describes new ways of doing old things, and innovative technologies that enable entirely new concepts.

These concepts are rapidly gaining attention and becoming trends. It is important for all commercial enterprises to be aware of these trends because they represent competitive advantage. We could argue that it is too late to be an early adopter in the Industrial IoT, and those arriving now are the early majority. Some would say that the risk is much lower for the late majority but so too may be the reward. For this reason, trends can help shape business decisions.

The trends now visible in the IIoT go beyond buzzwords. The important difference is they reflect significant buy-in from suppliers. This leads to investment and enablement. The intention of any investment is to see a return. The enablement impacts manufacturers looking to exploit these trends.

Understanding IIoT trends

As part of its ongoing support for customers looking to capitalize on the IIoT, Avnet offers a series of resources and solutions. In a new article, Avnet looks at three of the trends shaping the IIoT right now. The full impact of these trends is yet to be felt.

The article covers three trends that converge to enable the new industrial landscape. This could be characterized as being widely distributed, on-demand, service-based manufacturing. The trends include micromanufacturing, additive manufacturing and digital twins.

These three distinct but connected aspects of Industry 4.0 are in various stages of their development. Arguably, additive manufacturing is the most mature but the way it will be used to enable new manufacturing services is still developing. Digital twins is becoming a common term but it can mean different things to different people, depending on what they need from technology. Micromanufacturing is the relative newcomer but it could be the most impactful, particularly as more companies look towards onshoring for future growth and stability.

To read the full article, visit

Discovering IIoT Platforms

Industrial systems are moving beyond routine control. Manufacturers see this and appreciate that their systems need to understand more about the operating environment. Connected sensors bring that understanding through real-time data. This presents its own challenges, but the industry is reacting to this challenge through the development of platforms.

These platforms are intended to balance simplicity with complexity and provide scalability. Abstraction is fundamental to this approach, but it also demands a new way of developing solutions. Platforms aimed at the IIoT need to understand how to provide a robust yet flexible approach to system development.

Software development is one of the biggest demands on engineer resources. Not only that, but it also provides perhaps the biggest potential for design errors. A large amount of embedded software doesn’t contribute to the OEM’s value-add, making it even more cost-intensive.

explains how abstraction is implemented in IIoT platforms. It describes how developers can meet their objective of designing and maintaining a network of smart endpoints. It achieves this by reducing the burden of software development, potentially removing it completely.

Machine Learning in the IIoT

Artificial intelligence is becoming almost synonymous with cloud computing. It already influences commercial activities across all vertical markets but is perhaps mostly felt in the service industry today. That is changing rapidly as AI and machine learning find their way into smaller systems at the network’s edge.

Machine vision is already widely used in industrial manufacturing. There is a clear connection between machine vision and machine learning, but how simple is it to implement? provides one expert’s view on taking that next step.

Image sensors are a key element of machine vision. The data they generate is essential in any system that uses machine learning. Avnet explores this common thread to give engineers an insight into how to bring machine learning to their machine vision applications.

Take the next step

At its core, the IIoT relies on the same electromechanical, hardware and software components that enable all modern life. As a leading distributor working with a broad supplier base, Avnet also provides design services to help its industrial customers throughout their digital transformation journey.

Avnet has been doing business the right way since 1921. It has over 1,800 FAEs available to customers, support from its extensive engineering communities, subject matter expertise and access to the latest development kits. These resources will help you move from proof of concept to production faster. Avnet supports you through rapid prototyping and small volume production.

Customers have access to over a century of experience in supply chain management, to ensure your production scales. We provide assurance of supply and flexible inventory programs through a global distribution and logistics infrastructure.

Avnet’s capabilities cover technologies and solutions, design services and expertise, and supply chain and logistics. Accelerate your digital transformation to the IIoT with Avnet.

The post Going beyond buzzwords in the Industrial IoT appeared first on IoT Business News.

The IoT now touches every aspect of modern life, in every nation. Its accessible nature means it has impacted and improved lives everywhere. This is being felt most strongly across industrial sectors.

The Industrial IoT underpins Industry 4.0. This quiet revolution is getting louder, changing not only the way individual companies work internally but how they collaborate with each other. The IIoT creates a new kind of connection between vertically and horizontally integrated manufacturing companies and suppliers.

Apart from the technical implications of that, there are wider considerations. Choosing not to embrace Industry 4.0 and the IIoT is rapidly becoming a moot point. How to embrace it is now very much the topic of discussion.

Building a business case for the IIoT isn’t necessarily easy, as it will require new ways of calculating capital and operational expenditure. It will depend on the age of equipment and the cost of replacement or upgrading. Eventually all manufacturers will be part of the IIoT, at least at some level, but it will take some longer than others.

If the end goal is the same, how you get there depends on where you start from. Not all companies will be new to the IIoT, but most will be new to at least some of the practices. There are mid to long-term trends emerging that manufacturers will need to consider. This includes the impact of disruptive but complementary technologies, such as additive manufacturing.

These topics are covered in more detail in an Avnet White Paper ‘’, and article . Both are part of Avnet’s wider resources supporting the Industrial IoT.

On-demand manufacturing

Other challenges are emerging, which are a direct result of the IIoT. This includes a ‘manufacture on demand’ approach to mass production. Consumer habits indicate a growing demand for more customization. While modern, connected and ‘smart’ factories may be able to accommodate this demand, it has the effect of reducing volumes. Part of the reason why industry works is because volumes are high, using equipment designed to manufacture the same thing over and over. Change is not the friend of high-volume manufacturing.

Although there are obvious challenges, the financial incentive is huge. Countries stand to benefit from growth in the region of trillions of US dollars. This figure is comparable to the new resources that will need to be installed. Sensors, the manufacturing heartland of data, will be deployed in their billions. This is driving change in the infrastructure and its associated solutions.

All this data needs to be processed. Much like a manufacturing production line, data will need to be inspected, analyzed, and put to good use. The impact artificial intelligence will have here is hard to quantify. Current developments indicate that the intelligence is moving closer to the data, to reduce latency and the burden of moving large files around a network and the internet.
The information technology (IT) and operational technology (OT) domains are colliding to make this vision a reality. Interfacing these two worlds requires new solutions, both technical and operational. Innovations here include new protocols that speak both languages, and new platforms that accommodate IT and OT demands.

The IIoT is a simple concept

Conceptually, the IIoT is simple. It involves capturing information and turning it into actionable data. This masks the reality; that change comes with risk. Successful and profitable manufacturing facilities rely on a careful, almost delicate balance between risk and reward. The IIoT provides demonstrable rewards but the risk must also be quantified.

Digital transformation is happening everywhere. Another key trend that will impact manufacturers is the shift towards a service-based economy. This shift is accelerating, not least because leaders in this field already recognize the value in developing new services. It requires more than just good intent to move from a product-based business model to a service-based revenue stream. The IIoT is enabling that evolution. This will result in hybrid approaches that must coexist, at least initially and possibly for the long term.

At its core, the IIoT relies on the same electromechanical, hardware and software components that enable all modern life. As a leading distributor working with a broad supplier base, Avnet also provides design services to help its industrial customers throughout their digital transformation journey.

Avnet has been doing business the right way since 1921. It has over 1,800 FAEs available to customers, support from its extensive engineering communities, subject matter expertise and access to the latest development kits. These resources will help you move from proof of concept to production faster. Avnet supports you through rapid prototyping and small volume production.

Customers have access to over a century of experience in supply chain management, to ensure your production scales. We provide assurance of supply and flexible inventory programs through a global distribution and logistics infrastructure.

Avnet’s capabilities cover technologies and solutions, design services and expertise, and supply chain and logistics. Accelerate your digital transformation to the IIoT with Avnet.

The post Industry 4.0: the quiet revolution that is getting louder appeared first on IoT Business News.

Rebounding from dips in consumer spending to the post-pandemic surge in demand is catching many manufacturers “flat-footed.” The Wall Street Journal reports that, while consumer spending on long-lasting goods rose 6.4% in the U.S. in 2020, production of those goods fell 8.4%. What will come to the aid of frazzled manufacturers in the coming months? A digital thread that provides end-to-end connectivity is becoming a reality.

The interconnectivity of devices, equipment and all corresponding operations provided by the Internet of Things (IoT) is taking over manufacturing operations today, and it’s playing a transformational role in industries around the world. This is critical as our global economy navigates the imminent post-recession surge in consumer spending coupled with historic labor shortages.

The Industrial Internet of Things (IIoT), then, is the application of connected IoT in factories and process facilities that introduces a new level of efficiency, reliability and performance. It operates on the concept of feedback loops through which the heartbeats of machines, equipment and production systems continually flow back through the complex systems and processes that produce manufactured products. The IIoT can offer manufacturers new business opportunities, cost savings and improved machine monitoring and maintenance. IIoT solutions are already offering endless benefits to modern-day manufacturers.

Here are the top 10 benefits of IIoT deployment:

1. Production System Awareness and Monitoring: At the core of IIoT solutions is constant communication between systems and machines, which ensures throughput is optimized and machine defects are identified in real-time.

2. Manufacturing Process Optimization: Machines and equipment enabled with sensors and managed with IIoT systems can monitor conditions, equipment and workflows—like machine performance, assembly line management, supply chain optimization, workforce safety or quality assurance processes—for optimization.

3. Predictive Maintenance: More than 75% of equipment and system failures occur without notice. With IIoT, preventative maintenance incorporates analytics to predict machine failures.

4. Optimizes Quality: It can address problems on the production line immediately and reduce downtime, lost productivity and product defects. IIoT equipment is programmed to monitor the quality of materials, analyze equipment performance in real-time, and measure and test finished products.

5. Inventory and Supply Chain Management: Data, analytics, insights and contextual intelligence makes inventory systems run seamlessly, which gives more accurate estimates of available material, the work-in-progress and the estimated arrival time of new materials—which helps optimize the supply chain and cuts costs.

6. Customer Service Levels and Satisfaction: Sensor-equipped production systems and inventory make it possible for customers to stay apprised of the progress of their orders in near real-time. Sensors offer insights about customer usage that can help manufacturers improve features, alert customers to problems and bottlenecks, and competitively differentiate their business.

7. Worker Safety and Health: Intelligent wearables allow managers to monitor the health and safety of production workers by tracking histories for illness and injury, absences, near-misses, machinery or vehicle accidents or life-threatening events such as gas leaks.

8. Energy Management and Sustainability: Industrial manufacturing is responsible for consuming 54% of the world’s electricity. Manufacturers that use IIoT can significantly increase energy efficiency by optimizing energy consumption.

9. Service Provisioning and Orchestration: Field services delivery enabled by IIoT is a value-based approach based on factors provided, such as the timing, context and technical personnel involvement for a given service activity.

10. Service Contract Compliance and Performance: IIoT enables data visibility in real-time so both the Original Equipment Manufacturer (OEM) and the user are aware of the risks and issues as they arise.

The post Top 10 Benefits of Industrial IoT Deployment appeared first on IoT Business News.

IoT Analytics this month launched the Smart Factories Insights Report 2021 discovering 80 smart factories, portraying 10 of them in detail, and highlighting commonalities and best-practices.

The analysis of these also uncovered some common misconceptions about what it takes to realize a smart factory.

Key findings of the research include:

• Factories are becoming more intelligent, flexible, and sustainable through the power of technology, data, and the Internet of Things (IoT).
• IoT Analytics researched how 80 of the world’s best factories became so smart and discovered that, among other things, there is much more to smart factories than technology.

Introduction – The smart factory as a competitive advantage

Moderna, the American biotechnology company and maker of the well-known Moderna vaccine, is the poster child of how a digital-first vision applied to a smart factory can be a business game-changer in 2021 and beyond.

In 2018, the company invested in a new 200,000-square-foot manufacturing site in Norwood, Massachusetts as part of its 10-year vision to “integrate automation and digital technology into everything we do.” The company equipped the factory with various state-of-the-art digital technologies that enabled end-to-end testing, developing, and scalable manufacturing capabilities for mRNA drug candidates. Some of the latest robotics, automation, AI, data analytics, and blockchain technology were deployed to create a digitally connected ecosystem or a “smart factory.” The goal was to be able to use the digitally advanced factory to scale up production quickly and with agility, according to the market need.

In 2020, Moderna’s smart factory was put to the test by the COVID-19 pandemic. In July 2020, the company became one of the first US companies to enter phase III of a clinical trial for a potential coronavirus vaccine, just months after the genetic code of the virus was released.

The smart factory became a cornerstone for Moderna to accelerate the vaccine development process in several ways, including using AI-based algorithms for massive sequence optimization and using digital twin technology to rapidly simulate and test the related production processes. This vaccine development process, which usually takes an average of 10 years, was reduced to months, in part thanks to Moderna’s previous investments in smart factory technologies.

Our latest research on smart factories shows that Moderna’s facility is a prime example of a new generation of factories that are more agile, sustainable, collaborative, and efficient than many of the factories that exist today.

What a smart factory really is – a definition

Based on our research, a smart factory is “the holistic transformation of people, processes, and technologies along with the use of data to achieve the intended performance/business goals of one or more production site(s).”

Commenting on the findings of the research Knud Lasse Lueth, CEO at IoT Analytics, says:

“It is important to understand that a smart factory is not a destination or an end goal, but a journey that all manufacturing organizations can embark on at their own pace.”

Sharmila Annaswamy, senior analyst at IoT Analytics, adds:
“The “smartness” of a factory is measured on a spectrum. Factories of all shapes and sizes can assess their maturity and begin their journeys (e.g., by using digital maturity models, such as the Acatech model or Fraunhofer’s I4.0 assessment model, which are discussed in the report). According to our research, this amorphous nature of smart factories has led to the formation of seven common misconceptions, which are discussed below.”

Seven misconceptions about smart factories

1. Smart factories must be started from scratch (greenfield projects).

WRONG. For sure,greenfield smart factory projects (i.e., new factories built from scratch) are easier to realize because there is no existing infrastructure to upgrade and no existing processes that could be disrupted, making planning and implementation much easier. Our research, however, shows that many existing (brownfield) facilities are also becoming smarter, in some cases with worldwide recognition.

Industrial automation vendors are at the forefront of showcasing how a brownfield smart factory transformation can be achieved. Here are two examples:

• Schneider Electric has showcased how its own technologies (e.g., EcoStruxure platform, AVEVA platform) are in use at their own factories. The Schneider Electric facility in Lexington, USA, was recognized as an advanced lighthouse factory by the World Economic Forum in 2020.
• Siemens Electronics Works plant in Amberg, Germany was also recognized as an advanced lighthouse factory by the World Economic Forum in 2021. Using the company’s own solutions, from Siemens Cloud infrastructure to digital twins, the plant has achieved a 50% increase in efficiency and now serves as a guideline for other smart factories.

2. Only large organizations can realize smart factories.

WRONG. It is true that IoT Analytics research from 2019 shows that big companies are further along and less budget-constrained, but there are ways that make realizing smart factories viable for smaller companies as well. Here are some examples:

• Financial support. There are several industry associations, like CESMII in the U.S. or the Gaia-X community in Europe, that provide subsidies and support for SMEs who are looking to make their factories smart.
• Determine viability. Non-profit smart factory initiatives, like Smart Factory OWL and Smart Factory KL, allow manufacturers to witness and test certain technologies off-site and discuss them with like-minded professionals before spending on them.
• Pay per use. A notion exists that smart factory initiatives involve a lot of CAPEX, infrastructure, and data upgrading and hence are not suitable for SMEs. The rise of as-a-service business models (mostly for software but also increasingly for everything else, including expensive equipment), pushes those costs to OPEX, bringing smart factories within the reach of SMEs.

3. There is a one-size-fits-all blueprint for realizing a smart factory.

WRONG. Unfortunately, no two smart factories look exactly alike because each has different production characteristics, and, perhaps even more importantly, each has its own performance/business goals.

In our research, we identified eight typical performance/business goals, which we grouped into three types: operational, commercial, and R&D. The examples below highlight how different goals can lead to different types of smart factory deployments (in this case the Moderna plant has a different goal than the Trumpf factory). Examples:

• Moderna (Operational goal—Improving agility). The Moderna example discussed earlier is an example of how increased agility created by a smart factory created tremendous value. In this case, time to market was crucial, not just for the company, but for the entire world, and investments in the smart factory ended up eventually paying huge dividends for Moderna, whose stock ended 2019 at <$20/share and today is trading at ~$400/share.
• TRUMPF (Commercial goal—Showcasing new offerings). TRUMPF’s €30M Smart Factory in Chicago realizes TRUMPF’s vision of networked production and acts as a showcase for TRUMPF customers. One of the main goals of the factory is to showcase the art of the possible and help customers of TRUMPF realize their own smart factories.

4. Smart factory initiatives are mostly about technology.

WRONG. While new technology is often a key part of a smart factory transition, it is not the only part and, according to our research, not the most important one. Almost all smart factory practitioners that we have interviewed in the last two years have pointed us toward the importance of less tangible aspects, mostly related to people and processes within an organization. In fact, when asked about the success factors of Industry 4.0 initiatives, only one of the top 10 success factors revolved around technology.

One particularly important concept to consider is change management, which is required to ensure that the transformation process is transparent and that employees feel included throughout the process. Example:

• Infineon introduced change ambassadors at their factory in Singapore. In 2017, Infineon, a Germany-based global electronic and semiconductor manufacturing company, announced a five-year transformation roadmap for its manufacturing site in Singapore. To engage employees right from the start, members of the workers union were appointed as change ambassadors to facilitate two-way communication of new initiatives and promote feedback between shopfloor staff and management.

5. Smart factory initiatives replace existing continuous process improvement projects.

WRONG. Smart factory initiatives need to work alongside existing process improvement tools (e.g., Lean Manufacturing, Six Sigma) rather than replace them.

Our research shows that Smart factory technologies play a vital part in automating aspects of data collection and analysis, and in eliminating inefficiencies and errors associated with manual processes, thereby improving the efficiency of the continuous improvement process.

Even though the “Lean” methodology in its purest form avoids significant technological assistance, many practitioners now try to use various smart factory technologies to help eliminate different wastes. The picture below shows some selected smart factory technologies and which of these can help with which forms of waste.

6. Scaling the successes at one factory to other factories in the network is easy.

WRONG. Scaling from one smart factory to several others is often the most difficult part because each factory setup is inevitably different from the predecessors, with both the tangible (machine conditions, IT landscape, product features) and intangible conditions (employee mindset, management mindset) being entirely different in different locations.

The journey of a smart factory often begins with a single digital use case and quickly grows into a mesh of technologies and use cases. Approaches on how to navigate the strategic decisions around scaling a smart factory approach vary.

Some companies find that it is easier to first try out some of the technologies and use cases in an existing factory before building a new smart factory from scratch (greenfield). Example:

• Siemens. The German conglomerate invested heavily into digitizing its existing Amberg plant before deploying a similar setup, with very minor local modifications, at its greenfield Chengdu plant in China. Siemens claims that, on a technology and process level, the plant is a one-on-one replication of the Amberg plant.

Other companies have employed the opposite approach. Rather than building a new smart factory based on best practices from an existing plant, they build a new flagship plant and use it as the central element in their smart manufacturing network strategy. Example:

• Bonfiglioli. In 2016, Bonfiglioli, an Italian manufacturer of power transmission components, introduced its “EVO” (short for evolution) strategy to build technologically advanced smart factories. The company built a new flagship factory at its Clemintino Bonfiglioli site, Italy, and is now using the plant to recruit new employees, train existing employees, and continuously transfer technologies and methodologies to its other plants in various locations worldwide.

7. A smart factory must be fully automated.

WRONG. While many smart factories contain highly automated systems, automation is not a prerequisite for realizing the goals of smart factories.

According to our research, data, not automation, is the key foundation underpinning all smart factory use cases. Technology that enables the acquisition, orchestration, and analysis of relevant data will empower the humans running the factory to make faster, more informed, and ultimately better decisions. Example:

Hugo Boss. Apparel manufacturing is considered one of the most labor-intensive industries. Hugo Boss’s apparel manufacturing smart factory in Izmir, Turkey, has implemented technologies such as augmented reality, machine learning, cloud computing, and data analytics alongside its 3,500 workers to produce suits, jackets, shirts, and coats. The factory improves efficiency by using more than 1,600 Wi-Fi-enabled tablets to capture data and empower front-line employees with order and operational data.

Conclusion

As the example of the Moderna smart factory has shown, investing in production technology, personnel, and processes can pay enormous dividends. Today’s generation of smart factories is not necessarily characterized by heavily high-tech greenfield installation. Due to the rapid change in customer preferences and the availability of promising technologies, companies of all shapes and sizes can (and should) have a comprehensive smart factory strategy in 2021—regardless of their current digital maturity. The messaging is clear: Companies that have already embarked on their smart factory journey will continue to adapt better to the dynamic business environment and grow faster than those that have not.

Here are three action points that, based on our research, have worked for several manufacturers to start their smart factory journey:

• Action point 1: Focus on a use case and not a technology. Out of the many use cases out there, pick an applicable high-ROI, high benefit use case and then experiment with suitable technologies. (Note: IoT Analytics has performed several IoT use case analyses and measured ROI of each, e.g., in 2019 as part of the Industry 4.0 Adoption Report. We will publish a new IoT Use Case Adoption Report in September 2021, based on input from 200+ practitioners.)
• Action point 2: Do not wait for employee engagement. Engage front-line workers from the beginning of the smart factory project. Alleviate their doubts through transparent communication and empower them through upskilling efforts. Communicate, communicate, communicate.
• Action point 3: Tie the smart factory to an existing process improvement initiative. Support the initiative with smart factory technologies to make processes more efficient with technology.

The post What are smart factories? 7 misconceptions and a definition appeared first on IoT Business News.

Even though the number of connected IoT devices in the consumer space currently exceeds those in the industrial one, Industrial IoT investments are seeing strong growth, in terms of cross-sector solutions as well as devices made to meet the needs of specific sectors.

Reply’s latest research “Industrial IoT: A Reality Check” explores two key areas that are driving the growth of IoT within the industrial market: smart factory and smart transport & logistics.

By connecting machinery and tools, the Industrial IoT (IIoT) enables manufacturing companies to improve the visibility of their production in real time. The huge amount of data generated by Industrial IoT devices constitutes the fuel for optimising production, improving the delivery quality, introducing predictive maintenance, automating the supply chain and much more.

Filippo Rizzante, Reply CTO, explains:

“Without the Industrial IoT, Industry 4.0 cannot exist. Data are the fuel for all “smart” use cases in the industrial world; Industrial IoT is the crucial element that guarantees the infrastructure to collect data, to send them to the cloud and to manage the feedback post-analysis, all as part of a virtuous circle of benefits to business.”

Market growth

The research, carried out thanks to the data collected with the use of the proprietary Trend Sonar platform and the support provided by the Teknowlogy Group, also examines the main markets for smart factories and smart transport & logistics, grouped into two clusters: “Europe-5” (Germany, Italy, France, Belgium and the Netherlands) and “Big-5” (the USA, China, India, Brazil and the UK).

Despite the though economic climate of 2020, both clusters saw a small growth in investments in smart factories, as well as in the smart transport & logistics area, while further and much more significant growth is expected by 2025. Overall, the smart factory market of the “Big-5” cluster, led by the US, is expected to exceed €86 billion by 2025, with strong investments in platforms, predictive solutions and remote monitoring. The smart transport & logistics market is set to exceed €15 billion. In the “Europe-5” cluster, on the other hand, the smart factory market is expected to nearly triple in all countries, reaching a total of over €23 billion in the five countries in question, with Germany in the lead. Platforms are set to experience exponential growth and businesses will invest to manage quality better and to reduce costs. Germany will also remain the leader in the smart transport & logistics area, but the other countries in the cluster will still see significant growth. This Cluster is expected to reach a total value of €3.6 billion in 2025.

The drive of 5G and edge computing

The adoption of low-cost sensors and 5G networks, driven by large investments by Telcos, will further improve the diffusion of Industrial IoT. For example, improved communication between autonomous vehicles/robots, artificial intelligence and machinery, combined with increased computing power and very low latency, is expected to improve not only the efficiency of plants, but also their safety. Moreover, the ability to create high-density private networks will enable a wider deployment of the Industrial IoT, as well as the connection of a significant number of sensors, machinery, vehicles and robots, complemented by a greater use of augmented and virtual reality to support “connected workers”.

Cybersecurity is a crucial factor

The constant growth of connected devices and their heterogeneity requires bold security management of the setup and maintenance policy of both devices and networks. Based on its experience, Reply believes that organisations need to adopt micro-segmented environments (on-premises and/or cloud-based), which are stable and ready to react to both traditional and new dangerous technologies and techniques, thus reducing the probability that new types of attacks will be successful. The analysis of IoT architecture, of industrial components and of entire infrastructures will help companies to eliminate existing gaps, vulnerabilities and threats in advance. But this is much more than merely a technological issue: training programmes aimed at employees, together with the study and continuous testing of all devices used will also be crucial.

From factories to consumers

If, in recent years, Industrial Internet of Things technologies have been adopted and used, above all, to improve the efficiency of factories and logistics centres, during the pandemic, new investments have been directed primarily towards improving worker safety. The long-term trend, however, is expected to directly involve final consumers. The success of so-called “connected products” is, in fact, accelerating investments towards solutions in which the collection and processing of usage data does not only involve production machinery, but also the use of finished products. The redesign of design, production and distribution processes of IoT-connected products is enabling the creation of value-added services and facilitating the ability to remotely update and maintain household appliances, cars, robots, electronics and entertainment devices.

The post The IoT, Together With the Evolution of 5G and Edge Computing, Is Driving Industrial Innovation appeared first on IoT Business News.

Data is growing by every second and in total compliance to the big data’s 3V rule – volume, velocity, and value that the world has been witnessing in the past decade. Today, with various methods of data storage like private, public, hybrid and on-premise storage methods, collecting and storing data is no longer a challenging task. But with such massive amounts of data to handle, the ability of enterprises to harness, analyze, and take quick business decisions has become increasingly complex. To bridge this gap between the big data expertise to bigger data readiness, data fabrics are a clear winner.

Data fabric does the conversion of raw data sets to the most appropriate, actionable and worth investing data insights. Many companies have evolved from the traditional methods of data preparation techniques to providing insightful approaches.

One such approach is called the K2View approach. In this approach, a patented micro-DB methodology is used to store data through a digital entity wherein every entity represents a specific business partner. Every time the fabric captures data, the schema processes and distributes it into a micro-DB. While every micro-DB represents a specific digital entity, it is encrypted with a primary key thereby ensuring highly configurable data synchronization. With a focus on making applications smarter, be it domestic or industrial, the data fabric performs end-to-end automation of the data preparation pipeline.

Data Fabric for IIoT: Weaving The Right Architecture for the Industrial Floor

Data is the core of the evolution of predictive models. While capturing & storing more data is just one part of it, distilling and refining it into a valuable asset class is a real challenge. With data fabrics, this data is filtered at an early stage thereby making it easier to prepare the data. This means, collecting, integrating, analyzing, and archiving data are all performed, automatically. Not to miss, the process evolves gradually and as the models understand the raw data; their performance in automating the industrial equipment improves too. According to data fabric analysis, the fabric also helps in the transition from manual monitoring to the self-governed evaluation of detecting abnormalities.

Over a period, these models would mature into prescriptive entities that execute guidelines more accurately and have an impact on the physical world. Next comes the on-demand deployment of predictive models for a wide variety of industrial use cases. Hosted in the cloud, these models would be accessible from anywhere to the business requirements. Ultimately, these models will lay the foundation for enhanced automation wherein industrial processes learn and repair themselves.

Data Fabric for Edge: Optimize Communication With The Core

While we are discussing IoT, Edge deserves a mention too. After all, the technology’s disruptive demand cannot be met without fabrics. Now the edge is bound to grow because it is easier to build sustainable IoT in a location that is geographically closer to its end customers. This reflects the bottom line costs due to the lesser number of sensors and other essential devices. Moreover, it is easier to monitor the distributed computations across the edge clusters and the core.

One of the major issues with edge computing is also now resolved. For many years, edge computing did not get mainstream partially because of insufficiencies in real-time data preparation and partially because of the unforeseen environmental conditions that may vary from edge-to-edge. While fabrics have addressed the data preparation issues, improved quality of the hardware is doing the essential data processing. The high-quality hardware casing ensures uninterrupted operations in different conditions, no matter how extreme they are.

However, there are additional complexities involved in adopting edge.

The ability to stream data continuously between the core and the edge has now become a major concern. The edge-core communication is a universal business requirement and fabrics have a solution for this as well.

Consider the use case of a service that provides continuous & on-demand content to millions of users. Most common examples include video streaming platforms (Netflix etc.), social media or e-learning platforms. Now, to maximize uptime, Edge computing could help in eliminating latency by providing streaming nearest to the end consumers. However, without analytics, the very objective of automated digital services is incomplete. The problem with most Edge solutions is the inability to compute and stream analytics data (customer consumption, preferences, etc.) back to the core and ultimately the business CRM landscape.

Using a distributed data fabric the complexity can be reduced to revolutionary levels. This is a simple and secure approach to provide on-demand data from the edge to the system landscape and ultimately to the sales, marketing & support teams.

Conclusion

It is safe to say that fabrics and IoT‘s growth are interoperable with each other. To make smarter apps & processes, we need to send/receive filtered data at the moment across a network of devices. Automated data preparation pipelines are the potential solution to exchange high-quality data.

The post The Interoperable Growth of Data Fabric and IoT appeared first on IoT Business News.

Putting machines to communicate with humans is a beauty called the internet-of-things. To put industrial machines to communicate is a breakthrough and perhaps the biggest of all we know. Since everyone is talking about 5G, it is equally important to discuss the optimal potential of the next-gen network bandwidth. In all honesty, industry 4.0 was an upcoming trend until the networking hassles were put to rest with the inception of private 5G networks.

They are pacing to touch USD 15.7 billion by 2026 at a CAGR of 79% and that’s huge. As a result, multiple IIoT applications that had waited for speedier networks are finally actualized to their optimal potential.

Why 5G is better for IIoT?

No matter 4G, networks have provided the needed start to the 4.0 revolution, it is time to upscale the strength and achieve bigger, better and speedier processes on the floor. For IIoT businesses, 5G is leaps & bounds ahead of 4G; the delay in sending/receiving data is 1 millisecond in 5G compared to 200 milliseconds in 4G. Now imagine the change for industries that work in highly agile environments such as FMCG, healthcare, etc. Moreover, 5G can connect up to a million devices per .38 square miles. This is 100 times more devices than 4G and a sure-shot boon for unifying private networks for cross-location units. Briefly, it can handle more volume of data at a greater speed yet lesser cost.

However, for industries, it means more than just fast internet.

The operational technology suite (OT) in an industrial setup uses a wide variety of networking technologies both fixed and mobile. To control and communicate with devices and floor processes, the software application has to stream data in real-time. For example, digital twins, automated production lines, remote monitoring through live videos, predictive maintenance etc. cannot perform in average networks. Their performance requirement is demanding and the usual Wi-Fi or LTE networks will not suffice.

5G bandwidths are strong enough to support automated guided vehicles (AGVs) at limited densities. To upscale the production, it can support a greater number of AGVs per service area. Others such as extended reality headsets are also making progress with the backing of faster streaming. Also known as data goggles, the emergency device helps maintenance engineers seek real-time insights into complicated industrial requirements. Not to miss, it ensures round-the-clock remote control of equipment and processes, no matter how complex the IIoT landscape is.

For any corporate setup, slicing is an integral strategy in the networking stack. Through virtualization, it is used to split the incoming network into multiple networks for distinctive customers. Moreover, no traffic from other networks would influence the quality of service on the particular slice. Just like a tenant on a cloud platform, slice obtains similar exclusivity. 5G network slicing makes it faster to enable or disable network fragments and create on-demand sub-networks for particular departments in an industrial setup.

5G is what big data has dreamt about. Over the years, organizations have built data management infrastructure to store, process and stream real-time analytics. Since IoT works closely with big data, 5G networking is a step ahead to implement edge computing. For industries, it is a trend in the making.

While we are at it, the omnipresence of the 5G private network should not be ignored.

Since most production units operate from different locations, it is essential that the network capacity in all the units is the same. floLIVE, for example, provides a private 5G networking solution to support a wide variety of use cases for small as well as global enterprises. So be it the network for a private campus, multiple campuses within a country or a cross-country service, their private network solution supports multiple RAN providers. The IoT solution is built over a Software-defined Connectivity (SDC) infrastructure to ensure flexibility and security.

Through pre-allocated IMSIs from an internal IMSI library, enterprises can avail multiple operators and yet create a unified private network for their enterprise. Working as a single private network, their platform simplifies cross-border regulatory compliance hassles. With an emphasis on performance, the cloud-native solution enables policies in line with enterprise verticals, staff safety, corporate security and confidentiality.

The solution resolves a key network infrastructure challenge for the industries – centralized management of the multisite & multi-facility with on-demand deployment options.

Conclusion

Industry 4.0 could never be a possibility without remote & predictive analysis of data. The ability to track using process and performance sensors and generate on-request insights in real-time is the very foundation of what we call smart industrialization today. Everything else that you know about IIoT is related in one way or the other. With the evolution of technology, remote monitoring in IIoT will become mainstream just like AI. The differentiator, however, will be the QoS in controlling the equipment anytime and from anywhere.

The post 5G is here and so is the mainstream adoption of IIoT Startups appeared first on IoT Business News.

IoT strategy consulting firm Oxelar taps Octave to cut time to value for Thermigas’ preventive maintenance Industrial IoT application by 600 percent.

Sierra Wireless today announced that Thermigas is using Octave, its all-in-one edge-to-cloud solution, to increase uptime and reduce maintenance costs for industrial water heaters.

Using Octave, IoT services & strategy consulting firm Oxelar developed and deployed a preventive maintenance Industrial IoT (IIoT) application in less than two months. By connecting its water heaters to the cloud, the application alerts Thermigas and its customers if one of these heaters needs a settings adjustment or other maintenance to prevent downtime.

Patrice Le Du, Thermigas CEO, said: “Our water heaters are business critical. If they stop working, our customers have to shut down their production lines. We’ve been trying to develop a predictive maintenance IIoT application to guarantee our water heaters’ reliability for years. But connecting our water heaters to the cloud was more challenging than we expected, and we didn’t make the progress we needed to. That changed this year. Using Octave, Oxelar delivered a working prototype at a speed we didn’t think was possible. Launching this application is a significant achievement that will drive improvements in uptime of our water heaters, cut our maintenance costs, and create a new revenue stream for our company.”

Reducing Equipment Downtime with Preventive Maintenance

Thermigas’ industrial water heaters are used by its customers to process food, chemicals, and pharmaceuticals, as well as to treat or clean industrial surfaces. Even a few minutes of downtime can stop the customers’ production lines, costing them thousands of euro for short stoppages and even higher losses for longer ones.

The new preventive IoT platform developed and managed by Oxelar extracts temperature, pressure, and other data from Thermigas’ industrial water heaters and orchestrates its integration into the cloud. If this data indicates a problem, the cloud-based IIoT application then sends real-time alerts (via email and/or SMS) to Thermigas and the customer, informing them that they need to act to prevent equipment downtime. For example, customers can avoid downtime by logging into the IIoT application to remotely adjust the heater’s settings, manually do maintenance themselves, or schedule a maintenance visit by a Thermigas technician.

Improving Loyalty, Lowering Costs, and Creating Revenue with Industrial IoT

Thermigas’ industrial water heaters are extremely reliable, with average uptimes higher than 99.9 percent. However, considering how business-critical the heaters are for its customers, Thermigas has been exploring how to use the IIoT to further increase that uptime percentage since 2016. Such an IIoT application would boost customer loyalty and satisfaction by increasing water heater reliability, but it would also allow Thermigas to ensure their water heaters are working properly prior to annual inspections by government inspectors. This would cut their costs by reducing the need to send service technicians to customers’ facilities prior to inspections.

Additionally, Thermigas planned to create new, recurring revenue by providing customers with predictive maintenance as a service, since it would lower its customers’ total cost of ownership.

Building and Commercializing an Industrial IoT Application In Less Than Two Months

For more than four years Thermigas struggled to build this application from scratch, with the extraction of data from its water heaters’ controllers, sensors, and other Operational Technology (OT) systems in particular causing them problems.

Thermigas recently partnered with Oxelar to help them find a way to overcome the barriers that were preventing them from launching a predictive maintenance application. Oxelar initially estimated it would take six to nine months for it to build the IIoT infrastructure needed to extract data from Thermigas’ water heaters and integrate it into the cloud.

However, Oxelar then tested Octave, and realized that the Modbus protocol and APIs built into the edge-to-cloud solution would allow them to easily extract data from Thermigas’ OT systems. With Octave, Oxelar was able to move from the start of application development to deployment of a commercial application prototype in less than eight weeks. Having rigorously tested the application with one of their largest customers, Thermigas currently plans to make its new predictive maintenance IIoT application generally available to all its customers in January 2021.

François Dumoulin, Oxelar CEO, said: “We have been working with customers for years on IIoT applications for remote monitoring of industrial equipment. It can take months to have the software written and to assemble the other infrastructure needed to extract, orchestrate, and act on data from this equipment. Sierra Wireless Octave simplifies this entire process. We plugged the Octave FX30 IoT gateway into the Thermigas water heater’s controllers and sensors, and were able to begin extracting data almost immediately. We are already looking at more opportunities to use Octave to provide our customers with even higher return on their IIoT investments.”

Freeing Firms to Focus on Industrial IoT Data, Not Infrastructure

In addition to reducing time to value for the application, with Octave Thermigas can send firmware and application updates over the air to their water heaters, making it easy for them to update the application with new energy efficiency and other capabilities over time.

Octave also offers Thermigas predictable message-based pricing, allowing them to accurately estimate their data transmission costs, and extensive wireless coverage, thanks to its ability to automatically connect to the best available 2G or LTE-M Low Power Wide Area (LPWA) network in an area.

Olivier Pauzet, VP Product IoT Solutions at Sierra Wireless, said: “For years, Thermigas’ efforts to create smart, IIoT-enabled assets has been hampered by complexity. This happens to too many companies, and its why we introduced Octave. By freeing Oxelar from having to worry about how to extract data from Thermigas’ water heaters and orchestrate its integration into the cloud, they could focus on using this data to predict when these heaters might need maintenance. With Octave companies can stop dreaming of how they will use the IIoT to transform their business – and start doing it.”

The post Thermigas Increases Industrial Water Heater Uptime with Sierra Wireless Octave appeared first on IoT Business News.

• Amazon Monitron provides customers an end-to-end machine monitoring solution comprised of sensors, gateway, and machine learning service to detect abnormal equipment conditions that may require maintenance
• Amazon Lookout for Equipment gives customers with existing equipment sensors the ability to use AWS machine learning models to detect abnormal equipment behavior and enable predictive maintenance
• AWS Panorama Appliance enables customers with existing cameras in their industrial facilities with the ability to use computer vision to improve quality control and workplace safety
• AWS Panorama Software Development Kit (SDK) allows industrial camera manufacturers to embed computer vision capabilities in new cameras
• Amazon Lookout for Vision uses AWS-trained computer vision models on images and video streams to find anomalies and flaws in products or processes
• Axis, ADLINK Technology, BP, Deloitte, Fender, GE Healthcare, and Siemens Mobility among customers and partners using new AWS industrial machine learning services

Today at AWS re:Invent, Amazon Web Services, Inc. (AWS), an Amazon.com company, announced Amazon Monitron, Amazon Lookout for Equipment, the AWS Panorama Appliance, the AWS Panorama SDK, and Amazon Lookout for Vision.

Together, these five new machine learning services help industrial and manufacturing customers embed intelligence in their production processes in order to improve operational efficiency, quality control, security, and workplace safety. The services combine sophisticated machine learning, sensor analysis, and computer vision capabilities to address common technical challenges faced by industrial customers, and represent the most comprehensive suite of cloud-to-edge industrial machine learning services available. This is why more than a hundred thousand customers are using AWS for machine learning, and why customers of all sizes and across all industries are using AWS services to make machine learning core to their business strategy.

Companies are increasingly looking to add machine learning capabilities to industrial environments, such as manufacturing facilities, fulfillment centers, and food processing plants. For these customers, data has become the connective tissue that holds their complex industrial systems together. Industrial systems typically have numerous interdependent processes that operate with small tolerances for error, and even minor issues can have major ramifications. Being able to analyze data about the equipment operating in their facilities helps customers address this challenge, and many customers have embraced services like AWS IoT SiteWise as a way to collect data and generate real-time performance metrics from their industrial equipment. As customers have begun to use the cloud to collect and analyze industrial data, they have also asked for new ways to incorporate machine learning to help make sense of the data and further drive operational efficiency. In some cases, customers want to use machine learning to help them realize the promise of predictive maintenance to reduce costs and improve operational efficiency. In other cases, customers running in disconnected or latency-sensitive environments want to use computer vision at the edge to spot product defects and improve workplace safety. With these evolving needs and opportunities, industrial companies have asked AWS to help them leverage the cloud, the industrial edge, and machine learning together to get even more value from the vast amounts of data being generated by their equipment.

Amazon Monitron and Amazon Lookout for Equipment enable predictive maintenance powered by machine learning

A major challenge facing industrial and manufacturing companies today is the ongoing maintenance of their equipment. Historically, most equipment maintenance is either reactive (after a machine breaks) or preventive (performed at regular intervals to ensure a machine doesn’t break). Reactive maintenance can result in significant costs and downtime, while preventive maintenance can be costly, result in over-maintenance, or fail to prevent breakdown if not performed often enough. Predictive maintenance (the ability to foresee when equipment is likely to need maintenance) is a more promising solution. However, in order to make it work, companies have historically needed skilled technicians and data scientists to piece together a complex solution from scratch. This included identifying and procuring the right type of sensors for the use case and connecting them together with an IoT gateway (a device that aggregates and transmits data). Companies then had to test the monitoring system and transfer the data to on-premises infrastructure or the cloud for processing. Only then could the data scientists on staff build machine learning models to analyze the data for patterns and anomalies, or create an alerting system when an outlier was detected. Some companies have invested heavily in installing sensors across their equipment and the necessary infrastructure for data connectivity, storage, analytics, and alerting. But even these companies typically use rudimentary data analytics and simple modeling approaches that are expensive and often ineffective at detecting abnormal conditions compared to advanced machine learning models. Most companies lack the expertise and staff to build and refine the machine learning models that would enable highly accurate predictive maintenance. As a result, few companies have been able to successfully implement predictive maintenance, and those that have done it are looking for ways to further leverage their investment, while also easing the burden of maintaining their homegrown solutions.

Here’s how the new AWS machine learning services can help:

• For customers who do not have an existing sensor network, Amazon Monitron offers an end-to-end machine monitoring system comprised of sensors, a gateway, and a machine learning service to detect anomalies and predict when industrial equipment will require maintenance. Amazon Monitron enables customers to remove cost and complexity from building a sophisticated, machine learning-driven predictive maintenance system from scratch, and it also allows them to focus on their core manufacturing, supply chain, and operations functions. Amazon Monitron detects when machines are not operating normally based on abnormal fluctuations in vibration or temperature, and notifies customers when to examine machinery in order to determine if preventative maintenance is needed. The end-to-end system includes IoT sensors to capture vibration and temperature data, a gateway to aggregate and transfer data to AWS, and a machine learning cloud service that can detect abnormal equipment patterns and deliver results in minutes with no machine learning or cloud experience required. With Amazon Monitron, maintenance technicians can start tracking machine health in a matter of hours, without any development work or specialized training. Amazon Monitron can be used on a variety of rotating equipment, such as bearings, motors, pumps, and conveyer belts in industrial and manufacturing settings. Use cases range from monitoring a few critical machines like the cooling fans or water pumps used in data centers, to large scale installations in manufacturing facilities with production and conveyance systems. Amazon Monitron also includes a mobile app for a customer’s onsite maintenance technicians to monitor equipment behavior in real time. With the mobile app, a technician can receive alerts of any abnormal equipment conditions across different machines, check up on the health of the machine, and decide if they need to schedule maintenance. To increase the accuracy of the system, technicians can enter feedback on the accuracy of the alerts in the mobile app, and Amazon Monitron learns from that feedback to continually improve over time. Amazon Monitron is now generally available.

• For customers that have existing sensors but don’t want to build machine learning models, Amazon Lookout for Equipment provides a way to send their sensor data to AWS to build models for them and return predictions to detect abnormal equipment behavior. To get started, customers upload their sensor data to Amazon Simple Storage Service (S3) and provide the S3 location to Amazon Lookout for Equipment. Amazon Lookout for Equipment can also pull data from AWS IoT SiteWise, and works seamlessly with other popular machine operations systems like OSIsoft. Amazon Lookout for Equipment analyzes the data, assesses normal or healthy patterns, and then uses the learnings from all of the data on which it is trained to build a model that is customized for the customer’s environment. Amazon Lookout for Equipment can then use the machine learning model to analyze incoming sensor data and identify early warning signs for machine failure. This allows customers to do predictive maintenance, saving them money and improving productivity by preventing the crash of an industrial system line. Amazon Lookout for Equipment allows customers to get more value from their existing sensors, and it helps customers make timely decisions that can materially improve the entire industrial process.

AWS Panorama uses computer vision to improve industrial operations and workplace safety

Many industrial and manufacturing customers want to be able to use computer vision on live video feeds of their facility and equipment to automate monitoring or visual inspection tasks and to make decisions in real time. For example, customers routinely need to inspect high-speed processes to determine if adjustments are needed (e.g. fine milling or laser tooling), to monitor site and yard activity to ensure operating compliance (e.g. ensure pedestrians and forklifts remain in designated work zones), or to assess worker safety within their facilities (e.g. appropriate social distancing or use of PPE). However, the typical monitoring methods used today are manual, error prone, and difficult to scale. Customers could build computer vision models in the cloud to monitor and analyze their live video feeds, but industrial processes typically need to be physically located in remote and isolated places, where connectivity can be slow, expensive, or completely non-existent. This problem is even more difficult for industrial processes that involve manual review like quality checks on manufactured parts or security feeds. For example, if a quality issue emerges on a high throughput production line, customers want to know immediately because the costs of letting the problem persist is steep. This type of video feed could be automatically processed in the cloud using computer vision, but video feeds are high bandwidth and can be slow to upload. As a result, customers are required to monitor video feeds in real time, which is hard to do, error prone, and expensive. While there is a desire to use smart cameras that have enough processing power to run these models, getting low latency performance with good accuracy from these cameras can be challenging. Most customers end up running unsophisticated models that can’t be programmed to run custom code that integrates into the industrial machines.

Here’s how AWS can now help:

• The AWS Panorama Appliance provides a new hardware appliance that allows organizations to add computer vision to existing on-premises cameras that customers may already have deployed. Customers start by connecting the AWS Panorama Appliance to their network, and the device automatically identifies camera streams and starts interacting with the existing industrial cameras. The AWS Panorama Appliance is integrated with AWS machine learning services and IoT services that can be used to build custom machine learning models or ingest video for more refined analysis. The AWS Panorama Appliance extends AWS machine learning to the edge to help customers make predictions locally in sites without connectivity. Each AWS Panorama Appliance can run computer vision models on multiple camera streams in parallel, making possible use cases like quality control, part identification, and workplace safety. The AWS Panorama Appliance works with AWS and third party pre-trained computer vision models for retail, manufacturing, construction, and other industries. Also, customer-developed computer vision models developed in Amazon SageMaker can be deployed on the AWS Panorama Appliance.

• The AWS Panorama Software Development Kit (SDK) enables hardware vendors to build new cameras that can run meaningful computer vision models at the edge. Cameras that are built with the AWS Panorama SDK run computer vision models for use cases like detecting damaged parts on a fast-moving conveyor belt or spotting when machinery is outside of a designated work zone. These cameras can use chips designed for computer vision from NVIDIA and Ambarella. By using the AWS Panorama SDK, manufacturers can build cameras with computer vision models that can process higher quality video with better resolution for spotting issues. They can also build more sophisticated models on low-cost devices that can be powered over Ethernet and placed around a site. Customers can train their own models in Amazon SageMaker and deploy them on cameras built with the AWS Panorama SDK with a single click. Customers can also add Lambda functions to cameras built with the AWS Panorama SDK to be alerted to potential issues via text or email. AWS also offers pre-built models for tasks like PPE detection and social distancing, and can deploy these models in minutes without doing any machine learning work or special optimizations.

Amazon Lookout for Vision offers automated fast and accurate visual anomaly detection for images and video at a low cost

One use case where AWS customers are excited to deploy computer vision with their cameras is for quality control. Industrial companies must maintain constant diligence to maintain quality control. In the manufacturing industry alone, production line shutdowns due to overlooked errors result in millions of dollars of cost overruns and lost revenue every year. The visual inspection of industrial processes typically requires human inspection, which can be tedious and inconsistent. Computer vision brings the speed and accuracy needed to identify defects consistently, but implementation can be complex and require teams of data scientists to build, deploy, and manage the machine learning models. Because of these barriers, machine learning-powered visual anomaly systems remain out of reach for the vast majority of companies.

Here’s how AWS can now help these companies:

• Amazon Lookout for Vision offers customers a high accuracy, low-cost anomaly detection solution that uses machine learning to process thousands of images an hour to spot defects and anomalies. Customers send camera images to Amazon Lookout for Vision in batch or in real-time to identify anomalies, such as a crack in a machine part, a dent in a panel, an irregular shape, or an incorrect color on a product. Amazon Lookout for Vision then reports the images that differ from the baseline so that appropriate action can be taken. Amazon Lookout for Vision is sophisticated enough to handle variances in camera angle, pose, and lighting arising from changes in work environments. As a result, customers can accurately and consistently assess machine parts or manufactured products by providing as few as 30 images of the baseline “good” state. Amazon Lookout for Vision also runs on Amazon Panorama appliances. Customers can run Amazon Lookout for Vision in AWS starting today, and beginning next year, customers will be able to run Amazon Lookout for Vision on AWS Panorama Appliances and other AWS Panorama devices so customers will be able to use Amazon Lookout for Vision in locations where Internet connectivity is limited or non-existent.

“Industrial and manufacturing customers are constantly under pressure from their shareholders, customers, governments, and competitors to reduce costs, improve quality, and maintain compliance. These organizations would like to use the cloud and machine learning to help them automate processes and augment human capabilities across their operations, but building these systems can be error prone, complex, time consuming, and expensive,” said Swami Sivasubramanian, Vice President of Amazon Machine Learning for AWS.

“We’re excited to bring customers five new machine learning services purpose-built for industrial use that are easy to install, deploy, and get up and running quickly and that connect the cloud to the edge to help deliver the smart factories of the future for our industrial customers.”

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IT/OT Integration is critical for answering the $77 billion need for IIoT

With the IIoT market expected to grow from $77.3 billion in 2020 to $110.6 billion by 2025, 73% of manufacturers plan to increase their investment in smart factory technology over the next year.

As IIoT sensors produce 1.44 billion data points per plant per day, IT/OT integration is critical to improving operational efficiency while accelerating success through digital transformation initiatives. However, enterprises have traditionally been challenged with converting real-time, historic OT data from legacy systems into higher-level IT insights.

Data produced on the factory floor needs to maintain its rich context (such as process conditions, time stamps, machine states, and other production states) to provide maximum insights to factory staff. Previously, aggregating the data generated by machines in processes required significant manual effort and pulling information from many disparate sources. Instead, by automatically capturing high-speed, contextualized OT data from industrial controllers in real-time, organizations can generate predictive insights and operational excellence across the enterprise. By applying context to data pulled from the factory floor, OT teams will create more powerful analytics to better understand the data and how it impacts the machines, lines, plants, and processes they are responsible for. In 2021, IT/OT integration will directly impact whether enterprises remain or become more competitive in the global manufacturing landscape.

Edge is the new cloud

For companies scaling smart factory initiatives in 2021, real-time availability of mission-critical workloads will be necessary to ensure business outcomes. Edge computing will complement existing cloud infrastructure by enabling real-time data processing where the work takes place (e.g., motors, pumps, generator, or other sensors). Implementing integrated analytics from the edge to the cloud will help these enterprises maximize the value of investments in digital systems.

The industry will continue to move toward more decentralized compute environments, and the edge will add significant value to digital transformation initiatives. By integrating edge functionalities with existing cloud infrastructure, organizations will worry less about logistical IT considerations and, instead, focus on rethinking what’s possible in a smart machine: What questions can it answer faster? What new problems can it solve? How can it protect operations better? Analysts note that by 2022, 90% of industrial enterprises will utilize edge computing for this reason.

Digital twins save $1 trillion in manufacturing costs

Over the next 12 months, by interconnecting business systems via digital thread, organizations will virtually commission new production lines. Using digital twins, manufacturers will run machines virtually before parts are ordered, discover control issues before support staff goes on-site, predict future performance challenges/opportunity, simulate line changes to keep up with ever-changing customer demands and train new staff on systems without consequence. Gartner estimates that businesses will save US$1 trillion each year in asset maintenance by using IoT through digital twins. IDC suggests that 30% of Global 2000 companies will be using data from digital twins of IoT connected products and assets to improve product innovation success rates and organizational productivity, achieving gains of up to 25%. In 2021, organizations will use digital twins, enabled by digital thread, to solve lifecycle challenges in the digital world before they turn into “if only” moments in the real world, lowering overall manufacturing costs and increasing factory productivity.

Pandemic promotes AR training as the new standard for a distributed workforce

About 70% of manufacturers say the biggest impacts of robotics on the workforce in the next five years will be an increased need for talent to manage in a more automated, flexible production environment and the opening of new jobs to engineer robotics and their operating systems. Since on the job training is no longer possible due to social distancing requirements, manufacturers will fill the gap with remote training tools, such as augmented reality (AR) and 3D-based work instructions, to allow workers to train with experts remotely and optimize capture and delivery. Using advanced technologies to train workers will enable them to analyze performance in real-time, troubleshoot issues more quickly, improve productivity, and avoid significant downtime for unforeseen repairs. This will ultimately make the manufacturing workforce more connected and data-driven, narrowing the skills gap while avoiding safety and compliance risks.

Automation accelerates employee advancement through human-machine interface

With COVID-19’s impact on factory capacity and new social distancing regulations, technology is vital for success, but people are still the heart of the operation. With Industry 4.0 comes the opportunity to evolve jobs. Automation will prevent a crowded factory floor while replacing tedious, repetitive tasks. This frees up workers to leverage their creativity and expertise to find unique ways to use technology to improve operations or solve critical issues. For example, a worker may oversee analyzing data from connected machines to forecast downtime or find ways to consolidate processes to reduce the number of steps needed to create a product. By upskilling workers with human-machine collaboration, manufacturers will safely comply with new health regulations and accelerate production amid the pandemic and beyond.. Since people are critical for machines to reach their full potential, manufacturers must enter projects with a human-first mindset focusing on employee needs and adapting processes accordingly to remain competitive.

The post Rockwell Automation Predictions for 2021 appeared first on IoT Business News.

Smart Manufacturing to Represent 60% of Global Industrial IoT Connections.

A new study from Juniper Research has found that the global number of Industrial IoT connections will increase from 17.7 billion in 2020 to 36.8 billion in 2025; representing an overall growth rate of 107%.

The research identified smart manufacturing as a key growth sector of the Industrial IoT market over the next five years; accounting for 22 billion connections by 2025.

The new research, “Industrial IoT: Future Market Outlook, Technology Analysis & Key Players 2020-2025”, predicted that 5G and LPWA (Low Power Wide Area) networks will play pivotal roles in creating attractive service offerings to the manufacturing industry, and enabling the realisation of the ‘smart factory’ concept, in which real-time data transmission and high connection densities allow highly-autonomous operations for manufacturers.

5G to Maximise Benefits of Smart Factories

The report identified private 5G services as crucial to maximising the value of a smart factory to service users, by leveraging the technology to enable superior levels of autonomy amongst operations. It found that private 5G networks will prove most valuable when used for the transmission of large amounts of data in environments with a high density of connections, and where significant levels of data are generated. In turn, this will enable large-scale manufacturers to reduce operational spend through efficiency gains.

Software Revenue to Dominate Industrial IoT Market Value

The research forecasts that over 80% of global Industrial IoT market value will be attributable to software spend by 2025; reaching $216 billion. Software tools leveraging machine learning for enhanced data analysis and the identification of network vulnerabilities are now essential to connected manufacturing operations.

Research author Scarlett Woodford noted:

‘Manufacturers must exercise caution when implementing IoT technology; resisting the temptation to introduce connectivity to all aspects of operations. Instead, manufacturers must focus on the collection of data on the most valuable areas to drive efficiency gains.’

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New alliance connecting AI and IoT.

• Holistic production optimization with AI + IoT = AIoT
• Intelligent automation by linking IoT data and smart AI algorithms including reinforcement learning
• Companies increase production efficiency by up to 30 percent

Device Insight, established provider of IoT and IIoT solutions, and Sentian, a Swedish specialist in industrial AI, are pooling their expertise to help companies optimize their production processes along the lines of a Smart Factory.

This cooperation combines both of the most important current fields of technology, AI and IoT, to form an “Artificial Intelligence of Things” (AIoT) and at the same time take the intelligent automation of industrial manufacturing processes to a whole new level, enabling companies to increase the efficiency of their production by up to 30 percent.

Until now, most industrial companies have concentrated on predictive maintenance, leaving the opportunity to optimize their core processes with the help of artificial intelligence unused. In fact, it is precisely these gradual improvements in production processes that offer promising business value, enabling companies to significantly increase their product quality level as well as the efficiency of their operations.

AIoT makes industrial production “smart” – consistent and durable

The goal of the innovative AIoT approach is to continuously reduce deviations from the optimum within manufacturing processes. Fewer deviations mean improved machine and system performance, less waste and lower costs – and above all, more highest-quality products. The result: income and profit, as well as customer satisfaction will increase noticeably. Production will be transformed into a Smart Factory.

Linking IoT know-how and AI expertise

For the implementation of AIoT projects, Device Insight brings its expertise in connecting machines, aggregating and managing IoT data and linking AI applications into the partnership. Additional added value is created by the Munich-based IoT pioneer’s many years of expertise in the analysis and visualization of evaluations based on high-performance IoT components.

Swedish AI specialist Sentian contributes its advanced algorithms and solutions that help reduce deviations within individual production processes or even entire plants. Sentian’s mathematical optimization approach is groundbreaking, allowing fast and extremely precise planning as well as flexible replanning throughout production. Another special component is Sentian’s novel, model-based approach to “Reinforcement Learning” – the latest development in deep learning.

Why predictive maintenance is not enough

Thanks to this unique combination of AI and IoT, Device Insight and Sentian are now able to accompany companies on the way to intelligent production – away from individual solutions and selective improvements, such as those possible with predictive maintenance, and towards a holistically optimized smart factory.

“Predictive maintenance is still very important for the industry. When it comes to process optimization, however, predictive maintenance can only be of limited help,” says Marten Schirge, Managing Director at Device Insight.

“The real challenge within industry lies elsewhere. These days, many control systems are outdated and not very adaptable, while at the same time machines are becoming increasingly complex. This is the conflict area where we begin with AIoT. Together with our partner Sentian, we want to help companies fully exploit the hidden potential for better efficiency, higher quality and ultimately more profit.”

“Bringing AI to the core of production enables companies to truly benefit from AI”, says Martin Rugfelt, CEO at Sentian. “The potential of AIoT and our cooperation to deliver fully scalable solutions provides proof of value rather than just technical proofs. AI is ready to be operationalized.”

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A large portion of pump OEMs’ growth will be driven by services in the next 5 years.

Frost & Sullivan’s recent analysis, 2025 Vision: Future of Pumps in a Connected World, finds that an average of 50% to 60% of pump original equipment manufacturers’ (OEMs’) revenue is expected to be generated from services-related activities, such as real-time monitoring and reliability services.

This will result in the pump industry transitioning from a product-based to a service-based model in the wake of Industrial Internet of Things (IIoT) in this digitalization era. Global pump revenue is estimated to reach $46.92 billion by 2025 from approximately $38.34 billion in 2019.

Kiravani Emani, Industrial Automation & Process Control Research Analyst at Frost & Sullivan, said:
“In the next five years, a large portion of pump OEMs’ growth will be driven by services that leverage analytics to provide insights on improving pump reliability and lifetime. Additionally, service-based business models are expected to become more predominant; as a result, pump OEMs are expected to diversify their revenues and deliver standalone services to unlock new revenue streams.”

Emani added:

“Intelligent pumps with analytics capabilities are expected to be the new norm as customers require meaningful data insights on pump performance as opposed to a device that will merely display data. Further, the water and wastewater, chemicals, refining, and oil and gas production industries are expected to embrace IoT-based pump solutions as these industries are actively working towards digitalization.”

The advent of IIoT has unlocked innovative and profitable business models for pump vendors. The need to shift from a traditional business model (hardware) to services is presenting tremendous growth prospects for pump OEMs, including:

• Expanding service capabilities with a focus on building innovative business models to unlock revenue opportunities.
• Offering high-quality, reliable and precise services for customers by investing in IIoT-based technologies such as cloud computing and edge analytics.
• Adopting an integrated approach by leveraging the strength of channel partners to enhance the customer experience.
• Reducing energy consumption and controlling operational costs to boost the demand for energy-efficient pumps.

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Latest Release of Leading IIoT Platform to Enhance Scalability, Simplify Solution Development, and Expand OPC UA Support for Enterprise Deployments.

PTC today announced the upcoming release of the latest version of its market-leading ThingWorx® Industrial IoT platform.

Designed to accelerate Industrial IoT deployments across the enterprise value chain, ThingWorx 9.0 will deliver new and expanded features to help industrial companies create, implement, customize, and scale their solutions.

ThingWorx was the first platform that focused exclusively on the industrial market for digital transformation use cases. Since its initial launch, thousands of industrial companies, including discrete product and process manufacturers, have used ThingWorx to successfully optimize business processes, improve manufacturing operations, modernize field service delivery, and more. ThingWorx is an essential platform for delivering Industrial IoT solutions at scale, across the enterprise, and enables organizations around the world to gain competitive advantage and reduce costs.

ThingWorx 9.0 will deliver advances in several core development areas and introduce many new features and capabilities:

Enhanced Scalability and Availability

With Industrial IoT central to companies’ digital transformation efforts, ThingWorx 9.0 will introduce a new, optimized clustered configuration that will significantly improve the horizontal scalability and availability of the platform. This improvement will fortify the ability of ThingWorx to scale to vast populations of devices, manage demanding data processing requirements, and support a greater number of application users. Additionally, the clustered configuration will further strengthen ThingWorx deployments for critical operations, systems, services, and assets that need to remain highly available under the most important circumstances.

Accelerated Application and Solution Enablement

Expanding on the platform’s heritage as a standout tool for rapid application enablement, ThingWorx 9.0 will formally introduce solution building blocks. These building blocks are pre-defined, pre-built configurations of connectors, domain models, business logic, and UI elements, which will simplify implementations of the highest-value Industrial IoT use cases, such as status monitoring, digital work instructions, and manufacturing job order management.

Unleashing Data Models with Microsoft Using OPC Unified Architecture (OPC UA)

PTC and Microsoft share the vision to drive openness and interoperability in industrial IoT and support the industrial interoperability standard OPC UA. ThingWorx 9.0 will bring seamless integration with OPC UA components that Microsoft contributed to the OPC Foundation, including OPC UA Publisher, OPC UA Twin, and OPC UA Global Discovery Server, offering the ability to deliver intelligence and data richness from the edge to the cloud. OPC UA helps integrate the ThingWorx Kepware® connectivity solution and Microsoft Azure with ThingWorx, where data models get automatically standardized for simplified solution enablement.

Analytics Advancements

ThingWorx 9.0 will introduce enhanced predictive analytics scoring at the edge to reduce data transmission costs and latency challenges, and improve the accuracy of asset performance predictions. These advantages are important for deployments of ThingWorx at the enterprise level, as these companies rely on the insights from the ThingWorx Analytics solution to improve decision making, optimize operational processes, and reduce unplanned downtime.

Scaling ThingWorx with Solution Central

ThingWorx 9.0 will simplify the scaling of Industrial IoT deployments across the enterprise with enhancements to the Solution Central tool. Administrators will have access to a broader range of self-service functions for deploying ThingWorx solutions, managing ThingWorx environments, and enabling development team collaboration. Since its introduction in ThingWorx 8.5, Solution Central has been adopted by hundreds of ThingWorx customers and partners that are scaling ThingWorx deployments across their organizations.

“The introduction of ThingWorx 9.0 will mark an important advancement for PTC’s Industrial IoT strategy,” said Joe Biron, Divisional General Manager and Chief Technology Officer, IoT Segment, PTC.

“Our added investments in scalability, solution development, OPC UA support, and stronger analytics are critical as our customers continue to scale their Industrial IoT deployments across the enterprise. These enhancements come at an especially important time, as companies look for new ways to innovate and address challenges from the impact of COVID-19, such as the increased need for remote monitoring, control, and services.”

In addition to strong customer adoption and success, ThingWorx continues to see consistent acknowledgment as a leading Industrial IoT platform from industry analyst firms. Analysts continue to credit the advancements of ThingWorx as a platform, as well as PTC’s strategic alliances with Rockwell Automation and Microsoft.

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• 74% of manufacturing decision-makers surveyed plan to upgrade comms and control networks in next two years to advance digital transformation and Industry 4.0
• More than 90% are investigating use of 4G/5G for their operations
• 84% that are considering 4G/5G will deploy their own local private wireless network in their manufacturing operations

Nokia has partnered with ABI Research, an independent research firm, to survey more than 600 manufacturing decision-makers to assess investment strategies related to 4G/LTE, 5G and Industry 4.0.¹

The survey found that 74% of respondents are looking to upgrade their communications and control networks by the end of 2022 with more than 90% investigating the use of either 4G and/or 5G in their operations. Just over half of respondents (52%) believe that the latest generation of 4G/LTE and 5G will be necessary to meet their transformational goals.

The research also identified key business use cases that would drive investment in 4G or 5G. Respondents reflected the need to digitalize and improve existing infrastructure (63%), automation with robotics (51%) and achieve new levels of employee productivity (42%).

Manish Gulyani, Vice President Marketing, Nokia Enterprise said:

“We have reached an inflection point in Industry 4.0 transformation as the fast, secure, low latency connectivity underpinning its implementation now becomes available. This research indicates the strong marketplace appetite for industrial-grade wireless networking to capture the transformational benefits of digitalization and automation. We believe that demand, combined with easy-to-deploy private wireless solutions, will drive adoption.”

The research examined near-term drivers influencing buying decisions for new industrial systems across IT (information technology) and OT (operations technology). IT drivers primarily focus on reducing downtime (53%), improving operations efficiency (42%), and enhancing security (36%). In comparison, OT drivers reflect a desire to replace aging infrastructure (43%), improve efficiency (40%) and increase capacity (38%).

Further highlights indicate:

• 88% of respondents stated that they were familiar with private wireless (4G/5G) networking
• 84% that are considering 4G/5G will deploy their own local private wireless network in their manufacturing operations
• leading priority buying areas are automation and machine upgrades (47%), IIoT initiatives (41%), with cloud infrastructure following at (37%).

Ryan Martin, Principal Analyst, ABI Research said:
“Importantly, research findings indicate a preference for deploying private fully-owned and operated wireless networks, with manufacturers favoring in-house management to allay security concerns. It’s evident that respondents are not entirely committed to Wi-Fi/WLAN and will consider latest generations of wireless technologies. As a result, 2020 is a critical year for networking suppliers to educate the market regarding the merits of 4G/LTE and 5G.”

“Based on this research we also observe a pan-industry need to quantify not only the potential ROI of investing in private wireless, but also to clearly indicate the cost of inaction – vendors need to make the case for investing in Industry 4.0 today to gain a clear competitive advantage over those who choose to wait.”

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Open-Source Platform Complements PTC’s Commercial Vuforia Offering.

PTC has announced the release of the Vuforia® Spatial Toolbox platform. Created by the PTC Reality Lab, the powerful new open-source platform enables developers to create, innovate, and solve spatial computing problems in a whole new way.

Innovators and academic researchers can explore the power of Industrial Internet of Things (IoT) and Spatial Computing, accelerate prototyping for machines, and develop leading-edge spatial Augmented Reality (AR) and IoT use cases to support digital transformation initiatives.

With this groundbreaking new spatial computing platform, teams can improve the operation of complex manufacturing environments and make IoT-enabled machines easier and more intuitive to control with on-the-fly programming. Robots can be operated and controlled through more intuitive user interfaces (UIs), and intuitive Human Machine Interfaces (HMIs) can be quickly built, enabling improved human-machine interaction and merging the digital world and physical screens.

Mike Campbell, executive vice president and general manager, AR, PTC, said:

“Many developers, innovators, and researchers recognize that AR can help democratize the programming and control of connected machines.”

“What they need are solutions that help alleviate development overhead for prototyping these innovative, next-gen AR tools. PTC is helping them develop tools and interfaces to spatially interact with and program the world of interconnected things around them.”

As the newest addition to the Vuforia AR product portfolio, Vuforia Spatial Toolbox is designed to complement the current commercial Vuforia offering. The Vuforia Spatial Toolbox is a system consisting of two components which combine to provide an industrial AR/Spatial Computing prototyping environment with pre-built UI/UX elements, spatial programming services, an intuitive UI app, and simplified connectivity to IoT with the Vuforia Spatial Edge Server. The open-source environment is designed to drive further exploration around the convergence of the physical and digital worlds and help to push the boundaries of innovation.

To enable users to take advantage of the new Vuforia Spatial Toolbox while working from home during this crisis, PTC created a basic hardware interface add-on that allows them to connect the Vuforia Edge Server with Arduino projects, children’s LEGO® BOOST and LEGO® Education WeDo 2.0 sets, and the Philips Hue smart lighting system.

The technology is the brainchild of Valentin Heun, Ph.D., vice president, Innovation Engineering, PTC, and former scientist at the MIT Media Lab’s Fluid Interfaces Group, where he led the Reality Editor human-machine interface research. Dr. Heun is a leader in the AR industry, and an active author and speaker on topics related to AR.

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• Siemens’ MindSphere Named an Industrial IoT Software Platforms Leader by Independent Research Firm

• Partner ecosystem now includes SAS, AWS, Microsoft, Alibaba and Arrow Electronics

• MindSphere extends Xcelerator portfolio to enable tomorrow’s engineering, today

The Industrial Internet of Things (IIoT) has the potential to significantly improve global productivity and growth, with Accenture estimating that this latest wave of digital innovation will accelerate the reinvention of sectors that account for almost two-thirds of world output and add US$14.2 trillion to the global economy by 2030.

As companies around the world continue to develop IIoT strategies and implement solutions, they are increasingly partnering with Siemens and choosing MindSphere®, the cloud-based, open Internet of Things (IoT) operating system, as the foundation of their IIoT programs. Recently, Siemens was among the select companies that Forrester invited to participate in The Forrester Wave: Industrial IoT Software Platforms, Q4 2019 evaluation. In this evaluation, Siemens’ MindSphere was cited as a Leader in Industrial IoT Software Platforms.

Over the past year, MindSphere significantly grew its partner program, and announced major new customer wins and Siemens’ expansion of the Mendix platform to include cloud and app services for digital engineering and IoT powered by MindSphere, which is at the heart of its powerful Xcelerator portfolio.

Siemens’ MindSphere helps companies understand data by quickly and securely connecting products, plants, systems and machines to the digital world.

According to Forrester: “MindSphere builds on Siemens’ strength in industrial equipment and controllers but isn’t limited to interacting with Siemens hardware. The company continues to tell a strong story about the importance of the digital twin, and MindSphere plays a key part in turning this vision into something pragmatic and implementable.”

“MindSphere is well suited to customers with existing investments in the Siemens ecosystem but also deserves the attention of any industrial company interested in tapping Siemens’ deep domain knowledge and the experience it’s gained from its own internal digital transformation.”

MindSphere helps companies understand data by quickly and securely connecting products, plants, systems and machines to the digital world. By unlocking the wealth of data from every machine and system in a business, MindSphere can transform this data into productive business results using powerful industrial applications with advanced analytics. MindSphere is a secure and scalable industrial end-to-end solution from asset connectivity to actionable business insights utilized to increase productivity and efficiency across the entire enterprise.

“MindSphere has continued to grow and strengthen over the past year, and we now have over 500 MindSphere partners including Microsoft, AWS, Arrow and Alibaba, which has helped fuel success in the Chinese market where we recently launched the partner program,” said Ray Kok, Senior Vice President and General Manager, Cloud Application Solutions at Siemens Digital Industries Software.

“We are proud to be recognized as a Leader within the Forrester Wave report and will work to build on this momentum to continue to help our customers achieve their goals.”

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