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Industrial IoT and smart manufacturing technologies that boost business value
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Key Points
- Leveraging smart technologies is essential to enabling the manufacturing industry to become more efficient and flexible, thus enabling businesses to grow at pace and scale effectively.
- Industrial IoT (IIoT) in smart manufacturing initiatives help businesses to create value by
reducing downtime, enhancing product quality, optimising the supply chain, and increasing operational efficiency. - Key technologies in smart manufacturing that improve operational efficiency, reduce costs, and increase productivity include sensors and data collection, machine learning and AI, robotics and automation, and digital twins.
- The global IIoT market is experiencing rapid growth and is forecast to reach USD 87.9 billion by 2026 – a significant increase from its USD 50.0 billion valuation in 2021.
- This growth will be further enhanced by emerging technologies such as 5G connectivity, edge computing, and advanced AI.
- The continued evolution of IIoT will lead to even smarter factories, with increased automation, greater customisation, and more efficient resource use. This could result in more sustainable manufacturing practices that reduce waste and energy consumption.
- As with all industries that leverage IoT technologies to enhance their products and performance, challenges such as security and data privacy concerns, interoperability, and skills and workforce adaptation remain.
- However, finding solutions to overcome challenges that exist across diverse industries presents a unique opportunity for increased cross-industry partnerships.
- Call us to collaborate!
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Ben Mazur
Managing Director
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The UK is an innovation nation. Small and medium enterprises (SMEs) have a critical role to play in driving regional growth, and thus, investment in manufacturing is the key to enabling them to develop new technologies and products at pace and scale. One such groundbreaking advancement is the Industrial Internet of Things (IIoT), which integrates smart sensors, devices, and advanced analytics to create a more connected and efficient manufacturing environment. Industrial IoT in smart manufacturing initiatives help businesses to create value by:
- Reducing downtime
- Enhancing product quality
- Optimising supply chains
- Increasing operational efficiency
Ignitec’s product manufacturing services offer streamlined end-to-end solutions designed to enable businesses to leverage cutting-edge technologies and develop scalable and sustainable products and services. Contact us to find out more about our in-house manufacturing services, which can help you optimise your supply chain and product quality efficiently and cost-effectively.
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What is Industrial IoT and smart manufacturing?
Shifting to more adaptive, responsive, and connected manufacturing is essential to developing a flexible infrastructure that adapts efficiently to a changing marketplace. The Industrial Internet of Things (IIoT) refers to the extension and use of the Internet of Things (IoT) in industrial sectors and applications. With a strong focus on machine-to-machine (M2M) communication, big data, and machine learning, IIoT enables industries to collect and analyse data from various devices and systems, leading to more intelligent decision-making and optimised processes.
Unlike consumer IoT, which focuses on enhancing everyday life (e.g. healthcare wearables and smart home technologies), IIoT is geared towards improving operational efficiency, reducing costs, and increasing productivity in industrial settings. Critical technologies in smart manufacturing include:
- Sensors and Data Collection: Advanced sensors monitor and collect data from machinery, production lines, and environmental conditions. For example, temperature sensors in a chemical manufacturing plant can ensure that processes remain within optimal ranges, preventing costly deviations.
- Machine Learning and AI: Machine learning algorithms analyse vast amounts of data to uncover patterns and insights, enabling predictive maintenance and process optimisation. For instance, IBM’s Watson IoT platform helps manufacturers predict equipment failures before they occur, saving time and money.
- Robotics and Automation: Automation technologies, including collaborative robots (cobots), streamline repetitive tasks, enhance precision, and increase production speed. In automotive manufacturing, companies like Tesla use robotic arms to assemble vehicles with high accuracy and efficiency.
- Digital Twins: Virtual replicas of physical assets, processes, or systems allow manufacturers to simulate and analyse performance in a virtual environment, leading to better decision-making and proactive maintenance. For example, Siemens uses digital twin technology to optimise the design and operation of its gas turbines.
How does IIoT help businesses to create value?
By leveraging real-time data, predictive insights and key technologies in smart manufacturing, IIoT enables manufacturers to make informed decisions and streamline operations – enabling businesses to stay competitive in an increasingly digital world:
- Improved operational efficiency: Real-time monitoring and data analysis enable manufacturers to optimise processes, reduce downtime, and improve overall efficiency. For example, Harley-Davidson reduced its production schedule from 21 days to six hours by implementing a fully integrated IIoT system.
- Enhanced product quality: Continuous monitoring of production processes ensures that quality standards are consistently met, reducing defects and recalls. For instance, Intel uses IIoT to monitor its semiconductor manufacturing process, achieving near-zero defects.
- Predictive maintenance: By analysing data from equipment, IIoT systems can predict when maintenance is needed, preventing unexpected breakdowns and reducing maintenance costs. Caterpillar uses IIoT to monitor its heavy machinery, predicting failures before they happen and ensuring timely maintenance.
- Supply chain optimisation: IIoT enhances visibility across the supply chain, allowing for better inventory management, demand forecasting, and logistics coordination. For example, DHL uses IIoT to track real-time shipments, optimising routes and delivery schedules.
Smart manufacturing challenges and limitations
While adopting IIoT technologies offers numerous benefits and transformative potential for manufacturing, it also presents several challenges and considerations. These are, by large, the same challenges faced by all industries leveraging the power of IoT technologies to optimise, enhance, and improve outcomes such as healthcare, automotive, education, agriculture, and retail:
- Security and Data Privacy Concerns: The increased connectivity of devices raises cybersecurity concerns. Ensuring robust security measures and data privacy protections is crucial. For example, manufacturers must implement end-to-end encryption and regular security audits to safeguard data.
- Integration with existing systems: Integrating IIoT technologies with legacy systems can be complex. Companies need to plan for smooth integration and interoperability. This may involve using middleware solutions or adopting industry standards for data exchange.
- Skills and Workforce Adaptation: Adopting IIoT requires a workforce skilled in data analysis, cybersecurity, and new technologies. Investing in training and education is essential. For instance, companies can partner with educational institutions to develop specialised training programs for their employees.
As these challenges are common across many sectors, increased collaboration and partnerships are the solution to addressing them. Contact us if you’re looking for a multidisciplinary partner to help find the best-fit solution to enable you to leverage new manufacturing technologies and boost business growth.
Global IoT in manufacturing market: Trends and forecasts
Industrial automation has resulted in significant growth in manufacturing sectors, with high market competition and end-user demand compelling manufacturers to ensure the production of high-volume and quality products. However, IIoT isn’t limited to automation and significantly impacts maintenance, integrated software, production flexibility, and reliability.
This helps explain the significant market growth of the global IIoT market, valued at USD 50.0 billion in 2021 and forecast to reach USD 87.9 billion by 2026.
Emerging technologies such as 5G connectivity, edge computing, and advanced AI will further enhance this growth. For example, 5G will enable faster and more reliable communication between devices, while edge computing will allow for real-time data processing at the source.
The continued evolution of IIoT will lead to even smarter factories, with increased automation, greater customisation, and more efficient resource use. This could result in more sustainable manufacturing practices, reduce waste and energy consumption, and help balance economic commerce and environmental conservation.
Final thoughts on industrial IoT initiatives
Industrial IoT and smart manufacturing initiatives are revolutionising the industrial sector, driving significant improvements in efficiency, quality, and business value creation. By embracing these technologies, companies can stay competitive, meet evolving market demands, and pave the way for a more sustainable and innovative future.
If you’re interested in exploring the tangible benefits of IIoT and smart manufacturing, schedule a free consultation with an expert on our team.
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Suggested reading
Sustainable manufacturing success stories: Real world examples of positive impact
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FAQ’s
Why is Industrial IoT important for smart manufacturing?
Industrial IoT is crucial for smart manufacturing. It integrates advanced technologies like sensors, data analytics, and AI to enhance operational efficiency and product quality. It enables real-time monitoring and predictive maintenance, reducing downtime and maintenance costs. IIoT helps manufacturers stay competitive and innovative by optimising processes and supply chains.
How does Industrial IoT improve operational efficiency?
Industrial IoT improves operational efficiency by providing real-time data from interconnected devices and sensors. This data allows manufacturers to optimise production processes, reduce waste, and streamline operations. Predictive analytics also help anticipate and prevent equipment failures, further enhancing efficiency.
What technologies are used in smart manufacturing?
Smart manufacturing utilises various technologies, including sensors, machine learning, AI, robotics, and digital twins. These technologies work together to collect and analyse data, automate processes, and simulate operations for better decision-making. This integration leads to improved efficiency, quality, and customisation in manufacturing.
When did smart manufacturing become popular?
Smart manufacturing began gaining popularity in the early 2010s with the advent of advanced IoT technologies and the Industry 4.0 movement. As digital transformation initiatives increased, more manufacturers adopted smart manufacturing practices. The COVID-19 pandemic further accelerated the adoption of these technologies to enhance resilience and efficiency.
Which industries benefit the most from Industrial IoT?
Industries such as automotive, aerospace, electronics, and pharmaceuticals benefit significantly from Industrial IoT. These sectors rely on high precision, efficiency, and quality control, which IIoT technologies provide. Additionally, industries with complex supply chains, like logistics and retail, also gain from enhanced visibility and optimisation.
Who are the leading companies in smart manufacturing?
Leading companies in smart manufacturing include Siemens, General Electric (GE), IBM, and Intel. These companies have implemented advanced IIoT technologies in their operations, achieving significant improvements in efficiency and quality. They also provide solutions and platforms to help other manufacturers adopt smart manufacturing practices.
How does predictive maintenance work in Industrial IoT?
Predictive maintenance in Industrial IoT involves using sensors and data analytics to monitor equipment health and predict failures before they occur. This approach relies on machine learning algorithms to analyse data trends and identify potential issues. Manufacturers can reduce downtime and maintenance costs by performing maintenance only when needed.
What is a digital twin in smart manufacturing?
In smart manufacturing, a digital twin is a virtual replica of a physical asset, process, or system. It allows manufacturers to simulate and analyse performance in a virtual environment, leading to better decision-making and proactive maintenance. This technology enhances product development, operational efficiency, and fault detection.
Why is cybersecurity important in Industrial IoT?
Cybersecurity is crucial in Industrial IoT to protect sensitive data and ensure the safe operation of interconnected devices. As IIoT systems become more prevalent, they become attractive targets for cyberattacks, which can disrupt operations and cause significant financial loss. Implementing robust security measures helps safeguard against these threats.
How do robotics enhance smart manufacturing?
Robotics enhance smart manufacturing by automating repetitive and precision tasks, increasing production speed and accuracy. Collaborative robots (cobots) work alongside human workers, improving safety and efficiency. This automation frees up human workers to focus on more complex and creative tasks.
What role does machine learning play in Industrial IoT?
Machine learning plays a vital role in Industrial IoT by analysing large volumes of data to uncover patterns and insights. It enables predictive maintenance, process optimisation, and quality control, improving overall efficiency and productivity. Machine learning algorithms continuously learn and adapt, enhancing their effectiveness over time.
When should a company adopt smart manufacturing technologies?
A company should adopt smart manufacturing technologies when it aims to enhance operational efficiency, reduce costs, and improve product quality. Adoption is particularly beneficial during times of digital transformation or when facing competitive pressures. It’s also crucial when existing systems are becoming outdated and less efficient.
Which smart manufacturing initiative has the most impact?
Predictive maintenance often has the most significant impact on smart manufacturing initiatives. Companies can drastically reduce downtime and maintenance costs by using IIoT to anticipate and address equipment failures. This proactive approach leads to higher efficiency and extended equipment life.
Who is responsible for implementing IIoT in a company?
The responsibility for implementing IIoT in a company typically falls to the IT department, in collaboration with operations and manufacturing teams. Leadership from the CIO or CTO is often crucial for strategic planning and integration. Successful implementation also requires cross-functional collaboration and buy-in from all levels of the organisation.
How does IIoT enhance supply chain management?
IIoT enhances supply chain management by providing real-time visibility into inventory levels, production status, and shipment tracking. This data helps in optimising inventory management, improving demand forecasting, and coordinating logistics more efficiently. Enhanced visibility reduces delays and disruptions, leading to a more responsive supply chain.
What are the main challenges in implementing IIoT?
The main challenges in implementing IIoT include ensuring cybersecurity, integrating new technologies with legacy systems, and upskilling the workforce. Companies must address data privacy concerns and establish robust security protocols to protect their IIoT systems. Additionally, smooth integration and employee training are critical for successful adoption.
Why should manufacturers consider digital twins?
Manufacturers should consider digital twins because they provide a powerful tool for simulation, analysis, and optimisation. Digital twins help predict performance issues, improve design processes, and enhance operational efficiency. They offer a virtual environment to test changes before implementing them in the real world, reducing risks and costs.
How can IIoT improve product quality?
IIoT can improve product quality by enabling real-time monitoring and control of manufacturing processes. Advanced sensors and data analytics ensure that production parameters stay within optimal ranges, reducing defects. Continuous quality monitoring helps maintain high standards and minimises the risk of product recalls.
What future trends are expected in IIoT and smart manufacturing?
Future trends in IIoT and smart manufacturing include adopting 5G connectivity, advanced AI, and edge computing. These technologies will enable faster, more reliable data transmission, smarter automation, and real-time data processing at the source. The ongoing evolution of these trends promises even greater efficiency and innovation in manufacturing.
When will 5G impact smart manufacturing?
5G is expected to impact smart manufacturing significantly as it becomes more widely available over the next few years. Its high-speed, low-latency connectivity will enable real-time data transmission and more reliable communication between devices. This will enhance the capabilities of IIoT, leading to further advancements in automation and efficiency.
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