Unlocking Operational Flexibility in Manufacturing with Industrial IoT
SOURCE: TTI MarketEye Blog
In the fast-paced manufacturing industry, operational flexibility is a critical factor for success. Manufacturers must swiftly adapt to changing market demands, optimize production processes, and stay competitive.
Traditionally, manufacturing processes have been bound by long changeover times, rigid production lines, and a lack of real-time visibility. Industrial IoT (IIoT) addresses these challenges and opens up new avenues for operational flexibility.
IIoT Accelerates Design Iterations
Sravani Bhattacharjee
With ubiquitous connectivity and advanced analytics, IIoT has redefined operations in manufacturing plants. For example, innovations in augmented reality (AR) and virtual reality (VR) have empowered manufacturers with more flexible ways to plan, model, and optimize processes. AR can provide real-time guidance to workers simplifying complex tasks. Using immersive VR, system engineers can perform very accurate assembly simulations, virtual prototyping, and industrial layout planning, adding flexibility and efficiency to design and production workflows.
Digital twins, another IIoT-enabled technology, helps system engineers digitally model physical objects and processes, dynamically mimicking their properties. With real-time insights from digital twins, manufacturers can predict faults, make changes faster, customize the design to match customer needs, and iterate faster.
Adopting digital twins, AR, and VR in factories requires reliable components such as discrete semiconductors that operate accurately, even in the most rugged conditions.
Autonomous Manufacturing Enhances Flexibility
IIoT connectivity and advances in aArtificial iIntelligence (AI) and rRobotics are redefining autonomous manufacturing, unlocking new opportunities for operational flexibility. Manufacturers have been using automated machinery and processes for decades. Autonomous manufacturing is relatively new. With autonomous manufacturing, manufacturers can leverage intelligent, data-driven technologies to create a persistent cycle of evolution in producing and delivering quality products.
In autonomous manufacturing, sensor data is collected from connected devices. AI systems ingest this data and run machine learning algorithms on the processed data that facilitate making intelligent decisions to support entire workflows.
For example, when the inventory of manufacturing parts goes below a certain threshold, new supplies for that part are automatically ordered without requiring human intervention.
Autonomous systems can also proactively stop an operation when a problem is detected, adapt to demand fluctuations by scaling up or down assembly lines, dynamically predict machine failures and send alerts, adding flexibility and agility to manufacturing operations.
Advances in robot operation technologies are also driving the fast transition to full autonomy in operations. Instead of being limited in space by motors and cables, robots can now use contactless components from suppliers like TTI that improve throughput and flexibility.
Adaptive Strategies Optimize Production
Predictive maintenance is an example of how IIoT technologies are enabling manufacturers to implement adaptive planning for optimizing production cycles. Predictive maintenance leverages real-time sensor data and predictive analytics to identify potential equipment failures before they occur, which minimizes downtime. TTI supports the increased adoption of predictive maintenance by supplying sensors for various IIoT applications.
IIoT Enhances Product Customization
In a software-driven digital economy, customers increasingly lean toward custom products tailored to their needs. Smart factories open new opportunities for product customization and personalization. By leveraging real-time IoT data, plant engineers can adapt production processes to meet specific customer requirements.
Machines and sensors connected to IIoT platforms can leverage automation, making reconfiguring production lines faster, reducing changeover times, and facilitating the production of personalized products at scale. This flexibility with design and configurations allows manufacturers to meet customer demands efficiently and cost- effectively.
Predictive Capabilities Improve Product Quality
Ensuring product quality is of utmost importance for system engineers. Industrial IoT plays a vital role in enhancing quality control processes.
IIoT platforms integrated with AI and cloud computing systems have opened doorways for advanced predictive analytics for manufacturers. IIoT devices generate massive volumes of data which can be used to train AI models. Once trained, the models ingest real-time data and run analytics to provide data-driven predictive insights. By monitoring key performance indicators, engineers can promptly detect any anomalies or deviations in the production process. This early detection allows quick intervention, preventing defective products from reaching customers.
Moreover, system engineers can leverage IoT-enabled predictive analytics to identify potential quality issues before they arise, enabling proactive measures to maintain high product standards.
Flexibility Improves Safety and Sustainability
IIoT contributes to various sustainable practices in manufacturing plants. Connected devices and sensors provide real-time monitoring and optimization of energy consumption. System engineers can identify energy-saving opportunities and implement data-driven energy management strategies. This not only reduces environmental impact but also keeps costs lower.
Industrial IoT empowers factories to achieve sustainability goals while maintaining operational flexibility.
Autonomous systems like drones and robots can now augment the human workforce to perform tasks in hazardous environments. Drones can collect data from production units in unsafe terrains like a deep-sea oil rig. Robots can perform tasks in extreme temperatures unsafe for humans. These options improve industrial safety and allow more agility and flexibility in operations.
The uptick in the usage of drones and robots is driving the demand for components like antennas, GPS and position sensors, communications components for RF, and faster microprocessors. Design engineers must ensure these components meet the requirements of harsh and rugged environments where drones and robots typically operate.
Conclusion
IIoT integration in manufacturing processes introduces operational flexibility, improving efficiency, agility, and productivity. As factories adopt IIoT technologies to be fully connected and automated, machines need to be equipped with connectors, wiring systems, and sensors that are robust, scalable, and can send and receive data without fail. These smart components are best paired with reliable network interfaces, simulation software, and programmable logic controllers that can manage large amounts of data, enabling machine learning capabilities.
Engineers can weigh the value of certification, cost, and performance of the components while designing autonomous machines and systems. TTI allows engineers to have more flexibility in design by catering to different components for different markets and use cases.
The fast-emerging global IIoT market is projected to reach $106.1 billion by 2026 from $88.2 billion in 2023. TTI is supporting the growth of IIoT and driving flexibility in manufacturing by supplying components and parts engineers can trust.
Sravani Bhattacharjee has worked as a tech leader at Cisco, Honeywell and other companies where she delivered many successful innovations to the market. As the principal of Irecamedia, she collaborates with Industrial IoT innovators to create compelling vision, strategy and content that drives awareness and business decisions.
