How IIoT Is Defining The Future Of Equipment Manufacturers
With new-age technology disruptions and the IIoT advent in almost every vertical, the manufacturing industry is not left untouched, leading to many OEMs strategizeing for their business model transition. Here, smart manufacturing–the enviable goal of every Original Equipment Manufacturer (OEM), is now not just a theoretical concept but a reality. This is where Industrial IoT comes into the picture. The technology -driven approach drives internet-connected equipment/machinery to communicate with the cloud-based solution, turning asset data into valuable machine usage pattern/analytics to enable remote monitoring, predictive/preventive maintenance through AI/ML, or to enable servitization through As-a-Service Models for OEMs to secure newer revenue streams and gain competitive advantage.??
Industrial IoT is the key enabler for smart manufacturing as it facilitates the digitalization of the manufacturing processes, supply chain transformation into interconnected digital networks, and promotes integration. To put it briefly, IIoT is enabling intelligent and interconnected processes for varying teams at OEMs by harnessing the power of AI/ML and data analytics.??
By bringing multiple advantages to the sector, IIoT is becoming the catalyst that is redefining the future of manufacturing.??
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Reasons why Industrial OEMs are leveraging IIoT:??
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1. Ensuring equipment’s uptime:??
Equipment manufacturers can gather vast amounts of data from multiple equipment and machines with the help of IIoT. The siloed data could further be used in identifying patterns, detecting anomalies and enabling customer-centric solutions. By analyzing data in real-time, industrial OEMs can detect problems as or before they occur and build real-time solutions.??
2. Smarter inventory management??
Managing inventory is a concern for major OEMs that could be resolved when smart solutions are in the picture. When device/equipment data is transformed into analytics through an industrial application, it is easier to keep a check on the inventory, thereby avoiding stock shortages and overstocking.???
3. Asset remote monitoring??
IIoT provides the efficacy of remote controlling and monitoring to Industrial OEMs. With smart IoT devices and solutions, they can monitor facilities, augment logistics, prevent quality issues in products without being worried about geographical constraints. This allows real-time tracking and improvements in production lines.??
?4. Advanced predictive maintenance??
One of the major innovations that IIoT has brought into the manufacturing industry is the ability to predict future anomalies in the equipment’s ideal operations. Smart IoT solutions provide proactive maintenance by tracking and analyzing sensor data to predict product/equipment failures. When manufacturers know the possibility of equipment failure, they already have a strategy to avoid the failure, which reduces downtime, maximizes equipment lifespan, and saves repair or warranty costs (truck rolls).??
?5. Enhanced cybersecurity??
Safeguarding crucial information such as traceability of equipment, material calculation, production notes, etc. is essential. Manufacturers need to put security measures such as encryption, security assessments and access limits to protect themselves from cyberattacks.??
Smart inventory management, asset tracking, quality control and improved cybersecurity results in business transformation which overall helps the Industrial manufacturers to stay competitive and onboard newer revenue streams.??
As OEMs are already advancing toward future-proof business models, it is important for them to not only adopt smart solutions but also aim for scalability, flexibility, ownership, and long-term strategic advantage to stay competitive. There might be multiple options to implement IIoT solutions, but Application Enablement Platforms (AEPs) stand as a neoteric to grant them what they exactly want.???
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Industrial IoT in Action: Use Cases??
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Case 1: Semiconductor Component Manufacturer Leverages Flex Platform?
?Background: A leading semiconductor manufacturer encountered challenges in enabling remote usage of their advanced AI/ ML SoC execution boards. Their solution involved creating a user-friendly cloud-based AI model and hardware evaluation environment to streamline access to OEM's RZ/V series Microprocessors (MPUs) and facilitate AI/ML model development, deployment, and performance evaluation, including real-world feedback on algorithms.??
Challenges: To evaluate the performance of AI/ML models on their semiconductor boards, they were required to conduct testing within laboratory environments. This restricted their ability to assess the models remotely and limited their capacity to showcase hardware capabilities to clients effectively. Additionally, the process involved multiple manual steps for training various types of AI/ML models and evaluating them on the boards. Furthermore, presenting these complex procedures to their customers from laboratory settings posed considerable difficulty.??
?Solution: The AEP enabled seamless integration for creating, deploying, and evaluating AI models designed explicitly for Vision AI applications.??
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The platform provided pre-built AI/ML models for tenant accounts and guest users and supported a cloud-based evaluation of DRP-AI by accessing EVK devices and onboarding personal devices. Additional features included remote device connectivity, dataset uploading, an ETL pipeline for data processing, and MLOps services for model conversion and training using Amazon Sagemaker with Pytorch and TensorFlow. It also offered ONNX conversion, DRP translation, model evaluation, Flex83 authentication, notification services, and real-time board power consumption trends.??
Outcomes:??
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Case 2: Enabling digital transformation for leading OEM in refrigeration??
?Background: The client is a prominent American manufacturer of commercial refrigeration products established in 1945. It is known for exceptional customer service, quality materials, innovative designs, and high product performance and holds a strong market reputation for providing intelligent and durable refrigeration solutions.??
Challenges: The client's refrigeration units were equipped with a Cellular Connectivity IoT Device Modem for transmitting asset data to a cloud platform. Despite data accessibility, the client faced limitations in performing remote operations such as asset management, alarm setting, and warranty diagnostics.??
?Solution: The AEP resolved the client’s operational challenges. With various modules and API services, the AEP facilitated the creation of a custom web portal for monitoring and managing refrigeration units. It provided real-time user behavior, unit performance, and operational insights. The solution also ensured risk identification & mitigation, streamlined asset management, and optimized resource allocation & cost control.?
Outcomes: Enabled the development of low-code custom applications tailored to the client's specific business requirements using the pre-built microservices.??
Using open-source tools, the platform offered robust support for database connectivity (both SQL and NoSQL), identity and access management (IAM), reduced warranty cost, significant drop in lines of codes, 6X reduction in total cost, and 10x acceleration in TTM.??
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Case 3: Transforming Employee Recognition with AI/ML capabilities??
Background: To present the development and optimization of an advanced Employee Recognition System utilizing AEP’s AI ML capabilities. The goal is to showcase a robust solution for accurate and real-time employee identification for security management and time tracking within organizational settings.?
Challenges: Varying illumination, intricate backgrounds, and diverse facial angles have impeded optimal performance when it comes to accurate face recognition. Limitations in high accuracy and real time performance is another challenge.??
Solution: The solution is built upon a multi-stage pipeline integrating Multi-task Cascaded Convolutional Networks (MTCNN) for face detection and FaceNet for face embedding. Leveraging the platform’s AI ML capabilities, the system continuously evolves through a Feedback Network, adapting to user input and refining recognition accuracy. Integrating AI ML capabilities significantly enhances the accuracy and efficiency of employee recognition. The MTCNN based face detection and FaceNet powered embedding contribute to precise identification, even in dynamic scenarios. Fueled by user feedback, the iterative model refinement process optimizes ongoing performance.??
Outcomes: By harnessing AI ML capabilities, the organization can deploy a state-of-the-art Employee Recognition System beyond traditional methods. The solution's adaptability, accuracy, and real-time capabilities are powerful tools for enhancing security and optimizing employee management.??
?Also read: How AEP proves itself as the change-maker in the Industrial OEM’s success:??
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