Unlock the Power of IIoT with Low-Code/No-Code Platforms

A Step-by-Step Guide to IoT Low Code/No Code

In today’s fast-paced digital landscape, leveraging the power of IIoT (Industrial Internet of Things) with low-code/no-code application development platforms is changing the way businesses operate.

Low-code/no-code application development platforms are inherently beneficial due to their speed, cost-efficiency, and flexibility. They enable businesses to quickly develop and deploy applications without requiring extensive coding knowledge. However, despite their advantages, these platforms often miss out on crucial operational data, leading to significant blind spots. These blind spots can account for up to 80% of missing data, including the location of assets, environmental conditions, and equipment utilization.

To fully unlock the potential of low-code/no-code platforms and enhance their effectiveness, integrating IIoT technology is essential. In this blog, I will walk you through the steps to achieve this integration:

1. Deploying IIoT Solutions
2. Keys to IIoT Data Strategy
3. Integrating IIoT with Low-Code/No-Code Platforms
4. Building Value Adding Applications

By following these steps, you can enhance the quality of your low-code/no-code applications, fill in the data gaps throughout your operations, and enable your team to make decisions with a complete data picture.

Deploying IIoT Solutions

Deploying IIoT solutions is the first step toward filling operations blind spots with connected devices. Potential IIoT solutions include real-time asset tracking, environmental monitoring, and equipment utilization tracking. By using no-code IIoT data contextualizing applications, operators can gain the insights they need to make informed decisions today. Here’s how to get started:

• Identify Objectives: Clearly define what you aim to achieve with IIoT, such as reducing expenses by replacing misplaced assets, reducing downtime, or minimizing raw materials waste.

• Select IoT Devices: Choose the right sensors and devices that can capture the necessary data points for your specific use case.

• Ensure Connectivity: Establish reliable connectivity for your IoT devices, whether through BLE, cellular, or other communication protocols.

• Set Up Data Collection: Implement a system to collect and store the data generated by your IIoT devices in a centralized location.

Keys to IIoT Data Strategy

Before diving into an IIoT project, it’s crucial to define your organization’s data strategy clearly. This ensures that the data collected is accurate, consistent, and useful. A well-defined IIoT data strategy lays the foundation for successful integration with existing systems like an ERP, BI tool, or low-code platforms. Consider these key points:

• Data Quality: Ensure the data collected is accurate, consistent, and useful. High-quality data is essential for making informed decisions.
• Data Security: Protect your data from unauthorized access and breaches. Implement robust security measures, including encryption and access controls. These requirements are important factors to look for when evaluating solution vendors.
• Scalability: Design your data infrastructure to handle increasing volumes of data as your IIoT deployment grows.
• Integration: Make sure your IIoT data can be easily integrated with other business systems and applications. Choosing IIoT solution vendors whose platform incorporates various communication protocols (MQTT, OData, REST APIs) will help you be more flexible with integration requirements.

Integrating IIoT with Low-Code/No-Code Platforms

Integrating IIoT data with low-code/no-code platforms allows businesses to create customized applications that leverage real-time data insights. Historically, you may have only relied on data from ERP, MES, and other traditional systems. However, with IIoT, you now have access to a wealth of additional data, such as real-time asset location, environmental conditions, and equipment utilization KPIs.

This enriched data set provides a more comprehensive view of your operations, enabling more informed decision-making and strategic planning for application development. Planning your application integration is crucial for smooth operation. Here’s how to do it:

• Data Ingestion: Use built-in connectors (MQTT, OData) or APIs to bring IIoT data into your low-code/no-code platform. By integrating this new IIoT data with existing ERP, MES, and other system data, you create a unified data ecosystem.
• Data Visualization: Create dashboards and visualizations to monitor and analyze IIoT data in real-time. This combined data set allows you to see correlations and trends that were previously hidden.
• Automation: Set up automated workflows to respond to IIoT data, such as triggering alerts or adjusting operations based on sensor readings. Integrating IIoT data enhances the functionality of your applications, providing more accurate and timely responses to operational conditions.

With this integrated approach, you can develop applications that offer deeper insights and greater operational efficiency. The enriched data set from IIoT, combined with traditional system data, provides a robust foundation for innovative applications that drive business success.

Building Value-Adding Applications

Previously, the lack of comprehensive data sets made it challenging to develop applications that could provide a complete picture of operations. Now, with the integration of IIoT data, building robust and insightful applications in low-code/no-code application development platforms is possible. Here are two specific examples:

Production Dashboards

Production dashboards consolidate data into a single view, allowing operators to make real-time decisions based on comprehensive insights. The addition of IIoT data brings new dimensions to these dashboards:

• OEE (Overall Equipment Effectiveness): Measure the efficiency of production processes with real-time data.
• Correlation Analysis: Analyze the relationship between environmental conditions and optimal/suboptimal yields using sensor data.
• Predictive Maintenance Analysis: Compare MES quality data with IIoT data to predict maintenance needs.
• Yield and Scrap Analysis: Monitor production yields and identify areas to reduce scrap.
• Productivity Rates: Track overall productivity and identify bottlenecks using real-time machine data.

Predictive Maintenance and Equipment Calibration

Building triggers and alerts into your production dashboard can enhance predictive maintenance and equipment calibration. This application allows for:

• Machine Health Monitoring: Use data from ERP, Thinaer, and MES to assess machine health in real-time.
• Tool Calibration Requirements: Determine calibration needs based on combined data sources.
• Real-Time Alerts: Generate alerts based on real-time data calculations to ensure timely maintenance and calibration.

Historically, these applications could be built but they were missing key data sets across your operations. Now, getting data from the shop floor, warehouse, or hangar allows you to build applications that enable you to make decisions with a complete data picture.

Conclusion

Integrating IIoT with low-code/no-code platforms empowers businesses to quickly and efficiently develop applications that leverage real-time data insights. By deploying IIoT solutions, having a solid data strategy, and building value-adding applications, organizations can unlock the full potential of their IIoT investments. Embrace this transformative approach to drive innovation and stay ahead in the competitive landscape.