Article
Top 10 Smart Industry Use Cases
In today’s industrial landscape, enterprises are continuously looking for smarter ways to manage operations, reduce costs, and make data-driven decisions. In this article, we explore the top 10 smart industry use cases based on years of experience working with customers. These use cases show how data can be harnessed to drive greater efficiency and innovation.
1. Condition-Based Monitoring
Many businesses face the challenge of unplanned downtime caused by equipment failure. Condition-based monitoring enables companies to stay ahead of these issues by continuously monitoring thousands of sensors. By setting specific conditions and combining data from various sources (like OPC servers, MQTT clients, or HTTP inputs), businesses can trigger automated actions such as notifications, work orders, or messages to platforms like Slack and Teams. This proactive approach ensures that maintenance teams can address potential issues before they escalate, reducing downtime and improving operational efficiency.
2. Send Files to Database or Cloud Warehouse
Handling data from files can often be cumbersome, but by sending files to a database or cloud warehouse, companies can streamline this process. Crosser’s solution enables businesses to monitor specific folders for new files, such as CSVs, and automatically parse and upload them to a database. This process also allows data transformation, ensuring that the data is ready for further analysis. By making file-based data easily accessible in modern data architectures, businesses can simplify the integration of valuable data into their broader operations.
3. Scheduled Execution of SQL
Routine database operations are essential for maintaining performance. With scheduled execution of SQL, businesses can automate regular SQL queries, such as data clean-up or aggregation tasks. This automation ensures that important database maintenance tasks happen on schedule, without manual intervention, thereby optimizing database efficiency. The ability to schedule and execute these operations at specific intervals (e.g., hourly, daily) frees up valuable time for IT teams and improves database performance over time.
4. Send Machine Data to a Cloud Warehouse
In industrial settings, real-time machine data is crucial for decision-making. Sending machine data to a cloud warehouse is a solution that enables businesses to efficiently integrate their machine data into cloud platforms like Databricks and Snowflake. Instead of sending data directly as streams, the data is first stored locally in file formats such as CSV or Parquet and then uploaded to a cloud staging area. This method ensures efficient integration of on-premise machine data into cloud environments, making it easier to perform analysis and access insights from factory floors.
5. Energy Forecasting
For industries with high energy consumption, effective energy forecasting can lead to significant cost savings. By integrating internal production forecasts with external real-time data such as weather conditions, businesses can accurately predict energy needs for the next 48 hours. Using machine learning models built in Python, companies can ensure that energy consumption aligns with operational demand, avoiding unnecessary energy expenses. This type of forecasting model is particularly valuable in energy-intensive industries like pulp and paper, where optimizing energy usage can have a substantial financial impact.
6. Unified Namespace
A Unified Namespace (UNS) is an increasingly popular approach for centralizing operational data. It brings together data from various sources into a single, accessible hub. By using Crosser’s tools, companies can standardize and adapt data from protocols like OPC UA and Modbus to fit the UNS data model. This centralized data hub enables real-time monitoring, application development, and machine learning. Insights gained from these applications can be fed back into the UNS, creating a seamless loop for real-time decision-making, historical analysis, and continuous optimization.
7. Process Optimization with Machine Learning
Industries are turning to machine learning to drive process optimization at the edge, close to their data sources. In this use case, data streams are collected from machines (using Modbus, for example), processed in real-time, and fed into machine learning models to derive optimized settings. These insights are then used to adjust machine parameters, improving performance metrics like production yield, energy consumption, or resource utilization. This local processing not only ensures low-latency operations but also enhances security by keeping sensitive data within the factory environment.
8. On-Premise Database to Cloud Warehouse
Migrating data from on-premise databases to a cloud warehouse offers businesses scalability and flexibility. Two main methods are used: file-based transfers (e.g., Parquet files) and SQL interfaces. For large, frequent data updates, businesses query local databases and store the data in compact files before uploading them to cloud services like AWS and Snowflake. Alternatively, for smaller, less frequent updates, SQL queries are used for direct ingestion. These approaches ensure that businesses can efficiently transition from traditional data storage to cloud-based architectures while maintaining data accessibility and performance.
9. Energy Measurements
Accurately measuring and managing energy consumption is critical for industries looking to reduce costs and meet regulatory demands. Energy measurements involve collecting data from various devices across large or distributed locations. Using Crosser’s tailored data flows, businesses can standardize measurements from diverse devices and aggregate them for centralized analysis. This allows companies to monitor and optimize energy use in real-time, helping reduce energy consumption and improve sustainability initiatives. Centralized data also supports compliance with energy regulations across multiple sites.
10. Send Machine Data to Database
Collecting and storing machine data is essential for businesses looking to analyze and optimize their operations. Sending machine data to a database enables companies to gather data from machines on the factory floor and store it either on-premises or in the cloud. This allows for continuous data collection from devices such as PLCs and OPC UA servers. By aligning the data structure with database requirements, businesses can efficiently manage data from thousands of sources, turning raw machine data into actionable insights that drive improved operational performance.
Final Thoughts and Next Steps
These use cases are just a sample of the thousands of applications that can be leveraged to address key operational challenges. The Crosser platform plays a key role in enabling these solutions, providing the tools and flexibility needed for fast and effective implementation.
To discover how the Crosser platform can support your smart industry initiatives and digital projects, we invite you to watch the full webinar or contact our team of experts for personalized insights and solutions tailored to your needs.
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