Leveraging Real-Time Data: Solving Industrial Operational Challenges

Real-time data is becoming increasingly important for industrial businesses to stay competitive and efficient. It enables companies to make informed decisions quickly and improve different aspects of their operations.

How industrial businesses can utilise real-time data to improve different aspects of their operations.

Real-time data can be used to improve various aspects of industrial businesses' operations, including:

1. Safety + environmental harm prevention: Real-time data applications equipped with predictive analytics can be used to forecast potential problems and stop machinery or alert personnel to impending problems, giving them time to evacuate the area, shutdown machinery or fix assets that are starting to malfunction ahead of a catastrophic failure. Some advanced applications built on advanced integrated data, AI + IoT platforms, such as our Dynamix platform, are even able to use automation capabilities to prevent incidents without human intervention.
2. Predictive maintenance: Real-time data can be used to predict equipment failures before they occur. By using machine learning algorithms and analytics, industrial businesses can detect patterns in data and predict when machines are likely to break down. This can help businesses schedule maintenance work during downtime, reducing the impact of unplanned maintenance on productivity.
3. Asset monitoring: beyond predicting maintenance failures, real-time data can be used to track asset location, monitor how its being operated, trace productivity and utilization, as well as be used to compare and contrast performance under different conditions and circumstances. This enables businesses to find better ways of operating, compare operator performance, as well as guide future purchasing and scheduling decisions.
4. Quality control: Real-time data can be used to monitor product quality in real-time. By using sensors, cameras, and other IoT devices, businesses can capture data on products as they move through the production process. This data can be used to detect defects and quality issues early, reducing waste and improving product quality.
5. Supply chain optimization: Real-time data can be used to optimize supply chain operations. By using sensors and IoT devices to track inventory levels, industrial businesses can avoid stock-outs and optimize inventory levels. They can also track shipments in real-time, improving delivery times and reducing costs.
6. Energy efficiency: Real-time data can be used to monitor energy consumption in real-time. By using sensors and analytics, businesses can identify opportunities to reduce energy usage and optimize energy efficiency.
7. Compliance: Real-time data can be sued to determine if thresholds are soon to be breached, personnel aren't complying with required regulations, or to identify another potential compliance breach before it occurs. This enable businesses to mitigate issues and prevent fines.

The technology that is needed to collect real-time data from lots of different systems and software, with a particular note of IoT.

To collect real-time data from different systems and software, businesses need a range of technologies, including:

1. IoT devices: IoT devices, such as sensors, cameras, and wearables, can collect data in real-time from machines, products, and people.
2. Edge computing: Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This can reduce latency and improve real-time data processing, but is only required when milliseconds matter - do not be upsold unnecessarily!
3. Integrated data, AI + IoT platform: A platform is required upon which you can easily integrate data sources and build applications for your different use cases (as listed above). This platform should be capable of extreme interoperability, enabling you to integrate all your relevant data sources from systems, sources, devices and data feeds to create a real-time single source of truth for your operational data, before then providing visualizations and functionality to make changes to assets/operations via IoT and real-time data applications. Advanced systems will also include automation, machine learning and predictive analytics capabilities to deliver new insights and affect change to operations automatically (AI).
4. Communications and connectivity: it will be necessary to communicate data from infield devices and business systems to your chosen platform (and then back again to personnel in the field), so WiFi, LoRaWAN or simple mobile network connections will be required.

In addition to the above it's important to note that things such as personal devices, servers (if platforms and application are to be self-hosted vs. Cloud-based), and other technologies may be required to fill out the full technology stack, but this will be dependent on individual circumstances and use cases.

The benefits of baking real-time data use into the business.

There are many benefits of using real-time data in industrial businesses, including:

1. Improved efficiency: Real-time data can help businesses identify and address inefficiencies in real-time, improving productivity and reducing costs.
2. Better decision-making: Real-time data provides businesses with real-time insights, enabling them to make informed decisions quickly.
3. Reduced downtime: Real-time data can be used to predict equipment failures before they occur, reducing unplanned downtime and improving equipment uptime.
4. Improved quality: Real-time data can be used to monitor product quality in real-time, improving product quality and reducing waste.
5. Enhanced sustainability: Real-time data can be used to more accurately monitor sustainability efforts and prevent incidents that are going to create immediate harm.

Where and how to get started with ingraining real-time data into operations

To get started with ingraining real-time data into operations, businesses can follow these steps:

1. Identify use cases: Businesses should identify the use cases where real-time data can provide the most value and which align with current objectives. This will ensure budget and buy-in.
2. Choose the right technology: Businesses should choose the right technology to collect and process real-time data, key to which would be the platform that you choose.
3. Develop a data strategy: Businesses should develop a data strategy that defines the data they need to collect, how they will collect it, where they will store it, and how they will analyze it.
4. Build a data infrastructure: Businesses should build a data infrastructure that can collect, store, and analyze real-time data. This will involve deploying IoT devices, edge computing infrastructure, and integrating existing legacy technology with your chosen platform.
5. Hire data professionals: Businesses should hire data professionals, such as data scientists and data engineers, who can help them collect, analyze, and derive insights from real-time data.
6. Test and iterate: Businesses should test their real-time data infrastructure and iterate on it as needed. They should monitor data quality and make adjustments to their data strategy as needed.
7. Integrate with operations: Finally, businesses should integrate real-time data into their operations, such as by using predictive maintenance algorithms to schedule maintenance work, using real-time quality control data to adjust production processes, or using real-time supply chain data to optimize inventory levels.

To discover more about how our Rayven's integrated data, AI + IoT platform can achieve this for you or to explore our ready-to-use real-time IoT solutions built for industry, speak to us today.