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How to identify the best use cases for real-time Industrial IoT solutions

Paul Berkovic, 31 March 2023

Industrial businesses operate in dynamic environments with many variables that can affect their performance.

To stay competitive and achieve operational excellence, these businesses need to leverage real-time data to gain insights into their processes, products, and customers. However, not all data is created equal, and not all data is worth collecting in real-time. Therefore, identifying the best use cases for real-time industrial IoT solutions and data applications is crucial for businesses to maximise the value of their data.

In this article, we will explore some practical tips for industrial businesses to identify the best use cases for real-time data applications.

1. Define business goals and KPIs

The first step in identifying the best use cases for real-time data applications is to define the business goals and key performance indicators (KPIs) that the business wants to achieve. For instance, a manufacturer may want to reduce downtime, increase production capacity, improve product quality, or optimise inventory levels. By defining these goals and KPIs, the business can identify the data that is relevant to these goals and determine whether it needs to be collected in real-time or at a slower frequency.

2. Map the data flow

Once the business goals and KPIs have been defined, the next step is to map the data flow across the business processes and systems. This involves identifying the sources of data, such as sensors, machines, or applications, and the destinations of data, such as databases, analytics platforms, or dashboards. By mapping the data flow, the business can identify the data that is critical to its operations, as well as any data bottlenecks or gaps that need to be addressed.

3. Prioritise data use cases

After mapping the data flow, the business can prioritise the data use cases based on their relevance to the business goals and KPIs. This involves assessing the value and feasibility of each use case, as well as its potential impact on the business. For instance, a use case that can improve product quality by 10% may be more valuable than a use case that can reduce downtime by 5%. Similarly, a use case that requires a major investment in IoT infrastructure may be less feasible than a use case that leverages existing data sources.

4. Conduct a proof of concept

To validate the feasibility and value of the prioritised data use cases, the business can conduct a proof of concept (POC) that involves collecting and analysing real-time data from a subset of the systems and processes. The POC can help the business test its assumptions, identify any technical or operational challenges, and demonstrate the potential benefits of the use case. By conducting a POC, the business can reduce the risk of investing in a use case that may not deliver the expected ROI.

5. Monitor and refine

After implementing the real-time data applications, the business should monitor the performance and outcomes of the use cases and refine them as needed. This involves tracking the KPIs, identifying any data anomalies or errors, and adjusting the data processing and analysis algorithms. By monitoring and refining the use cases, the business can ensure that it continues to derive value from the real-time data and that it stays aligned with its business goals and KPIs.


Examples of real-time data use cases

To illustrate how industrial businesses can identify the best use cases for real-time industrial IoT solutions and data applications, let's explore some examples of use cases in different industries:

  • Predictive maintenance: A manufacturer of heavy machinery can use real-time sensor data to predict when a machine is likely to fail and schedule maintenance work before it breaks down. By avoiding unplanned downtime, the manufacturer can improve productivity, reduce maintenance costs, and increase customer satisfaction.
  • Environmental monitoring: A mining company needs to ensure that tailings dams are secure at all times and that mine activities, such as blasting and hauling, don't create excess dust that can be blown onto surrounding land. Real-time data monitoring can be used to ensure that assets are failing and that weather conditions are conducive to certain activities at any given moment.
  • Water demand forecasting: A water company can utilise real-time data to understand current water flows and predict needs. Layering over climatic and weather data, they can then forecast likely demand and prepare for the coming months.
  • Quality control: A food processing plant can use real-time data from cameras and sensors to inspect the products for defects, such as discoloration, contamination, or size variations. By detecting and rejecting the faulty products in real-time, the plant can reduce waste, ensure compliance with regulations, and maintain its reputation for high-quality products.
  • Energy management: A data centre can use real-time data from energy meters and HVAC systems to optimise its energy consumption and reduce its carbon footprint. By adjusting the cooling, lighting, and power settings based on the workload and ambient conditions, the data centre can save energy, reduce operating costs, and demonstrate its commitment to sustainability.
  • Supply chain visibility: A logistics company can use real-time data from GPS trackers and telematics devices to track the location, status, and condition of its vehicles, containers, and cargo. By providing customers with real-time updates on their shipments, the company can improve customer satisfaction, reduce delivery times, and mitigate the risk of theft or damage.

Industrial businesses have a vast amount of data at their disposal, but not all data is equally valuable or relevant. By identifying the best use cases for real-time data applications, these businesses can gain insights into their operations, products, and customers that can help them achieve their business goals and KPIs. The key to identifying the best use cases is to define the business goals and KPIs, map the data flow, prioritise the data use cases, conduct a proof of concept, and monitor and refine the use cases. By following these steps, industrial businesses can harness the power of real-time data and stay ahead of their competition.

Rayven's integrated data, AI + IoT platform enables you to create a real-time, accurate single source of truth for all your data; gives you all-new ways to visualise and access it; as well as providing you with a set of easy-to-use tools that enable you to build your own industrial IoT solutions that improve performance, simply.

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