“Overall, combining external data with IoT and machine data allows organisations to make more informed and accurate decisions, which can lead to improved efficiency, cost savings, and reduced risk.”
Combining external data with time-series and machine data can enable organisations to gain a more complete and holistic view of their operations. By bringing in a wider range of data sources, organisations can gain a deeper understanding of the factors that are driving their operations and make more informed decisions about how to optimize them.
Including external data such as energy prices, weather forecasts and ERP-data with IoT and machine data can be extremely useful for a variety of applications. Here are just a few examples of how this combination can be leveraged:
- Improved energy balancing: By combining weather forecasts with time series data of energy consumption and production, district heating organisations can better forecast what the production mix should be, when having multiple energy sources and a variable demand. For example, winds from a certain direction may lead to a higher demand than the temperature would imply and thus this should be planned for in production.
- Improved profitability: In the manufacturing industry, IoT sensors can be used to collect data on the performance of machinery and the production process. This data can be used to identify inefficiencies and optimize performance. However, when this is combined with external data such as ERP data, it creates a link to the financial data, allowing for a better understanding of the costs associated with different production processes and the underlying machine-data.
- Improved energy management: By integrating energy price data with IoT and machine data, companies can optimize their energy usage and reduce costs. For example, if energy prices are high at a certain time of day, a manufacturing plant can schedule energy-intensive tasks for a different time when prices are lower. Similarly, weather forecasts can be used to adjust heating and cooling systems to reduce energy consumption
- Improved supply chain management: By integrating external data such as energy prices and weather forecasts with IoT and machine data, companies can better predict demand and optimize their supply chain. For example, if energy prices are expected to rise, companies may choose to ramp up production in advance to take advantage of lower energy costs. Similarly, if a storm is forecasted to disrupt transportation, companies can adjust their production and shipping schedules accordingly.
- Enhanced predictive maintenance: By combining machine data with external data such as weather forecasts, companies can more accurately predict when equipment is likely to fail. For example, if a machine typically breaks down when it is subjected to extreme heat or cold, combining weather data with machine data can help companies proactively schedule maintenance to avoid unplanned downtime. Or it might be that extreme weather leads to a ramped up production and, thus, the machines should be prepared for more wear.
Overall, combining external data with IoT and machine data allows organisations to make more informed and accurate decisions, which can lead to improved efficiency, cost savings, and reduced risk.
In Beacon Tower the way we make this happen is by using asset models based on the Digital Twin Definition Language (DTDL). An asset model is a digital replica of a physical asset or system that can be used for a variety of purposes, including design, simulation, testing, and operations.
Using Beacon Tower’s asset models to structure IoT and machine data is useful because it allows for the creation of hierarchical asset models. These models represent the relationships between different assets and systems within an organisation.
For example, an organisation might use an asset model to model a manufacturing plant, with individual assets such as machines, conveyor belts, and sensors represented as sub-components within the overall model. By using asset models to structure this data, the organisation can track how changes to one machine might affect the performance of the entire plant, or how a sensor failure could impact production.
In addition to helping organisations understand the relationships between different assets and systems, Beacon Tower’s hierarchical asset models also make it easier to integrate and analyse data from multiple sources. This can be especially useful in complex, interconnected systems where data from many different sources needs to be analysed to make informed decisions.
Check out this video about Beacon Tower asset models and see examples of integration of external data sources:
Read more about how Beacon Tower is a natural part of an integrated data platform in an enterprise IT environment.
For more information:
Partner, Mikael Rönde, email@example.com
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