Towards stronger business cases with smart asset management from Allinq

It starts with insight

With our asset management, we pursue four goals for our customers: reduce risk, reduce costs, increase uptime, and extend the lifecycle of the asset. Understandably, many companies tend to put what matters most first. In practice, this means: focusing primarily on reducing costs and increasing uptime. What measures are required to accomplish this? The answer to this requires an understanding of all the factors that affect cost and uptime.

Causes and coherence

Consider, for example, a 24/7 Network Operations Center that responds to alarms from sensors. A sensor measures a temperature above X degrees; this results in an alarm. A temperature warning is issued when it has already reached an excessive level. This can also be done differently. By analyzing the increase over the past few days, you can detect – or in other words – predict whether the limit has been exceeded. The coherence of various sensor data such as temperature, energy, and humidity is also very interesting. A diagnosis of this connection can clarify what caused an event.

For our asset management, this means that we do not focus exclusively on the ‘primary goals.’ After all, effective asset management is based on a full understanding of the causes and consequences of incidents and maintenance.

Data and functions

Allinq’s Smart Asset Management distinguishes between three different types of data:

• Descriptive data. The description of the asset type: device, model number, location, date of placement, etc.
• Ticket data. The registration of maintenance moments, incidents, classifications, and maintenance actions.
• Sensor data. The behavior and performance of the asset: for example, data on temperature, humidity, and energy consumption.

Our own database and the assets of our customers generally generate enough data that we can use. Sometimes, we may also install sensors for more specific information. We bring all data from the above sources together in one clear dashboard.

Sort by value

Using our dashboard, we define answers to the following questions: What risk is occurring? What is causing this? What risk may occur? How can we prevent this? By following this common thread, our data management is evolving from Business Intelligence (informing) to Advanced Analytics (analysing) to Data Science (predicting). We assign the data to the four goals of our asset management:

• Reduce risks. We record the historical data of the asset. This helps us to analyse different scenarios and prevent the recurrence of incidents.
• Reduce costs. We derive all information from the tickets that can contribute to cost reduction. We identify the performance and possible optimisations based on the sensor data.
• Increase uptime. We use sensor technology and advanced analytics to predict and prevent downtime.
• Extend life cycle. The asset’s performance is automatically and continuously analysed. We prevent malfunctions because – thanks to the analysis – we apply the most effective maintenance.

Summary and conclusion

Smart Asset Management from Allinq centralises all asset data and ranks it according to its role and significance for risks, costs, uptime, and life cycle. In the course of the data management process, its value increases: from informing to analysing to predictive. In this way, Allinq’s Smart Asset Management directly contributes to better business cases for its customers.

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