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Many organizations are currently in a digital transformation in the field of asset management. A maturity model is a systematic approach to analyze and rate technologies, capabilities, and processes. It can help organizations identifying fields of improvement. In the following blog entry, we look on a maturity model for asset management, which helps your organization discovering new technologies for their asset management.
Maturity model for Asset Management:
The maturity model is composed of five levels. The first level Initial describes a company, which did not yet start with the implementation of a technology. The highest level Optimized defines a company which uses all possible technologies to optimize their asset management. The maturity levels are defines as following:
Level 1 – Initial: In this stage an organization has no asset management. It does not store asset data and is not able to share asset information with for example a service provider. This organization does not use sensors to monitor the assets and cannot predict asset failure. Maintenance engineers do not get supported by 3D models or mobile applications. Maintenance activities are only executed when an asset fails.
Level 2 – Managed: In this stage an organization uses basic processes for their asset management. Asset information are analog documented and can be shared by mail, e-mail, or phone. Some sensors are used for maintenance purpose but can only read out onside and manually. Information from the manufacturer is used to approximately predict an asset failure. Maintenance engineers can use analog documents and plans for their maintenance work. Maintenance activities are executed time based or when an asset fails.
Level 3 -Defined: In this stage an organization uses advanced processes for their asset management. The asset data is stored digitally in a semi-structured way. Documents and data can be shared in a collaborative cloud. Sensor data can be read out and monitored over an IT-system. With the help of a central database asset failure can be predicted. Maintenance engineers can use mobile devices to access asset information. Maintenance activities are executed time based or condition based.
Level 4 – Quantitatively Managed: In this stage an organization uses predictable processes. The asset data is stored in a hierarchically in one or more IT-Systems. Asset data can be shared with the help of special access accounts. Sensor data relate to the asset data and can be read out and monitored fully integrated. In addition, limit values for the sensor data are defined and when a value exceeds maintenance workers get informed. Algorithms can predict asset failure with the help of sensor data. Maintenance engineers can use mobile devices to access asset information. 3D models are provided for the asset visualization. Based on the importance and the condition an individual maintenance strategy for every asset is defined.
Level 5 – Optimized: In this stage an organization optimizes their processes. The asset Data is stored hierarchically across the whole organization in one central IT-System. Asset data can be shared in real time over an asset network with other organizations. Sensor data are read out, monitored, and analyzed. In case of anomalies a message gets send to a maintenance worker with an individual maintenance recommendation. Machine learning algorithms are used to predict asset failure. These algorithms are improving continually and take new contributing factors into account. Maintenance engineers can use mobile devices to access asset data. 3D models of assets are connected to the asset data. Maintenance worker can use VR and AR to visualize assets. Based on the importance and the condition an individual maintenance strategy for every asset is defined, which continually get improved.
A survey:
I conducted a survey and asked companies how they are currently doing the asset management and where they fit in the maturity model. As a result, 39 companies from Germany, Austria and Switzerland participated in this survey. Lets have a closer look on the results:
The survey shows that most of companies still either no or analog way for their asset management. Around 62% of the participators can be distributed to the maturity level Initial, which means that they have not yet implemented at least in one category an asset management process. Another 36% of the companies have implemented analog methods for their asset management. Only less than 3% of the participants use digital workflows in all categories. There are strong differences between the different categories. Most of the companies store their data digital, but still 17,9% either do not store asset data or save the information paper based. The maturity of the companies uses mail, email, fax, or phone to share asset information. Only around 5% percent of the participants already using a modern asset network to share asset information. The most less pronounced category is how organizations use modern technologies to predict asset failure. Most of the companies, more than 50%, still not use any process for prediction. Only around 5% of the participants stated that they are using machine learning algorithm to predict asset failure. The maturity of companies also uses sensor data for their asset management, except 28% which do not use any sensors. The most common maintenance strategies are time based and condition based. The survey data shows that it is more likely that big companies are already advanced maintenance techniques than smaller companies. Except for the category of maintenance strategy, where no correlation was measurable. The survey also asked the companies, what asset management categories are most relevant. The results are that most relevant for the companies is a good way to store date, followed by a good maintenance strategy. On the third place of the most relevant topics is predictive maintenance. While in the first two categories most companies already use advanced techniques, have only a minority of the companies implemented modern way for predictive maintenance.
I hope this article series gave you a quick overview on how an organization can design their asset management. I looked on many new techniques to boost productivity and reduce cost. With the help of the maturity model, you can now rate your organization and compare it with the results of the survey.
Maturity model for Asset Management:
The maturity model is composed of five levels. The first level Initial describes a company, which did not yet start with the implementation of a technology. The highest level Optimized defines a company which uses all possible technologies to optimize their asset management. The maturity levels are defines as following:
Level 1 – Initial: In this stage an organization has no asset management. It does not store asset data and is not able to share asset information with for example a service provider. This organization does not use sensors to monitor the assets and cannot predict asset failure. Maintenance engineers do not get supported by 3D models or mobile applications. Maintenance activities are only executed when an asset fails.
Level 2 – Managed: In this stage an organization uses basic processes for their asset management. Asset information are analog documented and can be shared by mail, e-mail, or phone. Some sensors are used for maintenance purpose but can only read out onside and manually. Information from the manufacturer is used to approximately predict an asset failure. Maintenance engineers can use analog documents and plans for their maintenance work. Maintenance activities are executed time based or when an asset fails.
Level 3 -Defined: In this stage an organization uses advanced processes for their asset management. The asset data is stored digitally in a semi-structured way. Documents and data can be shared in a collaborative cloud. Sensor data can be read out and monitored over an IT-system. With the help of a central database asset failure can be predicted. Maintenance engineers can use mobile devices to access asset information. Maintenance activities are executed time based or condition based.
Level 4 – Quantitatively Managed: In this stage an organization uses predictable processes. The asset data is stored in a hierarchically in one or more IT-Systems. Asset data can be shared with the help of special access accounts. Sensor data relate to the asset data and can be read out and monitored fully integrated. In addition, limit values for the sensor data are defined and when a value exceeds maintenance workers get informed. Algorithms can predict asset failure with the help of sensor data. Maintenance engineers can use mobile devices to access asset information. 3D models are provided for the asset visualization. Based on the importance and the condition an individual maintenance strategy for every asset is defined.
Level 5 – Optimized: In this stage an organization optimizes their processes. The asset Data is stored hierarchically across the whole organization in one central IT-System. Asset data can be shared in real time over an asset network with other organizations. Sensor data are read out, monitored, and analyzed. In case of anomalies a message gets send to a maintenance worker with an individual maintenance recommendation. Machine learning algorithms are used to predict asset failure. These algorithms are improving continually and take new contributing factors into account. Maintenance engineers can use mobile devices to access asset data. 3D models of assets are connected to the asset data. Maintenance worker can use VR and AR to visualize assets. Based on the importance and the condition an individual maintenance strategy for every asset is defined, which continually get improved.
A survey:
I conducted a survey and asked companies how they are currently doing the asset management and where they fit in the maturity model. As a result, 39 companies from Germany, Austria and Switzerland participated in this survey. Lets have a closer look on the results:
The survey shows that most of companies still either no or analog way for their asset management. Around 62% of the participators can be distributed to the maturity level Initial, which means that they have not yet implemented at least in one category an asset management process. Another 36% of the companies have implemented analog methods for their asset management. Only less than 3% of the participants use digital workflows in all categories. There are strong differences between the different categories. Most of the companies store their data digital, but still 17,9% either do not store asset data or save the information paper based. The maturity of the companies uses mail, email, fax, or phone to share asset information. Only around 5% percent of the participants already using a modern asset network to share asset information. The most less pronounced category is how organizations use modern technologies to predict asset failure. Most of the companies, more than 50%, still not use any process for prediction. Only around 5% of the participants stated that they are using machine learning algorithm to predict asset failure. The maturity of companies also uses sensor data for their asset management, except 28% which do not use any sensors. The most common maintenance strategies are time based and condition based. The survey data shows that it is more likely that big companies are already advanced maintenance techniques than smaller companies. Except for the category of maintenance strategy, where no correlation was measurable. The survey also asked the companies, what asset management categories are most relevant. The results are that most relevant for the companies is a good way to store date, followed by a good maintenance strategy. On the third place of the most relevant topics is predictive maintenance. While in the first two categories most companies already use advanced techniques, have only a minority of the companies implemented modern way for predictive maintenance.
I hope this article series gave you a quick overview on how an organization can design their asset management. I looked on many new techniques to boost productivity and reduce cost. With the help of the maturity model, you can now rate your organization and compare it with the results of the survey.
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