Decision Making and Risk Management Control Installing sensors either within individual, or more commonly within distributed networks of assets, can provide challenges associated with collecting and manipulating the vast amounts of data in order to support effective decision making. The increasing volumes of data provided by these assets can have a detrimental impact on decision making by presenting information overload situations.
The focus for the Decision Making and Risk Management Control stage is to consider what the most appropriate approach is to collect, manipulate and present this data to support strategic, tactical and operational decision making with respect to these assets. In addition the performance of the assets and the sensor technology provide a basis to make risk-based decisions related to reliability, availability, maintainability and safety. For example, the output of the Data Processing, Interpretation and Control stage may be to identify key trends in the data to indicate faults. However, this data alone is not necessarily useful for an organisation. Ultimately those who make decisions are indifferent to the technology used to support the decisions; instead they are interested in the confidence they have with their decision.
The Decision Making and Risk Management Control stage of the asset management system uses the processed data (information) provided by the Data Processing, Interpretation and Control stage to support decision making. This decision making could take any of the following forms:
- Logistics modelling
- Maintenance modelling
- Degradation modelling
- Warranty modelling
- Non-destructive testing
- Reliability prediction
- Prognostic modelling
For example, we may use the output of sensors to predict the residual life of a system, i.e. model how the system degrades over time. Based on this information, maintenance actions could be carried out to repair the system, or if necessary, replace the system. If we were to consider a group of systems rather than an individual system, we could use logistics models to optimise our delivery of spares. Other applications could include using models to estimate the reliability of a system without forcing the system to fail. This is particularly attractive when systems are very expensive or where a system cannot be recovered upon failure. Understanding the asset-based context and requirements for decision making is also the first step in making the assets either autonomous or semi-autonomous and letting the assets decide what data to provide.
The University of Strathclyde has a world class reputation for developing risk-based modelling techniques that can support decisions such as these. The Department of Management Science and the Department of Manufacturing and Engineering Management have published in leading journals and engaged with a wide range of industrial clients, such as BAE Systems, NASA, MOD, Scottish Power, Scottish Water and SELEX Galileo, to develop asset and risk management models that can impact on operational decision making. The expertise of both departments is strongly linked to the quality of their engagement with these industries.