Assessing Return on Investment (ROI) Opportunities of Prognostics
Prognostics involves the estimation of remaining useful life (RUL) in terms that are
useful to the maintenance decision making process. The estimation of RUL
provides useful information, but additional information is necessary to form a decision or to determine
a corrective
action.
CALCE has developed a new stochastic decision model that
determines when scheduled maintenance makes good business sense, i.e., makes
possible a business objective such as a balance of cost and availability.
The model enables the optimal interpretation of life consumption monitoring
damage accumulation or health monitoring precursor data, and applies to failure
events that appear to be random or appear to be clearly caused by defects.
Specifically the model is
targeted at addressing the following questions:
- How do we determine, on an application-specific basis, when the reliability of electronics has become predictable enough to warrant the application of PHM-based scheduled maintenance concepts?
- Given that the forecasting ability of PHM is subject to uncertainties in the sensor data collected, the data reduction methods, the failure models applied, the material parameters assumed in the models, etc., how can PHM results be interpreted so as to provide value? This boils down to determining an optimal safety margin on life consumption monitoring prediction and prognostic distance for health monitoring.
- How can a business case be constructed to show the usefulness of PHM approaches for electronic systems?
Return on Investment (ROI):
Two elements are being considered in the evaluation of ROI:
- The cost of implementation of the PHM approach
- Development costs (hardware, software, and integration)
- Product manufacturing recurring costs (hardware, testing and installation)
- Infrastructure costs (documentation, training and changing the logistics/maintenance culture)
- Sustainment costs (data collection, data archiving, logistics footprint of the PHM structures and the cost of false positives)
- Financial costs (cost of money)
- The cost avoidance associated with using PHM
- Failures avoided (minimizing unscheduled maintenance, increasing availability, reduced risk of system loss and increased safety)
- Minimizing the loss of remaining life
- Reduction in logistics footprint of the system (better spares management, minimization of external test equipment)
- Reduction in repair costs (better fault isolation, reduced collateral damage during repair)
- Reduction in redundancy
- Reduction in no-fault-founds
- Reduced waste stream costs
- Reduced liability
- Eases design and qualification of future systems
CALCE Tools:
A web-based tool has been developed to address PHM cost
avoidance. The tool implements a stochastic discrete event
simulation applied to single and multi LRU systems where the LRUs
can have no PHM structures, fixed interval maintenance, life
consumption monitoring, or precursor to failure health monitoring.
The tool can be used to optimize safety margins and prognostic
distances for single LRUs and to determine best maintenance
strategies for multiple LRU systems. The tool has been used to
perform ROI studies on single LRU systems.
The tool is available as a web applet and as a standalone application, and
includes documentation and a tutorial. Link to ROI tool page (a member password is required to access the tool).
Proposed Projects:
| ROI Thrust | Year 1 | Model for estimating implementation costs |
| Year 2 | Integration of implementation cost and maintenance model and generation of ROI and business case metrics | |
| Year 3 | Real system case study (integrated ROI model) | |
| Maintenance-Logistics Thrust | Year 1 | Real system case study (maintenance model) and expanded revenue model |
| Year 2 | Inclusion of missing attributes:
|
|
| Year 3 | Logistical impacts on PHM |
For questions or additional information on ROI of PHM, contact: Peter
Sandborn, (301) 405-3167, sandborn@calce.umd.edu.