Identifying system failures by accessing prior troubleshooting information
A system, method, and computer-readable medium for performing a system failure identification operation, comprising: receiving information regarding a device a repair depot; performing a depot triage process on the device, the depot triage recording possible causal factors contributing to failure of the device; determining suspected failures associated with the device based upon symptoms exhibited by the device; and, correlating the suspected failures with commodities for use in repairing the device.
1. A computer-implementable method for performing a system failure identification operation, comprising:
receiving information regarding a device in a repair depot;
performing a depot triage process on the device, the depot triage recording possible causal factors contributing to failure of the device, the possible causal factors being identified based upon technical support personnel accessing prior troubleshooting steps;
determining suspected failures associated with the device based upon symptoms exhibited by the device;
identifying underlying patterns in causal factors and customer complaints using text mining analytics;
correlating the suspected failures with part commodities for use in repairing the device; and,
performing a machine-learning operation to direct repair operations, the machine learning operation providing information to a system failure identification user interface to allow the system failure identification user interface to provide information regarding symptom classification, correlation of systems with part commodities and part commodity recommendations to assist in directing repair operations.
2. The method of claim 1 , wherein:
the text mining analytics identify specific symptoms associated with the device.
3. The method of claim 1 , further comprising:
presenting a system failure identification user interface, the system failure identification user interface providing information regarding symptom classification, correlation of symptoms with commodities and commodity recommendations to assist in directing repair operations.
4. The method of claim 1 , wherein:
possible causal factors contributing to a device failure are accessed via a function command.
5. A system comprising:
a processor;
a data bus coupled to the processor; and
a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus, the computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for:
receiving information regarding a device in a repair depot;
performing a depot triage process on the device, the depot triage recording possible causal factors contributing to failure of the device, the possible causal factors being identified based upon technical support personnel accessing prior troubleshooting steps;
determining suspected failures associated with the device based upon symptoms exhibited by the device;
identifying underlying patterns in causal factors and customer complaints using text mining analytics;
correlating the suspected failures with part commodities for use in repairing the device; and
performing a machine-learning operation to direct repair operations, the machine learning operation providing information to a system failure identification user interface to allow the system failure identification user interface to provide information regarding symptom classification, correlation of systems with part commodities and part commodity recommendations to assist in directing repair operations.
6. The system of claim 5 , wherein:
the text mining analytics identify specific symptoms associated with the device.
7. The system of claim 5 , wherein the instructions executable by the processor are further configured for:
presenting a system failure identification user interface, the system failure identification user interface providing information regarding symptom classification, correlation of symptoms with commodities and commodity recommendations to assist in directing repair operations.
8. The system of claim 5 , wherein:
possible causal factors contributing to a device failure are accessed via a function command.
9. A non-transitory, computer-readable storage medium embodying computer program code, the computer program code comprising computer executable instructions configured for:
receiving information regarding a device in a repair depot;
performing a depot triage process on the device, the depot triage recording possible causal factors contributing to failure of the device, the possible causal factors being identified based upon technical support personnel accessing prior troubleshooting steps;
determining suspected failures associated with the device based upon symptoms exhibited by the device;
identifying underlying patterns in causal factors and customer complaints using text mining analytics;
correlating the suspected failures with part commodities for use in repairing the device; and,
performing a machine-learning operation to direct repair operations, the machine learning operation providing information to a system failure identification user interface to allow the system failure identification user interface to provide information regarding symptom classification, correlation of systems with part commodities and part commodity recommendations to assist in directing repair operations.
10. The non-transitory, computer-readable storage medium of claim 9 , wherein:
the text mining analytics identify specific symptoms associated with the device.
11. The non-transitory, computer-readable storage medium of claim 9 , wherein the computer executable instructions are further configured for:
presenting a system failure identification user interface, the system failure identification user interface providing information regarding symptom classification, correlation of symptoms with commodities and commodity recommendations to assist in directing repair operations.
12. The non-transitory, computer-readable storage medium of claim 9 , wherein:
possible causal factors contributing to a device failure are accessed via a function command.
13. The non-transitory, computer-readable storage medium of claim 9 , wherein:
the computer executable instructions are deployable to a client system from a server system at a remote location.
14. The non-transitory, computer-readable storage medium of claim 9 , wherein:
the computer executable instructions are provided by a service provider to a user on an on-demand basis.