Computer implemented methods and systems for determining fleet conditions and operational management thereof

Abstract

A method for determining fleet conditions and operational management thereof, performed by a central system includes receiving fleet data from at least one distributed data repository. The fleet data is substantially representative of information associated with a fleet of physical assets. The method also includes processing the received fleet data for the fleet using at least one process of a plurality of processes. The plurality of processes assess the received fleet data into processed fleet data. The method additionally includes determining a fleet condition status using the processed fleet data and the at least one process of the plurality of processes. The method further includes generating a fleet response. The fleet response is substantially representative of a next operational step for the fleet of physical assets. The method also includes transmitting the fleet response to at least one of a plurality of fleet response recipients.

1 . A method for determining fleet conditions and operational management thereof, wherein said method is performed by a system having at least one computing device including a processor and a memory device coupled to the processor, said method comprising:
 receiving data at the system from at least one data repository, the data substantially representative of information associated with two or more physical assets, wherein the two or more physical assets form a fleet;   processing, at the system, the received data for the fleet of physical assets using at least one process of a plurality of processes, the plurality of processes assessing the received data into processed data;   determining, at the system, a fleet condition status, including a condition for at least a first physical asset in the fleet using the processed data and the at least one process of the plurality of processes;   generating, at the system, a fleet response that is substantially representative of a next operational step for the two or more physical assets in the fleet based on the condition of at least the first physical asset in the fleet; and   transmitting the fleet response to at least one of a plurality of fleet response recipients.   
     
     
         2 . The method in accordance with  claim 1 , wherein the fleet condition status is substantially representative of at least one of a plurality of condition states of the at least one asset associated with an asset type associated with the fleet of physical assets. 
     
     
         3 . The method of  claim 1 , wherein receiving fleet data comprises:
 receiving data, wherein the received fleet data is one of incomplete or partially complete;   generating estimated fleet data comprising estimating a portion of the fleet data not received based upon previously included in the received fleet data; and   incorporating the estimated fleet data with the received fleet-data.   
     
     
         4 . The method of  claim 1 , wherein generating the fleet response comprises:
 identifying an availability of parts to maintain the fleet of physical assets;   identifying an availability of human resources for service of the fleet of physical assets; and   applying at least one of a plurality of decision algorithms to determine the next operational step.   
     
     
         5 . The method of  claim 1 , wherein generating the fleet response comprises prioritizing, for each asset of the fleet of physical assets, ordering of parts and scheduling human resources to service the physical assets based upon an asset condition. 
     
     
         6 . The method of  claim 1 , wherein determining the fleet condition status comprises:
 determining a first forecast for an availability of parts to maintain the fleet of physical assets;   determining a second forecast for an availability of human resources for service of the fleet of physical assets; and   determining a third forecast of material conditions of each asset of the fleet of assets, the third forecast substantially representative of a predicted condition.   
     
     
         7 . The method of  claim 1 , wherein determining the fleet condition status includes at least one or a combination of:
 analyzing a repository of historical data and historical fleet responses;   creating an expert data model; and   machine learning.   
     
     
         8 . A network-based system for determining fleet conditions and operational management thereof for a fleet of physical assets, said system comprising:
 a system having at least one computing device including a processor and a memory device coupled to said processor;   a database associated with said system;   a plurality of supplier client devices associated with a plurality of fleet suppliers;   a plurality of supplier databases associated with the plurality of fleet suppliers;   a plurality of servicer client devices associated with a plurality of fleet servicers; and   a plurality of servicer databases associated with the plurality of fleet servicers, said network-based system configured to:
 receive data at the system from at least one data repository, the data substantially representative of information associated with two or more physical assets, wherein the two or more physical assets form a fleet; 
 process, at the system, the received data for the fleet of physical assets using at least one process of a plurality of processes, the plurality of processes assessing the received data into processed data; 
 determine, at the system, a fleet condition status, including a condition for at least a first physical asset in the fleet using the processed fleet data and the at least one process of the plurality of processes; 
 generate, at the system, a fleet response that is substantially representative of a next operational step for the two or more physical assets in the fleet based on the condition of at least the first physical asset in the fleet; and 
 transmit the fleet response to at least one of a plurality of fleet response recipients. 
   
     
     
         9 . The network-based system in accordance with  claim 8 , wherein the fleet condition status is substantially representative of at least one of a plurality of condition states of at least one asset associated with an asset type associated with the fleet of physical assets. 
     
     
         10 . The network-based system of  claim 8 , wherein said network-based system configured to receive data is further configured to:
 receive data, wherein the received data is one of incomplete or partially complete;   generate estimated data, comprising estimating a portion of the data not included in the received data; and   incorporate the estimated data with the received data.   
     
     
         11 . The network-based system of  claim 8 , wherein said network-based system configured to generate the fleet response is further configured to:
 identify an availability of parts to maintain the fleet of physical assets;   identify an availability of human resources for service of the fleet of physical assets; and   apply at least one of a plurality of decision algorithms to determine the next operational step.   
     
     
         12 . The network-based system of  claim 8 , wherein said network-based system configured to generate the fleet response is further configured to prioritize, for each asset of the fleet of physical assets, ordering of parts and scheduling human resources to service the physical assets based upon an asset condition. 
     
     
         13 . The network-based system of  claim 8 , wherein said network-based system configured to determine the fleet condition is further configured to:
 determine a first forecast for an availability of parts to maintain the fleet of physical assets;   determine a second forecast for an availability of human resources for service of the fleet of physical assets; and   determine a third forecast of material conditions of each asset of the fleet of assets, the third forecast substantially representative of a predicted condition.   
     
     
         14 . The network-based system of  claim 8 , wherein said network-based system configured to determine the fleet condition is further configured to include at least one or a combination of:
 analyze a repository of historical data and historical fleet responses;   create an expert data model; and   machine learning.   
     
     
         15 . A computer for determining fleet conditions and operational management thereof, said computer comprising a processor and a memory device coupled to said processor, said processor programmed to:
 receive data from at least one data repository, the data substantially representative of information associated with two or more same physical assets, wherein the two or more physical assets form a fleet;   process the received data for the fleet of physical assets using at least one process of a plurality of processes, the plurality of processes assessing the received data into processed data;   determine a fleet condition status, including a condition for at least a first physical asset in the fleet using the processed fleet data and the at least one process of the plurality of processes;   generate a fleet response, the fleet response substantially representative of a next operational step for the two or more physical assets in the fleet based on the condition of at least the first physical asset in the fleet; and   transmit the fleet response to at least one of a plurality of fleet response recipients.   
     
     
         16 . The computer in accordance with  claim 15 , wherein the fleet condition status is substantially representative of at least one of a plurality of condition states of at least one asset associated with an asset type associated with the fleet of physical assets. 
     
     
         17 . The computer of  claim 15 , wherein said processor programmed to receive fleet data is further programmed to:
 receive fleet data, wherein the received fleet data is one of incomplete or partially complete;   generate estimated data, comprising estimating a portion of the fleet data not included in the received data; and   incorporate the estimated data with the received data.   
     
     
         18 . The computer of  claim 15 , wherein said processor programmed to generate the fleet response is further programmed to:
 identify an availability of parts to maintain the fleet of physical assets;   identify an availability of human resources for service of the fleet of physical assets; and   apply at least one of a plurality of decision algorithms to determine the next operational step.   
     
     
         19 . The computer of  claim 15 , wherein said processor programmed to generate the fleet response is further programmed to prioritize, for each asset of the fleet of physical assets, ordering of parts and scheduling human resources to service the physical assets based upon an asset condition. 
     
     
         20 . The computer of  claim 15 , wherein said processor programmed to determine the fleet condition status is further programmed to:
 determine a first forecast for an availability of parts to maintain the fleet of physical assets;   determine a second forecast for an availability of human resources for service of the fleet of physical assets; and   determine a third forecast of material conditions of each asset of the fleet of assets, the third forecast substantially representative of a predicted condition.   
     
     
         21 . The method of  claim 1 , wherein the fleet condition status includes an indication of at least one of a health assessment, remaining useful life, need for diagnostics, need for maintenance, need for replacement and need for deactivation.