Case Study: USCG Deepwater Program

Acquisition Logistics Engineering (ALE) was instrumental in supporting the shipbuilder, Northrop Grumman Ship Systems (NGSS), in total logistics support for a new class of ships, the National Security Cutter (NSC). The NCS is a modern ship that is an integral part of the U.S. Coast Guard's Deepwater Program. We orchestrated that effort by concentrating in three major areas: Process Definition, Skill Development, and Analysis Completion.

Regarding the first area, Process Definition, we established the rationale and methodology for revising the Mission Criticality Codes (MCCs) for the ship systems. Next, we created the parameters and provided the effort to enhance the ship's Mean Time Between Failure (MTBF) values, thus assisting in the development of a sparing tool to determine repair parts and spares to be purchased for sailaway ship support. Subsequent to that effort, ALE developed the model to calculate and display Operational Availability (Ao) values for each of the 14 NSC mission profiles matrixed to both the 11 critical systems and for the total ship systems. And finally, within the area of Process Definition, we devised the rationale, assumptions and mathematical calculations to assign Mean Logistics Delay Time (MLDT) values to the Aoequation.

In the second major area of Skill Development, we increased the skill level and capabilities of the NGSS Reliability, Maintainability and Supportability (RMS) team by providing ALE-instructed Relex training at our Gautier ALE facility. In addition, we provided reinforcement to the ALE RMS team on the use of the Relex Reliability Prediction model. Lastly, we instructed NGSS personnel in the logic, rationale, and operation of the ALE-devised Aomodeling tool.

For the third major area, Analysis Completion, we analyzed the data developed during the program to determine reliability drivers. Then we completed the analysis of MLDT impacts to influence the Aocalculation. Then we revised some of the original MTBF values as a result of analysis of sustainment outcomes.

In summary, the ALE-developed Aomodel brought visibility and allowed the analysis of key logistics parameters such as:

  • Mission requirements decomposition analysis - 14 missions were broken down to the requirement level.
  • Piece part/Line Replaceable Unit (LRU) criticality analysis - Over 10,000 parts were analyzed to determine the critical failure mode of the part and the redundancy of the part.
  • System level redundancy analysis - All Hull, Mechanical, and Electrical (HM&E) systems were reviewed to determine system level redundancy, redundancy between systems, and alternate means of completing functionality.
  • Key Aodrivers and the opportunities for possibility of Aoimprovements - The model automatically created Pareto charts for each of the missions so the availability drivers for each mission can be quickly determined.