RGA Was Developed in Cooperation with Dr. Larry Crow
RGA was jointly developed by ReliaSoft and Dr. Larry H. Crow, with input from corporate partners which include John Deere and the US Navy. Dr. Crow is known by many as the original "Reliability Engineer" and is the father of Reliability Growth. ReliaSoft's RGA software applies Dr. Crow's "tried and true" methods for reliability growth, repairable systems and related analysis. It is the only software that was designed with, endorsed by and validated by Dr. Crow, and also includes some of his recently developed methodologies and refinements. A brief summary of Dr. Crow's professional background is presented next.
Larry H. Crow
Dr. Larry H. Crow is an independent consultant as well as an instructor and consultant for ReliaSoft Corporation in the areas of reliability growth and repairable system data analysis. Previously, Dr. Crow served as Vice President, Reliability and Sustainment Programs at Alion Science and Technology in Huntsville, Alabama. He held this position at IIT Research Institute before Alion was established in 2002 by 1600 former IITRI employees. Prior to that, Dr. Crow was Director, Reliability at General Dynamics Advanced Technology Systems (formerly Bell Laboratories ATS). Before joining Bell Laboratories in 1985, Dr. Crow was chief of the Reliability Methodology Office at the US Army Materiel Systems Analysis Activity (AMSAA). He developed the Crow (AMSAA) model and the Crow Projection model, which have been incorporated into US DoD military handbooks as well as national and international standards and service regulations on reliability. Dr. Crow chaired the Tri-Service Committee to develop US MIL-HDBK-189, Reliability Growth Management and is the principal author of that document. He is also the principal author of the IEC 61164, Reliability Growth-Statistical Tests and Estimation Methods. He developed the widely used N.H.P.P. Power Law model for analyzing repairable systems reliability, which is featured in the new IEC 61710, Goodness-of-Fit and Estimation Methods for the Power Law Model.