Abstract: An apparatus comprises a processing platform configured to implement a machine learning system for automated generation of predicted reliability measures and associated early warning indicators for product and part combinations. The machine learning system comprises a data aggregation module configured to extract product and part data from a big data repository, and a reliability predictor configured to generate predicted reliability measures for respective ones of the product and part combinations utilizing a shared model that is determined based at least in part on the extracted product and part data. The machine learning system processes the predicted reliability measures to generate early warning indicators relating to particular ones of the product and part combinations having predicted reliability measures that fail to meet one or more specified criteria.

Abstract: An apparatus comprises a processing platform configured to implement an analysis and visualization tool utilizing a mixture of multiple reliability measures to characterize each of a plurality of product and part combinations. The analysis and visualization tool comprises a data aggregation module configured to extract product and part data from a big data repository, a reliability measure generator configured to process the extracted product and part data to generate a plurality of reliability measures for each of a plurality of different product and part combinations, a mixture model module configured to compute a score from the plurality of reliability measures for each of the different product and part combinations, and a visualization module configured to generate at least one visualization as a function of the scores computed for the respective different product and part combinations. The generated visualization may comprise a quadrant plot view visualization displaying the computed scores.