We propose a method for identifying the sources of prob lems in complex production systems where, due to the pro hibitive costs of instrumentation, the data available for analysis may be noisy or incomplete. In particular, we may not have complete knowledge of all components and their interactions. We define influences as a class of component interactions that includes direct communication and re source contention. Our method infers the influences among components in a system by looking for pairs of components with time-correlated anomalous behavior. We summarize the strength and directionality of shared influences using a Structure-of-Influence Craph (SIC). This paper explains how to construct a SIC and use it to isolate system mis behavior, and presents both simulations and in-depth case studies with two autonomous vehicles and a 9024-node pro duction supercomputer.

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