Root Cause Analysis (RoCA) is a computer program that assists analysts in understanding the root causes of process anomalies. As used here, “process anomalies” includes incidents that have caused, or that can potentially cause, injuries to personnel, damage to facilities, abnormal costs, or delays in processing. RoCA could be used, for example, in industry to investigate anomalies in production and by government agencies and airlines in investigating airplane accidents. Older software developed to aid such investigations offers limited capabilities for mapping the contribution of each root cause to a given process anomaly. Unlike the prior software, RoCA not only identifies root causes of process anomalies but also supports the identification of trends over multiple anomalies.

RoCA implements a causal-network format for root-cause analysis. The analyst can specify a causal structure (that is, what root causes contributed to each ultimate effect). The analyst can also specify the strengths of relationships between causes and effects. Given the causal structure and qualitative strength-of-relationship judgements, Bayesian networks can be used to evaluate the relative contributions of various root causes to a given anomaly. RoCA makes it possible to perform causal analysis to be performed on individual events and for the results of analyses of multiple events to be summarized and analyzed to support trend analysis.

RoCA also maintains an event database and provides for the entry of new events into the database as well as for editing the names, locations, times, or other attributes of previously entered events. An analyst can display and edit an analysis on a time line of a sequence of events; thus, the analyst can concentrate on the flow and causes of events rather than on background details or on the details of computer- code representations of causes. RoCA also enables communication of information about events among analysts.

In a RoCA analysis, an analyst breaks down an investigation of a complicated event into investigations of smaller event components. Each event component represents a relevant factor that constitutes a piece of the puzzle in the event investigation and that the analyst considers useful to include in the analysis. By creating smaller units that describe aspects of the event and by specifying the causal relationships among them, the analyst can structure the analysis in such a way that anyone reviewing it can understand it. In a RoCA analysis, the network and time-line displays force the analyst to be explicit about any assumptions concerning what caused what and what happened when. Two or more analysts sharing their respective network and time-line displays can see and comprehend each other’s assumptions.

RoCA can produce reports of many different types that can help in the identification of trends. Using these reports, an analyst can view data in a variety of ways and identify potential trends in the data that may warrant further investigation for systemic root causes. The analyst can enter a potential trend into the RoCA database and associate the trend with a set of events.

RoCA is written in Microsoft Visual Basic, using the Microsoft Jet/Access database drivers. RoCA utilizes interfacecontrol software from FarPoint, DataDynamics, KL Group, and AddSoft.

This program was developed by Tim S. Barth and Lisa Grace Kestel of Kennedy Space Center; Robert Fung and Brendan Del Favero of Prevision, Inc.; and Donna M. Blankmann-Alexander and Jeffrey R. Ewald of United Space Alliance. For further information, access the Technical Support Package (TSP) free on-line at  under the Software category.

In accordance with Public Law 96-517, the contractor has elected to retain title to this invention. Inquiries concerning rights for its commercial use should be addressed to

Robert Fung
Prevision, Inc.
120 N. Redwood Drive
San Rafael, CA 94903

Refer to KSC-12142, volume and number of this NASA Tech Briefs issue, and the page number.

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This article first appeared in the January, 2002 issue of NASA Tech Briefs Magazine.

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