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Parsons, Vickie S.

Publications and source records attributed to Parsons, Vickie S..

New Method for Updating Mean Time Between Failure for ISS Orbital Replaceable Units Consultation Report

A request to conduct a peer review of the International Space Station (ISS) proposal to use Bayesian methodology for updating Mean Time Between Failure (MTBF) for ISS Orbital Replaceable Units (ORU) was submitted to the NASA Engineering and Safety Center (NESC) on September 20, 2005. The results were requested by October 20, 2005 in order to be available during the process of reworking the current ISS flight manifest. The results are included in this report.

Parsons, Vickie S.

Taxonomy Working Group Final Report

The purpose of the Taxonomy Working Group was to develop a proposal for a common taxonomy to be used by all NASA projects in the classifying of nonconformances, anomalies, and problems. Specifically, the group developed a recommended list of data elements along with general suggestions for the development of a problem reporting system to better serve NASA's need for managing, reporting, and trending project aberrant events. The Group's recommendations are reported in this document.

Parsons, Vickie S.

Searching for 'Unknown Unknowns'

The NASA Engineering and Safety Center (NESC) was established to improve safety through engineering excellence within NASA programs and projects. As part of this goal, methods are being investigated to enable the NESC to become proactive in identifying areas that may be precursors to future problems. The goal is to find unknown indicators of future problems, not to duplicate the program-specific trending efforts. The data that is critical for detecting these indicators exist in a plethora of dissimilar non-conformance and other databases (without a common format or taxonomy). In fact, much of the data is unstructured text. However, one common database is not required if the right standards and electronic tools are employed. Electronic data mining is a particularly promising tool for this effort into unsupervised learning of common factors. This work in progress began with a systematic evaluation of available data mining software packages, based on documented decision techniques using weighted criteria. The four packages, which were perceived to have the most promise for NASA applications, are being benchmarked and evaluated by independent contractors. Preliminary recommendations for "best practices" in data mining and trending are provided. Final results and recommendations should be available in the Fall 2005. This critical first step in identifying "unknown unknowns" before they become problems is applicable to any set of engineering or programmatic data.

Parsons, Vickie S.

A Framework for Categorizing Important Project Variables

While substantial research has led to theories concerning the variables that affect project success, no universal set of such variables has been acknowledged as the standard. The identification of a specific set of controllable variables is needed to minimize project failure. Much has been hypothesized about the need to match project controls and management processes to individual projects in order to increase the chance for success. However, an accepted taxonomy for facilitating this matching process does not exist. This paper surveyed existing literature on classification of project variables. After an analysis of those proposals, a simplified categorization is offered to encourage further research.

Parsons, Vickie S.

Plausible Explanations for Trends in Project Programmatic Findings

This paper investigates several published hypotheses to determine which might explain the observed cyclic pattern in programmatic findings by independent review teams over a ten-year period. The data does not neatly support any of the intra-project theories considered; however, something is obviously triggering these findings. A closer examination of the narratives associated with the programmatic findings did reveal consistency. The recurring themes that appeared with more numerous findings were unclear roles and staffing problems. Other explanations, external to the project teams, were also explored and the data supported a distributed political influence on project performance.

Parsons, Vickie S.