Billions of dollars are spent by government organizations to accomplish their mission. Organizations are faced with the increased growth of complex software systems to handle higher velocity, volume, and variety of data captured at higher speeds to achieve mission goals. Software and hardware evolution continue to drive challenges in modifying legacy systems towards dependability and autonomy while forcing a change in software developers verification and validation approaches from traditional acquisition development methodology (waterfall, “V,” incremental, etc.) to agile and DevOps. At the same time, government organizations are wrestling with a tremendous technical change in the acquisition lifecycle process as they adopt Open System Architectures to meet the surge in cross-team interoperability while balancing budgetary constraints as they move towards a modernization strategy. A significant concern is the development of learning mechanisms to preserve organizational and programmatic knowledge, in the face of a transitory workforce, to improve information sharing as the organization undergoes enterprise evolution.For over a decade, the National Defense Industrial Association (NDIA) Systems Engineering Division repeatedly recommended the U.S. Department of Defense (DoD) and other government organizations integrate systems engineering (SE) and collaborative methods and tools throughout system acquisitions to better manage risk. A common theme reported in the findings is that insufficient understanding of both the individual and System-of-System (SoS) interdependency of product, process, and people/organizational environments affect information sharing. A similar note is taken in GAO-0615, GAO-09-1011T, and GAO-12-1022 to highlight the lack of interagency collaboration mechanisms.This praxis assesses cross-organizational links among three Elements of an acquisition organization using Social Network Analysis (SNA) to gain insight into their heuristic relationships and examines the human factors impacting process integration and collaboration in the “as-is” network. A Knowledge Management System (KMS) is introduced in a “to-be” network to investigate the probability of improving information sharing between three Elements in the network. The KMS is developed using ten criteria and five sub-criteria based on insight from literature reviews and substantiated by experts from the three Element and industry leaders. An Analytic Hierarchy Process (AHP) model aggregates the expert judgments to analyze the probability of implementing the KMS. An outline of a web-based knowledge portal leveraging the integration of current disparate tools into a "total system" approach to enable knowledge capture and information distribution is shown in Appendix 3. The primary objective for implementing a KMS is to allow seamless communication between teams allowing Communities of Practice (CoP) and management to observe and analyze the enterprise in a holistic manner to manage interdependencies with fact-based data integrated into metrics to make insightful decisions.
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