Publication

Engineered Resilient Systems (ERS) is one of the seven DoD Science and Technology (S&T) Priorities. The ERS program is evolving a framework and an integrated, trusted computational environment supporting all phases of acquisition and operational analyses. Resilient systems necessitate data-driven, richly informed decisions.  Achieving resilience in the past has relied on expertise of experienced senior decision makers using deep knowledge of their systems within their context.  In support of the ERS tradespace analysis goals, this research is motivated by the pressing concern in the defense-related industry and government of the aging workforce. The resulting wave of impending retirements of experienced personnel may lead to lost knowledge and inability to recreate past systems, let alone develop new ones of growing complexity. The concern over lost expertise, including both in-domain as well as systems-level thinking is a real one, and one that has received growing attention. Using a structured technique, it has been shown that novices can approach expert-like strategies in the use of visual tradespace exploration for decision-making.  Additionally, tradespace exploration has been shown to be useful as a “boundary object” fostering cross-domain conversations, and facilitating decision making for complex systems. Such tradespace results are promising in representing a means for capturing and transferring expert knowledge and skills.  In this project, the research team gathered expert knowledge and synthesized emerging ERS-related research, toward a goal enabling novices to have expert-like decision capability through encoded knowledge and data-driven tradespace analysis framework and integrated tool suite.

WORK ACCOMPLISHED

Task 1. Knowledge and Information Gathering. The research team investigated current and recent past briefings and literature related to engineered resilient systems, and specifically to tradespace exploration. Multiple discussions were held with government leaders, university researchers, practitioners. Given the importance of tradespace exploration (TSE), the team focused its efforts in this regard given the limitations of time and resources in this research project to date.

Task 2. Artifact and Knowledge Coding. Using the gathered information and insights, and the ongoing research of the team members, the key ERS-relevant artifacts were identified. The research team mapped these artifacts to the MPTs derived from the knowledge and information gathering performed in Task 1.

Task 3. Exploration Case Study. This task was initially intended to conduct an exploratory case study to identify tradespace exploration artifacts in practice and how they relate to knowledge goals within ERS. Unfortunately delays in data availability cut short the intended timeframe across which to perform this case study. As an alternative approach, the research team was able to generate some observations based on limited data availability to two Navy tradespace exploration activities conducted in concert with the ERS Program.

Task 4. Full Scale Case Study Design. Based on the results of the information gathering and observed exploration case studies, the research team developed some initial requirements for designing a larger full scale case study.

Task 5. Synthesize Preliminary Prescription for ERS Artifacts for Knowledge Capture/Transfer. Using the results of tasks 1 – 4, the research team enumerated some potential gaps and findings, along with suggested enablers for consideration in evolving the ERS framework and tool suite environment in support of the its tradespace analysis goals.

FINDINGS

A number of next steps were identified over the course of this research which would enhance and enable the ERS vision, specifically as related to tradespace exploration activities. These include:

• Efforts to begin compiling appropriate knowledge relative to the core constructs identified in this projects including past needs, contexts, constraints, design space, performance space, value space, performance model, value model, lessons learned, language, case examples, and tools.

• Research into effort vs. confidence tradeoffs so that projects can scale effort on various activities within the TSE process to match their needs subject to available resources.

• Development of fidelity tradeoff guidance and associated tools so that studies can scale TSE implementation appropriately.

• Explicit incorporation of resilience-related ilities evaluation into the ERS architecture, including model libraries and decision analyzer toolset.

• Inclusion of value models along with performance models in the model data store.

• Continued piloting of parts of the ERS associated TSE processes, as well as full end-to-end studies.

• Continued community building by the ERS program, including offices such as the various A9 (e.g. AF/A9, OAS, AFSPC/A9, etc.) and other entities with responsibilities overlapping with proposed ERS vision and capabilities

• Further research into supporting enabling methods, processes, and tools that can facilitate TSE knowledge capture and reuse, as well as resource-effective studies that can quantify and identify resilient, high value system solutions in diverse application.