Human Systems Integration at GTRI: Improving Human Performance for the U.S. Navy

Ensuring that human performance factors are adequately considered during the system engineering process has proven to be a challenging task for Human Factors and Human Systems Integration (HSI) practitioners. Programs that do not sufficiently include HSI as an integral aspect of planning and execution are at risk of diminished user performance and total system performance, leading to costly and time-consuming re-work. To encourage a greater involvement of HSI in systems engineering, the HSI Framework (HSIF) was developed to explicitly incorporate HSI tasks and products in all stages of system acquisition. The HSIF is a web application that contains general and domain-specific HSI activities, references, and related products. For HSI Practitioners and System Engineers, the HSIF provides technical guidance and best practices, thereby fostering early, explicit, and properly-scoped HSI efforts. In turn, Program Managers and Technical Authorities are provided with the information needed to accurately assess and manage human performance-related risks, leading to relevant, effective, and integrated system performance.

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Human Systems Integration Risk Management Tool

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1541931218621075 ◽

2018 ◽

Vol 62 (1) ◽

pp. 325-329

Author(s):

William Kosnik ◽

Patrick O’Neill ◽

Zachary Zimmerlin

Keyword(s):

Risk Management ◽

Human Performance ◽

Assessment Tool ◽

Systems Integration ◽

Management Tool ◽

Management Approach ◽

Performance Risk ◽

Human Systems Integration ◽

System Acquisition ◽

Human Systems

The Human Systems Integration Risk Management Tool (HSI-RMT) is a software-based interactive application designed to track, analyze, and mitigate human performance risk associated with the development of systems. It spans system development from concept formation to sustainment, that is – across the entire system acquisition lifecycle. HSI-RMT combines two previously developed tools: the HSI Capabilities and Requirements Tool (HSI-CART) and the HSI Program Risk Assessment Tool (HSI-PRAT). The former addresses HSI in capability requirements planning and the latter human performance considerations in system acquisition. HSI-RMT overlays a risk management approach onto the two tools in order to help the HSI practitioner identify, analyze, and mitigate human performance risk to program success. Tool content, in the form of best practice questions, was developed by Air Force HSI and industry subject matter experts. HSI-RMT promises to be a useful tool to help HSI practitioners manage human-centric risk across the system lifecycle. A demonstration will be given.

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Assessing the Value of Human Performance Modeling in Exploring Pilot-System Dynamics

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/107118139704100268 ◽

1997 ◽

Vol 41 (2) ◽

pp. 1032-1036 ◽

Cited By ~ 1

Author(s):

Holly S. Bautsch ◽

Michael D. McNeese ◽

S. Narayanan

Keyword(s):

Decision Making ◽

System Dynamics ◽

Task Analysis ◽

Human Performance ◽

Performance Modeling ◽

Systems Integration ◽

Cognitive Engineering ◽

Design Decision ◽

Human Systems Integration ◽

Human Systems

All too often human-systems integration is not addressed until the final stages of systems development. Because of constraints in the time-cost-schedule, it is typically too late in the acquisition process to make adaptations that address cognitive engineering and user-centered performance. Traditionally, designers have not had methods/tools that comprehensively integrate them in the concept exploration stage of design decision-making. Human-systems integration may currently include cognitive engineering or human performance modeling but rarely combines these methods to comprehensively establish human design requirements. This paper assesses the value of both cognitive engineering and human performance modeling by evaluating pilot-system dynamics in an advanced mission. One model is informed through traditional task analysis, while the other model utilizes cognitive task analysis. An experiment is reported which analyzes model outcomes in contrast to a “benchmark” (pilot-in-the-loop data). The results assess model similarities and differences. The discussion evaluates how human performance models can enhance cognitive engineering in design decision-making.

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