ITEC 2020 ITEC Extended Abstract Presentation Raising a Digital Twin, Avoiding the “Terrible Twos” John Meyers, Naval Air Warfare Center Training Systems Division, 32826, USA Michael Merritt, Naval Air Warfare Center Training Systems Division, 32826, USA Abstract - Data analytics and Digital Twins are becoming popular tools in many organizations, this includes government organizations. However, the realities and processes to bring these new tools into an organization is not always well understood and shared. This paper captures lessons learned specific to the use of data analytics and creating a digital twin to better understand a very complex and high priority Fleet issue (in this case aircraft physiological episodes). These lessons learned begin with understanding the "problem at hand", standing up a functional team, enablers (e.g. software tools, computing power), barriers (e.g. data availability, data quality, business and regulatory issues), successes and best practices. Current capabilities will be presented as well as consideration of the potential benefit of creating human digital twins (both physical and cognitive). Data analytics and digital twin technology is invaluable to better understanding complex problems with the potential to greatly improve human performance in complex systems. Introduction - The Team Lead requires excellent leadership skills in addition to knowing the organization and being recognized Data analytics is a powerful tool in modern businesses. as having strong technical credibility from the For this paper following, data analytics is the science of organization. analysing raw data to make conclusions to guide decisions. - Individual team member skills are critical. Performance A digital twin is a virtual model of a physical object, requires computer science, statistics and domain process or system. The problem, or use case, requiring the knowledge in the area that you are applying data analytics organization to develop and use these tools is referred to to. See Figure 1. Realistically, hiring the right academic as an aircraft physiological episode or PE. The Navy's background is the most practical approach. For this effort, definition of a physiological episode is when a pilot since the effort required logisticians and engineers, the experiences loss in performance related to insufficient initial thought was to train personnel currently in these oxygen, depressurization, or other factors during flight. It disciplines to be good data scientists. What was found to is not the purpose of this paper to cover specifics related to be the best approach was to teach data scientists to the issues, suspected causes or corrective actions resulting understand the engineering and logistics involved in the from efforts to better understand PEs. Information is available in the public domain regarding the Navy’s work issues at hand. Collaboration with subject matter experts for domain experience was necessary (specifically aircraft on PEs. This paper covers how data analytics and digital systems and maintenance processes). twin technology was used in understanding the problem and lessons learned as an organization. Background PEs are dangerous and effect safety of flight. Reported PEs were increasing over historical rates. There are many causes that can result in an official PE. Different aircraft were affected and responsibilities crossed multiple organizational boundaries. Aircrews were concerned and Senior Leadership wanted answers. Several potential causes were hotly debated; however, no single root cause was clearly identifiable at the time. The result was a need for a data driven approach to provide a sound foundation to make safety of flight and fiscal decisions. Creating the Team In the early phases, data visualization tools were utilized in an effort to link disparate data sets to identify PE casual factors. It was then decided to contract out some of the data analytics tasking to get an independent perspective and Fig. 1: Team Skills glean insight into data analytic technics applied by industry. Because of the magnitude, the complexity, the - Recruiting talent was not just a Human Resource need for in-depth domain expertise, and the desire to Department effort, it took required senior leadership establish a strong data analytics capability within engagement to build the team. NAVAIR, the decision was made to use in-house - Enterprise level support is required to remove resources to continue the data analytic efforts. Building a organizational and bureaucratic barriers, including the solid data analytics team is not an easy task. team being able to have direct access to senior leaders. A NAWCTSD Public Release 20-ORL001 Distribution Statement A - Approved for public release; distribution is unlimited.
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