Accommodating Marine New Technology Insertion and Design Changes Through Use of a Management Flight Simulator

Several Navies have established policies and initiatives to manage assets and new technologies more efficiently and effectively and implement analytical decision tools and simulation models. In warship design, new technology, changes, ambiguity in expectations, and managing multiple stakeholders add to system and program complexity. Engineers, management and suppliers often work in isolation leading to inconsistencies in product information, tracking of design changes and challenges with decision-making. Despite the many disparate discipline-specific models, tools and techniques in addressing these challenges, product design and program failures are pervasive. Many defence projects have incurred significant cost overruns and delays, with the causes attributed to program pressure, changing requirements, immature technology, and under-estimation of risks. Traditional practices and measures are unable to predict the impact of new technology and design changes. Moreover, there is not a practical approach or tool to help integrate multiple disciplines to better understand system and program complexity and the impact of changes and new technology insertion. Understanding risks, potential changes and technologies through knowledge gain early in the design can help reduce costs and schedule delays. Using set-based design, application of engineering principles and an agile management approach can provide for a robust design that can better accommodate changes and new technology. Along with these principles, systems thinking, system dynamics, a decision support system, and techno-socio-economic and cultural factors are considered in development of a novel management flight simulator. This simulator is presented through application of a case study on an advanced marine integrated power system. The simulator represents a digital twin of the physical system and associated life cycle management curves. It links key discipline specific models through a digital thread that includes the intrinsic properties of the physical system and the intangible assets of knowledge, organizational integration, culture and work performance. Adaptive intelligence and augmentation are possible through recognition and response to system attribute and management behavioural patterns and trends, as visualized within the simulator. As part of the simulator, the decision support system allows for trade-off and what-if analysis where six-sigma attributes are managed. Along with a set-based design and agile approach, disruptive and sustaining technologies can be better managed with the help of the decision support system. The system dynamics sub-model provides visualization and control of design life cycle management curves as impacted by system state. In particular, key system, policy and process interactive levers can be adjusted to improve the behaviour of these curves. With adjustment to a few levers, knowledge can be gained early in design and system ease-of-change increased, leading to reduced design change costs and schedule delays. Other management curves positively influenced by adjusted levers include the number of design changes, vendor-furnished information maturity, design maturity, and work performance. Use of the simulator allows for critical thinking, adaptation to complexity, early knowledge gain, identification of problems early in design, integration of disciplines, and ultimately product and program success. The simulator provides a ‘big picture’ perspective and total solution not possible with the use of separate engineering and management models.