The early stage design of large-scale engineering systems challenges design teams to balance a complex set of considerations. Established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice sub-optimal systemlevel results are often reached due to factors such as satisficing, ill-defined problems or other project constraints. Twelve sub-system and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate sub-systems. Responses showed sub-system team members often presented conservative, worst-case scenarios to other sub-systems when negotiating a trade-off as a way of hedging their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding "margins," is modeled with a series of optimization simulations. Three "bias" conditions were tested: no bias, a constant bias and a bias which decreases with time. Results from the simulations show that biased information passing negatively affects both the number of iterations needed to reach and * Corresponding author.the Pareto optimality of system-level solutions. Results are also compared to the interview responses and highlight several themes with respect to complex system design practice.
Parameter estimates in large-scale complex engineered systems (LaCES) affect system evolution, yet can be difficult and expensive to test. Systems engineering uses analytical methods to reduce uncertainty, but a growing body of work from other disciplines indicates that cognitive heuristics also affect decision-making. Results from interviews with expert aerospace practitioners suggest that engineers bias estimation strategies. Practitioners reaffirmed known system features and posited that engineers may bias estimation methods as a negotiation and resource conservation strategy. Specifically, participants reported that some systems engineers “game the system” by biasing requirements to counteract subsystem estimation biases. An agent-based model (ABM) simulation which recreates these characteristics is presented. Model results suggest that system-level estimate accuracy and uncertainty depend on subsystem behavior and are not significantly affected by systems engineers' “gaming” strategy.
Communication has been shown to affect the design of large-scale complex engineered systems. Drawing from engineering design, communication, and management literature, this work defines miscommunication as when communication results in a “deficiency” or “problem” that hinders parties from fulfilling their values. This article details a consequential example of miscommunication at a Fortune 500 engineering firm with the potential to affect system performance. In phase 1, interviews with engineering practitioners (n = 82) identified disagreement about what constitutes a parameter “estimate” in the design process. Phase 2 surveyed engineering practitioners (n = 128) about whether estimates communicated for system-level tracking approximate “current” design statuses or “future” design projections. The survey found that both definitions existed throughout the organization and did not correlate with subsystem, position, or design phase. Engineers inadvertently aggregated both current and future estimates into single system-level parameters that informed decision-making, thereby constituting widespread or systemic miscommunication. Thus, even technical concepts may be susceptible to miscommunication and could affect system performance.
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