scholarly journals On the use of coordination strategies in complex engineered system design projects

2020 ◽  
Vol 6 ◽  
Author(s):  
Arianne X. Collopy ◽  
Eytan Adar ◽  
Panos Y. Papalambros
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Large Scale ◽  
System Level ◽  
Engineering Project ◽  
Design Work ◽  
Coordination Strategies ◽  
Technical Leadership ◽  
Complex Engineered Systems ◽  
Design Projects

Abstract Coordination of distributed design work is an important activity in large-scale and complex engineered systems (LSCES) design projects. Coordination strategies have been studied formally in system design optimization and organizational science. This article reports on a study to identify what strategies are used in coordination practice. While the literature primarily offers prescriptive coordination strategies, this study focussed on the contribution of individuals’ behaviours to system-level coordination. Thus, a coordination strategy is seen as a particular set of individual actions and behaviours. We interviewed professionals with expertise in systems engineering, project management and technical leadership at two large aerospace design organizations. Through qualitative thematic analysis, we identified two strategies used to facilitate coordination. The first we call authority-based and is enabled by technical know-how and the use of organizational authority; the second we call empathetic leadership and includes interpersonal skills, leadership traits and empathy. These strategies emerged as complementary and, together, enabled individuals to coordinate complex design tasks. We found that skills identified in competency models enable these coordination strategies, which in turn support management of interdependent work in the organization. Studying the role of individuals contributes an expanded view on how coordination facilitates LSCES design practice.

Gaming the System: An Agent-Based Model of Estimation Strategies and their Effects on System Performance

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
John Meluso ◽  
Jesse Austin-Breneman
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
System Level ◽  
Conservation Strategy ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Bias Estimation ◽  
Agent Based Model ◽  
Agent Based ◽  
Complex Engineered Systems

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.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
Systems Design ◽  
System Level ◽  
Worst Case ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

During the early stage design of large-scale engineering systems, design teams are challenged to balance a complex set of considerations. The established structured approaches for optimizing complex system designs offer strategies for achieving optimal solutions, but in practice suboptimal system-level results are often reached due to factors such as satisficing, ill-defined problems, or other project constraints. Twelve subsystem and system-level practitioners at a large aerospace organization were interviewed to understand the ways in which they integrate subsystems in their own work. Responses showed subsystem team members often presented conservative, worst-case scenarios to other subsystems when negotiating a tradeoff as a way of hedging against their own future needs. This practice of biased information passing, referred to informally by the practitioners as adding “margins,” is modeled in this paper 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 and 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.

scholarly journals The Naval Postgraduate School’s Department of Systems Engineering Approach to Mission Engineering Education through Capstone Projects

Systems ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 38 ◽  
Author(s):  
Douglas L. Van Bossuyt ◽  
Paul Beery ◽  
Bryan M. O’Halloran ◽  
Alejandro Hernandez ◽  
Eugene Paulo
Keyword(s):  
Systems Engineering ◽  
System Analysis ◽  
System Modeling ◽  
System Level ◽  
Naval Postgraduate School ◽  
Engineering Project ◽  
Educational Approach ◽  
Capstone Courses ◽  
Engineering Department ◽  
Capstone Projects

This article presents an educational approach to applied capstone research projects using a mission engineering focus. It reviews recent advances in mission engineering within the Department of Defense and integrates that work into an approach for research within the Systems Engineering Department at the Naval Postgraduate School. A generalized sequence of System Definition, System Modeling, and System Analysis is presented as an executable sequence of activities to support analysis of operational missions within a student research project at Naval Postgraduate School (NPS). That approach is detailed and demonstrated through analysis of the integration of a long-range strike capability on a MH-60S helicopter. The article serves as a demonstration of an approach for producing operationally applicable results from student projects in the context of mission engineering. Specifically, it demonstrates that students can execute a systems engineering project that conducts system-level design with direct consideration of mission impacts at the system of systems level. Discussion of the benefits and limitations of this approach are discussed and suggestions for integrating mission engineering into capstone courses are provided.

Automatic Integration of System-Level Design and System Optimization Based on SysML

2012 ◽  
Vol 249-250 ◽  
pp. 1154-1159
Author(s):  
Yu Sheng Liu ◽  
Wen Qiang Yuan
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Optimization Model ◽  
System Modeling ◽  
System Optimization ◽  
System Level ◽  
Design Parameters ◽  
System Level Design ◽  
Model Based ◽  
Level Design

Model based systems engineering (MBSE) is becoming a promising approach for the system-level design of complex mechatronics. And several MBSE tools are developed to conduct system modeling. However, the system design cannot be optimized in current MBSE tools. In this study, an approach is presented to conduct the task. A set of optimization stereotype is defined at first which is used to formalize the optimization model based on the system design model. Then the design parameters and their relationships applied optimization stereotypes are extracted and transferred to construct the tool-dependent optimization model. Finally, the optimization model is solved and the results are given back and then modify the corresponding system model automatically. In this paper, MagicDraw is used to model the whole system whereas Matlab optimizer is used for optimization. The combustion engine is chosen as the example to illustrate the proposed approach.

scholarly journals Biased Information Passing Between Subsystems Over Time in Complex System Design

2014 ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Bo Yang Yu ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
Large Scale ◽  
Early Stage ◽  
System Level ◽  
Worst Case ◽  
Team Members ◽  
Early Stage Design ◽  
Complex System Design ◽  
Biased Information

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 system-level 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 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.

Estimate Uncertainty: Miscommunication About Definitions of Engineering Terminology

2019 ◽  
Vol 142 (7) ◽  
Author(s):  
John Meluso ◽  
Jesse Austin-Breneman ◽  
Jose Uribe
Keyword(s):  
System Performance ◽  
Large Scale ◽  
Phase 1 ◽  
Parameter Estimate ◽  
Fortune 500 ◽  
System Level ◽  
Future Design ◽  
Complex Engineered Systems ◽  
Current Design ◽  
Engineered Systems

Abstract 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.

Technology Characterization Models and Their Use in Systems Design

2014 ◽  
Vol 136 (7) ◽  
Author(s):  
Robert R. Parker ◽  
Edgar Galvan ◽  
Richard J. Malak
Keyword(s):  
Decision Making ◽  
Systems Engineering ◽  
Systems Design ◽  
Computational Effort ◽  
Engineering Problem ◽  
Component Technology ◽  
System Level ◽  
Support Vector ◽  
Engineering Project ◽  
Set Based Design

Prior research suggests that set-based design representations can be useful for facilitating collaboration among engineers in a design project. However, existing set-based methods are limited in terms of how the sets are constructed and in their representational capability. The focus of this article is on the problem of modeling the capabilities of a component technology in a way that can be communicated and used in support of system-level decision making. The context is the system definition phases of a systems engineering project, when engineers still are considering various technical concepts. The approach under investigation requires engineers familiar with the component- or subsystem-level technologies to generate a set-based model of their achievable technical attributes, called a technology characterization model (TCM). Systems engineers then use these models to explore system-level alternatives and choose the combination of technologies that are best suited to the design problem. Previously, this approach was shown to be theoretically sound from a decision making perspective under idealized circumstances. This article is an investigation into the practical effectiveness of different TCM representational methods under realistic conditions such as having limited data. A power plant systems engineering problem is used as an example, with TCMs generated for different technical concepts for the condenser component. Samples of valid condenser realizations are used as inputs to the TCM representation methods. Two TCM representation methods are compared based on their solution accuracy and computational effort required: a Kriging-based interpolation and a machine learning technique called support vector domain description (SVDD). The results from this example hold that the SVDD-based method provides the better combination of accuracy and efficiency.

scholarly journals A Study of Student Design Team Behaviors in Complex System Design

2012 ◽  
Vol 134 (12) ◽  
Author(s):  
Jesse Austin-Breneman ◽  
Tomonori Honda ◽  
Maria C. Yang
Keyword(s):  
Complex System ◽  
System Design ◽  
System Integration ◽  
Large Scale ◽  
Engineering Students ◽  
System Level ◽  
Design Parameters ◽  
Local Searches ◽  
Wide Range ◽  
Complex System Design

Large-scale engineering systems require design teams to balance complex sets of considerations using a wide range of design and decision-making skills. Formal, computational approaches for optimizing complex systems offer strategies for arriving at optimal solutions in situations where system integration and design optimization are well-formulated. However, observation of design practice suggests engineers may be poorly prepared for this type of design. Four graduate student teams completed a distributed, complex system design task. Analysis of the teams' design histories suggests three categories of suboptimal approaches: global rather than local searches, optimizing individual design parameters separately, and sequential rather than concurrent optimization strategies. Teams focused strongly on individual subsystems rather than system-level optimization, and did not use the provided system gradient indicator to understand how changes in individual subsystems impacted the overall system. This suggests the need for curriculum to teach engineering students how to appropriately integrate systems as a whole.

Large Scale Coordination: The Study of Groups at Work

2005 ◽  
Vol 49 (3) ◽  
pp. 527-528 ◽  
Author(s):  
Christopher P. Nemeth
Keyword(s):  
Information And Communication Technology ◽  
Communication Technology ◽  
Systems Engineering ◽  
Group Work ◽  
Large Scale ◽  
System Level ◽  
Cognitive Systems Engineering ◽  
Naturalistic Decision Making ◽  
Information And Communication ◽  
Safety Countermeasures

Naturalistic decision making (NDM) methods have previously been applied to understand individual and group cognition. The systemic aspects of work that are unavailable through the study of individuals or a single group can be revealed by cognitive research at large scale, among and across groups. The papers in this symposium explore the use of NDM methods including cognitive systems engineering to reveal how groups of operators have developed ways to perform inter-group work in real world settings. Insights from such studies inform the development of system-level products, including safety countermeasures and information and communication technology (ITC) that is intended to support this work.

scholarly journals Theoretical Foundations for Preference Representation in Systems Engineering

Systems ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 55
Author(s):  
Hanumanthrao Kannan ◽  
Garima V. Bhatia ◽  
Bryan L. Mesmer ◽  
Benjamin Jantzen
Keyword(s):  
Systems Engineering ◽  
Large Scale ◽  
Solution Space ◽  
Complex Engineered Systems ◽  
Preference Representation ◽  
Rigorous Framework ◽  
Stakeholder Preferences ◽  
Engineered Systems ◽  
The Relationship ◽  
Theoretical Foundations

The realization of large-scale complex engineered systems is contingent upon satisfaction of the preferences of the stakeholder. With numerous decisions being involved in all the aspects of the system lifecycle, from conception to disposal, it is critical to have an explicit and rigorous representation of stakeholder preferences to be communicated to key personnel in the organizational hierarchy. Past work on stakeholder preference representation and communication in systems engineering has been primarily requirement-driven. More recent value-based approaches still do not offer a rigorous framework on how to represent stakeholder preferences but assume that an overarching value function that can precisely capture stakeholder preferences exists. This article provides a formalism based on modal preference logic to aid in rigorous representation and communication of stakeholder preferences. Formal definitions for the different types of stakeholder preferences encountered in a systems engineering context are provided in addition to multiple theorems that improve the understanding of the relationship between stakeholder preferences and the solution space.

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