scholarly journals Potentials for the Integration of Design Thinking along Automotive Systems Engineering Focusing Security and Safety

2019 ◽  
Vol 1 (1) ◽  
pp. 2883-2892 ◽  
Author(s):  
Julian Tekaat ◽  
Aschot Kharatyan ◽  
Harald Anacker ◽  
Roman Dumitrescu
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Autonomous Vehicles ◽  
Design Thinking ◽  
Early Stage ◽  
Systems Design ◽  
Automotive Systems ◽  
Complex Technical Systems ◽  
Systematic Security ◽  
Early Phases

AbstractThe increasingly intelligent, highly complex, technical systems of tomorrow - for instance autonomous vehicles - result in the necessity for a systematic security- and safety-oriented development process that starts in the early phases of system design. Automotive Systems Engineering (ASE) as one approach is increasingly gaining ground in the automotive industry. However, this approach is still in a prototype stage. The consideration of security and safety within the early stages of systems design leads to so- called ill-defined problems. Such are not covered by ASE, but can be addressed by means of Design Thinking. Therefore we introduce an approach to combine both approaches. Based on this combination, we derive potentials in the context of the consideration of security and safety. Essential advantages are the possibility to think ahead of threat scenarios at an early stage in system design. Due to an incomplete database, this is not supported or only partially supported by conventional approaches. The resulting potentials are derived based upon a practical example.

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 Early Stage Model Based System Design under Uncertainties

2019 ◽  
Vol 1 (1) ◽  
pp. 3621-3630
Author(s):  
Kazuya Oizumi ◽  
Akio Ito ◽  
Kazuhiro Aoyama
Keyword(s):  
Complex System ◽  
System Design ◽  
Systems Engineering ◽  
Process Model ◽  
Early Stage ◽  
Design Team ◽  
Support Design ◽  
Model Based ◽  
Design Process Model ◽  
High Level

AbstractSystem design at the early stage of design plays an important role in design process. Model based systems engineering is seen as a prominent approach for this challenge. System design can be explored by means of system simulation. However, as the system is a complex system, system model tends to have high level of abstraction. Therefore, the models cannot depict every details of the system, which makes optimization unreasonable.Furthermore, at the early stage of design, there are many uncertainties such as success of technological developments. By properly incorporating uncertain factors in system design, the system can be tolerant. Currently system design is conducted by experienced experts. However, for more complex system, it would be difficult to continue the current practice. Therefore, a method to support design team to make decision in system design is needed.This paper proposes a computational support for the system design. Design constraints, which seems the core information that design team wants at system design, are modeled. By visualizing constraints quantitatively and intuitively, the proposed method can support design team to conduct system design and design study.

scholarly journals PATTERN BASED SYSTEMS ENGINEERING – APPLICATION OF SOLUTION PATTERNS IN THE DESIGN OF INTELLIGENT TECHNICAL SYSTEMS

2020 ◽  
Vol 1 ◽  
pp. 1195-1204
Author(s):  
H. Anacker ◽  
R. Dumitrescu ◽  
A. Kharatyan ◽  
A. Lipsmeier
Keyword(s):  
System Design ◽  
Systems Engineering ◽  
Engineering Application ◽  
Systems Design ◽  
Mechatronic Systems ◽  
Knowledge Space ◽  
Technical Systems

AbstractFor the development of intelligent technical systems, Systems Engineering and Solution Patterns are the guarantee for success. In order to avoid cost-intensive iterations, the documentation and reuse of solution knowledge is addressed during the systems design. Using an interdisciplinary specification technique, a uniform structuring of Solution Patterns as well as the composition in a multidimensional knowledge space takes place. This is the basis of an associated systematics for a solution pattern-based system design of mechatronic systems, which is validated by two cooperating DeltaRobots.

scholarly journals THE CHALLENGING COMBINATION OF AGILITY AND CONVERGENCE IN HYBRID PRODUCT DEVELOPMENT PROCESSES: AN EMPIRICAL ANALYSIS OF STANFORD'S ME310 PROCESS MODEL

2021 ◽  
Vol 1 ◽  
pp. 2991-3000
Author(s):  
Frank Koppenhagen ◽  
Tim Blümel ◽  
Tobias Held ◽  
Christoph Wecht ◽  
Paul Davin Kollmer
Keyword(s):  
Empirical Analysis ◽  
Systems Engineering ◽  
Process Model ◽  
Design Thinking ◽  
Solution Space ◽  
Problem Formulation ◽  
Study Approach ◽  
Development Processes ◽  
Student Projects

AbstractCombining agility and convergence in the development of physical products is a major challenge. Rooted in a design thinking approach, Stanford's ME310 process model attempts to resolve the conflicting priorities of these two design principles. To investigate how successful Stanford's hybrid process model is in doing so, we have used a qualitative case study approach. Our paper begins by outlining this process model's fundamental principles in terms of engineering design methodology. Subsequently, we present the results of our empirical analysis, which tracks the coevolution of problem and solution space by meticulously examining all prototype paths in ten of Stanford's ME310 student projects. We have discovered that convergence during solution finding does not correspond to the process model's theoretical specifications. Even in the phase of the final prototype, both the technical concept and the underlying problem formulation changed frequently. Further research should focus on combining the prototype-based ME310 approach with methods from systems engineering which allow for a more comprehensive theoretical exploration of the solution space. This could lead to improved convergence during solution development.

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