Modellierung von Unsicherheit in der Produktentwicklung

Wiebel, Marion; Eifler, Tobias; Mathias, Johannes; Kloberdanz, Hermann; Bohn, Andrea; Birkhofer, Herbert

doi:10.1007/978-3-658-00897-0_10

Cited by 7 publications

(6 citation statements)

References 16 publications

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“…Dazu gilt es das Bewusstsein der Produktentwickler für die Marktunsicherheiten mit denen Zielsystemelemente behaftet sind, zu erhöhen, und die Produktentwickler dabei zu unterstützen die kritischen Zielsystemelemente zu identifizieren, um Validierungsaktivitäten abzuleiten und zu planen. Da Unsicherheit als solche nicht objektiv bewertbar ist [21]…”

Section: Forschungsdesignunclassified

Methode zur gezielten Identifikation von Marktunsicherheiten zur Unterstützung der Zielsystementstehung

Zimmermann¹,

Prinz²,

Albers³

2020

Proceedings of the 31st Symposium Design for X (DFX2020)

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Product development and the associated processes are characterized by uncertainties. Thus, the elements of the system of objectives are also subject to uncertainties, which influence the product, its development and its success on the market. The systematic handling of these uncertainties is crucial for the success of product development. Uncertainty can be reduced by validation. However, validation has only a low priority, since, among other things the awareness of the necessity of validation and the uncertainties is only partly existing. In the course of this publication, a method to systematically evaluate the elements of the system of objectives concerning market uncertainty to increase the awareness of the uncertainties and to support the planning of validation activities is proposed.

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Section: Forschungsdesignunclassified

Methode zur gezielten Identifikation von Marktunsicherheiten zur Unterstützung der Zielsystementstehung

Zimmermann¹,

Prinz²,

Albers³

2020

Proceedings of the 31st Symposium Design for X (DFX2020)

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“…Especially during the early phase of engineering design processes uncertainty is often prevailing (Lévárdy & Browning, 2009;Wiebel et al, 2013). Engineering design processes can therefore also be seen as complex systems (e.g., Smith & Morrow, 1999).…”

Section: Initial Situation and Problem Descriptionmentioning

confidence: 99%

“…(Lévárdy & Browning, 2009;Sarbacker & Ishii, 1997;Schrader et al, 1993) As ambiguity characterizes engineering design processes, the traditional methods and tools of project management provide less value. Therefore, the attitudes and aptitudes of project managers and participants must change (De Neufville, 2004;Loch et al, 2006;Wiebel et al, 2013) along with their models, methods and tools. (Lévárdy & Browning, 2009) While unforeseen uncertainty is a source of risk for engineering design processes, it can also provide opportunities for organizations capable of effectively sensing the endogenous and exogenous changes and adapting to the changed conditions (Haeckel, 1999).…”

Section: Initial Situation and Problem Descriptionmentioning

confidence: 99%

“…Yin, 2009). Especially the early phase of engineering design processes is predominated by a high amount of uncertainty (Lévárdy & Browning, 2009;Wiebel et al, 2013). The early phase thereby includes the phases of planning, task clarification and conceptual design and is elaborated in detail in section 2.4.…”

Section: 24mentioning

confidence: 99%

“…Hubka (1984) considers the conceptual design phase to be among the most demanding phases in design work and indeed the whole of engineering. (Blessing, 1994) The early phase of engineering design processes is predominated by a high amount of uncertainty (Lévárdy & Browning, 2009;Wiebel et al, 2013). …”

Section: Early Phase Of Engineering Design Processesmentioning

confidence: 99%

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Structure‐Based System Dynamics Analysis of Engineering Design Processes

Kasperek

Schenk

Kreimeyer³

et al. 2016

Systems Engineering

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The dynamic process behavior of Engineering Design Processes (EDPs) needs to be understood in order to distribute resources appropriately as well as for cost and schedule calculation. Currently, structural and behavioral modeling approaches are used in the context of EDP management. In many use cases, it is essential to have at hand both the static structural view and the dynamic behavioral one. Currently, however, the two modeling approaches cannot be sufficiently combined. Within this work, a framework for the structure‐based System Dynamics analysis of EDPs is presented. It uses structural models as a basis for constructing System Dynamics models to simulate the process behavior and thereby supports understanding the correlation of process structure and behavior. This framework was iteratively developed by conducting case studies and consists of three layers, a five‐step procedure and special support for the transformation of the models. For the validation, a subset of the design process at a large German commercial vehicle manufacturer has been chosen. Based on the application of the framework during a six‐month project at the company, the industrial experts rated the framework as beneficial for the purpose of optimizing EDPs. Furthermore, they stated that the transparent representation of the sequence of the process, influencing factors and control variables in combination with the quantitative data had improved their understanding of the overall process.

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