A novel method to enable trade-offs across the whole product life of an aircraft using value driven design

Price, Mark; Soban, Danielle; Mullan, Claudia; Butterfield, Joseph; Murphy, Adrian

doi:10.3233/aop-120028

Cited by 22 publications

(23 citation statements)

References 16 publications

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“…All aspects of value were quantified in monetary terms, enabling easy trade-off with more traditional requirements. This quantification process was based on the implementation of Net Present Value (NPV) from the VDD literature [25]. The dataset also contained information enabling mapping each design variable directly to components or subsystems in the structural hierarchy.…”

Section: Case Studymentioning

confidence: 99%

Design for Tomorrow—Volume 1

2021

Smart Innovation, Systems and Technologies

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A data analysis method based on artificial neural networks aiming to support cause-and-effect analysis in design exploration studies is presented. The method clusters and aggregates the effects of multiple design variables based on the structural hierarchy of the evaluated system. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, dataset is comparable to the original, unmodified, one. The proposed method is evaluated using coefficient-of-determination, root mean square error, average relative error, and mean square error. Data analysis approach with artificial neural networks is believed to significantly improve the comprehensibility of the evaluated cause-and-effect relationships studying PSS concepts in a cross-functional team and thereby assisting the difficult and resource-demanding negotiations process at the conceptual stage of the design.

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Section: Case Studymentioning

confidence: 99%

Design for Tomorrow—Volume 1

2021

Smart Innovation, Systems and Technologies

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“…Monetary units are convenient, practical, and universally understood metrics for value, and are beneficial in the design process to stress the potential success of investments [59]. This quantification process in the arena is driven by the implementation of Net Present Value (NPV) and Surplus Value (SV) from the Value Driven Design (VDD) literature [60]. Different models, which exploit a probabilistic approach using Monte Carlo simulation, are then developed and 'called' in the MDDA to generate the necessary data for the NPV function to be populated.…”

Section: Value Analysismentioning

confidence: 99%

The Model-Driven Decision Arena: Augmented Decision-Making for Product-Service Systems Design

Wall

Bertoni

Larsson

2020

Systems

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The shift towards Product-Service Systems (PSS) stresses the need to embed new and unique capabilities in Decision Support Systems, with the aim of helping the engineering team in handling the pool of information and knowledge available during decision events. Emerging from a multiple case study in the Swedish manufacturing industry, this paper describes the development of the Model-Driven Decision Arena (MDDA), an environment for collaborative decision-making that focuses on the early design phases of PSS. Based on the findings from multiple case studies, this paper illustrates the main goals of the MDDA, detailing its main functions, its physical environment, and its software architecture and models. This paper demonstrates the use of the MDDA in a case study related to the development of an asphalt compactor, presenting and discussing the results of verification activities conducted with industrial practitioners on the current MDDA prototype.

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“…The use of value functions has been often linked to the need to monetary quantify the profits, or losses, linked to a specific design solution (e.g., Castagne, Curran & Collopy 2009;Cheung et al 2012). In these cases, the value function took the form of a net present value assessment or of surplus-value calculation (e.g., Price et al 2012;Selva & Crawley 2013).…”

Section: Value and Sustainability Challenges In Aerospace Product Devmentioning

confidence: 99%

Integration of value and sustainability assessment in design space exploration by machine learning: an aerospace application

et al. 2020

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The use of decision-making models in the early stages of the development of complex products and technologies is a well-established practice in industry. Engineers rely on well-established statistical and mathematical models to explore the feasible design space and make early decisions on future design configurations. At the same time, researchers in both value-driven design and sustainable product development areas have stressed the need to expand the design space exploration by encompassing value and sustainability-related considerations. A portfolio of methods and tools for decision support regarding value and sustainability integration has been proposed in literature, but very few have seen an integration in engineering practices. This paper proposes an approach, developed and tested in collaboration with an aerospace subsystem manufacturer, featuring the integration of value-driven design and sustainable product development models in the established practices for design space exploration. The proposed approach uses early simulation results as input for value and sustainability models, automatically computing value and sustainability criteria as an integral part of the design space exploration. Machine learning is applied to deal with the different levels of granularity and maturity of information among early simulations, value models, and sustainability models, as well as for the creation of reliable surrogate models for multidimensional design analysis. The paper describes the logic and rationale of the proposed approach and its application to the case of a turbine rear structure for commercial aircraft engines. Finally, the paper discusses the challenges of the approach implementation and highlights relevant research directions across the value-driven design, sustainable product development, and machine learning research fields.

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A novel method to enable trade-offs across the whole product life of an aircraft using value driven design

Cited by 22 publications

References 16 publications

Design for Tomorrow—Volume 1

Design for Tomorrow—Volume 1

The Model-Driven Decision Arena: Augmented Decision-Making for Product-Service Systems Design

Integration of value and sustainability assessment in design space exploration by machine learning: an aerospace application

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