Methodical Approach for Rough Energy Assessment and Compliance Checking of Energy-related Product Design Options

Abramovici, Michael; Quezada, Akamitl; Schindler, Thomas

doi:10.1016/j.procir.2014.03.135

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…A variety of tools for evaluating product design concerning environmental standards have been produced by other researchers (Blanchini & Miani, 1999; Harjula et al, 1996; Kirst et al, 2009; Tuck & Hague, 2006). Further researchers have proposed several guidelines for assessing environmental issues (Abramovici et al, 2014; Benabdellah, Benghabrit, et al, 2020; Bonvoisin et al, 2010; Feldman, 1999; Hatcher et al, 2011; Korpalski, 1996; Ljungberg, 2007; Luttropp, 2000; Rose, 2000). Others linked DFE with Industry 4.0 technologies to achieve greenness (Asiimwe & de Kock, 2019; Mendoza et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…Bonvoisin et al (2010) suggested a scenario model to accurately quantify the environmental effect of the use process, particularly for energy‐intensive components, by including all occurrences that might or would occur during a typical user's usage of the product. Finally, Abramovici et al (2014) created a technique to assist designers in conducting energy analyses of mass‐produced Enterprise Resource Planning (ERP) design choices and ensuring compliance with energy‐related limit‐value restrictions. With the same perspective, Rossi et al (2016) presented an analysis of the key barriers that prevent the implementation of eco‐design approaches in industrial companies.…”

Section: Discussionmentioning

confidence: 99%

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Design for the environment: An ontology‐based knowledge management model for green product development

et al. 2021

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Through appropriate operations and policies, such as green processes and product development process (PDP), companies can respond to environmental sustainability.To remain competitive, one such approach, Design for X (DFX), involves considering different environment and sustainable strategies through different factors Xs. With regard to the availability of different DFX techniques that consider environmental issues, the decision as to which approach needs to be adopted remains absent. This paper aims at presenting an overview from 1980 to 2020 of the developed research, applications, and DFX techniques for assessing green issues. Selected DFX techniques are linked with strategies used in organizations. Following a literature analysis, a collaborative knowledge-based framework that addresses the design concepts needed to assess environmental, safety, and health concerns in the development of green products is proposed. Furthermore, as a pillar for considering the Semantic Web and an evolving approach linked with natural language processing (NLP) and artificial intelligence (AI), an ontology-based knowledge management model for green assessment is developed for the representation, acquisition, organization, and capitalization of knowledge in a computer interpretable manner. The findings are useful for both managers and practitioners as they provide a coherent domain ontology that can help them manage knowledge, improve teamwork, and make decisions in a collaborative green PDP. Besides, an understanding of the essential design considerations that are required to implement environmental, safety, and health issues, as well as competencies used in the PDP is presented. Key barriers, managerial and strategic implications, and mitigation actions are also identified in this paper.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Design for the environment: An ontology‐based knowledge management model for green product development

et al. 2021

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“…Within each cell, the practitioner is asked to rate the studied system on a scale, 0 to 4, based on its perceived performance. Similar management tools have been proposed for assessing the priority of specific environmentally related concerns [40, 41]. However, all of these methods include ratings based on expert (or practitioner) knowledge, introducing significant bias.…”

Section: Studying Phases Of the Sima Reference Architecturementioning

confidence: 99%

Analyzing environmental sustainability methods for use earlier in the product lifecycle

Brundage

Bernstein

Hoffenson

et al. 2018

Journal of Cleaner Production

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Environmental sustainability information in the manufacturing industry is not easily shared between stages in the product lifecycle. In particular, reliable manufacturing-related information for assessing the sustainability of a product is often unavailable at the design stage. Instead, designers rely on aggregated, often outdated information or make decisions by analogy (e.g., a similar manufacturing process for a similar product yielded X and Y results). However, smart manufacturing and the Internet of Things have potential to bridge the gap between design and manufacturing through data and knowledge sharing. This paper analyzes environmental sustainability assessment methods to enable more accurate decisions earlier in design. The techniques and methods are categorized based on the stage they apply to in the product lifecycle, as described by the Systems Integration of Manufacturing Applications (SIMA) reference architecture. Furthermore, opportunities for aligning standard data representation to promote sustainability assessment during design are identified.

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“…However, as Schöggl et al [11] have shown, their high level of generality renders it difficult to translate them into concrete design choices, and they do not provide possible solution strategies. Finally, the design for X approaches enable designers to optimize specific product requirements, such as disassembly [12,13], remanufacturing [14,15], or energy efficiency [16]. They are inherently not very well suited to taking into account the whole life cycle of a product [17,18].…”

Section: Introductionmentioning

confidence: 99%

The Inclusion of End-of-Life Modeling in the Life Cycle Energy Optimization Methodology

Bouchouireb

Jank

O’Reilly

et al. 2020

Journal of Mechanical Design

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In this work, an End-Of-Life (EOL) model is included in the Life Cycle Energy Optimisation (LCEO) methodology to account for the energy burdens and credits stemming from a vehicle's EOL processing phase and balance them against the vehicle's functional requirements and production and use phase energies. The substitution with a correction factor allocation method is used to model the contribution of recycling to the EOL phase's energy. The methodology is illustrated through the optimisation of the design of a simplified vehicle sub-system. For the latter, multiple recycling scenarios with varying levels of assumed recycling induced material property degradation were built, and their impact on the vehicle sub-system's optimal solutions was compared to that of scenarios based on landfilling and incineration with energy recovery. The results show that the vehicle sub-system's optimal designs are significantly dependent on the EOL scenario considered. In particular, the optimal designs associated with the recycling scenarios are on average substantially heavier, and less life cycle energy demanding, than their landfilling or incineration with energy recovery-related counterparts; thus, demonstrating how the inclusion of EOL modelling in the LCEO methodology can significantly alter material use patterns, thereby effecting the very mechanisms enabling the embodiment of the resulting life cycle energy optimal designs.

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Methodical Approach for Rough Energy Assessment and Compliance Checking of Energy-related Product Design Options

Cited by 11 publications

References 2 publications

Design for the environment: An ontology‐based knowledge management model for green product development

Design for the environment: An ontology‐based knowledge management model for green product development

Analyzing environmental sustainability methods for use earlier in the product lifecycle

The Inclusion of End-of-Life Modeling in the Life Cycle Energy Optimization Methodology

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