New product development for mass customization: a systematic review

Fettermann, Diego de Castro; Echeveste, Márcia Elisa Soares

doi:10.1080/21693277.2014.910715

Cited by 25 publications

(24 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The process perspective concerns how customized products are developed (see e.g. Fettermann & Echeveste, 2014). The content perspective instead covers different aspects of the actual customization such as if it is related to whether the properties of the product are unique to a particular customer order or whether the product only has standard properties that are the same irrespective of the customer (see e.g.…”

Section: Open Accessmentioning

confidence: 99%

Triadic perspective on customization and supplier interaction in customer-driven manufacturing

Wikner

Bäckstrand

2017

Production & Manufacturing Research

View full text Add to dashboard Cite

Customization and customer-driven manufacturing are both explicitly based on the focal actor's interaction with the customer actor. The impact of these aspects on the internal operations of the focal actor is relatively well known compared to the limited number of studies performed on the impact on the focal actor's supplier. The purpose here is therefore to investigate how the concept of customization complements the concept of customer-driven and how customer requirements affect interaction between a supplier and the supplier's supplier in a triad. Frameworks for different perspectives are developed where match and mismatch between the actors on the perception of flow driver and flow differentiation is highlighted. The frameworks are then applied on a case company to illustrate application.

show abstract

Section: Open Accessmentioning

confidence: 99%

Triadic perspective on customization and supplier interaction in customer-driven manufacturing

Wikner

Bäckstrand

2017

Production & Manufacturing Research

View full text Add to dashboard Cite

show abstract

“…In those articles, computational intelligence techniques were discussed to address both functional and affective components for new product development. The literatures reviews reveal that many review articles have been written to discuss systematical and empirical methods for new product development (Graner andMissler-Behr 2012, Fettermann andEcheveste 2014). However, there is still a lack of a specific review article to solely survey machine learning techniques for affective design which is an important component in new product development despite optimizing the functional aspects in product development.…”

Section: Introductionmentioning

confidence: 99%

Affective design using machine learning: a survey and its prospect of conjoining big data

Chan

Kwong

Clark

et al. 2018

International Journal of Computer Integrated Manufacturing

View full text Add to dashboard Cite

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today's competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design.

show abstract

“…Aplicações bem-sucedidas da CM podem ser verificadas em empresas das mais variadas áreas de atuação (FOGLIATTO et al, 2012), como nas companhias Bosch, Nokia, Philips e Siemens (LINDEN et al, 2007), setor de serviços (PETERS; SAIDIN, 2000), automobilística (ALFORD et al, 2000;BRABAZON et al, 2010) e móveis modulados ECHEVESTE, 2014). No setor de alimentos é verificada uma maior quantidade de aplicações na área de nutrição personalizada (BOLAND, 2008) e de produção de alimentos (McINTOSH et al, 2010).…”

Section: Introductionunclassified

“…Embora se identifiquem diversos estudos sobre o tema na literatura, ainda há dificuldades de compreensão de alguns limites da adoção de CM (PILLER et al, 2005) e da aplicação de seus conceitos nas empresas (FOGLIATTO et al, 2012;ECHEVESTE, 2014). Esta dificuldade é mais crítica na indústria alimentícia (FISHER et al, 2005;MATTHEWS et al, 2006;BOLAND, 2006).…”

Section: Introductionunclassified

Uma revisão sistemática para identificação das possibilidades de aplicações e das barreiras de adoção da customização em massa na produção de alimentos

Calegari

Fettermann

2018

Braz. J. Food Technol.

Self Cite

View full text Add to dashboard Cite

Resumo Em um cenário de indústria 4.0 e integração de processos, torna-se necessária a discussão sobre a produção de produtos personalizados em massa ao consumidor. As empresas possuem dificuldades na implementação da estratégia de Customização em Massa (CM), especialmente em empresas produtoras de alimentos, o que pode ser explicado pela falta de estudos sobre fatores que afetam o sucesso da adoção desta estratégia. A partir de uma revisão sistemática da literatura, este artigo possui o objetivo de realizar uma análise bibliométrica com a finalidade de identificar as possibilidades da adoção de CM para produtos alimentícios customizados em massa e, ainda, identificar barreiras relacionadas à implementação da CM. Os resultados apresentam a perecibilidade dos alimentos, a dificuldade no processamento, a quantificação dos valores nutricionais do produto customizado e a complexidade de percepção do valor agregado da customização pelos clientes como barreiras para a implementação no setor alimentício. Os resultados apresentados contribuem para a identificação de oportunidades de novos produtos e também para a reunião de informações que possibilitem um direcionamento da implementação de CM no setor alimentício.

show abstract

New product development for mass customization: a systematic review

Cited by 25 publications

References 103 publications

Triadic perspective on customization and supplier interaction in customer-driven manufacturing

Triadic perspective on customization and supplier interaction in customer-driven manufacturing

Affective design using machine learning: a survey and its prospect of conjoining big data

Uma revisão sistemática para identificação das possibilidades de aplicações e das barreiras de adoção da customização em massa na produção de alimentos

Contact Info

Product

Resources

About