Connected Baby Bottle

Bogers, Sander; Frens, Joep; Kollenburg, Janne van; Deckers, Eva; Hummels, Caroline

doi:10.1145/2901790.2901855

Cited by 69 publications

(42 citation statements)

References 13 publications

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“…This data feedback can give insights into actual product use and performance, and thereby inform design improvements [41]. For example, this strategy can be used to identify weaknesses in current designs [44], or to evaluate how users react in real life to design interventions [45]. The idea of taking advantage of product-in-use data for design improvements is well established in the design of websites and software (e.g., [46]).…”

Section: Categories In Literaturementioning

confidence: 99%

“…The idea of taking advantage of product-in-use data for design improvements is well established in the design of websites and software (e.g., [46]). In the design of physical products, the phenomenon is emerging in academic literature using terms like closed-loop design evolution [44] and data-enabled design [45]. Table 1 shows IoT capabilities described in previous literature, and how we map them to seven main categories: tracking, monitoring, control, optimization, design evolution, autonomy, and processing/networking/communication.…”

Section: Categories In Literaturementioning

confidence: 99%

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Circular Strategies Enabled by the Internet of Things—A Framework and Analysis of Current Practice

et al. 2019

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This paper focuses on how the Internet of Things (IoT) could contribute to the transition to a circular economy (CE), through supporting circular business model and design strategies. While literature has highlighted the opportunities for IoT to support circular strategies in business, little has been published about actual implementations in practice. The aim of this study was therefore to understand how companies to date have implemented IoT for circular strategies, and how these implementations compare to the range of opportunities described in literature. To that end, a two-step approach was followed. Firstly, building on academic literature, a framework was developed which categorizes different IoT-enabled circular strategies. The framework recognizes tracking, monitoring, control, optimization, and design evolution as IoT capabilities. Efficiency in use, increased utilization, and product lifetime extension are distinguished as circular in-use strategies, while reuse, remanufacturing, and recycling are distinguished as circular looping strategies. The framework complements previously published work, as it adds additional detail to the categorization, and allows for easy mapping of diverse cases. Secondly, 40 cases from practice were analyzed and mapped to the framework. This way, practice-based insights were derived about the current distribution of IoT-enabled circular strategies implemented in practice. The results show that current implementation of IoT-enabled circular strategies mainly supports two strategies in the use phase: efficiency in use and product lifetime extension. Only a small number of the reviewed cases display IoT-enabled looping (reuse, remanufacturing, and recycling). Similarly, few cases describe ‘design evolution’ for CE, i.e., the feedback of data from products in use to support circular design. Based on these results, this study identifies the need for future research to further investigate why IoT-enabled looping strategies and design evolution for circular strategies have not been implemented to scale.

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Section: Categories In Literaturementioning

confidence: 99%

Section: Categories In Literaturementioning

confidence: 99%

Circular Strategies Enabled by the Internet of Things—A Framework and Analysis of Current Practice

et al. 2019

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“…Nowadays, instead of relying merely on expert knowledge, "Data-Driven Product Development" can be used to derive good, robust, and feasible designs, driven by product data (Zheng et al, 2018). Bogers, Frens, van Kollenburg, Deckers, and Hummels (2016) present a framework, called "Data-Enabled Design", for the integration of use phase data in the design of a product-service system. They integrated real-time use phase data as well as user interactions to iteratively improve the product at hand.…”

Section: Related Methods and Frameworkmentioning

confidence: 99%

Data-Driven Engineering – Definitions and Insights from an Industrial Case Study for a New Approach in Technical Product Development

Trauer¹,

Schweigert-Recksiek²,

Okamoto³

et al. 2020

Balancing Innovation and Operation

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The growing digitization affects all areas of engineering. Together with fast-paced trends, it drives complexity and uncertainty in many domains. Yet, its potentials are manifold and, in most cases, outweigh the disadvantages. Beneath terms such as "big data", "digital twin", the term "data-driven engineering" has evolved over the last years. However, neither in literature nor in industry, there is a unified definition or understanding of the term. The presented research is based on a literature review as well as an industrial case study. Several databases were screened systematically for the literature review and forward and backward searches were used additionally. The case study was conducted in a collaboration with a company in the climate system sector. First, a literature-based distinction between the terms model-based, modeldriven, data-based, and data-driven as well as definitions of data-driven engineering were investigated. Representatives of the company then evaluated these findings in a workshop and together with the industry partner a consistent definition was developed. The authors define data-driven engineering as a framework for product development in which the goal-oriented collection and use of sufficiently connected product lifecycle data guides and drives decisions and applications in the product development process. Further, promising use cases for the industry partner regarding data-driven engineering were formulated. The use cases were initially evaluated and prioritized regarding their cost-benefit ratio. Symbioses with other strategies of the company such as Digital Twins, model-based engineering, and solution space engineering are outlined. For academia, the presented findings provide a consistent definition that can be used as a promising direction for future research. Especially a procedure model for the systematic conception and implementation of data-driven engineering would be beneficial. For industry, this paper provides insights on potentials of data-driven engineering, a differentiation from related concepts, and very concrete use-cases serving as a starting point for a company-specific implementation.

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“…By using the method, designers managed to conclude designerly insights from data, which could be used as complimentary to traditional user and contextual research methods, such as contextmapping (Visser, Stappers, van der Lugt and Sanders, 2005) or design probes (Mattelmäki, 2006). Other researchers have explored complementing qualitative research in the fuzzy front-end with data collection by sensors: emerging examples, such as using everyday objects as data-collecting ethnographers to collect rich contextual insights (Giaccardi, Cila, Speed and Caldwell, 2016), or the use of data-collecting technology probes to augment rich and contextual data with sensor data (Bogers, Frens, van Kollenburg, Deckers and Hummels, 2016) have shown alternative paths to bring data techniques into the fuzzy front-end. However, both of these examples are technologically complex, often beyond the scope and resources available for a design team.…”

Section: The Value Of Digital Data For Generative Design Researchmentioning

confidence: 99%

Data Exploration for Generative Design Research

Kun¹,

Mulder²,

Kortuem³

2018

Proceedings of DRS

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The current work elaborates upon a Generative Data Exploration method, which is a design technique aiming at supporting designers in integrating data in their design activities. Digital data offers new opportunities in all sort of professional domains, yet existing approaches and tools to manipulate data are predominantly targeted at data experts. As access to data is becoming democratised, new types of techniques are needed to leverage the agency of designers and to empower them to utilise data in the design process. Designers without prior data experience can benefit from the techniques, know-how, best practices of experts, if such expert knowledge is codified in design methods and tools. The aims of a Generative Data Exploration method are twofold. First, the method facilitates a learning curve on gaining holistic data literacy. Second, the method supports designing where digital data, exploration of data and sense-making of data is part of the process.

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Connected Baby Bottle

Cited by 69 publications

References 13 publications

Circular Strategies Enabled by the Internet of Things—A Framework and Analysis of Current Practice

Circular Strategies Enabled by the Internet of Things—A Framework and Analysis of Current Practice

Data-Driven Engineering – Definitions and Insights from an Industrial Case Study for a New Approach in Technical Product Development

Data Exploration for Generative Design Research

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