Data driven design for reliability

Geiger, Celine; Sarakakis, Georgios

doi:10.1109/rams.2016.7448023

Cited by 12 publications

(7 citation statements)

References 1 publication

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“…The last research areas identified concerning data-driven design activities is 'assess, predict and improve the system performance'. Operational data collected in the text and validation and in-service phase can enhance the health of the product, which will then increase the product reliability (Geiger and Sarakakis, 2016;Shin et al, 2015). As such, an integrated design improvement approach consisting of data modelling and analysis techniques are proposed by a couple of researchers (Ma et al, 2017;Riesener et al, 2019;Shin et al, 2015).…”

Section: Assess Predict and Improve The System Performancementioning

confidence: 99%

Four Patterns of Data-Driven Design Activities in New Product Development

Lee

Ahmed‐Kristensen

2023

Proc. Des. Soc.

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In the midst of Industry 4.0 where digitalisation is stimulated through the Internet of Things (IoT), Big Data, and machine learning technologies, an increasing volume of valuable data has been acquired from sensors and interconnected devices. This data-driven paradigm can enable organisations to create new or improved products and services, build long-term customer relationships in a value co-creation manner, adapt to continuous business reconfiguration or address societal challenges such as sustainability. Scientific research addressing Data-driven design has increased steadily in the last few years. However, despite this, there is still a need for a comprehensive understanding of data-driven design processes. Thus, through a systematic literature review, we review the data-driven design activities observed in the new product and service development and types of data utilised in New Product Development (NPD) process. This paper contributes to design research and through reviewing the current landscape of Data-driven design identifies ten data-driven design activities and four-dimensional aspects in NPD process.

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Section: Assess Predict and Improve The System Performancementioning

confidence: 99%

Four Patterns of Data-Driven Design Activities in New Product Development

Lee

Ahmed‐Kristensen

2023

Proc. Des. Soc.

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“…However, there is also a stream of research that focuses on improving the reliability of an already existing part. Geiger and Sarakakis (2016) provide a new approach of DFR based on field reliability data. They use information on the predecessor of the to-be-redesigned component and they state that a good understanding of the failure modes is key in the DFR process.…”

Section: Prediction Of the Reliability Improvement After A Component ...mentioning

confidence: 99%

Component Redesigns and The Impact of Their Implementation Policy

Neefs,

Lamghari-Idrissi,

Basten

et al. 2023

2023 Winter Simulation Conference (WSC)

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The semiconductor industry is under a lot of pressure due to major chip shortages. Downtime of lithography systems can cost semiconductor companies up to ten thousands of euros per hour (ASML Holding N.V., 2014). To improve their systems' reliability, ASML replaces old or frequently failing components with new and more reliable redesigns. In this research, we develop a tool that is able to determine the best policy to implement these redesigned components in the field. Seven distinct policies are considered that differ in (1) roll-out strategy, (2) whether or not to use old parts that are still on stock, and (3) whether or not to rework failed old parts that are built out of the machines. The roll-out strategy indicates whether parts are preventively replaced through a Field Change Order (FCO) or correctively through a Replace on Failure (ROF). Using a Markov Chain approach and a simulation approach, we test two models: one in which old parts can be reworked into the redesigned version, and one where this is not possible. Generally, the policies where the old parts can be reworked are preferred as they result in lowest costs and failures. ROF is preferred when the aim is to reduce costs, and FCO is preferred when the aim is to reduce the number of failures. When the focus is on reducing waste (circular economy) we recommend to rework parts, but not in combination with an FCO roll-out strategy as the parts on stock are then at high risk of becoming excess or obsolete. i Probability that an old part can be repaired s Base stock level w n Salvage value of new parts w o Salvage value of old parts z n Costs to repair a new part (including repair and normal shipment) z o Costs to rework an old part to the new version x

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“…MRO reports supported Abramovici et al (2017) to analyze influence factors and causes for failure-prone components. (Geiger and Sarakakis, 2016) have presented the usage of various data, including field data and actual user data, to understand the current reliability performance and critical failure modes of automobiles. In understanding product behaviors, ICED21 Klein et al (2019) presented a use case using PUI from embedded sensors (i.e., spin value, load weight) to calculate the washing machine's bearing load during washings.…”

Section: Figure 3 Pui For the Understanding Of Users And Productsmentioning

confidence: 99%

Understanding Users and Products in Product Development: The Application of Product Usage Information and Its Challenges

Deng¹,

Wellsandt²,

Hribernik³

et al. 2021

Proc. Des. Soc.

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The need to understand users and products is one of the cores for product success. There are many ways to reach this understanding,such as,interviews and consumer cocreation. This study put a focus on the application of product usage information (PUI). Although amount of product-related information is available in the middle of a product's life, producers have not realized the full potential of these data in product development. The academic literature describes various use cases that outline how PUI supports this kind of understanding, and eventually benefits product development. Each of them provides fragmented information from certain perspectives. This diversity and the lack of systematic overview facilitates a fragmentation in the research of PUI usage in product development. To have an initial and unified overview of PUI's value on the understanding of users and products, this paper conducted a combination of systematic and descriptive review to form a sample with 12 papers. The result indicates that PUI increases producer's understanding about the product, its users and the context of usage. However, producers need to address several challenges if they want to apply PUI in product development successfully.

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Data driven design for reliability

Cited by 12 publications

References 1 publication

Four Patterns of Data-Driven Design Activities in New Product Development

Four Patterns of Data-Driven Design Activities in New Product Development

Component Redesigns and The Impact of Their Implementation Policy

Understanding Users and Products in Product Development: The Application of Product Usage Information and Its Challenges

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