Machine Learning Supporting Experimental Design for Product Development in the Lab

Babutzka, Jens; Bortz, Michael; Dinges, Andreas; Foltin, Gregor; Hajnal, David; Schultze, Hergen; Weiß, Horst

doi:10.1002/cite.201800089

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…Moreover, machine learning and text mining can be effectively used for idea generation and validation through an online community, which will be a more reliable tool for listening to the VOCs (Christensen et al, 2017). Besides, machine learning can be effectively used in product development in the experimentation phase for multi-criteria decision-making and parameter design (Babutzka et al, 2019). This will assist the project team to ensure robust product development within the stipulated time frame.…”

Section: Linking Dfss With the 4th Industrial Revolutionmentioning

confidence: 99%

Ten commandments for successful implementation of Design for Six Sigma

Gijo

Bhat²,

Antony

et al. 2021

TQM

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PurposeThis article aims to provide Design for Six Sigma (DFSS) practitioners, researchers and academicians with Ten Commandments to successfully deploy projects.Design/methodology/approachThe commandments are the brainchild of four authors' experience and expertise for more than 15 years of DFSS deployment in the spectrum of fields as a consultant, researcher, academic and Master Black Belt in Six Sigma and general quality management and engineering disciplines. Thus ascertained commandments were validated and classified through the “Delphi Study” to ensure its applicability.FindingsThe Ten Commandments from authors' perspective include: alignment of DFSS with organisational strategy; top management support and involvement; listening to the voice of the customers (VOC); effective training programme for right project teams; project selection and prioritisation; linking DFSS with ISO international standards; linking DFSS with organisational learning and innovation; linking DFSS with the 4th Industrial Revolution; effective use of DFSS methodology and the integrated tools within the methodology and reward and recognition schemes.Research limitations/implicationsThe commandments presented in this article are the authors' personal experience in different industrial scenarios and settings and demographical locations. The authors are planning to conduct a longitudinal survey to understand further insights of these commandments with the input of several DFSS Black Belts and Master Black Belts, academicians and leading researchers from various countries.Originality/valueAll the organisation's stakeholders can use this article as general guidelines to ensure effective deployment of the DFSS approach.

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Section: Linking Dfss With the 4th Industrial Revolutionmentioning

confidence: 99%

Ten commandments for successful implementation of Design for Six Sigma

Gijo

Bhat²,

Antony

et al. 2021

TQM

View full text Add to dashboard Cite

show abstract

“…Multiple response surfaces methodology (Jun & Suh 2008), ordinal logistical regression (Demirtas et al 2009) and genetic algorithms (Hsiao & Tsai 2005;Kim & Cho 2005) have been attempted for instance to determine the optimal settings of the design attributes that achieve maximum customer satisfaction. Case-based and neural network approaches have been used extensively during idea generation, either for leveraging decisions on previous design cases (Hu et al 2017) or for simulating design alternatives concerning specific performance parameters (Dering & Tucker 2017;As et al 2018;Babutzka et al 2019). Optimization tools have been employed mainly with predictive purposes during the detail design phase.…”

Section: Stream 3: Analytics For Design or Design Analyticsmentioning

confidence: 99%

Data-driven design: the new challenges of digitalization on product design and development

et al. 2020

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Digitalization and the momentous role being assumed by data are commonly viewed as pervasive phenomena whose impact is felt in all aspects of society and the economy. Design activity is by no means immune from this trend, and the relationship between digitalization and design is decades old. However, what is the current impact of this ‘data revolution’ on design? How will the design activity change? What are the resulting research questions of interest to academics? What are the main challenges for firms and for educational institutions having to cope with this change? The paper provides a comprehensive conceptual framework, based on recent literature and anecdotal evidence from the industry. It identifies three main streams: namely the consequences on designers, the consequences on design processes and the role of methods for data analytics. In turn, these three streams lead to implications at individual, organizational and managerial level, and several questions arise worthy of defining future research agendas. Moreover, the paper introduces relational diagrams depicting the interactions between the objects and the actors involved in the design process and suggests that what is occurring is by no means a simple evolution but a paradigmatic shift in the way artefacts are designed.

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“…Since the standard deviation directly quantifies the uncertainty about a prediction, it can be used to suggest a new simulation to improve the model. Such an approach has already been proposed, e.g., in , and is closely related to, e.g., adaptive sampling and Bayesian optimization .…”

Section: Supervised Learningmentioning

confidence: 99%

“…The recognition of handwriting is a well‐known example , realized by deep neural networks (DNNs), that have proven successful in many other scenarios . Kernel‐based methods like Gaussian process regression are an alternative, especially for smaller data sets , . Knowledge‐based models, on the other hand, are based on more fundamental assumptions with – compared to the data‐based models – relatively few model parameters.…”

Section: Introductionmentioning

confidence: 99%

Some Aspects of Combining Data and Models in Process Engineering

Heese

Nies

Bortz

2020

Chemie Ingenieur Technik

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Observing phenomena under defined conditions and building mathematical models to make further predictions are essential ingredients of natural and engineering sciences. Recent technological and methodical advances make large and highdimensional simulation data accessible to model building and therefore to optimization. In this article, selected machine learning methods are highlighted and applied to example data from simple flow sheet simulations. Furthermore, the essential outcomes of the workshop dealing with combination of data and models during the Tutzing Symposium 2019 are summarized.

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Machine Learning Supporting Experimental Design for Product Development in the Lab

Cited by 9 publications

References 22 publications

Ten commandments for successful implementation of Design for Six Sigma

Ten commandments for successful implementation of Design for Six Sigma

Data-driven design: the new challenges of digitalization on product design and development

Some Aspects of Combining Data and Models in Process Engineering

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