Data Mining in Product Portfolio and Variety Management – Literature Review on Use Cases and Research Potentials

Mehlstäubl, Jan; Braun, Felix; Paetzold, Kristin

doi:10.1109/temscon-eur52034.2021.9488575

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…The state of the art is based on the literature review conducted by Mehlstäubl et al (2021) on the use of data mining in product portfolio and variety management. For this article, the relevant approaches are those that use clustering for variety reduction and control as well as for market analysis.…”

Section: State Of the Artmentioning

confidence: 99%

“…Although the complexity is no longer manageable manually, the activities in product portfolio and variety management are driven by the experiential knowledge of the developers (Mehlstäubl, Braun, et al, 2022). Machine learning techniques offer great potential in these disciplines and make it possible to gain insights from large amounts of data and thus provide a meaningful basis of information for decision-making processes in product portfolio and variety management (Mehlstäubl et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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Methodical Approach to Cluster Configurations of Product Variants of Complex Product Portfolios

Mehlstäubl

Pfeiffer

Kraul³

et al. 2023

Proc. Des. Soc.

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Companies are increasingly struggling to manage their complex product portfolios. Since they do not fully understand the complexity, intelligent solutions are required. Emerging technologies and tools offer new ways to deal with existing problems. With the help of clustering, similarities between product variants can be identified automatically, and complexity can be systematically reduced. This article aims to develop a methodological approach to identify correlations between product variants in complex product portfolios automatically by using clustering algorithms. The approach includes the systematic cleaning and transformation of product portfolio data. In addition, a guide for algorithm selection and evaluation of clustering results is presented. As the last step, the results are systematically analysed and visualised. To validate the methodical approach, it is applied to a real-world data set of a commercial vehicle manufacturer and the usefulness of the results is confirmed in an expert workshop.

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Section: State Of the Artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Methodical Approach to Cluster Configurations of Product Variants of Complex Product Portfolios

Mehlstäubl

Pfeiffer

Kraul³

et al. 2023

Proc. Des. Soc.

Self Cite

View full text Add to dashboard Cite

show abstract

Machine Learning to Generate Knowledge for Decision-Making Processes in Product Portfolio and Variety Management

Mehlstäubl¹,

Braun²,

Gadzo³

et al. 2023

Design in the Era of Industry 4.0, Volume 1

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Using Machine Learning for Product Portfolio Management: A Methodical Approach to Predict Values of Product Attributes for Multi-Variant Product Portfolios

Mehlstäubl

Braun²,

Denk

et al. 2022

Proc. Des. Soc.

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To satisfy customer needs in the best way, companies offer them an almost infinite number of product variants. Although, an identical product was not built before, the values of its attributes must be determined during the product configuration process. This paper introduces a methodical approach to predict the values of product attributes based on customer feature configurations using machine learning. Machine learning reduces the effort compared to rule-based expert systems and is both, more accurate and faster. The approach was validated by predicting vehicle weights using industrial data.

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Data Mining in Product Portfolio and Variety Management – Literature Review on Use Cases and Research Potentials

Cited by 4 publications

References 30 publications

Methodical Approach to Cluster Configurations of Product Variants of Complex Product Portfolios

Methodical Approach to Cluster Configurations of Product Variants of Complex Product Portfolios

Machine Learning to Generate Knowledge for Decision-Making Processes in Product Portfolio and Variety Management

Using Machine Learning for Product Portfolio Management: A Methodical Approach to Predict Values of Product Attributes for Multi-Variant Product Portfolios

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