Identification and evaluation of opportunities and risks for data-driven validation of systems of objectives

Contemporary scientific research underscores the importance of data-driven validation within the product development process, particularly in relation to a system of objectives. Leveraging field-gathered machine data to inform decision-making in developing intricate mechatronic systems holds considerable promise. Despite the existence of various established models in the literature for data analysis, such as the widely adopted CRISP-DM process model for data mining, there remains a need for a specialized process model explicitly designed to assist developers involved directly in the product development process. This model should not only aid in conducting data analysis but also provide insights into the associated opportunities and risks inherent in the data analysis process. While it is tempting to focus solely on the potential benefits and opportunities offered by data analysis, it is equally essential to carefully consider the accompanying risks. Therefore, the objective of this study is to develop a systematic approach for evaluating both opportunities and risks in the data-driven validation of a system of objectives.

Subjects

Data Analytics

Data-driven validation

Design: challenges & Innovation

DDC Class

004: Computer Sciences

006: Special computer methods

620: Engineering

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publishedVersion

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https://creativecommons.org/licenses/by-nc-nd/4.0/

Name

1-s2.0-S2212827124006474-main-1.pdf

Type

Main Article

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766.16 KB

Format

Adobe PDF

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Identification and evaluation of opportunities and risks for data-driven validation of systems of objectives