Derivation of Description Features for Engineering Change Request by Aid of Latent Dirichlet Allocation

Riesener, Michael; Dölle, Christian; Mendl-Heinisch, Michael; Schuh, Günther; Keuper, Alexander

doi:10.1017/dsd.2020.98

Cited by 3 publications

References 27 publications

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AI-artifacts in engineering change management – a systematic literature review

Burggräf,

Wagner,

Saßmannshausen

et al. 2024

Res Eng Design

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Changes and modifications to existing products, known as engineering changes (EC), are common in complex product development. They require appropriate implementation planning and supervision to mitigate the economic downsides due to complexity. These tasks, however, take a high administrative toll on the organization. In response, automation by computer tools has been suggested. Due to the underlying process complexity, the application of artificial intelligence (AI) is advised. To support research and development of new AI-artifacts for EC management (ECM), a knowledge base is required. Thus, this paper aims to gather insights from existing approaches and discover literature gaps by conducting a systematic literature review. 39 publications applying AI methods and algorithms in ECM were identified and subsequently discussed. The analysis shows that the methods vary and are mostly utilized for predicting change propagation and knowledge retrieval. The review’s results suggest that AI in EC requires developing distributed AI systems to manage the ensuing complexity. Additionally, five concrete suggestions are presented as future research needs: Research on metaheuristics for optimizing EC schedules, testing of stacked machine learning methods for process outcome prediction, establishment of process supervision, development of the mentioned distributed AI systems for automation, and validation with industry partners.

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