Experiences with big data: Accounts from a data scientist’s perspective

This work focuses on the effective utilisation of varying data sources in injection moulding for process improvement through a close collaboration with an industrial partner. The aim is to improve productivity in an injection moulding process consisting of more than 100 injection moulding machines. It has been identified that predicting quality through Machine Process Data is the key to increase productivity by reducing scrap. The scope of this work is to investigate whether a sufficient prediction accuracy (less than 10% of the specification spread) can be achieved by using readily available Machine Process Data or additional sensor signals obtained at a higher cost are needed. The latter comprises Machine Profile and Cavity Profile Data. One of the conclusions is that the available Machine Process Data does not capture the variation in the raw material that impacts element quality and therefore fails to meet the required prediction accuracy. Utilising Machine Profiles or Cavity Profiles have shown similar results in reducing the prediction error. Since the cost of implementing cavity sensors in the entire production is higher than utilising the Machine Profiles, further exploration around improving the utilisation of Machine Profile Data in a setting where process variation and labelled data are limited is proposed.

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“…and not 'What type of data can support the given challenge?' (Kulahci et al 2020). An example of such a data mapping is illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Rønsch

Külahçı

Dybdahl

2021

International Journal of Production Research

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Design of Agricultural Product Traceability System and Consumption Information Big Data System

Zhiqing

Fang²,

Cui³

2021

2021 6th International Symposium on Computer and Information Processing Technology (ISCIPT)

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Digitalization’s Effect on Chinese Employment Mechanism Study

Zhao,

Tang

2024

Sustainability

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The influence of the digital revolution on the labor market is undeniably profound. While much of the existing research has concentrated on the role of digitalization in boosting employment rates, its impact on the long-term sustainability of employment remains largely unexplored. In the context of prevailing uncertainties, the imperative to bolster employment resilience through digitalization becomes increasingly apparent. This study aims to bridge the existing gap by introducing an innovative analytical framework that integrates digitalization, enterprise resilience (ER), and sustainable employment (SE). Focusing on publicly listed companies within China’s manufacturing sector, the research employs fixed effects models and mediation analysis to intricately explore the interactions among these variables. The investigation yields several pivotal insights: (1) digitalization has a significantly positive impact on sustainable employment; (2) enterprise resilience acts as a positive mediator in the relationship between digitalization and sustainable employment; (3) through heterogeneity analysis, it is demonstrated that digitalization plays a more pronounced role in bolstering employment stability in non-high-tech industries and companies exhibiting superior financial health in the eastern region. These findings offer critical perspectives for informing government policy and devising corporate strategies that capitalize on digitalization and enhance enterprise resilience, thereby promoting more sustainable employment trajectories.

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Experiences with big data: Accounts from a data scientist’s perspective

Cited by 4 publications

References 34 publications

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

An investigation of the utilisation of different data sources in manufacturing with application in injection moulding

Design of Agricultural Product Traceability System and Consumption Information Big Data System

Digitalization’s Effect on Chinese Employment Mechanism Study

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