Production line optimization using data analysis

OData support
Supervisor:
Dr. Dudás Ákos
Department of Automation and Applied Informatics

This thesis was written as a result of the cooperation with the Budapest branch of Continental

Automotive Systems Ltd., one of the leading automotive manufacturing companies in the world.

The plant produces a large volume of measurement data during the production of various

automotive products on dozens of production lines. Currently there exists only a limited scope of

predictive data analysis solutions that operate on the production data. In order to better understand

possible connections between production quality metrics, the Vehicle Dynamics (VED) business

unit proposed several case studies about currently existing production quality issues observed

during the ER100 electronic brake system product family.

In this thesis, I first analyze the existing traceability workflow based on the central

Manufacturing Execution System (MES) and also investigate the data analysis capabilities of the

current standard Business Intelligence (BI) and reporting tool. Afterwards, I design and implement a

complete machine learning data pipeline starting from the integration of heterogeneous data sources

using SQL Server Integration Services (SSIS) to provide input data for several different machine

learning models that can eventually provide predictions after successful training.

After the theoretical overview of the used software tools and machine learning techniques, two

real-life case studies provided by the VED engineering team are analyzed by deploying multiple

machine learning models using popular machine learning and deep learning frameworks, such as

Scikit-learn and TensorFlow. In order for an effective understanding of the underlying data during

the data exploration phase, I created an interactive web dashboard using Bokeh. In addition to the

on-premise implementation, I carried out the training and deployment of various prediction models

in the cloud, as well using Amazon SageMaker, which is a recent addition to Amazon Web Services

(AWS) machine learning product portfolio.

Finally, I summarize the data analysis results of the case studies along with the potential future

work to further improve results and facilitate a production-ready rollout

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