Abstract
It is becoming increasingly important to take advantage of Big Data in order to be able to understand industrial processes and improve their efficiency and effectiveness. This work presents an application on a glass container manufacturing plant, to detect and characterize patterns in the efficiency of the production process. Besides the inherent complexity of the pattern discovery task, the challenge is increased by the multivariate time series nature of the data. The main goal of this project, other than minimizing production losses, creating knowledge from data and therefore improving the company’s overall efficiency, aims to contribute to literature in describing patterns on an univariate time series leveraging multivariate time series data, specially in manufacturing applications.
Supported by FEUP-PRIME program and BA GLASS PORTUGAL.
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Oliveira, M.A., Guimarães, L., Borges, J.L. (2020). Understanding Production Process Productivity in the Glass Container Industry: A Big Data Approach. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_21
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DOI: https://doi.org/10.1007/978-3-030-64583-0_21
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