http://orcid.org/0000-0001-8596-7541
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Abstract
Manufacturing has been rejuvenated by automation and digitalization. This has brought forth the new industrial era also called Industry 4.0. During the last few years, we have collaborated with companies from various industries that have all been going through this transformation. Through these collaborations, we have collected numerous examples of (sometimes troublesome) experiences with Big Data applications of production analytics. These experiences reflect the current state of production data and the challenges it poses. Our goal in this paper is to share those experiences and lessons learned in dealing with practical issues from data acquisition to data management and finally to data analytics.
Acknowledgments
The authors would like to thank the industrial partners and MADE SPIR, MADE DIGITAL, BioPro2 and Innovation Fund Denmark for making this work possible.
About the authors
Murat Kulahci is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark and a Professor in the Department of Business Administration, Technology and Social Sciences at Luleå University of Technology in Sweden. He is an applied statistician with particular focus on design of experiments, statistical process control, time series analysis, and financial engineering. His current research primarily focuses on large data analytics for descriptive, inferential and predictive purposes. Many of his research applications involve high dimensional, high frequency data requiring methods in chemometrics and machine learning. He has been collaborating with various industries in industrial statistics projects and digital manufacturing. He has more than 85 refereed journal articles and 3 books published with co-authors from Europe and USA.
Flavia Dalia Frumosu holds an MSc in Dynamic Modelling from the Technical University of Denmark where she is currently a PhD student. Her current research lies in the field of big data analytics with applications to the manufacturing industry. She also has expertise in the domain of risk and safety after working as a consultant for three years in the oil and gas industry.
Abdul Rauf Khan joined the Department of Applied Mathematics and Computer Science at the Technical University of Denmark, as a Postdoc in October 2017, after finishing his PhD from Aalborg University Denmark. During his PhD, he developed advanced machine learning methods for industrial manufacturing. Currently he is doing research on online machine learning methods for industrial manufacturing. More specifically, the development of novel machine learning/data mining methods to enable fast and rapid decision-making.
Georg Ørnskov Rønsch holds an MSc in Chemical Engineering from the Aalborg University Esbjerg and have worked with data utilization form manufacturing for the last 15 years. Currently he is enrolled as an Industrial PhD student at the Technical University of Denmark. The focus of the PhD is model-based process monitoring and control for fault detection and diagnostic using chemometric and Deep Learning methods.
Max Peter Spooner is a Postdoctoral researcher in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark where he obtained his PhD in 2018. His research interests currently include statistical process control and monitoring of industrial processes, time series analysis and climate data analysis for the design of electronic products.