Scientific Publications

@inproceedings{Burkhardt2025b,

title = {DPPviewer: A Visual Analytics Approach for Optimizing Production Chains on Digital Product Passports},

author = {Dirk Burkhardt and Moritz Bock and Arjan Kuijper},

editor = {Ebad Banissi and Nuno Datia and João Moura Pires and Anna Ursyn and Kawa Nazemi and Cristian A. Secco and Boris Kovalerchuk and Razvan Andonie and Marina Gavrilova and Minoru Nakayama and Mountaz Hascoet and Amalia Rusu and Quang Vinh Nguyen and Mabule Samuel Mabakane and Adrian Rusu and Jie Hua and Fatma Bouali and Gilles Venturini and Alice Dong and Sebastian Kernbach and Gaetano Cimin and Urska Cvek and and Tony Huang},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2025/11/2025iv-ii.pdf, Paper as PDF},

doi = {10.1109/IV68685.2025.00061},

isbn = {979-8-3315-7741-4},

year  = {2025},

date = {2025-10-31},

urldate = {2025-08-08},

booktitle = {2025 29th International Conference Information Visualisation (IV)},

pages = {311-319},

publisher = {IEEE},

abstract = {The development of a Digital Product Passport (DPP) is an important step in the process of sustainable production and an increasingly mandatory tool for the manufacturing industry, particularly in the EU. However, DPPs have so far been understood in the industry as a purely technical implementation for the exchange of relevant data toward product manufacturing, which is not suitable for reading and understanding by humans. This paper, therefore, describes an approach for a visual analytics system that enables human decision making through the visualization and analysis of DPPs, with a particular focus on optimizing CO2 efficiency. The system uses intuitive analytical visualizations to enable quick understanding and actionable insights. The main contribution is the concrete data processing and visualization of DPP information for human access, and is so far one of the first available approaches for a visual representation of DPPs in general.},

keywords = {Carbon Footprint, Circular Economy, Data Visualization, Industry 4.0, Internet of Things, Manufacturing Industries, Optimization, Product Carbon Footprint, Production, Regulation, Sustainable Development, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Burkhardt2025,

title = {Enabling Smart Manufacturing with Visual Analytics for Plant Workers},

author = {Dirk Burkhardt and Gerald Ristow},

editor = {Meike Klettke and Ralf Schenkel and Andreas Henrich and Daniela Nicklas and Maximilian E. Schüle and Klaus Meyer-Wegener},

url = {https://dirk.burkhardt.xyz/wp-content/uploads/2025/03/2025btw.pdf, Paper as PDF},

doi = {10.18420/BTW2025-33},

issn = {2944-7682},

year  = {2025},

date = {2025-02-28},

urldate = {2025-02-28},

booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2025)},

pages = {665-678},

publisher = {Gesellschaft für Informatik},

address = {Bonn},

series = {Lecture Notes in Informatics (LNI) - Proceedings, Volume P-361},

abstract = {Smart manufacturing is increasingly making use of visual analytics to optimize production or to identify early problem signs. However, current solutions and approaches require professionals, especially from the data science area, to make use of it, which is for most production companies not affordable. In this paper, we describe first a best practice to sensorize plants from the wood and beverage industry to enable smart manufacturing in general. Second, we describe a new approach that aims at providing easy-to-use visual analytics functionalities that are designed to be used directly by plant workers. Plant workers usually have encompassing experience in the production and the plant, but lack of computer experience and corresponding mathematical knowledge for data analysis. Through lowering the barriers for plant workers in performing data analysis of the IoT sensors with simplified and almost automated analysis functions would give them the ability to gain insights into the production and achieve similar production optimizations and problem preventions as data science experts could. The main contributions of this article are on the one hand the best practice of how production lines of the wood and beverage industry could be made ready for smart manufacturing, but also an approaches that enable non-data scientists, especially plant workers, to perform sufficient analysis about optimal production settings and early problem cause identification.},

keywords = {Human-Computer Interaction, Internet of Things, Smart Manufacturing, User-Centered Design, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}