Scientific Publications

@inproceedings{Burkhardt2025a,

title = {Process Mining for Production Optimization in Smart Manufacturing},

author = {Dirk Burkhardt and Juliane Harbarth and Andreas Görmer},

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-i.pdf, Paper as PDF},

doi = {10.1109/IV68685.2025.00058},

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

year  = {2025},

date = {2025-10-31},

urldate = {2025-10-31},

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

pages = {288-295},

publisher = {IEEE},

abstract = {Smart Manufacturing is currently the main objective when production manufacturers digitalize their plants to face current regulations and requirements to optimize costs, consumed resources, and sustainability. The focus is usually on extracting data from production and making it "analyzable". However, the results often neglect advanced options to optimize the production, may it in regards to the production process itself or in regards to consumed resources of the produced goods in specific. As the main contribution, this paper describes a novel approach to consider process mining appending to plant digitization and IoT analytics. As a result, the entire production process becomes transparent and therewith analyzable, but also the concrete consumed resources per produced good, per group, or as a whole can be analyzed. As the application benefit, the paper also outlines some advanced analysis capabilities to identify production optimizations based on process mining.},

keywords = {Fourth Industrial Revolution, Industry 4.0, IoT Analytics, Optimization, Process Mining, Production, Smart Manufacturing, Sustainable Development, Visual Analytics},

pubstate = {published},

tppubtype = {inproceedings}

}

@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}

}