![[Translate to English:]](/fileadmin/_processed_/a/f/csm_Kiefer_Daniel_2_9a705b0eca.webp "[Translate to English:]")
Daniel Kiefer M.Sc.
Research team Digitization and Management
+49 7121 271 1466
daniel.kiefer@reutlingen-university.de
Building 5, Room 011
By arrangement
Research assistant in the research team Digitization and Management
- Artificial Intelligence
- Machine learning
- Deep Learning
- Digitization & Management
- Industry 4.0
Professional Experience
- Science & Research, ESB Business School
- Management Consulting, Barkawi Management Consultants
- Management Consulting, goetzpartners
- Private Equity, CAPCELLENCE
- Lean Management Consulting, STAUFEN.SHANGHAI
- Management Consulting, TARGUS Management Consulting
- SYNCHRO Inhouse Consulting, TRUMPF North America
- Inhouse Consulting, Carl Zeiss Vision
Academic Education
- PhD Candidate, Artificial Intelligence, Karlsruhe Institute of Technology (KIT)
- Operations Management (M. Sc.), ESB Business School
- Business & Entrepreneurship, Cambridge Judge Business School
- Computer Science & Business Administration ,University Bologna
- Maschinenbau – Wirtschaft und Management (B. Eng.), Aalen University
Scholarships
- 100 KI Talente Stipendiat, Fraunhofer IAO
- Studienstiftung des deutschen Volkes
- Elevate VDI – Verein Deutscher Ingenieure
- DAAD - Deutscher Akademischer Austauschdienst
- McKinsey Firsthand Program
- BCG Emerald Talent Program
Kiefer, Daniel
2024 | |
| Kiefer, Daniel; Wezel, Stefan; Böttcher, Alexander; Grimm, Florian; Straub, Tim; Bitsch, Günter; van Dinther, Clemens (2024): Anomaly detection in hobbing tool images: using an unsupervised deep learning approach in manufacturing industry. - In: Procedia computer science 232 (5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)). - S. 2396-2405. - DOI: https://doi.org/10.1016/j.procs.2024.02.058 | BibTeX | RIS DOIURN |
| Grimm, Florian; Kiefer, Daniel; Straub, Tim; Bitsch, Günter; van Dinther, Clemens (2024): Automatic gear tooth alignment in vision based preventive maintenance. - In: Procedia computer science 232 (5th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2023)). - S. 1564-1572. - DOI: https://doi.org/10.1016/j.procs.2024.01.154 | BibTeX | RIS DOIURN |
2022 | |
| Kiefer, Daniel; Grimm, Florian; van Dinther, Clemens (2022): Artificial intelligence in supply chain management: investigation of transfer learning to improve demand forecasting of intermittent time series with deep learning. - In: Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS 2022), 4-7 January 2022, virtual event/Maui. - Honolulu : University of Hawai'i at Manoa. - S. 1656-1665. - ISBN: 978-0-9981331-5-7. - URL: http://hdl.handle.net/10125/79537 | BibTeX | RIS URN |
| Kiefer, Daniel; van Dinther, Clemens; Straub, Tim (2022): The time has come : application of artificial intelligence in small- and medium-sized enterprises. - In: Wirtschaftsinformatik 2022 : Proceedings, 21.-23. February 2022, Nuremberg (online). - Atlanta, GA : Association for Information Systems. - 4 S. - DOI: https://doi.org/10.34645/opus-3872 | BibTeX | RIS DOIURN |
2021 | |
| Kiefer, Daniel; Grimm, Florian; Bauer, Markus; van Dinther, Clemens (2021): Demand forecasting intermittent and lumpy time series: comparing statistical, machine learning and deep learning methods. - In: Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54), 4-8 January 2021, online. - Honolulu : University of Hawai'i at Manoa. - S. 1425-1434. - ISBN: 978-0-9981331-4-0. - DOI: https://doi.org/10.24251/HICSS.2021.172 | BibTeX | RIS DOIURN |
| Kiefer, Daniel; Spitzmüller, Julian; van Dinther, Clemens (2021): Digital innovation culture: a systematic literature review. - In: Innovation Through Information Systems : Volume III: A Collection of Latest Research on Management Issues. - Cham : Springer. - S. 305-320. - ISBN: 978-3-030-86799-7. - DOI: https://doi.org/10.1007/978-3-030-86800-0_22 | BibTeX | RIS DOI |
| Blessing, Gerald; Kiefer, Daniel (2021): Digital skills of procurement employees and their attitudes toward digital technologies. - In: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 173-182. - ISBN: 978-981-16-3264-8. - DOI: https://doi.org/10.1007/978-981-16-3264-8_17 | BibTeX | RIS DOI |
| Bauer, Markus; Kiefer, Daniel; Grimm, Florian (2021): Sales forecasting under economic crisis: a case study of the impact of the COVID19 crisis to the predictability of sales of a medium-sized enterprise. - In: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 163-172. - ISBN: 978-981-16-3264-8. - DOI: https://doi.org/10.1007/978-981-16-3264-8_16 | BibTeX | RIS DOI |
| Grimm, Florian; Kiefer, Daniel; Bauer, Markus (2021): Univariate time series forecasting by investigating intermittence and demand individually. - In: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 143-151. - ISBN: 978-981-16-3266-2. - DOI: https://doi.org/10.1007/978-981-16-3264-8_14 | BibTeX | RIS DOI |
| Kiefer, Daniel; Bauer, Markus; Grimm, Florian (2021): Univariate time series forecasting: machine learning prediction of the best suitable forecast model based on time series characteristics. - In: Human centred intelligent systems : proceedings of KES-HCIS 2021 conference ; Smart innovation, systems and technologies, volume 244. - Singapore : Springer. - S. 152-162. - ISBN: 978-981-16-3266-2. - DOI: https://doi.org/10.1007/978-981-16-3264-8_15 | BibTeX | RIS DOI |
2020 | |
| Bauer, Markus; van Dinther, Clemens; Kiefer, Daniel (2020): Machine learning in SME: an empirical study on enablers and success factors. - In: AMCIS 2020 proceedings - Advancings in information systems research : August 10-14, 2020, Online. - Atlanta, GA : Association for Information Systems. - S. 1-10. - URL: https://aisel.aisnet.org/amcis2020/adv_info_systems_research/adv_info_systems_research/3 | BibTeX | RIS |
2019 | |
| Kiefer, Daniel; Ulmer, Annette (2019): Application of artificial intelligence to optimize forecasting capability in procurement. - In: Wissenschaftliche Vertiefungskonferenz : Informatik-Konferenz an der Hochschule Reutlingen, 27. November 2019. - Reutlingen : Hochschule Reutlingen. - S. 69-80. - ISBN: 978-3-00-064236-4. - DOI: https://doi.org/10.34645/opus-3870 | BibTeX | RIS URNDOI |
2018 | |
| Eberl, Andreas; Fallert, Benedikt; Kiefer, Daniel; Leuschner, David; Mastroianni, Lisa; Richter, Patrick; Tittelbach, Frederik; Braun, Anja; Lucke, Dominik; Ohlhausen, Peter; Palm, Daniel (2018): Methoden zur Sofortpreiskalkulation von CNC-Drehteilen : Entwicklung und Bewertung der Anwendbarkeit von Algorithmen und prädiktiven Machine-Learning-Modellen. - In: Zeitschrift für wirtschaftlichen Fabrikbetrieb : ZWF 113 (12). - S. 835-839. - DOI: https://doi.org/10.3139/104.112020 | BibTeX | RIS DOI |
