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Daniel Kiefer

Daniel Kiefer, M.Sc.Research team Digitization and Management

Office Hours: By arrangement

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Building 5
Room 011
Tel. +49 7121 271 1466
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Areas of Responsibilty

Research assistant in the research team Digitization and Management

Research Topics
  • Artificial Intelligence
  • Machine learning
  • Deep Learning
  • Digitization & Management
  • Industry 4.0
Current Research Projects
Vita

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
Publications
  • G. Blessing and D. Kiefer: Digital Skills of Procurement Employees and their Attitudes toward Digital Technologies. In: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • D. Kiefer, M. Bauer and F. Grimm: Univariate Time Series Forecasting: Machine Learning Prediction of the Best Suitable Forecast Model based on Time Series Characteristics. In: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • M. Bauer, D. Kiefer and F. Grimm: 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: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • F. Grimm, D. Kiefer and M. Bauer: Univariate Time Series Forecasting by Investigating Intermittence and Demand Individually. In: Proceedings of the 14th International Conference on Human Centred Intelligent Systems (KES-HCIS-21). Rom 2021.
  • D. Kiefer, C. van Dinther and J. Spitzmüller: Digital Innovation Culture – A Systematic Literature Review. In: Proceeding of the 16. Internationale Tagung der Wirtschaftsinformatik (WI). 16. Auflage. Duisburg-Essen 2021.
  • D. Kiefer, F. Grimm, M. Bauer and C. van Dinther, “Demand Forecasting Intermittent and Lumpy Time Series: Comparing Statistical, Machine Learning and Deep Learning Methods,” in Proc. 54rd Hawaii Int. Conf. Syst. Sci., Maui, HI, USA, Jan. 2021.
  • Bauer M., Dinther C. van, & Kiefer D., “Machine Learning in SME: An Empirical Study on Enablers and Success Factors: An Empirical Study on Enablers and Success Factors,” AMCIS 2020 Proceedings 3, no. 3, 2020.
  • Kiefer, D., Ulmer, A. & Dinther, C. van. (2019). Application of Artificial Intelligence to optimize forecasting capability in procurement. In U. Kloos, N. Martinez, & G. Tullius (Hrsg.), Wissenschaftliche Vertiefungskonferenz. Tagungsband 2019 (1. Aufl., S. 69–80). Reutlingen: Reutlingen University. doi: https://doi.org/10.5281/zenodo.3539397
  • Eberl, A., Fallert, B., Kiefer, D., Leuschner, D., Mastroianni, L., Richter, P., . . . Palm, D. (2018). Methoden zur Sofortpreis-kalkulation von CNC-Drehteilen. ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb, 113(12), 835–839. doi.org/10.3139/104.112020