Principal Scientist at Royal Prince Alfred Hospital, Australia
Congratulations, Prof. Herbert Jodlbauer, on winning the esteemed Best Researcher Award from Citation Awards! Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done!🌿
Herbert Jodlbauer is Professor of Production at the Production Department located at the University of Applied Sciences Upper Austria, Campus Steyr, and heads the Josef Ressel Centre for Data-driven Business Model Innovation. Over the past 20 years, Jodlbauer has conducted research in production systems modelling, simulation, and analysis, production planning strategies, data analytics in production, digitalisation, smart production, and business model innovation. He worked as project manager and key researcher in several R&D projects on (smart) production, process optimisation, Industry 4.0, digital transformation, and business model innovation. Jodlbauer is the scientific head of the multidisciplinary Centre of Excellence for Smart Production at the FH OÖ.
Professional Profiles:
Scopus Profile
ORCID Profile
Google Scholar Profile
LinkedIn
ResearchGate Profile
Areas of Specialization:
Business, Management and Accounting, Graph Theory
Education
Mechanical Engineering (Dr.), University of Technology Vienna, Austria (1990 – 1992) , Technical Mathematics (Dipl.-Ing.), Johannes Kepler University Linz, Austria (1985 – 1989)
Academic and Professional Career
Head of the Josef Ressel Centre for Data-Driven Business Model Innovation (JRC DDBMI) since 2023, Scientific Head of Centre of Excellence for Smart Production at the University of Applied Sciences Upper Austria since 2014, Professor for Production, University of Applied Sciences Upper Austria, Head of Study for Smart Production and Management (since 1995) and Operations Management (since 2008) since 1995
Qualifications and Experience:
Head of JRC DDBMI and Head of Centre of Excellence for Smart Production, Experience as a scientific project leader in FFG, FWF, and EU projects, Key researcher in numerous projects
Research Focus:
Production Systems Modelling and Analysis, Production Planning Strategies, Simulation Modelling in Manufacturing, Data Analytics in Production, Digitalisation, Smart Production, Business Model Innovation
Memberships / Reviewing and Conference Activities:
Reviewer for IJPR, IJPE, EJOR, CEJOR, EuroCast, ISM, and more, Conference Chair for Industry 4.0 and Smart Manufacturing, Value Chain Management, and more, Member of VHB, ÖGOR, FIBBA, and more
Peer Reviewer & Academic Engagements:
Prof. Herbert Jodlbauer citation metrics and indices from Google Scholar are as follows:
Citations: 1379(All), 808 (Since 2018)
h-index: 26 (All), 26 (Since 2018)
i10-index: 36 (All), 22 (Since 2018)
Publications: 65 documents indexed in Scopus.
Selective Publications (JOURNALS):
Bachmann, N., & Jodlbauer, H. (2023). Iterative business model innovation: A conceptual process model and tools
for incumbents. Journal of Business Research, 168, 114177.
Jodlbauer, H., Brunner, M., Bachmann, N., Tripathi, S., & Thürer, M. (2023). Supply Chain Management: A
Structured Narrative Review of Current Challenges and Recommendations for Action. Logistics, 7(4), 70.
Jodlbauer, H., & Tripathi, S. (2023). Analytical comparison of cross impact steady state, DEMATEL, and page rank
for analyzing complex systems. Expert Systems with Applications, 225, 120154.
Jodlbauer, H., & Tripathi, S. (2023). Due date quoting and rescheduling in a fixed production sequence. International
Journal of Production Research, 1-15.
Tripathi, S., Mittermayr, C., & Jodlbauer, H. (2023). Exploring the time-lagged causality of process variables from
injection molding machines. Procedia Computer Science, 217, 1153-1167.
Brunner, M., Jodlbauer, H., Bachmann, N., & Tripathi, S. (2023). Implementing Virtuality in Production-a Design
Science Approach. Procedia Computer Science, 217, 988-997.
Tripathi, S., Riegler, A., Anthes, C., & Jodlbauer, H. (2022, October). Vsimgen: A Proposal for an Interactive
Visualization Tool for Simulation of Production Planning and Control Strategies. In Proceedings of the Future
Technologies Conference (FTC) 2022, Volume 1 (pp. 731-752). Cham: Springer International Publishing.
Jodlbauer, H., Tripathi S., Brunner, M., & Bachmann, N. (2022). Stability of Cross-Impact Matrices. Technological
Forecasting & Social Change, 182, 121822.
Bachmann, N., Tripathi, S., Brunner, M., & Jodlbauer, H. (2022). The Contribution of Data-Driven Technologies in
Achieving the Sustainable Development Goals. Sustainability, 14(5), 2497.
Fröhler, B., Anthes, C., Pointecker, F., Friedl, J., Schwajda, D., Riegler, A., Tripathi, S., Holzmann, C., Brunner, M.,
Jodlbauer, H., Jetter, H.-C., Heinzl, C. (2022). A Survey on Cross‐Virtuality Analytics. In Computer Graphics
Forum (Vol. 41, No. 1, pp. 465-494).
Kieroth, A., Brunner, M., Bachmann, N., Jodlbauer, H., & Kurz, W. (2022). Investigation on the acceptance of an
Industry 4.0 maturity model and improvement possibilities. Procedia Computer Science, 200, 428-437.
Wolfartsberger, J., Riedl, R., Jodlbauer, H., Haslinger, N., Hlibchuk, A., Kirisits, A., & Schuh, S. (2022). Virtual Reality
als Trainingsmethode: Eine Laborstudie aus dem Industriebereich. HMD Praxis der Wirtschaftsinformatik, 59(1), 295-308.
Tripathi, S., Jodlbauer, H., Mittermayr, C., & Emmert-Streib, F. (2022). Identifying key interactions between process
variables of different material categories using mutual information-based network inference method. Procedia
Computer Science, 200, 1550-1564.
Tripathi, S., Strasser, S., & Jodlbauer, H. (2021). A Network based Approach for Reducing Variant Diversity in
Production Planning and Control. In DATA (pp. 241-251).
Tripathi, S., Muhr, D., Brunner, M., Jodlbauer, H., Dehmer, M., & Emmert-Streib, F. (2021). Ensuring the robustness
and reliability of data-driven knowledge discovery models in production and manufacturing. Frontiers in Artificial
Intelligence, 4, 22.
Tripathi, S., Mittermayr, C., Muhr, D., & Jodlbauer, H. (2021). Large scale predictability analysis of process variables
from injection molding machines. Procedia Computer Science, 180, 545-560.
Muhr, D., Tripathi, S., & Jodlbauer, H. (2021). An adaptive machine learning methodology to determine
manufacturing process parameters for each part. Procedia Computer Science, 180, 764-771.
Jodlbauer, H., & Dehmer, M. (2020). An extension of the reorder point method by using advance demand spike
information. Computers & Operations Research, 124, 105055