Industry-informed researcher
AI adoption, compliance & innovation in industrial digitalization.
Head of Digitalization & IT, voestalpine Krems GmbH
Lecturer & researcher, Institute of Management Science, TU Wien
18 publications 260+ citations 7 h-index
Current focus
Building the governance layer that lets industrial organizations experiment with AI-generated software without losing compliance — the missing operational link between vibe coding speed and audit-grade accountability.
Three research lines connecting knowledge-based systems, industrial AI, and the organizational conditions under which AI use succeeds or fails.
Knowledge graphs, competence-based planning, federated learning architectures, and cognitive assistance systems that keep expert knowledge available and actionable in complex production environments. The doctoral foundation and the largest part of the publication record (CIRP Annals, Procedia CIRP, PHM Society, IEEE ETFA, WGAB).
LLM-based chatbots, explainable event extraction, and RAG-assisted maintenance planning and operations — with a human-centricity emphasis that predates the current agentic wave. Includes multi-structured data analytics for fault detection in semiconductor manufacturing.
Current agenda
How organizations adopt AI-generated software at speed while remaining auditable: sandboxed experimentation vs. hardened promotion, the economics of human review versus regeneration, and why compliance regimes push firms toward augmentation rather than automation.
Four papers in preparation. The lead paper establishes the framework; the others extend it into new settings.
Design-science artifact: a two-track governance model separating a sandboxed experimental lane from a hardened promotion lane, with an explicit promotion gate (code provenance, model/version logging, human accountable owner) and a threshold model for when human review costs more than regeneration.
Target: ICIS / ECIS · Business & Information Systems Engineering
Empirical test of a counterintuitive claim: regulatory load acts as a forcing function toward augmentation, because automating a regulated process inherits the full certification burden while augmenting an accountable human does not.
Target: Academy of Management Discoveries / Technovation
Extends the practitioner Leadership–Crowd–Lab model with a knowledge-retention loop: a graded knowledge graph of AI use cases that survives staff turnover and satisfies an auditor simultaneously.
Target: Journal of Management Information Systems / CIRP CMS
Adapts the framework to small-enterprise constraints — no IT department, no internal auditor, owner-operator accountability — where compliance load is lighter but expertise is scarcer.
Target: Journal of Small Business Management
Complete list — journal articles, conference papers, book chapters, dissertation and preprints. Source of record: Google Scholar · ORCID. Bold = Linus Kohl. Citation counts as indexed by Google Scholar.
Explainable Event Extraction in Knowledge-Based Maintenance
Procedia CIRP, 134, 711–716, 2025 · DOI
A Knowledge Graph-based Learning Assistance System for Industrial Maintenance
Procedia CIRP, 126, 87–92, 2024 · 6 citations · DOI
A competence-based planning methodology for optimizing human resource allocation in industrial maintenance
CIRP Annals, 72(1), 389–392, 2023 · 32 citations · DOI
AI-Enhanced Maintenance for Building Resilience and Viability in Supply Chains
Supply Network Dynamics and Control, 163–185, 2022 · 16 citations · DOI
Text mining for AI enhanced failure detection and availability optimization in production systems
CIRP Annals, 70(1), 373–376, 2021 · 54 citations · DOI
Künstliche Intelligenz im Kompetenzmanagement: Ein Fallbeispiel aus der Halbleiterindustrie
Zeitschrift für wirtschaftlichen Fabrikbetrieb (ZWF), 116(7–8), 534–537, 2021 · 11 citations · DOI
A Knowledge-Based Digital Lifecycle-Oriented Asset Optimisation
Tehnički glasnik, 15(2), 226–234, 2021 · 9 citations · DOI
Transforming STEM Education with Extended Reality: A Replicable Framework for University Integration
Conference on Learning Factories (CLF 2025), 53–61, 2025 · DOI
Large Language Model-based Chatbot for Improving Human-Centricity in Maintenance Planning and Operations
PHM Society European Conference, 8(1), 2024 · 27 citations · DOI
DigiTeachVR: Digitally-Enhanced Teaching Platform for Improving Data Science Skills and Virtual Reality Competences in Cross-Disciplinary Engineering Education
Conference on Learning Factories (CLF 2024), 50–57, 2024 · 5 citations · DOI
Knowledge-Based Digital Twin for Predicting Interactions in Human-Robot Collaboration
IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2021 · 50 citations · DOI
A Text Understandability Approach for Improving Reliability-Centered Maintenance in Manufacturing Enterprises
IFIP Advances in Information and Communication Technology (APMS 2021), 2021 · 7 citations · DOI
AI-Enhanced Fault Detection Using Multi-Structured Data in Semiconductor Manufacturing
In: Multimodal and Tensor Data Analytics for Industrial Systems Improvement (Springer), 297–312, 2024 · 2 citations · DOI
Knowledge-graph based approach for automated selection of spare parts suitable for additive manufacturing: a railway use-case
Cranfield University, 2024 · 1 citation
Maintenance-Free Factory: A Holistic Approach for Enabling Sustainable Production Management
In: WGAB 2023, 2023 · DOI
A Modular Federated Learning Architecture for Integration of AI-enhanced Assistance in Industrial Maintenance
In: WGAB 2021 — Academic Society for Work and Industrial Organization, 2021 · 17 citations · DOI
Knowledge-based maintenance framework for smart manufacturing: advancing efficient planning and cognitive assistance to enhance system availability
Doctoral dissertation, Technische Universität Wien, 2025 · 1 citation
Design and development of automatic recommendation generation module of prescriptive maintenance model (AutoPriMa)
Diploma thesis, Technische Universität Wien, 2019 · 2 citations
Combining Process Monitoring with Text Mining for Anomaly Detection in Discrete Manufacturing
Proceedings of the 12th Conference on Learning Factories (CLF 2022) / SSRN preprint, 2022 · 8 citations · DOI
Bridging research and operations — from applied research at Fraunhofer Austria to leading digitalization and IT in heavy industry.
Today
voestalpine Krems GmbH
Responsible for the digital transformation of a heavy-industry site and the applied deployment of AI in business and production processes. Direct ownership of the constraints that most AI research abstracts away: audit, safety, data protection, and production continuity.
Earlier
Fraunhofer Austria Research GmbH
Applied research projects on data-driven production processes, advanced analytics, and AI-based optimization for maintenance and production systems — with industry partners in automotive and semiconductor manufacturing.
Ongoing
TU Wien — Institute of Management Science
Teaching and thesis supervision in industrial engineering and digitalization; continued publication in the CIRP, PHM and IFIP communities.
Open to research collaboration, co-authorship, thesis supervision, and speaking invitations.