On the public citation graph
Business process management for Industry 4.0 – Three application cases in the DFKI-Smart-Lego-Factory
0 signed · 29 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-17906-3_10openalex-snapshot
Intelligent Systems in Maintenance Planning and Management
inferredcites →https://doi.org/10.1007/978-3-319-58457-7_24openalex-snapshot
A Deep Learning Approach for Predicting Process Behaviour at Runtime
inferredcites →https://doi.org/10.1007/978-3-642-97998-9openalex-snapshot
ARIS — Business Process Modeling
inferredcites →https://doi.org/10.1007/978-3-662-49851-4openalex-snapshot
Process Mining: Data Science in Action
inferredcites →https://doi.org/10.1007/s10270-015-0490-0openalex-snapshot
A graph-theoretic method for the inductive development of reference process models
inferredcites →https://doi.org/10.1016/j.dss.2017.04.003openalex-snapshot
Predicting process behaviour using deep learning
inferredcites →https://doi.org/10.1108/14637151011065946openalex-snapshot
Empirical research in business process management – analysis of an emerging field of research
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4690886openalex-extracted
Process Mining in Manufacturing and Logistics: A Systematic Mapping and New Taxonomy Proposal
inferred← citeshttps://doi.org/10.1007/978-3-030-29996-5_46openalex-snapshot
Development of a Modeling Architecture Incorporating the Industry 4.0 View for a Company in the Gas Sector
inferred← citeshttps://doi.org/10.1007/978-3-030-57993-7_42openalex-snapshot
Identifying Key Business Processes that Can Benefit from Industry 4.0 in the Gas Sector
inferred← citeshttps://doi.org/10.1007/978-3-030-66498-5_8openalex-snapshot
Using Physical Factory Simulation Models for Business Process Management Research
inferred← citeshttps://doi.org/10.1007/978-3-030-78424-9_57openalex-snapshot
Data Quality Assessment to Apply Process Mining in Production Processes
inferred← citeshttps://doi.org/10.1007/978-3-030-96293-7_54openalex-snapshot
Increasing the Efficiency and Workers Wellbeing in the European Bakery Industry: An Industry 4.0 Case Study
inferred← citeshttps://doi.org/10.1007/978-3-031-09062-2_4openalex-snapshot
Lab-Scale Smart Factory Implementation Using ROS
inferred← citeshttps://doi.org/10.1007/s10462-025-11399-0openalex-snapshot
Interpretable and explainable machine learning methods for predictive process monitoring: a systematic literature review
inferred← citeshttps://doi.org/10.1007/s11135-025-02186-8openalex-snapshot
A bibliometric analysis and review of the use of LEGO topic in the scientific literature
inferred← citeshttps://doi.org/10.1007/s13218-019-00586-1openalex-snapshot
Towards Explainable Process Predictions for Industry 4.0 in the DFKI-Smart-Lego-Factory
inferred← citeshttps://doi.org/10.1007/s44029-022-0716-1openalex-snapshot
Mit Composable Applications zur digitalisierten Fertigung
inferred← citeshttps://doi.org/10.1016/j.procs.2021.01.360openalex-snapshot
Explaining a Random Forest With the Difference of Two ARIMA Models in an Industrial Fault Detection Scenario
inferred← citeshttps://doi.org/10.1108/bpmj-05-2020-0181openalex-snapshot
Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends
inferred← citeshttps://doi.org/10.1108/scm-07-2021-0347openalex-snapshot
The effects of industry 4.0 technologies on relational performance: the mediating role of supply chain emergence in the transitive logistics service triads
inferred← citeshttps://doi.org/10.1145/3386164.3387293openalex-snapshot
Reengineering of the New Customer Gas Connection Process Utilizing Industry 4.0 Technologies
inferred← citeshttps://doi.org/10.17705/1cais.05344openalex-snapshot
Modeling 4.0: Conceptual Modeling in a Digital Era
inferred← citeshttps://doi.org/10.22214/ijraset.2022.40754openalex-snapshot
The Integration of Industry 4.0 in the Processes of a Company in the Gas Sector through a Holistic Enterprise Architecture
inferred← citeshttps://doi.org/10.23860/diss-abdelatti-marwan-2022openalex-snapshot
Empowering Industrial IoT Systems with GPU-Powered Optimization Algorithms and Cybersecurity Tools
inferred← citeshttps://doi.org/10.3390/designs4030017openalex-snapshot
A Conceptual Framework to Support Digital Transformation in Manufacturing Using an Integrated Business Process Management Approach
inferred← citeshttps://doi.org/10.5937/imcsm24028dopenalex-snapshot
Enhancing production planning efficiency with document management using BPM tools: The Bonita approach
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.