@thesis{Hartl2021, author = {Hartl, Jakob Joseph Lorenz}, title = {Digitaler Zwilling im Mittelstand}, subtitle = {ein Zukunftsmodell f{\"u}r die Produktionsplanung und -steuerung?}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-8062}, pages = {VI, 49}, year = {2021}, language = {de} } @article{HartlBock2024, author = {Hartl, Jakob and Bock, J{\"u}rgen}, title = {A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes}, volume = {18}, journal = {Tehnički glasnik}, number = {3}, publisher = {University North}, address = {Koprivnica}, issn = {1848-5588}, doi = {https://doi.org/10.31803/tg-20240513233046}, pages = {437 -- 444}, year = {2024}, abstract = {The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and medium sized enterprises (SMEs).}, language = {en} } @thesis{Hartl2023, author = {Hartl, Jakob Joseph Lorenz}, title = {A maturity model to determine the degree of utilization of machine learning in production planning and control processes}, publisher = {Technische Hochschule Ingolstadt}, address = {Ingolstadt}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-41482}, pages = {VIII, 104}, year = {2023}, abstract = {In the context of emerging technologies such as Machine Learning, which are increasingly shaping and taking on tasks in companies, gaining significance, this present work aims to create a maturity model that evaluates Machine Learning implementations, primarily in PPC processes but also in organizational and technical aspects. To achieve this goal, the following steps are undertaken: firstly, the topics related to Machine Learning, PPC processes, and maturity models are described. In order to define maturity levels, it is essential to analyze success factors and obstacles. Subsequently, these are categorized based on the defined dimensions and overarching design fields. Another component in this context involves examining existing work and current research trends related to Machine Learning. Ultimately, this work is intended to serve as a catalyst for addressing the research gap concerning models demanded in practice and the scholarly exploration of topics related to Machine Learning in corporate processes. The structure of the maturity model in this work is designed to be accessible for small and medium-sized enterprises (SMEs) as well.}, language = {en} }