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    <title language="eng">Implications from Legacy Device Environments on the Conceptional Design of Machine Learning Models in Manufacturing</title>
    <parentTitle language="eng">Journal of Manufacturing and Materials Processing</parentTitle>
    <identifier type="url">https://doi.org/10.3390/jmmp8010015</identifier>
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    <author>Bastian Engelmann</author>
    <author>Anna-Maria Schmitt</author>
    <author>Lukas Theilacker</author>
    <author>Jan Schmitt</author>
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    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
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    <title language="eng">Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data</title>
    <parentTitle language="eng">Applied Artificial Intelligence</parentTitle>
    <identifier type="url">https://doi.org/10.1080/08839514.2024.2381317</identifier>
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    <author>Bastian Engelmann</author>
    <author>Anna-Maria Schmitt</author>
    <author>Moritz Heusinger</author>
    <author>Vladyslav Borysenko</author>
    <author>Niklas Niedner</author>
    <author>Jan Schmitt</author>
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    <file>https://opus4.kobv.de/opus4-fhws/files/5708/Engelmann_Changeover_Events_Manufacturing.pdf</file>
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  <doc>
    <id>5609</id>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <issue>8</issue>
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    <title language="eng">G-code evaluation in CNC milling to predict energy consumption through Machine Learning</title>
    <abstract language="eng">Computerized Numeric Control (CNC) plays an essential role in highly autonomous manufacturing systems for interlinked process chains for machine tools. NC-programs are mostly written in standardized G-code. Evaluating CNC-controlled manufacturing processes before their real application is advantageous due to resource efficiency. One dimension is the estimation of the energy demand of a part manufactured by an NC-program, e.g. to discover optimization potentials. In this context, this paper presents a Machine Learning (ML) approach to assess G-code for CNC-milling processes from the perspective of the energy demand of basic G-commands. We propose Latin Hypercube Sampling as an efficient method of Design of Experiments to train the ML model with minimum experimental effort to avoid costly setup and implementation time of the model training and deployment.</abstract>
    <parentTitle language="eng">Advances in Industrial and Manufacturing Engineering</parentTitle>
    <identifier type="url">https://doi.org/10.1016/j.aime.2024.100140</identifier>
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    <licence>Creative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International</licence>
    <author>Anna-Maria Schmitt</author>
    <author>Eddi Miller</author>
    <author>Bastian Engelmann</author>
    <author>Rafael Batres</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>CNC machine tools</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>G-code</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Energy consumption</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-fhws/files/5609/Schmitt_G-code_evaluation_CNC_milling.pdf</file>
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    <title language="eng">Classification of Production Process Phases with Multivariate Time Series Techniques</title>
    <parentTitle language="deu">2024 22nd International Conference on Research and Education in Mechatronics (REM)</parentTitle>
    <identifier type="doi">10.1109/REM63063.2024.10735481</identifier>
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    <author>Anna-Maria Schmitt</author>
    <author>Anna Antonov</author>
    <author>Jan Schmitt</author>
    <author>Bastian Engelmann</author>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
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  <doc>
    <id>5595</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <edition/>
    <issue/>
    <volume>15</volume>
    <type>article</type>
    <publisherName>Frontiers Media SA</publisherName>
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    <title language="eng">Self-employed and stressed out? The impact of stress and stress management on entrepreneurs’ mental health and performance</title>
    <abstract language="eng">Introduction:&#13;
&#13;
Entrepreneurs play a central role in economic and social stability, yet the start-up rate in Germany has declined in recent years, possibly due to the stress associated with entrepreneurial endeavors. Stressors such as financial uncertainty and time pressure are prevalent among entrepreneurs and negatively affect their psychological well-being. However, research on stress management strategies among self-employed individuals remains limited.&#13;
&#13;
Methods:&#13;
This pilot study conducted a quantitative analysis with 117 self-employed participants in Germany. The study focused on typical entrepreneurial work demands and selected stress coping mechanisms.&#13;
&#13;
Results:&#13;
The analysis revealed a significant correlation between quantitative demands and mental exhaustion. Furthermore, a high positive correlation between presenteeism and workload suggests that presenteeism may partially explain the variance in workload. These findings underscore how high job demands can lead to self-endangering behaviors that are detrimental to mental health.&#13;
&#13;
Discussion:&#13;
Although no significant moderating effect of proactive coping on the relationship between job demands and mental exhaustion was observed, significant negative correlations between proactive coping and both job demands and mental exhaustion suggest a potential protective role of proactive coping against work-related stress. This study highlights the importance of understanding stress coping strategies among self-employed individuals and their impact on entrepreneurial success and mental well-being. Further research in this area is warranted to develop effective interventions to support the well-being and productivity of self-employed individuals in Germany.</abstract>
    <parentTitle language="eng">Frontiers in Psychology</parentTitle>
    <identifier type="issn">1664-1078</identifier>
    <identifier type="doi">https://doi.org/10.3389/fpsyg.2024.1365489</identifier>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-55955</identifier>
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Stressors such as financial uncertainty and time pressure are prevalent among entrepreneurs and negatively affect their psychological well-being. However, research on stress management strategies among self-employed individuals remains limited.&lt;\/jats:p&gt;&lt;\/jats:sec&gt;&lt;jats:sec&gt;&lt;jats:title&gt;Methods&lt;\/jats:title&gt;&lt;jats:p&gt;This pilot study conducted a quantitative analysis with 117 self-employed participants in Germany. The study focused on typical entrepreneurial work demands and selected stress coping mechanisms.&lt;\/jats:p&gt;&lt;\/jats:sec&gt;&lt;jats:sec&gt;&lt;jats:title&gt;Results&lt;\/jats:title&gt;&lt;jats:p&gt;The analysis revealed a significant correlation between quantitative demands and mental exhaustion. Furthermore, a high positive correlation between presenteeism and workload suggests that presenteeism may partially explain the variance in workload. 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    <author>Sophia Kiefl</author>
    <author>Sophie Fischer</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>eng</language>
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      <value>General Psychology</value>
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    <title language="eng">Self-employed and stressed out? The impact of stress and stress management on entrepreneurs’ mental health and performance</title>
    <parentTitle language="eng">Frontiers in Psychology</parentTitle>
    <identifier type="doi">10.3389/fpsyg.2024.1365489</identifier>
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    <author>Sophia Kiefl</author>
    <author>Sophie Fischer</author>
    <author>Jan Schmitt</author>
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    <title language="eng">Reentrant Flow Shop Production Control with the Theory of Constraints for Wafer Fabrication Having Parallel Machines</title>
    <abstract language="eng">Purpose of this paper:&#13;
In a production line with capacity constraint resources (CCR) or bottleneck stations, the&#13;
production control and the throughput rate are determined by the CCR station. In this&#13;
study, we consider a production system with parallel machines and there are many rounds&#13;
of reentrants during the production processes, and can be regarded as a reentrant flow&#13;
shop process (RFS). In most RFS systems, the release of jobs is a push type without&#13;
considering the WIP in each layer of reentrance. To resolve the excessive WIP in an RFS&#13;
system, we consider the theory of constraints (TOC) and implement the DBR (drum,&#13;
bottleneck and rope) to model RFS scheduling and to define appropriate buffer sizes at the&#13;
CCR stations of diverse reentrant layers.&#13;
Design/methodology/approach STS:&#13;
We obtain production data of a type of semiconductor chip where the photolithography&#13;
process is the most frequent reentrant. We designed nine scenarios based on the current&#13;
RFS of the case company by applying the TOC and considering the time differences in&#13;
parallel machines. A simulation model for the photolithography process was established&#13;
using AutoMod simulation. Thirty simulation runs were performed for the mean and&#13;
standard deviation. The goal is to identify the best scenario among the nine proposed ones.&#13;
Findings:&#13;
The simulation results demonstrated that Scenario 9, which involves dynamic buffers and&#13;
assigning higher priority to products with more processing reentrance layers, achieved the&#13;
highest throughput of finished products. The throughput of finished products in Scenario 9&#13;
is 22% higher than in the base scenario. It also shows improvements in maximum product&#13;
stack-up and input-output ratio compared to the base scenario.&#13;
Value:&#13;
The paper studies dispatching rules for reentrant flow lines having parallel machines with&#13;
data from the semiconductor industry, and this type of production system is common for&#13;
producing a wide range of electronic devices. With limited buffer sizes on the shop floor,&#13;
the number of WIPs is significant, especially for reentrant flow shops, to avoid being&#13;
clogged with too many WIPs. Implementing DBR modeling from TOC, the nine scenarios&#13;
demonstrate the effects of controlling buffer sizes on system performances.&#13;
Research limitations/implications:&#13;
The nine scenarios are designed based on the case company situations and may not be&#13;
readily implemented in other industries. Moreover, the DBR modeling of the buffer sizes&#13;
and CCR station schedule control should also be adjusted when employed in other&#13;
applications.</abstract>
    <parentTitle language="eng">Proceedings of the 28th International Symposium on Logistics (ISL 2024) Building Sustainable Connectivity through Logistics and Supply Chains</parentTitle>
    <identifier type="isbn">978-0-85358-354-7</identifier>
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    <author>Mei-Hui Chen</author>
    <author>Peik Bremer</author>
    <author>Jan Schmitt</author>
    <author>Kune-muh Tsai</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Flow Shop</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Production Control</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Theory of Constraints</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Semiconductor</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <thesisPublisher>Technische Hochschule Würzburg-Schweinfurt</thesisPublisher>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>18</issue>
    <volume>24</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
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    <title language="eng">Analysis of Factors Influencing the Precision of Body Tracking Outcomes in Industrial Gesture Control</title>
    <abstract language="eng">The body tracking systems on the current market offer a wide range of options for tracking the movements of objects, people, or extremities. The precision of this technology is often limited and determines its field of application. This work aimed to identify relevant technical and environmental factors that influence the performance of body tracking in industrial environments. The influence of light intensity, range of motion, speed of movement and direction of hand movement was analyzed individually and in combination. The hand movement of a test person was recorded with an Azure Kinect at a distance of 1.3 m. The joints in the center of the hand showed the highest accuracy compared to other joints. The best results were achieved at a luminous intensity of 500 lx, and movements in the x-axis direction were more precise than in the other directions. The greatest inaccuracy was found in the z-axis direction. A larger range of motion resulted in higher inaccuracy, with the lowest data scatter at a 100 mm range of motion. No significant difference was found at hand velocity of 370 mm/s, 670 mm/s and 1140 mm/s. This study emphasizes the potential of RGB-D camera technology for gesture control of industrial robots in industrial environments to increase efficiency and ease of use.</abstract>
    <parentTitle language="eng">sensors</parentTitle>
    <identifier type="doi">10.3390/s24185919</identifier>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-57575</identifier>
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    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Aleksej Weber</author>
    <author>Markus Wilhelm</author>
    <author>Jan Schmitt</author>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <file>https://opus4.kobv.de/opus4-fhws/files/5757/Wilhelm_Analysis_Factors_Body_Tracking.pdf</file>
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  <doc>
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    <publishedYear>2024</publishedYear>
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    <language>eng</language>
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    <type>report</type>
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    <title language="eng">Sustainability at HEIs: Mapping Good Practice</title>
    <abstract language="eng">Global climate change is a cognitive challenge for many people and often evokes negative associations due to its complexity and interactions with politics, social movements and economic developments. Therefore, the possession of green skills becomes central to the fight against climate change. The European Council conclusions recognize this urgency and underline the need for a transition to green skills. This recognition also extends to higher education, where institutions have a crucial role to play in tackling the climate crisis. Personal Green Skills in Higher Education (PeGSinHE) is an Erasmus+ KA2 project coordinated by Kauno Kolegija (KK, Lithuania), Tampere University of Applied Sciences (TAMK, Finland), Hochschule für Agrar- und Umweltpädagogik (HAUP, Austria), Universidad de Málaga (UMA, Spain) and Technical University of Applied Sciences Würzburg Schweinfurt (THWS, Germany). The strategically designed project aims not only to promote green skills among students and encourage personal behavioral change in line with the Sustainable Development Goals, but also to instill a sense of social responsibility in the partner institutions. The focus is on empowering lecturers at partner universities through innovative teaching and learning methods to effectively impart green skills to students. This report describes the objectives and methodology used to assess environmental and sustainability competencies in the higher education institutions involved in the project. Methodologically, the report uses an assessment template designed to provide a comprehensive overview of best practice and baseline levels of environmental and sustainability competencies. It advocates the involvement of key stakeholders from all five partner Higher Education Institutions to ensure a broad perspective on these practices and competences within their respective countries and organizations. Different methods and perspectives will be used to collect data to enable a holistic understanding of the topic. The joint completion of the assessment template serves as a catalyst for joint discussions on the level of environmental and sustainability competencies and the identification of best practices in each organization. The results show that national implementation strategies are relatively loose, although some competency descriptions set targets for undergraduate degree programs. Challenges faced by higher education staff include resource constraints, particularly lack of time, the need for a deeper understanding of sustainable development and pedagogical tools, and the need for improved opportunities for collaboration. Given the time and resource constraints of this study, the results must be considered preliminary. Nevertheless, they confirm the findings of previous studies.</abstract>
    <identifier type="doi">10.57714/b75p-n548</identifier>
    <note>Es wurde nachträglich am 28.03.2025 ein Logo in der Datei ausgetauscht, inhaltlich gab es jedoch keine Änderungen.</note>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Eveliina Asikainen</author>
    <author>Nina Eskola-Salin</author>
    <author>Sophie Fischer</author>
    <author>Vytautė Giedraitiené</author>
    <author>Povilas Beseckas</author>
    <author>Stephanie Mairhofer</author>
    <author>Lea Köder</author>
    <author>Jan Schmitt</author>
    <author>Holger Walter</author>
    <author>Olga Guerrero-Perez</author>
    <author>Elidia Beatriz Blázquez-Parra</author>
    <author>Ina Bikuviené</author>
    <author>Neringa Lisina</author>
    <author>Rasa Tamuliené</author>
    <author>Alina Liepinaitiené</author>
    <author>Anne-Maria Mäkelä</author>
    <author>Sanna Tahlo</author>
    <author>Hanna Selimaa</author>
    <author>Veronika Hager</author>
    <author>Joaquin Ortega-Casanova</author>
    <author>Patricia Mora-Segado</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sustainability</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>best practice</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>HEI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>green skills</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <file>https://opus4.kobv.de/opus4-fhws/files/5636/PEGSinHE_Report.pdf</file>
  </doc>
  <doc>
    <id>5603</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>14</volume>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
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    <belongsToBibliography>1</belongsToBibliography>
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    <title language="eng">Advancing Efficiency in Mineral Construction Materials Recycling: A Comprehensive Approach Integrating Machine Learning and X-ray Diffraction Analysis</title>
    <abstract language="eng">In the context of environmental protection, the construction industry plays a key role with significant CO2 emissions from mineral-based construction materials. Recycling these materials is crucial, but the presence of hazardous substances, i.e., in older building materials, complicates this effort. To be able to legally introduce substances into a circular economy, reliable predictions within minimal possible time are necessary. This work introduces a machine learning approach for detecting trace quantities (≥0.06 wt%) of minerals, exemplified by siderite in calcium carbonate mixtures. The model, trained on 1680 X-ray powder diffraction datasets, provides dependable and fast predictions, eliminating the need for specialized expertise. While limitations exist in transferability to other mineral traces, the approach offers automation without expertise and a potential for real-world applications with minimal prediction time.</abstract>
    <parentTitle language="eng">buildings</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:863-opus-56030</identifier>
    <identifier type="doi">10.3390/buildings14020340</identifier>
    <identifier type="issn">2075-5309</identifier>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Markus Wilhelm</author>
    <author>Frank Lotter</author>
    <author>Christian Scherdel</author>
    <author>Jan Schmitt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Architecture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Building and Construction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Civil and Structural Engineering</value>
    </subject>
    <collection role="institutes" number="fwi">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="idee">Institut Digital Engineering (IDEE)</collection>
    <collection role="oa-colour" number="">Gefördert (Gold)</collection>
    <file>https://opus4.kobv.de/opus4-fhws/files/5603/Wilhelm_Advancing_Efficiency_Mineral.pdf</file>
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    <id>5629</id>
    <completedYear>2024</completedYear>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
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    <volume>13</volume>
    <type>article</type>
    <publisherName>Elsevier BV</publisherName>
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    <title language="eng">A simplified machine learning product carbon footprint evaluation tool</title>
    <abstract language="eng">On the way to climate neutrality manufacturing companies need to assess the Carbon dioxide (CO2) emissions of their products as a basis for emission reduction measures. The evaluate this so-called Product Carbon Footprint (PCF) life cycle analysis as a comprehensive method is applicable, but means great effort and requires interdisciplinary knowledge. Nevertheless, assumptions must still be made to assess the entire supply chain. To lower these burdens and provide a digital tool to estimate the PCF with less input parameter and data, we make use of machine learning techniques and develop an editorial framework called MINDFUL. This contribution shows its realization by providing the software architecture, underlying CO2 factors, calculations and Machine Learning approach as well as the principles of its user experience. Our tool is validated within an industrial case study.</abstract>
    <parentTitle language="eng">Cleaner Environmental Systems</parentTitle>
    <identifier type="issn">2666-7894</identifier>
    <identifier type="doi">10.1016/j.cesys.2024.100187</identifier>
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