{"id":103503,"date":"2024-02-20T14:32:57","date_gmt":"2024-02-20T13:32:57","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103503"},"modified":"2024-05-14T05:00:31","modified_gmt":"2024-05-14T03:00:31","slug":"resource-efficiency","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/management\/resource-efficiency\/","title":{"rendered":"Resource Efficiency"},"content":{"rendered":"\n\n\n
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\n \n
\n \n \n \n \"Electric\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Electric Trucks in Intermodal Terminal Pre- and Post-Carriage<\/h4>\n
Impact on terminal processes in combined road-rail freight transport<\/div>
Ralf Elbert<\/a> \n \"ORCID<\/a>, Samira Ghaneian Sebdani<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Electric trucks (e-trucks) play an important role in reducing CO\u2082 emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 70-77<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Customized\n <\/picture>\n <\/div>\n
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Customized Organs from Space<\/h4>\n
How weightlessness could change human lives<\/div> <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>\nDue to its weightlessness, space offers enormous opportunities for production. The unique conditions of microgravity, for example, can simplify the development of organs and tissues from the body's own stem cells, allowing therapies to be developed in a more targeted manner. Even though many independent initiatives are currently emerging to explore this and other potential applications, their success is not a foregone conclusion. <\/div>\n <\/div>\n <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Machine\n <\/picture>\n <\/div>\n
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Machine Learning to Promote Sustainability\u00a0<\/h4>\n
Company analysis based on expert interviews<\/div>
Matthias Weigold<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 44-51<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Real-Time\n <\/picture>\n <\/div>\n
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Real-Time Monitoring of the Carbon Footprint for SMEs<\/h4>\n
Sustainability in real time \u2014 from operation to finished products<\/div>
Henning Strau\u00df<\/a> \n \"ORCID<\/a>, Julian Sasse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO\u2082 footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 102-109<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Enabling\n <\/picture>\n <\/div>\n
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Enabling the Future of Manufacturing with Digital Twins<\/h4>\n
Opportunities and obstacles<\/div>
Javad Ghofrani<\/a> \n \"ORCID<\/a>, Darian Lemke<\/a>, Tassilo S\u00f6ldner<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 72-81<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Open-Source\n <\/picture>\n <\/div>\n
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Open-Source and Cost-Effective Digital Twin<\/h4>\n
A case study with two weeks to succeed<\/div>
Shantall Cisneros Saldana<\/a> \n \"ORCID<\/a>, Sonali Pratap<\/a>, Parth Punekar<\/a>, Sampat Acharya<\/a>, Heike Markus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digital Twin (DT) adoption remains a challenge due to high costs, complexity and lack of skills. This study proposes a cost-effective, TRL 5-validated DT model that can be built using open-source and office suite tools within just two weeks. Integrating real-time sensor data, predictive analytics, anomaly detection and notification, the model improves efficiency and sustainability in agriculture. Even with cloud service constraints, the system delivers a 7.76% average relative error and rapid, automated notifications. The findings show how open-source in combination with common commercial tools technologies can make advanced digital tools accessible to all, creating scalable, human-centered, and affordable solutions in line with Industry 5.0 principles. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 3 | Pages 62-68 | DOI 10.30844\/I4SE.25.3.62<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Data\n <\/picture>\n <\/div>\n
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Data Quality in the Engineering of Circular Products<\/h4>\n
Decision support for circular value creation through data ecosystems<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Sven Rarbach<\/a>, Jens Pottebaum<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Decisions affecting the sustainability of products are made during the engineering process. As product engineering progresses, statements on sustainability can also be substantiated. Initially, only estimates based on related products and processes are possible, but later, operational and machine data can be used. When metrics are used for key figures, the traceability of the data should be ensured. For this purpose, relevant data quality criteria and indicators are selected and analyzed for correlations. Data availability can be increased by relying on partners within data ecosystems for product engineering. Data spaces such as Gaia-X, Catena-X and Manufacturing-X form a basis for this ambition. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 12-19 | DOI 10.30844\/I4SE.25.2.12<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Intelligent\n <\/picture>\n <\/div>\n
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Intelligent Load Carrier Management<\/h4>\n
AI-supported monitoring and reduction of losses in logistics<\/div>
Dominik Augenstein<\/a>, Lea Basler<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their \u2018simplicity\u2019, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution\u2014without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 78-84<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Circular\n <\/picture>\n <\/div>\n
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Circular Economy as a Holistic Strategy<\/h4>\n
Complexity management and sustainability<\/div>
Joseph W. D\u00f6rmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Over the past decades, circular economy has established itself as an important strategy for tackling sustainability challenges. Its holistic approach aims to use resources efficiently and minimize waste. This article aims to identify and evaluate the numerous challenges connected to the successful implementation and expansion of the circular economy approach. Economic, technological, social and political aspects are examined to provide a comprehensive insight into the complexity of the strategy and its implementation. The article concludes that a successful circular economy can only be achieved through the coordinated cooperation of different stakeholders and the development of innovative solutions to the identified challenges. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 1 | Pages 60-67<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Hybrid\n <\/picture>\n <\/div>\n
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\t\t

Hybrid Decision Support in Product Creation<\/h4>\n
Improving performance with data science and artificial intelligence<\/div>
Iris Gr\u00e4\u00dfler<\/a> \n \"ORCID<\/a>, Jens Pottebaum<\/a> \n \"ORCID<\/a>, Peter Nyhuis<\/a> \n \"ORCID<\/a>, Rainer Stark<\/a> \n \"ORCID<\/a>, Klaus-Dieter Thoben<\/a> \n \"ORCID<\/a>, Petra Wiederkehr<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of \"hybrid decision support\" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 1 | Pages 18-25 | DOI 10.30844\/I4SE.25.1.18<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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