{"id":103475,"date":"2024-02-20T13:12:24","date_gmt":"2024-02-20T12:12:24","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103475"},"modified":"2024-05-14T04:45:31","modified_gmt":"2024-05-14T02:45:31","slug":"sustainability","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/industry-4-0\/sustainability\/","title":{"rendered":"Sustainability"},"content":{"rendered":"\n\n\n
\n
\n \n
\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Digital Twins for Emission Reduction<\/h4>\n
Ex-ante case study on a pump test bench in industrial production<\/div>
Felix Bischoff<\/a>, Ingela Tietze<\/a> \n \"ORCID<\/a>, Peter Hertweck<\/a>, Nina van Hasz<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digital twins are frequently referred to as a promising approach for reducing greenhouse gas (GHG) emissions in industrial production; however, robust empirical evidence of their benefits under real-world conditions is largely lacking. In this case study, the emission reduction potential of a digital twin\u2014as a conceptually described target system\u2014is quantified ex-ante via the example of a test bench for hydraulic pumps. To this end, the GHG emissions of the original test plan for the year 2025 are determined based on actual measured energy consumption of the tested pumps and time-resolved grid electricity emission intensities. This is followed by a rule-based rescheduling, in which energy-intensive test processes are shifted to time intervals with lower emissions. The rescheduling takes operational constraints into account so that processes and equipment remain unchanged. The savings potential is determined by comparing the GHG emissions of the reference and the optimized case. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 16-24 | DOI 10.30844\/I4SE.26.3.2<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Has\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Has the Time Come for an Energy Revolution in Intralogistics?<\/h4>\n
The current status of hydrogen fuel cell-powered MHE<\/div>
Joseph W. D\u00f6rmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Hydrogen fuel cells promise to be a sustainable alternative to fossil fuel or battery-electric material handling equipment (MHE) in various production or warehouse contexts. Short refuelling times, an absence of carbon emissions, and constant power input put fuel cell-powered MHE at an advantage in high-intensity work environments. However, various barriers to the adoption of fuel cells remain, including considerations surrounding cost and efficiency. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 6 | Pages 74-80<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Loam\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Loam Construction and Wooden Shelving<\/h4>\n
A contribution to sustainability in warehouse logistics<\/div>
Viviano De Giacomo<\/a> \n \"ORCID<\/a>, Nathalie Fritsch<\/a> \n \"ORCID<\/a>, Jakob Kennert<\/a> \n \"ORCID<\/a>, Dieter Uckelmann<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 6 | Pages 82-89<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"The\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

The Bias of \u201cInstructional Systems for the Disabled\u201d<\/h4>\n
Ethnographic insights from deploying augmented reality in a sheltered workshop<\/div>
David Kostolani<\/a> \n \"ORCID<\/a>, Annemarie Ploss<\/a>, Sebastian Schlund<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The rehumanization of industrial work has emerged as a key focus in Industry 4.0 research, emphasizing the empowerment of human workers amidst advancing automation. Within this re-search, supporting workers with disabilities through digital assistance technologies serves as a prime example of a human-centric approach to industrial engineering. These technologies often claim to enhance productivity, which aims to promote the integration of workers with disabili-ties in industrial roles. But can they genuinely improve their work experience? This ethnograph-ic study presents insights from two years of developing and deploying augmented reality in a sheltered woodworking workshop. Over this period, we engaged in conversations and facilitat-ed over 30 technology sessions with workers with diverse disabilities. Our experiences chal-lenge the narrative of industrial research, in particular with digital instructional systems serving as \u201cenabler technology\u201d to help them work \u201cbetter.\u201d ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 102-110 | DOI 10.30844\/I4SE.25.5.102<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Machine\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Real-Time\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Enabling\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Open-Source\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Data\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

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
\n \n
\n \n \n \n \"Why\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Why Moving Toward a Circular Economy Is Crucial<\/h4>\n
The ten R-Strategies of sustainable management<\/div> <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>As environmental challenges such as climate change and resource scarcity intensify, with Earth Overshoot Day highlighting overconsumption, the circular economy emerges as a crucial solution. Legislation at the national and EU level obliges companies to become more sustainable. Simultaneously, the circular economy strengthens economic resilience, promotes innovation and creates competitive advantages. However, the impact on the labor market is controversial, as fewer primary resources and new products are needed. Sustainable corporate management requires a balanced consideration of the triple bottom line: Planet, People, and Profit, treating each as equally important. In contrast to the linear economy, the circular economy follows the \u2018cradle to cradle\u2019 principle and integrates the ten R-Strategies of sustainability. These strategies range from refuse (avoidance) and reduce (reduction) to recycling and repurpose (reuse). Companies should identify which strategies can be swiftly ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 2 | Pages 68-76<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
1<\/span>\n2<\/a>\n…<\/span>\n18<\/a>\n\u00bb<\/a><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":461,"featured_media":113755,"parent":103459,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-103475","page","type-page","status-publish","has-post-thumbnail","hentry"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien.webp",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-150x150.webp",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-666x375.webp",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-768x432.webp",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-1024x576.webp",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-1032x320.webp",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-764x376.webp",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-392x320.webp",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-608x496.webp",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-640x325.webp",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-274x376.webp",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-514x292.webp",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-320x440.webp",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-514x289.webp",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-196x180.webp",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien.webp",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien.webp",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-510x510.webp",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-510x287.webp",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-100x100.webp",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2026\/05\/I4S-Taxonomien-64x36.webp",64,36,true]},"uagb_author_info":{"display_name":"Nimesh Patel","author_link":"https:\/\/industry-science.com\/en\/author\/nimesh\/"},"uagb_comment_info":0,"uagb_excerpt":null,"_links":{"self":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103475","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/users\/461"}],"replies":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/comments?post=103475"}],"version-history":[{"count":1,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103475\/revisions"}],"predecessor-version":[{"id":103477,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103475\/revisions\/103477"}],"up":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/pages\/103459"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media\/113755"}],"wp:attachment":[{"href":"https:\/\/industry-science.com\/en\/wp-json\/wp\/v2\/media?parent=103475"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}