{"id":109128,"date":"2025-06-13T15:57:12","date_gmt":"2025-06-13T13:57:12","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=109128"},"modified":"2025-06-19T18:19:41","modified_gmt":"2025-06-19T16:19:41","slug":"customer-procurement-action","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/customer-procurement-action\/","title":{"rendered":"Transforming Customer Impulse into Procurement Action"},"content":{"rendered":"

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Customer demands are becoming increasingly heterogeneous and dynamic [1, 2]. To meet individual demand preferences, companies shorten product life cycles and differentiate their offerings. The retailer Zara, for instance, can bring a new item of clothing from design to sale within 15 days [2]. This development requires consistent customer focus within the company that goes beyond marketing [3]. The aim is to respond promptly to changing customer needs [4]. Accordingly, the supply management function is gaining in relevance [5, 6]. Supply management has traditionally been seen as a function focused on …<\/div>\n

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Subscription<\/th>\nincluded<\/th>\nPurchase<\/th>\n
without<\/td>\n−<\/td>\n29,00 \u20ac<\/td>\n
Digital<\/td>\n−<\/td>\n27,55 \u20ac<\/td>\n
Expert<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Digital Twin Technology and Architecture<\/h4>\n
A synthesis of concept and practice<\/div>
Arka Mukherjee<\/a> \n \"ORCID<\/a>, Shibaji Chandra<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Digital twins are a key enabling technology of the fourth industrial revolution, integrating physical systems with their digital counterparts to create intelligent, data-driven environments. This conceptual\/practice-oriented paper examines how to establish a modern architectural framework for digital twins leverages modern tech-stack like IoT, Data Fabric, AI\/ML, seamless integration and enterprise grade security. The paper is grounded in an abundance of literature by leading vendors and analysts in space. It offers a comparative study of different vendors implementing the solution stack in the proposed architecture. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 114-122<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Developing\n <\/picture>\n <\/div>\n
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Developing Virtual Reality in Learning Contexts<\/h4>\n
Navigating efficiency, content relevance and scalability<\/div>
Stella Kanatouri<\/a> \n \"ORCID<\/a>, Oliver Sosna<\/a> \n \"ORCID<\/a>, Alexander Kulik<\/a>, Sina C. Truckenbrodt<\/a> \n \"ORCID<\/a>, Friederike Klan<\/a> \n \"ORCID<\/a>, Christian Erfurth<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n While virtual reality can facilitate hands-on learning, its development faces barriers, including high costs and time demands and scalability challenges. This article presents two case studies that illustrate strategies for overcoming such barriers when training the next generation of skilled workers in environmental technologies. By examining approaches for streamlining development and increasing content relevance and scalability, we highlight lessons learned for future practice. We conclude by envisioning a future in which educational institutions can flexibly and cost-effectively prototype virtual reality in learning contexts, ensuring alignment with curricular goals and learners\u2019 needs. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 3 | Pages 26-34 | DOI 10.30844\/I4SE.26.3.3<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Immersive\n <\/picture>\n <\/div>\n
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Immersive Human Digital Twins for Industry 4.0<\/h4>\n
Supporting adaptive human-centric production by integrating cognitive and physical states<\/div>
Tajbeed A. Chowdhury<\/a> \n \"ORCID<\/a>, Eric Wagner<\/a> \n \"ORCID<\/a>, Paul Motzki<\/a> \n \"ORCID<\/a>, Martina Lehser<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The rapid advancement of immersive technologies has created new opportunities to transform human-machine collaboration in industry. This paper presents an immersive platform with a digital twin that combines both physical and cognitive characteristics of human dynamics. By integrating multimodal sensing, human biomechanics, and cognitive state into digital twin technology, the proposed system enhances operational safety and ensures better ergonomics. The main argument is that human digital twins are not only desirable but essential for next-generation industrial systems. We discuss the limitations of existing human modeling approaches, outline the conceptual foundations of human digital twins, and demonstrate their industrial relevance across safety, productivity, ergonomics and sustainability. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 6-13 | DOI 10.30844\/I4SE.26.3.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Digital\n <\/picture>\n <\/div>\n
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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 \"Experiencing\n <\/picture>\n <\/div>\n
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\t\t

Experiencing Digital Twins in Production and Logistics<\/h4>\n
The fischertechnik\u00ae Learning Factory 4.0 as a development platform for possible expansion stages<\/div>
Deike Gliem<\/a> \n \"ORCID<\/a>, Sigrid Wenzel<\/a> \n \"ORCID<\/a>, Jan Schickram<\/a>, Tareq Albeesh<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The fischertechnik\u00ae Learning Factory 4.0 has proven to be a suitable experimental environment for testing digital twins. Depending on the targeted maturity stage, the functions of a digital twin range from status monitoring and forecasting to the operational control of production and logistics systems. To systematically classify these functions, this article presents a maturity model that serves as a framework for the development of a digital twin. Building on this, selected use cases are implemented in a test and development environment based on a system architecture with multi-layered logic structure. These initial implementations serve to highlight application purposes, relevant methods, and typical challenges and potentials in the transfer to real factory environments. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | Edition 2 | Pages 30-37 | DOI 10.30844\/I4SE.26.2.30<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Enabler\n <\/picture>\n <\/div>\n
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Enabler for the Digital Twin<\/h4>\n
Requirements for Technical Documentation 4.0<\/div>
Christian Koch<\/a>, Lukas Schulte<\/a>, Ren\u00e9 W\u00f6stmann<\/a>, Jochen Deuse<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 4 | Pages 76-85<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Supply management provides an organization with the resources that it needs but does not produce itself. However, intraorganizational needs are not isolated. They ultimately serve to fulfill the demands of external (end) customers. Traditionally, supply management receives information from its internal customers, i.e. from other functional areas such as production planning, logistics, or marketing. Information on (end) customer demands reaches supply management, if at all, indirectly via these other functional areas, which often pass on information after interpreting it. This article discusses how digital twins of (end) customer demands can provide all functional areas with precise, near-real-time data.<\/p>\n","protected":false},"featured_media":108942,"menu_order":0,"template":"","categories":[79167,79298],"tags":[79631,79449],"product_cat":[79304],"topic":[67838],"technology":[79334],"knowhow":[],"industry":[],"writer":[82828,81388],"content-type":[83932],"potential":[],"solution":[],"glossary":[],"class_list":["post-109128","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","tag-digitaler-zwilling-en","tag-digitalisierung-en","product_cat-articles","topic-digital-twin","technology-business-model-en","writer-andreas-h-glas-en","writer-michael-essig-en","content-type-article","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2025\/06\/Oehlschlaeger_AdobeStock_1414992948-64x36.jpeg",64,36,true]},"uagb_author_info":{"display_name":"Florian Goldmann","author_link":"https:\/\/industry-science.com\/en\/author\/"},"uagb_comment_info":0,"uagb_excerpt":"Supply management provides an organization with the resources that it needs but does not produce itself. However, intraorganizational needs are not isolated. They ultimately serve to fulfill the demands of external (end) customers. Traditionally, supply management receives information from its internal customers, i.e. from other functional areas such as production planning, logistics, or marketing. 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