{"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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\n\n\n\n<\/thead>\n\n\n\n\n\n\n\n\n<\/tbody>\n<\/table>\n

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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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 \"Digital\n <\/picture>\n <\/div>\n
\n \t \n
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Digital Competence Lab (DCL) for Speech Therapy<\/h4>\n
Designing a learning platform to advance digital skills<\/div>
Anika Thurmann<\/a> \n \"ORCID<\/a>, Antonia Weirich<\/a> \n \"ORCID<\/a>, Kerstin Bilda<\/a>, Fiona D\u00f6rr<\/a> \n \"ORCID<\/a>, Lars T\u00f6nges<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n The digital transformation of healthcare results in lasting changes in speech therapy. Smart technologies and artificial intelligence (AI) are creating new opportunities to ensure therapy quality, address care bottlenecks, and actively involve patients in exercise processes. At the same time, these developments are expanding the role of speech therapists, who increasingly use digital systems as supportive tools in addition to their core therapeutic tasks. Based on a feasibility study of the AI-supported application ISi-Speech-Sprechen in a real-world setting of complex Parkinson's therapy (PKT), this article outlines the key challenges associated with implementing smart technologies. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 110-118 | DOI 10.30844\/I4SE.26.1.102<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"AI\n <\/picture>\n <\/div>\n
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AI Implementation in Industrial Quality Control<\/h4>\n
A design science approach bridging technical and human factors<\/div>
Erdi \u00dcnal<\/a> \n \"ORCID<\/a>, Kathrin Nauth<\/a> \n \"ORCID<\/a>, Pavlos Rath-Manakidis<\/a>, Jens P\u00f6ppelbu\u00df<\/a> \n \"ORCID<\/a>, Felix Hoenig<\/a>, Christian Meske<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) offers significant potential to enhance industrial quality control, yet successful implementation requires careful consideration of ethical and human factors. This article examines how automated surface inspection systems can be deployed to augment human capabilities while ensuring ethical integration into workflows. Through design science research, twelve stakeholders from six organizations across three continents are interviewed and twelve sociotechnical design requirements are derived. These are organized into pre-implementation and implementation\/operation phases, addressing human agency, employee participation, and responsible knowledge management. Key findings include the critical importance of meaningful employee participation during pre-implementation, and maintaining human agency through experiential learning, building on existing expertise. This research contributes to ethical AI workplace implementation by providing guidelines that preserve human ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 120-127 | DOI 10.30844\/I4SE.26.1.112<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"XAI\n <\/picture>\n <\/div>\n
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\t\t

XAI for Predicting and Nudging Worker Decision-Making<\/h4>\n
Feasibility and perceived ethical issues<\/div>
Jan-Phillip Herrmann<\/a> \n \"ORCID<\/a>, Catharina Baier<\/a>, Sven Tackenberg<\/a> \n \"ORCID<\/a>, Verena Nitsch<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Explainable artificial intelligence (XAI)-based nudging, while ethically complex, may offer a favorable alternative to rigid, algorithmically generated schedules that simultaneously respects worker autonomy and improves overall scheduling performance on the shop floor. This paper presents a controlled laboratory study demonstrating the successful nudging of 28 industrial engineering students in a job shop simulation. The study shows that the observed concordance between students\u2019 sequencing decisions and a predefined target sequence increases by 9% through nudging. This is done by using XAI to analyze students\u2019 preferences and adjusting task deadlines and priorities in the simulation. The paper discusses the ethical issues of nudging, including potential manipulation, illusory autonomy, and reducing people to numbers. To mitigate these issues, it offers recommendations for implementing the XAI-based nudging approach in practice and highlights its strengths relative to rigid, ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 70-78<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Improving\n <\/picture>\n <\/div>\n
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\t\t

Improving Documentation Quality and Creating Time for Core Activities<\/h4>\n
Success factors for implementing AI-based documentation systems in nursing care<\/div>
Sophie Berretta<\/a> \n \"ORCID<\/a>, Elisabeth Liedmann<\/a> \n \"ORCID<\/a>, Paul-Fiete Kramer<\/a> \n \"ORCID<\/a>, Anja Gerlmaier<\/a>, Christopher Schmidt<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Demographic change is accompanied by both a growing demand for care and a shortage of qualified nursing staff. Consequently, AI-based technologies are increasingly becoming a focus of care-related innovations. Their aim is to reduce workload pressure, save time, and enhance the attractiveness of the nursing profession. Using the example of AI-supported documentation systems for admission interviews, this article examines to what extent such systems can contribute to improvements in work processes and care quality, focusing on the perspectives of nursing professionals and nursing experts. The results indicate potential for workload relief, enhanced documentation quality, and the reallocation of time resources toward direct patient care. However, realizing these potentials requires a human-centered and context-sensitive implementation approach. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 154-160 | DOI 10.30844\/I4SE.26.1.146<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Applied\n <\/picture>\n <\/div>\n
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Applied AI for Human-Centric Assembly Workplace Design<\/h4>\n
An ethics-informed approach<\/div>
Tadele Belay Tuli<\/a> \n \"ORCID<\/a>, Michael Jonek<\/a> \n \"ORCID<\/a>, Sascha Niethammer<\/a>, Henning Vogler<\/a>, Martin Manns<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection\u00ae. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 1 | Pages 60-68 | DOI 10.30844\/I4SE.26.1.58<\/a><\/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":[79167,79168,79298],"tags":[70296,84147,80100,79631,80242,79449,84146,84148,68753,83930],"product_cat":[3300],"topic":[67838,79371,79333],"technology":[79334],"knowhow":[],"industry":[],"writer":[82828,81388],"content-type":[83932],"potential":[69462],"solution":[67610,78673],"glossary":[],"class_list":{"0":"post-109128","1":"article","2":"type-article","3":"status-publish","4":"has-post-thumbnail","6":"category-design-en","7":"category-translate-en","8":"category-typeset","9":"tag-customer-integration","10":"tag-demand-management","11":"tag-digital-twin-en","12":"tag-digitaler-zwilling-en","13":"tag-digitalisation-en","14":"tag-digitalisierung-en","15":"tag-purchasing-and-supply-management","16":"tag-responsiveness","17":"tag-supply-chains","18":"tag-transparency-en","19":"product_cat-article","20":"topic-digital-twin","21":"topic-logistics","22":"topic-process-optimization","23":"technology-business-model-en","24":"writer-andreas-h-glas-en","25":"writer-michael-essig-en","26":"content-type-article","27":"potential-resource-efficiency","28":"solution-logistik-en","29":"solution-logistics-technology","30":"product","31":"first","32":"instock","33":"downloadable","34":"virtual","35":"sold-individually","36":"taxable","37":"purchasable","38":"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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