{"id":103317,"date":"2024-04-15T12:00:00","date_gmt":"2024-04-15T10:00:00","guid":{"rendered":"https:\/\/industry-science.com\/?post_type=article&p=103317"},"modified":"2025-02-04T16:58:47","modified_gmt":"2025-02-04T15:58:47","slug":"motion-mining-compared-to-traditional-lean-tools","status":"publish","type":"article","link":"https:\/\/industry-science.com\/en\/articles\/motion-mining-compared-to-traditional-lean-tools\/","title":{"rendered":"Motion-Mining Compared to Traditional Lean Tools"},"content":{"rendered":"

<\/p>\n

Motion-Mining\u00ae is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining\u00ae is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case.<\/p>\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−<\/td>\n26,10 \u20ac<\/td>\n
Professional<\/td>\n✓<\/span><\/td>\n0,00 \u20ac<\/td>\n
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Cooperation Routines of Complementors in Digital Ecosystems<\/h4>\n
A microfoundation of integrative dynamic capability<\/div>
Christian Zabel<\/a> \n \"ORCID<\/a>, Tahir Schmidt<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Complementors are central to value creation in digital ecosystems yet have limited leverage and must adapt through dynamic capabilities. Building on the Profiting From Innovation Framework, this study examines how integrative capabilities manifest for complementors through cooperative routines. Based on a systematic literature review of Scopus-indexed studies from 2020 to mid-2025 focusing on the microfoundation \u201corchestrating ecosystem actors\u201d, we identify two routine clusters. Complementors cooperate with other complementors via partner sensing, scouting, coalitions, resource sharing, and risk allocation while protecting critical assets. They cooperate with platform owners via multichannel boundary spanning, quality signaling, governance compliance, boundary resource integration, and co-development, while facing the risk of owner entry. Research gaps concern the formalization of cooperation routines, taxonomy, and B2B contexts. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 22-28 | DOI 10.30844\/I4SE.26.4.3<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Data-Driven\n <\/picture>\n <\/div>\n
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\t\t

Data-Driven Supplier Selection in Industry 4.0<\/h4>\n
Towards an industrial platform for selection and configuration<\/div>
Purushothaman Ganesh<\/a> \n \"ORCID<\/a>, Baris E. Albayrak<\/a> \n \"ORCID<\/a>, J\u00f6rg Franke<\/a> \n \"ORCID<\/a>, Till Sindel<\/a> \n \"ORCID<\/a>, Jens F\u00fcrst<\/a>, Sebastian Lang<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844\/I4SE.26.4.6<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Open\n <\/picture>\n <\/div>\n
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Open Source as Enabler for Smart Data Ecosystems<\/h4>\n
How collaboration shapes sovereignty and interoperability across industrial applications<\/div>
Anna Maria Schleimer<\/a> \n \"ORCID<\/a>, Julia Pampus<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n From automotive supply chains to smart factories, data ecosystems promise seamless collaboration across company boundaries. But how can industries build the necessary infrastructure without creating new dependencies? This article explores how open-source software can serve as a foundation and impactful tool for sovereign, interoperable technologies and standards. Yet, open-source software is not a silver bullet; rather, it poses challenges for digital sovereignty in burgeoning data ecosystems. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 6-13 | DOI 10.30844\/I4SE.26.4.1<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"From\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

From Private Law to Private Ordering<\/h4>\n
Rule-making through industrial digital platforms<\/div>
Pia C. Romeike<\/a>, Frederik B\u00e4r<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Industrial digital platforms are considered the driving force behind digital value creation, yet legally they largely operate in a gray area. This article examines what defines industrial digital platforms and what legal framework applies to them. Existing platform regulations apply only to a limited extent to industrial digital platforms, which is why these platforms often establish their own legal framework through their terms and conditions. The article also explores the implications of European regulation, power asymmetries, and the opportunities and limitations of private regulation. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 98-105 | DOI 10.30844\/I4SD.26.4.11<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Classification\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Classification of Digital Supply Chain Management Platforms<\/h4>\n
Review of existing classification approaches from a circular economy perspective<\/div>
Sophia Botsch<\/a> \n \"ORCID<\/a>, Eva Mante<\/a> \n \"ORCID<\/a>, Marcel Papert<\/a> \n \"ORCID<\/a>, Alexander Pflaum<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Assessing the impact of digital industrial platforms on the dynamics and resilience of supply chains requires clear classification of such platforms. This article examines the extent to which a current classification proposal from the field of supply chain management must be further developed in the context of digital platforms for implementing the circular economy (CE). The authors conclude that a fundamental revision is not necessary, as the digital CE platforms under consideration fit well into the existing classification system. However, new research questions arise regarding the distinction between digital service platforms and digital data-oriented platforms, as well as the link between the circular economy and supply chain management\u2014particularly in connection with supply chain control towers, which are becoming increasingly established in supply chain management practice. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 4 | Pages 62-70 | DOI 10.30844\/I4SE.26.4.7<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\n \n \n \n \"Industry\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Industry 4.0\u2014Progress and Digitalization in Limbo<\/h4>\n
Status of sustainable transformation and digitalization in production engineering<\/div>
Christian Donhauser<\/a> \n \"ORCID<\/a>, Daniel Riepl<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 56-60<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

Motion-Mining\u00ae is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining\u00ae is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case.<\/p>\n","protected":false},"featured_media":107478,"menu_order":0,"template":"","categories":[79167,79298],"tags":[],"product_cat":[79304],"topic":[68005,67838,68206,69611,70058,79371,79333,79489,79490],"technology":[67790,79493,71297,67946,67596,67634],"knowhow":[],"industry":[79494],"writer":[82516,82766,83727],"content-type":[],"potential":[],"solution":[],"glossary":[],"class_list":["post-103317","article","type-article","status-publish","has-post-thumbnail","category-design-en","category-typeset","product_cat-articles","topic-automation","topic-digital-twin","topic-industry-4-0","topic-internet-of-things-en","topic-lean-production-en","topic-logistics","topic-process-optimization","topic-quality","topic-supply-chain-management-en","technology-artificial-intelligence","technology-digitalization","technology-machine-learning","technology-sensors","technology-software-en","technology-tools","industry-manufacturing-en","writer-carsten-feldmann-en","writer-christopher-borgmann-en","writer-hendrik-appelhans-en","product","first","instock","downloadable","virtual","sold-individually","taxable","purchasable","product-type-article"],"uagb_featured_image_src":{"full":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min.jpeg",1400,788,false],"thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-150x150.jpeg",150,150,true],"medium":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-666x375.jpeg",666,375,true],"medium_large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-768x432.jpeg",768,432,true],"large":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-1024x576.jpeg",1020,574,true],"front-page-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-1032x320.jpeg",1032,320,true],"post-entry":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-764x376.jpeg",764,376,true],"post-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-392x320.jpeg",392,320,true],"post-teaser-mobile":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-608x496.jpeg",608,496,true],"post-custom-size":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-640x325.jpeg",640,325,true],"whitepaper-teaser":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-274x376.jpeg",274,376,true],"card-big":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-514x292.jpeg",514,292,true],"card-portrait":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-320x440.jpeg",320,440,true],"card-big-company":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-514x289.jpeg",514,289,true],"gp-listing":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-196x180.jpeg",196,180,true],"1536x1536":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min.jpeg",1400,788,false],"2048x2048":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min.jpeg",1400,788,false],"woocommerce_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-510x510.jpeg",510,510,true],"woocommerce_single":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-510x287.jpeg",510,287,true],"woocommerce_gallery_thumbnail":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-100x100.jpeg",100,100,true],"dgwt-wcas-product-suggestion":["https:\/\/industry-science.com\/wp-content\/uploads\/2024\/04\/AdobeStock_289332658-min-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":"Motion-Mining\u00ae is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining\u00ae is compared with traditional lean management tools used to analyze manual activities. 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