{"id":103439,"date":"2024-02-25T11:54:42","date_gmt":"2024-02-25T10:54:42","guid":{"rendered":"https:\/\/industry-science.com\/?page_id=103439"},"modified":"2024-05-13T20:05:47","modified_gmt":"2024-05-13T18:05:47","slug":"analytics","status":"publish","type":"page","link":"https:\/\/industry-science.com\/en\/tools\/analytics\/","title":{"rendered":"Analytics"},"content":{"rendered":"\n\n\n
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\n \n
\n \n \n \n \"Site\n <\/picture>\n <\/div>\n
\n \t \n
\t\t

Site Assessment for Flexible Intralogistics in Brownfield Sites<\/h4>\n
Innovative decision support in practice<\/div>
Jolanda Schierbaum<\/a>, Carsten Feldmann<\/a>, Lars Renhof<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Due to its dynamic environment, space planning in intralogistics is not a one-time task but a recurring decision-making process subject to numerous constraints imposed by existing infrastructure. Decisions are often based on incomplete data, resulting in a high risk of poor planning decisions and inefficient use of space. This paper presents a practice-oriented process model for space evaluation in brownfield projects. The proposed approach improves the standardization and consistency of space evaluation and promotes best practices among all stakeholders. By supporting systematic decision-making, the process model contributes to optimized planning and resource allocation, thereby reducing risks and avoiding costly implementation errors.The process model is demonstrated through a case study conducted at a commercial vehicle manufacturer. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 42 | 2026 | Edition 3 | Pages 124-133<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Derivation\n <\/picture>\n <\/div>\n
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\t\t

Derivation of MTM Analyses from Motion Capture Data<\/h4>\n
Evaluation of the procedure and comparison with a manual MTM analysis<\/div>
Silas P\u00f6ttker<\/a> \n \"ORCID<\/a>, Maria Neumann<\/a> \n \"ORCID<\/a>, Martin Benter<\/a> \n \"ORCID<\/a>, Constantin Eckart<\/a> \n \"ORCID<\/a>, Ulrike Wolf<\/a> \n \"ORCID<\/a>, Peter Kuhlang<\/a> \n \"ORCID<\/a>, Hermann L\u00f6dding<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n For around 15 years, German labor productivity per working hour has been increasing at significantly less than 1% per year. At the same time, more detailed productivity analyses reveal high potential in companies. The issue is that the required MTM analyses are complex and not yet employed as broadly and frequently as would be necessary. One solution is the use of digital technologies such as motion capture. These make it possible to carry out productivity analyses with little effort, as they provide data that accelerates the analysis. The MTMmotion\u00ae tool from the MTM ASSOCIATION e.\u00a0V. was developed with the aim of carrying out valid and compliant MTM analyses using data provided by other technologies. This article compares the method developed for a motion capture system and MTMmotion\u00ae with a conventional MTM-1\u00ae analysis. The main result is that digital technologies can be used to create valid MTM analyses in early planning phases with little effort in order to make early ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 112-119 | DOI 10.30844\/I4SE.25.5.108<\/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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Data-Driven Assistance Systems in the Working Environment<\/h4>\n
Efficient development of target group-specific BI dashboards in companies<\/div>
Martin Schmauder<\/a> \n \"ORCID<\/a>, Gritt Ott<\/a> \n \"ORCID<\/a>, Martin Hahmann<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Dashboards play a key role in informed business decisions. Based on findings from an action research process, this article shows how company-specific solutions can be systematically developed and bad investments avoided. The provision of IT capacities, securing data access, formulating requirements, and developing the data model prove to be particularly critical. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 5 | Pages 136-143 | DOI 10.30844\/I4SE.25.5.130<\/a><\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Requirements\n <\/picture>\n <\/div>\n
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Requirements Analysis for Predictive Analytics in SCM<\/h4>\n
Decision support for research and practice<\/div>
Iris Hausladen<\/a> \n \"ORCID<\/a>, ABM Ali Hasanat<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>Predictive analytics opens up opportunities to improve decision-making in manifold areas, including in supply chain management (SCM). Yet, the complete realization of its potential requires the identification of the corresponding needs upfront. This paper provides a structured concept that guides through the complex and interdisciplinary endeavor of requirements analysis for predictive analytics in SCM. Due to the generic nature of this approach, it can be applied for any use case and be adapted or enhanced in case of need. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 4 | Pages 86-92<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"The\n <\/picture>\n <\/div>\n
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The Core Principles of the Digital Twin<\/h4>\n
Transformingorder processes and the automation pyramid<\/div>
Wilmjakob Herlyn<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n
\n\t\t\t \n\t\t\t <\/div>The digital twin [DT] is considered a key technology of Industry 4.0. Its basic concept is now being successfully applied in practice, as demonstrated by the commissioning of Mercedes' Factory56 in 2022. New identification technologies, tracking systems and communication solutions faciliate new ways of controlling production and managing material flows, particularly at the shop floor level. With precise technical data permanently available not only for products, but also for material availability and order fulfillment status, production processes can be managed more dynamically and efficiently. This is precisely where the concept of the DT comes into play, enabling the immediate use and evaluation of this data.Its relevance continues to grow, especially in the context of make-to-order production, the rising variety of product configurations, and the globalization of production and supply networks. This article introduces the basic concept of the DT and illustrates how it connects to ... <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 3 | Pages 92-101<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
\n \n
\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
\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
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\n \n \n \n \"Optimizing\n <\/picture>\n <\/div>\n
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Optimizing the Budgeting Process with Digital Twins<\/h4>\n
Dashboards and process mining for process-oriented performance measurement<\/div> <\/td>\n <\/tr>\n <\/table>\n
\n Traditional budgeting often resembles a marathon full of spreadsheets, manual reconciliations and time-consuming data collection. However, modern companies need agile, data-driven solutions that allow for transparency, efficiency and strategic foresight. Digital technologies such as digital twins, dashboards and process mining initiate this possibility: they transform the budgeting process from a static set of figures to a dynamic, simulation-capable management tool. Instead of getting lost in detailed work, companies can use them to analyze processes in real time, simulate scenarios and make well-informed decisions. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | Edition 2 | Pages 52-58<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Distributed\n <\/picture>\n <\/div>\n
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Distributed Application Integration in Industry<\/h4>\n
Employing microservices for enterprise application integration (EAI)<\/div>
Jan-Peer Rudolph<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n In line with current digital transformations, the number of software applications in use by companies is continuously increasing. This particularly affects industrial enterprises, which face challenges due to their often complex business processes. A holistic and sustainable integration of these business processes requires a strong link between the different information systems used. In this context, application integration, also known as enterprise application integration (EAI), is becoming more important. Modern approaches such as the use of microservices offer a particularly flexible and efficient solution for seamlessly connecting different applications and thus promoting the agility and scalability of a company\u2019s IT landscape. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 41 | 2025 | Edition 1 | Pages 74-80<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n
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\n \n \n \n \"Maturity\n <\/picture>\n <\/div>\n
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Maturity Levels of Smart Knowledge Services<\/h4>\n
Self-assessment and GAP analysis<\/div>
Isger Glauninger<\/a> \n \"ORCID<\/a>, Nick Tugarin<\/a> \n \"ORCID<\/a>, Christian van Husen<\/a> \n \"ORCID<\/a><\/div>\n <\/td>\n <\/tr>\n <\/table>\n
\n Digitalization opens up new forms of operational training. A growing focus is on smart services, which allow for proactive engagement with customer demands and empower businesses in times of digitalization. While traditional learning environments are rarely tailored toward individual needs, smart services offer new opportunities. Decentralization, previously only a utopic vision, is becoming the reality now. <\/div>\n <\/div>\n
Industry 4.0 Science<\/strong> | Volume 40 | 2024 | Edition 4 | Pages 50-56<\/div> <\/div>\n <\/div>\n <\/a>\n <\/div>\n<\/div>\n\n
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