Data Science

Hybrid Decision Support in Product Creation

Hybrid Decision Support in Product Creation

Improving performance with data science and artificial intelligence
Iris Gräßler ORCID Icon, Jens Pottebaum ORCID Icon, Peter Nyhuis ORCID Icon, Rainer Stark ORCID Icon, Klaus-Dieter Thoben ORCID Icon, Petra Wiederkehr ORCID Icon
Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.
Industry 4.0 Science | Volume 41 | Edition 1 | Pages 18-25 | DOI 10.30844/I4SE.25.1.18
Optimization of Line Feeding Strategy for the Assembly Line

Optimization of Line Feeding Strategy for the Assembly Line

A holistic approach for improving the intralogistics in production industry
Christina Braun, Lea Isfort
The logistics industry offers numerous opportunities for data-driven solutions, such as improving the part feeding problem in assembly line industries. A data-based approach for will lead to an improvement of cost-effectiveness through optimized processes, resource utilization, and consistent supply to the assembly line. The generated approach is a mixed integer programming model which considers limited storage space, uses constraints, and various cost factors related to transport, replenishment, and picking.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 5 | Pages 58-61
Industrial Data Science

Industrial Data Science

Machine learning (ML) for technical systems
Felix Reinhart
Data Science is an established tool for knowledge discovery, in particular from economic data. The progressing digitization of products and production systems enables the broader application of Data Science in technical systems. However, the requirements and constraints, e.g. for control and optimization of production processes, differ significantly from established Data Science applications. Industrial Data Science addresses the issues of applying machine learning to technical systems in industrial setups. This article characterizes challenges of Industrial Data Science, gives application examples of and general indicators for Industrial Data Science.
Industrie 4.0 Management | Volume 32 | 2016 | Edition 6 | Pages 27-30
Potentials of Data Science in Production and Logistics

Potentials of Data Science in Production and Logistics

Part 2—Procedure for data analysis and application examples
Michael Freitag ORCID Icon, Mirko Kück, Abderrahim Ait Alla ORCID Icon, Michael Lütjen ORCID Icon
The importance of data science for production and logistics continues to grow because more data are available due to Industry 4.0-Applications used for process and system optimization. In addition, the improved methods and tools for data analysis enable an easier processing of application-specific issues. This article is the second part relating to data science in production and logistics. While the first article dealt with the definition of terms and the potential of data analysis, the article at hand is dedicated to the application of data science in production and logistics by means of various application examples.
Industrie Management | Volume 31 | 2015 | Edition 6 | Pages 39-46
Potentials of Data Science in Production and Logistics Part 1

Potentials of Data Science in Production and Logistics Part 1

An Introduction into Current Approaches of Data Science
Michael Freitag ORCID Icon, Mirko Kück, Abderrahim Ait Alla ORCID Icon, Michael Lütjen ORCID Icon
The implementation of industry 4.0 concepts requires a new understanding of data processing and analysis. Data Science integrates approaches of mathematical modelling and performant implementation to analyse data of specific application areas. Within this first article, the basics of Data Science are presented and perspectives for a data-driven production and logistics are discussed. Within a second article in a following edition, the process steps for structured data analysis will be explained and illustrated by means of application examples.
Industrie Management | Volume 31 | 2015 | Edition 5 | Pages 22-26
Prediction of Return Shipment in E-Commerce by Means of Machine Learning

Prediction of Return Shipment in E-Commerce by Means of Machine Learning

Procedure and tools for the practical use of machine learning
Daniel Weimer, Till Becker
Customers in online shops return at least half of the placed orders. This huge amount of return shipments results in high costs e.g. from a logistic point of view. To predict the return rate based on customer data and order information, machine learning techniques can be applied which are able to learn a powerful model for return prediction based on historical order data. This article introduces a hands-on approach for successfully applying machine learning in real world processes and shows a case study to predict the return shipment probability in an e-commerce scenario.
Industrie Management | Volume 30 | 2014 | Edition 6 | Pages 47-50