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Planning Assistance in Production and Logistics

Planning Assistance in Production and Logistics

Supervised learning for predicting process steps in the planning of logistics processes
Marius Veigt, Lennart M. Steinbacher, Michael Freitag ORCID Icon
The competitive pressure in the contract logistics industry is intense. Logistics providers must respond to tenders quickly and with convincing concepts. This article presents initial approaches to how logistics process planning in tender management can be supported using supervised learning methods. Under the premise that similar processes from past projects can be transferred and adapted to a project to be planned, an AI-based assistance system suggests appropriate process steps and MTM (Methods-Time Measurement) codes during planning. This procedure can accelerate process planning and lead to an increased quality of logistics processes to be planned. (Only in German)
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 9-13
Optical Detection of Measured Values

Optical Detection of Measured Values

Machine Learning Methods for Digitalizing Manual Reading and Measuring Processes
Matthias Mühlbauer, Hubert Würschinger, Nico Hanenkamp, Svyatoslav Funtikov
In factory operations, measuring equipment is often used without automatic storage or further processing possibilities of the measured value. In this case, employees must capture and process the measured values manually. In this article, an approach for the optical detection and digitization of measured values with the help of machine learning methods is presented. This aims to reduce the workload of the employees, avoid reading errors and enable automated documentation.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 43-47
Qualitative Cause-Effect Relationships

Qualitative Cause-Effect Relationships

Planning and implementation of relocation projects in the reorganization of factories
Andreas Nitsche, Malte Stonis ORCID Icon, Peter Nyhuis ORCID Icon
The realization of reorganization projects represents a complex and independent planning task within the framework of factory layout planning. Only little methodical knowledge exists, which considers the temporal, spatial and organizational restrictions in the creation of a schedule. This paper aims to present the interdependencies in the planning and execution of realization projects and thus to provide a basis for discussion for further investigations in the field of scheduling factory relocations for the reorganization of factory objects.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 53-57
Automate Processes Strategically Instead of Selectively

Automate Processes Strategically Instead of Selectively

How and why a Center of Automation ignites the digitization booster—not only in related fields
Steffen Weiers
Many departments have already recognized the enormous increase in efficiency and personnel relief from routine activities through process automation. These digital thought leaders have begun to automate office processes using new technologies such as Robotic Process Automation (RPA), low code in the Microsoft Power Platform or in SAP. However, the positive experiences often remain in individual departments. Due to the lack of a strategic superstructure, companies as a whole have not yet succeeded in systematically transferring the added values to all areas. The organizational solution for this is called a "Center of Automation". Sometimes it is enough for the team to consist of two members to bring an overarching, digital process mindset into a company. (Only in German)
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 58-62
Potential of Platform-Based Business Models in the Plastics Industry

Potential of Platform-Based Business Models in the Plastics Industry

Benedict Bender, Stefanie Lewandowski
The potential of platform-based business models in the context of Industry 4.0 has not yet been fully realized. Approaches for platforms and ecosystem- based value creation vary between industries. The plastics industry has so far been widely ignored in this respect. Due to its industrial structure, in particular the uniform value creation structures, the plastics industry is suitable for digital platforms. In addition to approaches for platforms in the injection molding industry, the article offers a procedure model for the expansion of established business models. This can facilitate the entry into the field of platform-based business models for SMEs.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 14-18 | DOI 10.30844/IM_22-6_14-18
DataLab WestSax – R&D Setting for Regional Data-based Value Creation Experiments

DataLab WestSax - R&D Setting for Regional Data-based Value Creation Experiments

Ein regionaler Katalysator für datenbasierte Wertschöpfungsprozesse
Christian Leyh, Wibke Kusturica ORCID Icon, Sarah Neuschl, Christoph Laroque ORCID Icon
New types of value creation characterized by extensive data use and cross-company data sharing are becoming increasingly important for companies. However, many barriers slow down the path towards data-based value creation, especially for SMEs. Companies often lack specific ideas for data usage or digitalization and data competencies. As a result, there is often untapped value creation potential in companies. By describing a real laboratory setting with real experiments, this article demonstrates support options for companies to identify their own "data treasure" and to lift it.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 37-41 | DOI 10.30844/IM_22-6_37-41
Data as Basis for Business Models

Data as Basis for Business Models

Recommendations for Competitive Predictive Maintenance Business Models
Sven Seidenstricker, Saskia Ramm, Barbara Dinter
The combination of product service systems and big data requires a change in the existing, traditional business models and a repositioning of the companies. Since these changes are often a challenge, this article uses the example of predictive maintenance to present the influences of big data and product service systems on the business models of medium-sized companies in mechanical and plant engineering. Based on a systematic literature review in combination with expert interviews, numerous practical business model implications were obtained, providing sound guidance for industry representatives.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 33-36 | DOI 10.30844/IM_22-6_33-36
Demand Planning Falcon

Demand Planning Falcon

Precise stochastic demand calculation with a newly developed digital planning method
Alexander Schmid, Thomas Sobottka, Samuel Luthe, Wilfried Sihn
Precise stochastic demand calculation is the key to successful material planning, i. e. to always have exactly the right quantity on hand. However, decision-makers are faced with the dilemma of which of the many forecasting methods they should use, adapted to the item properties as much as possible. This paper examines the optimization potential of a self-developed automatically optimizing forecasting approach based on ten common forecasting methods, which are evaluated using two case studies from the capital goods industry.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 47-50 | DOI 10.30844/IM_22-6_47-50
Challenges of Digitalization in Intermodal Transport

Challenges of Digitalization in Intermodal Transport

Data models for the exchange of planning data for regional freight tram transportation
Jonas Ziegler, Ingo Dittrich, Theo Lutz ORCID Icon, Lisa Fäßler
The logistics industry is currently being confronted with various challenges, such as the lack of drivers, global disruptions to supply chains and the environmental impact of freight transport. In a comparison between the modes of transport, this speaks for a greater shift in freight transport from road to rail. In this article, the challenges for this shift are examined and it is shown to what extent data models can simplify the transport planning and economic assessment of regional freight tram transports. (Only in German)
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 59-62
Assessment of Technical Cleanliness in the Production Process of Lithium-Ion Battery Cells for Automotive Applications

Assessment of Technical Cleanliness in the Production Process of Lithium-Ion Battery Cells for Automotive Applications

Laura Meusel, Bernd Rosemann, Michael Morawiec
Technical cleanliness as a quality feature in the automotive industry is continuously growing in importance. In this context, particularly high cleanliness requirements are placed on battery cells for electric vehicles, which must be adhered to along the value chain. This paper will introduce an assessment method for the analysis of technical cleanliness in the production process of lithium-ion- cells as well as revealing potential failure causes.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 6 | Pages 19-23
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