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Use of Artificial Intelligence in Procurement

Use of Artificial Intelligence in Procurement

Possibilities of smart contracting
Andreas H. Glas, Kübra Ates, Michael Eßig
Procurement has the task to supply an organization with required but not self-produced goods. The goods vs. payment exchange with suppliers is laid down in contracts. “Electronic contracts" or “Smart Contracts” represent the logic digitally and thus enhance transparency. This can still evolve. In the future, improved algorithms and artificial intelligence will not only be able to administer contracts, but also to design them. This article presents the status quo of "Smart Contracting", places it in the "Legal Tech" topic and shows how artificial intelligence could be used in procurement.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 14-18
AI-Supported Optimization of Repetitive Processes

AI-Supported Optimization of Repetitive Processes

A coding technique for repetitive processes in evolutionary optimization
Christina Plump, Rolf Drechsler, Bernhard J. Berger
Optimisation is an essential task in many situations. The class of evolutionary algorithms is a population-based, heuristic technique for optimisation. They allow the optimisation of multi-modal problems even with distorted search spaces. They can propose several solutions instead of just one. An important aspect of evolutionary algorithms is encoding search space candidates. In the optimisation of processes, this is a non-trivial task. This article describes a successfully tested encoding.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 19-22
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
My Colleague Is a Robot

My Colleague Is a Robot

Acceptance of collaborative robotics in warehouses
Frederic Jacob, Eric Grosse ORCID Icon, Stefan Morana, Cornelius J. König
Warehousing is a very labor- and cost-intensive task in many companies. Digitization and automation of manual warehouse processes can increase efficiency, reduce costs and relieve employees. Collaborative robots that share work tasks with employees are increasingly used in warehouses. However, the pure techno-centric use of such robots can negatively influence the acceptance of human-robot collaboration. Various influences such as fear of job loss, higher cognitive stress, expected extra effort, or concerns about injuries can hinder human-robot collaboration and negatively impact economic benefits. This paper presents possible barriers to the acceptance of collaborative robotics in warehouses and discusses recommended actions for human-centered, sustainable human-robot collaboration.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 23-26
Methods for Designing Enterprise Architecture in Manufacturing Companies

Methods for Designing Enterprise Architecture in Manufacturing Companies

EAM as enabler for the design of transferable AI solutions
Arno Kühn, Arthur Wegel ORCID Icon, Jonas Cieply ORCID Icon
A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 37-42 | DOI 10.30844/I4SE.23.1.106
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
Predictive Manufacturing

Predictive Manufacturing

An intelligent monitoring system to detect anomalies in 3D printing
Benjamin Uhrich, Martin Schäfer, Miriam Louise Carnot, Shirin Lange
In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 27-31 | DOI 10.30844/I4SE.23.1.88
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
From Random Sampling to Real-time Data

From Random Sampling to Real-time Data

Integrated plant engineering to increase process capability
Alexander Seelig
The digitization of processes is complex and error-prone. That is why manufacturing processes are monitored using statistical process control methods. The aim of the presented project was to answer the questions how the data basis for the use of the quality control chart (QRC) can be extended from random samples to near real-time data and how the implementation of the solution should be done. The software solution was developed and tested in the Fischertechnik learning factory. It could be shown that the data from the learning factory is suitable to be displayed in a closely timed manner and to be evaluated by means of process indicators of the QRK. In this way, errors can be avoided and capacities saved. (Only in German)
Industrie 4.0 Management | Volume 39 | 2023 | Edition 1 | Pages 48-52
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