Automation

Additive manufacturing is an increasingly important manufacturing technology with huge economical potential. However, its popularity is accompanied by high material and time losses, as defects are often detected at a very late stage. One solution for a more sustainable production is the automated detection of manufacturing defects using artificial intelligence. This article describes the digitization of the defect detection process in additive manufacturing using a system based on a neural network. In addition to the steps for automated defect detection, system performance is also discussed.

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.

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)

Artificial intelligence has the potential to bring companies and entire industries to a completely new technological level. The prerequisite is data with a high degree of maturity, with which companies can automate complex processes, calculate forecasts or create analyses. With the right data strategy, structuring and achieving the necessary data quality are no longer dreams of the future.

Based on theoretical-empirical findings presented in the article "Technology Acceptance of Robotic Process Automation (RPA) (Part 1)", this article presents the results of an empirical study on the acceptance of robotic process automation (RPA) using structural equation modeling. This survey was conducted in a finance department of the business unit "Smart Infrastructure - Digital Grid" of a global technology company distributed throughout Germany.

To show SMEs a way to identify changes at an early stage, the interactive situation awareness monitor developed by Fraunhofer IPK is used. This gives companies an individually structured overview about their environment and their company (e. g. order situation). The area of the corporate environment is currently being expanded to include the compa- ny-specific component of automatic recognition of context-related data in order to provide relevant information to a company automatically. For this purpose, a machine learning model is developed and trained using company-specific data sets. First- ly, it is necessary to create the data sets with relevant and irrelevant environment information. For that, the PESTEL analysis is applied in this paper. Further- more, advantages and disadvantages of the analy- sis for generating data sets are discussed.

This article aims to develop a model for the acceptance of RPA technologies based on the presentation of the state-of-the-art. The model is the basis of an empirical study on the acceptance of robot-assisted process automation (RPA) at a global technology company. The results of this study are presented in a corresponding publication "An Empirical Analysis of Technology Acceptance of Robotic Process Automation (RPA) (Part 2)".