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Digital technologies have gained significant importance in the course of the 4th Industrial Revolution and these technologies are widely implemented, nowadays. However, it is necessary to bear in mind that an ill-considered use can quickly have a negative impact on the environment in which the technology is used. For more responsible and sustainable use, the regulation of digital technologies is therefore necessary today. Since the government is taking a very slow response, as the example of the AI Act shows, companies need to take action themselves today. In this context, one of the central questions for companies is: "Which digital technologies are relevant for manufacturing companies in terms of regulation? This paper conducted a quantitative Delphi study to answer this question. The results of the Delphi study are presented and evaluated within the framework of a data analysis. In addition, it will be discussed how to proceed with the results so that manufacturing companies can benefit from them. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of artificial intelligence applications.
Robotic Process Automation (RPA) gewinnt durch die Möglichkeit, repetitive Administrationsprozesse zu automatisieren und Effizienzpotenziale zu heben, zunehmend an Bedeutung. In der Praxis scheitern jedoch viele Implementierungsprojekte. Dies resultiert primär aus dem fehlenden Verständnis darüber, wie sich die Einführung von RPA auf das Gesamtsystem Organisation auswirkt. Es entsteht eine wachsende Kluft zwischen dem Leistungsversprechen von RPA und der Fähigkeit von Unternehmen, jenes auszuschöpfen. Trotz der exponentiellen Geschwindigkeit des technologischen Fortschritts mangelt es vielen Unternehmen an der notwendigen Adaptionsfähigkeit, welche für den nachhaltigen Erfolg einer RPA-Implementierung essenziell ist. In diesem Kontext spielt die Optimierung der im Einklang stehenden Dimensionen Mensch, Technik und Organisation eine zentrale Rolle. Durch eine systematische Literaturrecherche wird aufgezeigt, dass bisherige Ansätze diesen Zusammenhang nur unzureichend betrachten. In der heutigen Forschungslandschaft existiert kein Modell, welches die technischen, sozialen und organisatorischen Komponenten, die im Zuge der RPA-Einführung zu berücksichtigen sind, darlegt. Angelehnt an das soziotechnische Systemdenken und den Prozess der Fallstudienforschung werden theoriegeleitet Dimensionen und Elemente einer RPA-spezifischen soziotechnischen Systemarchitektur identifiziert und erläutert. Das daraus resultierende Modell zur Unterstützung von Unternehmen bei der RPA-Einführung wurde mit einer Vielzahl Industrievertretern im Rahmen des öffentlichen Forschungsprojekts RPAsset des FIR e. V. an der RWTH Aachen validiert.
Long-term production management defines the future production structure and ensures the long-term competitiveness. Companies around the world currently have to deal with the challenge of making decisions in an uncertain and rapidly changing environment. The quality of decision-making suffers from the rapidly changing global market requirements and the uniqueness and infrequency with which decisions are made. Since decisions in long-term production management can rarely be reversed and are associated with high costs, an increase in decision quality is urgently needed. To this end, four different applications are presented in the following, which support the decision process by increasing decision quality and make uncertainty manageable. For each of the applications presented, a separate digital shadow was built with the objective of being able to make better decisions from existing data from production and the environment. In addition, a linking of the applications is being pursued:
The Best Practice Sharing App creates transparency about existing production knowledge through the data-based identification of comparable production processes in the production network and helps to share best practices between sites. With the Supply Chain Cockpit, resilience can be increased through a data-based design of the procurement strategy that enables to manage disruptions. By adapting the procurement strategy for example by choosing suppliers at different locations the impact of disruptions can be reduced. While the Supply Chain Cockpit focuses on the strategy and decisions that affect the external partners (e.g., suppliers), the Data-Driven Site Selection concentrates on determining the sites of the company-internal global production network by creating transparency in the decision process of site selections. Different external data from various sources are analyzed and visualized in an appropriate way to support the decision process. Finally, the issue of sustainability is also crucial for successful long-term production management. Thus, the Sustainable Footprint Design App presents an approach that takes into account key sustainability indicators for network design. [https://link.springer.com/referenceworkentry/10.1007/978-3-030-98062-7_15-1]
In short-term production management of the Internet of Production (IoP) the vision of a Production Control Center is pursued, in which interlinked decision-support applications contribute to increasing decision-making quality and speed. The applications developed focus in particular on use cases near the shop floor with an emphasis on the key topics of production planning and control, production system configuration, and quality control loops.
Within the Predictive Quality application, predictive models are used to derive insights from production data and subsequently improve the process- and product-related quality as well as enable automated Root Cause Analysis. The Parameter Prediction application uses invertible neural networks to predict process parameters that can be used to produce components with desired quality properties. The application Production Scheduling investigates the feasibility of applying reinforcement learning to common scheduling tasks in production and compares the performance of trained reinforcement learning agents to traditional methods. In the two applications Deviation Detection and Process Analyzer, the potentials of process mining in the context of production management are investigated. While the Deviation Detection application is designed to identify and mitigate performance and compliance deviations in production systems, the Process Analyzer concept enables the semi-automated detection of weaknesses in business and production processes utilizing event logs.
With regard to the overall vision of the IoP, the developed applications contribute significantly to the intended interdisciplinary of production and information technology. For example, application-specific digital shadows are drafted based on the ongoing research work, and the applications are prototypically embedded in the IoP.
Ziel des Beitrags ist es, aufzuzeigen, wie produzierende Unternehmen entlang der Customer-Journey systematisch kundenbezogene Daten erheben können. Nach einer Einleitung zur Motivation der Themenstellung, einer Begriffserläuterung und einer Vorstellung des Studiendesigns wird ein Referenzprozessmodell der Kundeninteraktionen produzierender Unternehmen gestaltet, darauf aufbauend ein Datenmodell des digitalen Schattens der Kundeninteraktionen abgeleitet und zuletzt ein Vorgehensmodell zur Implementierung des digitalen Schattens der Kundeninteraktionen präsentiert.
Digital technologies such as 5G, augmented reality, and artificial intelligence (AI) are currently being used in various ways by manufacturing companies. As the fourth industrial revolution progresses, it has become apparent that reckless use and inadequate regulation of these technologies have a detrimental effect on the environment in which they are utilized. Therefore, regulation of digital technologies is imperative today to ensure more responsible and sustainable use. While governments usually establish regulations, progress is not keeping pace with the demands and hazards of employing digital technologies. The European AI law serves as an example of the considerable distance yet to be covered before binding guidelines are established. Consequently, companies must take proactive measures today to ensure that they use digital technologies responsibly in their environments. In this context, identifying which digital technologies are pertinent to manufacturing companies in terms of regulation is crucial. Furthermore, a comprehensive approach is required to design compliance holistically for digital technologies and to systematically derive the corresponding guidelines. This paper introduces a set of models that not only determine the importance of
compliance in the application of different technologies but also present a framework for methodically designing compliance. Furthermore, the paper contributes to the development of an AI platform in the German research project PAIRS by investigating the compliance relevance of applications such as artificial intelligence.
Companies in the manufacturing sector are confronted with an increasingly dynamic environment. Thus, corporate processes and, consequently, the supporting IT landscape must change. This need is not yet fully met in the development of information systems. While best-of-breed approaches are available, monolithic systems that no longer meet the manufacturing industry's requirements are still prevalent in practical use. A modular structure of IT landscapes could combine the advantages of individual and standard information systems and meet the need for adaptability. At present, however, there is no established standard for the modular design of IT landscapes in the field of manufacturing companies' information systems. This paper presents different ways of the modular design of IT landscapes and information systems and analyzes their objects of modularization. For this purpose, a systematic literature research is carried out in the subject area of software and modularization. Starting from the V-model as a reference model, a framework for different levels of modularization was developed by identifying that most scientific approaches carry out modularization at the data structure-based and source code-based levels. Only a few sources address the consideration of modularization at the level of the software environment-based and software function-based level. In particular, no domain-specific application of these levels of modularization, e.g., for manufacturing, was identified. (Literature base: https://epub.fir.de/frontdoor/index/index/docId/2704)
Pricing is one of the most important, but underestimated tools, to enhance a company's profitability. Especially value-based pricing has a high potential to reach higher levels of satisfaction because it equates the needs of providers and customers. Even though, it is a well-known price model and promises higher satisfaction, many companies struggle to implement it. Especially the manufacturing industry is characterized by cost-plus pricing and competition-based pricing. However, especially for digital products these pricing strategies are insufficient. Therefore, this paper aims at exploring the design fields for value-based pricing of digital products in the manufacturing industry. To achieve this, the basics of digital products and value-based pricing are explored. Furthermore, an expert workshop is conducted that follows a framework for value-based pricing consisting of four consecutive steps analysis, price strategy, pricing, and market launch to capture the design fields. This paper concludes with limitations, and practical and research implications.
Die pandemiebedingt angestiegene Homeofficequote in produzierenden
Unternehmen ist seit Juli 2020 deutlich rückläufig und indiziert ein
geringes Maß an langfristig gestalteten hybriden Arbeitsplatzkonzepten.
Angesichts des Fachkräftemangels besteht Handlungsdruck, eine
attraktive Arbeitsumgebung mit industriellen Tätigkeiten zu vereinbaren.
Um zukunftsorientierte Arbeitsplatzkonzepte zu gestalten, nennt
das vorgestellte Vorgehen systematisch die menschlichen Tätigkeiten
in produzierenden Unternehmen und bewertet deren Remotefähigkeit.
Gap Analysis for CO2 Accounting Tool by Integrating Enterprise Resource Planning System Information
(2023)
Detailed carbon accounting is the foundation for reducing CO2 emissions in manufacturing companies. However, existing accounting approaches are primarily based on manual data preparation, although manufacturing companies already have a variety of IT systems and resulting data available. The gap analysis carried out based on the GHG Protocol and an reference ERP system shows how much of the required information for CO2 accounting can be integrated from an ERP system. The ERP system can cover 20 % of the required information. The information availability can be increased to 49 % through additionally identified modifications of the ERP system. Integrating the CO2 accounting tool with other systems of the IT landscape, e. g. Energy Information System, enables an additional increase.