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Institut / FIR-Bereiche
Reinforced through the pandemic and shaped by digitalization, today's professional working environment is in a state of transformation. Working remotely has become a vital component of many professions' regular routines. The design of remote work environments presents challenges to organizations of all sizes. By providing a classification, this paper reveals a comprehensive understanding of the fields of design to be considered to establish lasting remote work concepts in organizations. A hierarchical classification with four dimensions consisting of human, technology, organization, and culture, seven design elements and, twenty design parameters indicates to organizations the fields of design that need to be examined. To satisfy both the theoretical foundation and the practical application, design elements are derived by implementing a systematic review of the literature that represents key areas of interest for remote work. Additionally, these are verified and complemented by a dedicated case study research to incorporate practice-oriented design parameters.
Systematisation Approach
(2023)
Current megatrends such as globalisation and digitalisation are increasing complexity, making systems for well-founded and short-term decision support indispensable. A necessary condition for reliable decision-making is high data quality. In practice, it is repeatedly shown that data quality is insufficient, especially in master and transaction data. Moreover, upcoming approaches for data-based decisions consistently raise the required level of data quality. Hence, the importance of handling insufficient data quality is currently and will remain elementary. Since the literature does not systematically consider the possibilities in the case of insufficient data quality, this paper presents a general model and systematic approach for handling those cases in real-world scenarios. The model developed here presents the various possibilities of handling insufficient data quality in a process-based approach as a framework for decision support. The individual aspects of the model are examined in more detail along the process chain from data acquisition to final data processing. Subsequently, the systematic approach is applied and contextualised for production planning and supply chain event management, respectively. Due to their general validity, the results enable companies to manage insufficient data quality systematically.
Industrial companies are moving to a solution driven business by offering smart product service systems (Smart PSS). In addition to an existing portfolio of physical goods and technical services, companies develop new digital services and combine all three offerings to an integrated digital solution business. While the development of new digital services does not pose any major challenges for companies, the successful sale of Smart PSS does. Due to changing customer requirements and value propositions of a solution, the sale of Smart PSS requires new design principles for the sales organization compared to the simple sale of physical goods or technical services. While there are already many publications on the topic of industrial sales in research, the description of Smart PSS in particular represents a new field of research. The combination of both topics is therefore not only interesting from a theoretical point of view, but also has a particularly high practical relevance and impact for industrial companies. This paper therefore describes on the one hand, which characteristics can be used to derive customer requirements for Smart PSS and on the other hand, which effects these requirements have on the sales organization of the industrial company. The design principles give recommendations for the organizational structure, the resources, the information systems and the culture of the company depending on the targeted customer type. In order to identify and describe both the customer requirements and the design principles, two morphological boxes were developed based on a literature research and semi-structured interviews with industrial companies. The paper gives an outlook on the different characteristics of the design recommendations and describes first best practices for the successful transformation of the sales organization.
More and more companies in the mechanical and plant engineering industry are transforming their business model and evolving from product to solution providers. Subscription business models play a key role in this development. They enable companies to enter long-term collaborative relationships with customers and thus monetize the potential of Industry 4.0. However, this development is not easy for many companies and is associated with numerous hurdles. One of these hurdles is the development of a suitable range of services tailored to customer needs. In this context, the bundling of individual services to service modules plays a key role in realizing new value propositions. In practice, however, companies often lack an understanding of which services need to be combined in what way to be able to realize new value propositions. Accordingly, the goal of this work is to identify relevant services for subscription business models, to cluster them into meaningful value-adding bundles, and to derive new value propositions accordingly. The new value propositions in turn enable mechanical and plant engineering companies to strengthen customer loyalty and thus achieve long-term economic success.
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.
Based on the increasingly complex value creation networks, more and more event-based systems are being used for decision support. One example of a category of event-based systems is supply chain event management. The aim is to enable the best possible reaction to critical exceptional events based on event data. The central element is the event, which represents the information basis for mapping and matching the process flows in the event-based systems. However, since the data quality is insufficient in numerous application cases and the identification of incorrect data in supply chain event management is considered in the literature, this paper deals with the theoretical derivation of the necessary data attributes for the identification of incorrect event data. In particular, the types of errors that require complex identification strategies are considered. Accordingly, the relevant existing error types of event data are specified in subtypes in this paper. Subsequently, the necessary information requirements and information available regarding identification are considered using a GAP analysis. Based on this gap, the necessary data attributes can then be derived. Finally, an approach is presented that enables the generation of the complete data set. This serves as a basis for the recognition and filtering out of erroneous events in contrast to standard and exception events.
The European Commission set out the goal of carbon neutrality by 2050, which shall be achieved by fostering the twin transition - sustainability through digitalization. A keystone in this transition is the implementation of a prospering Circular Economy (CE). However, product information required to establish a flourishing CE is hardly available or even accessible. The Digital Product Passport (DPP) offers a solution to that problem but in the current discussion, two separate topics are focused on: its architecture and its application on batteries. The content of the DPP has not been an essential part of the discussion, although access to high-quality data about a product's state, composition and ecological footprint is required to enable sustainable decision-making. Therefore, this paper presents a classification of product data for circularity in the manufacturing industry to emphasize the discussion about the DPP's content. Developed through a systematic literature review combined with a case-study-research based on common operational information systems, the classification comprises three levels with 62 data points in four main categories: (1) Product information, (2) Utilization information, (3) Value chain information and (4) Sustainability information. In this paper, the potential content structure of a DPP is demonstrated for a use case in the machinery sector. The contribution to the science and operations community is twofold: Building a guideline for DPP developers that require scientific input from available real-world data points as well as motivating manufacturers to share the presented data points enabling a circular product information management.
Competitive differentiation in the manufacturing sector is no longer based on product and service innovations alone but on the ability to monetize the usage phase of products and services. To this end, manufacturers are increasingly looking at so-called subscription business models as a way of supplementing the traditional sale of products and services. Since supplier success in the subscription business is directly dependent on customer success, the setup and expansion of a so-called Customer Success Management (CSM) is required. While CSM has already been established in the software industry for several years, companies in the manufacturing sector are often still in the conceptual phase of a CSM, parallel to the setup and expansion of their subscription business. Therefore, this paper aims to support the set-up of a CSM by providing a reference data model, based on case study research, that can be used to support the organizational or daily CSM tasks and to serve as a blueprint for conceptualizing CSM-specific IT systems.
With the development of publicly accessible broker systems within the last decade, the complexity of data-driven ecosystems is expected to become manageable for self-managed digitalisation. Having identified event-driven IT-architectures as a suitable solution for the architectural requirements of Industry 4.0, the producing industry is now offered a relevant alternative to prominent third-party ecosystems. Although the technical components are readily available, the realisation of an event-driven IT-architecture in production is often hindered by a lack of reference projects, and hence uncertainty about its success and risks. The research institute FIR and IT-expert synyx are thus developing an event-driven IT-architecture in the Center Smart Logistics' producing factory, which is designed to be a multi-agent testbed for members of the cluster. With the experience gained in industrial projects, a target IT-architecture was conceptualised that proposes a solution for a self-managed data-ecosystem based on open-source technologies. With the iterative integration of factory-relevant Industry 4.0 use cases, the target is continuously realised and validated. The paper presents the developed solution for a self-managed event-driven IT-architecture and presents the implications of the decisions made. Furthermore, the progress of two use cases, namely an IT-OT-integration and a smart product demonstrator for the research project BlueSAM, are presented to highlight the iterative technical implementability and merits, enabled by the architecture.
Manufacturing companies face the challenge of managing vast amounts of unstructured data generated by various sources such as social media, customer feedback, product reviews, and supplier data. Text-mining technology, a branch of data mining and natural language processing, provides a solution to extract valuable insights from unstructured data, enabling manufacturing companies to make informed decisions and improve their processes. Despite the potential benefits of text mining technology, many manufacturing companies struggle to implement use cases due to various reasons. Therefore, the project VoBAKI (IGF-Project No.: 22009 N) aims to enable manufacturing companies to identify and implement text mining use cases in their processes and decision-making processes. The paper presents an analysis of text mining use cases in manufacturing companies using Mayring's content analysis and case study research. The study aims to explore how text mining technology can be effectively used in improving production processes and decision-making in manufacturing companies.