IFIP advances in information and communication technology
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768
Information Requirements for Data-Driven Upgrade Engineering to Enable an Upgrade Circular Economy
(2025)
In today’s environment, organizations face mounting challenges to reconcile eco-nomic efficiency with ecological responsibility and social sustainability. Upgrade Engineering offers a promising alternative to traditional maintenance strategies by combining technical upgrades with broader organizational and strategic sustaina-bility goals. Through a qualitative and exploratory research design that includes in-depth expert interviews across various industrial sectors and a systematic liter-ature review, a lifecycle-based information model was developed. This model de-scribes the information and data needs from the planning stage through construc-tion, usage, and improvement phases. The findings reveal that adopting an inte-grated data infrastructure that captures both quantitative sensor readings and qual-itative expert feedback is essential for effective decision-making and continuous improvement. The study concludes with practical recommendations and empha-sizes the necessity of interdisciplinary collaboration. Future research should fur-ther investigate interrelationships between the information collected in the plant life cycle phases and the technical, economic, and social dimensions of Upgrade Engineering in order to be able to steer upgrade decisions even more effectively.
769
Towards a Framework for Value-Based Pricing of Digital Products in the Manufacturing Industry
(2025)
The implementation of value-based pricing is still scarce, especially in the manufacturing industry. However, with the growing number of digital offerings in this industry and the shift towards customer centricity, the need for a new pricing method is clear. Even though there are several frameworks for value-based pricing, professionals at manufacturing companies do not know how to implement this pricing method within their own organization. It is apparent that the existing frameworks are not applicable for digital products in the manufacturing industry. Therefore, a new practical and easy-to-use framework needs to be developed. This paper gives an overview of the current research regarding value-based pricing and digital products in the manufacturing industry. Then the methodology is explained, which focuses on insights from industry experts. Consequently, the result, a framework with main factors for value-based pricing is introduced and discussed. The last chapter finishes with concluding remarks and an outlook on further research.
766
Supporting Strategic Decision Making for Data Monetization in the Era of Digital Transformation
(2025)
Unlocking the value of data remains one of the most pressing and promising strategic challenges in the age of digital transformation. While data is widely recognized as a key asset, many organizations still face difficulties in converting its potential into tangible business value. This paper presents a research-based framework for data monetization strategies, developed through literature review, expert input, and ecosystem analysis. To support practical application, the framework is complemented by a diagnostic questionnaire that helps organizations assess their current capabilities and reflect on suitable strategic directions.
Designed for early-stage use, the tool fosters alignment between monetization efforts and the company’s transformation context. The approach was developed within the EU-funded DATAMITE project and contributes to advancing strategy development in industrial data use and digital production management.
766
The transition from linear to circular value-preserving models poses significant challenges, particularly in the context of Reverse Logistics. Variability in returned products in terms of time and quality and logistical complexities referring to decentralized and central factories hamper implementation. While digitalization offers the potential to optimize these processes, many companies lack a structured, scalable, and interoperable approach for integrating digital tools into Reverse Logistics. Moreover, the concept has largely been neglected and limited to managing returns from quality and warranty claims. Existing Enterprise Architecture Models frequently fall short in addressing challenges such as product condition and variability, independence from proprietary systems, and the integration of multiple Circular Economy (CE) strategies thereby constraining their practical applicability. The present paper aims to address these gaps by proposing a generic, vendor-independent, data-driven architecture model that enables companies to implement efficient, sustainable Reverse Logistics in alignment with CE goals. This model fulfills the pressing need for practical frameworks that support interoperability, reuse of existing IT infrastructure, and comprehensive process transparency. Consequently, organizations will be empowered to implement smart Reverse Logistics and to meet regulatory demands, resource efficiency targets, and consumer expectations for sustainable products. Furthermore, the model should serve to meet national and supranational environmental protection targets.
664
In the contention of the current industrial landscape, an increasing number of manufacturing firms are experimenting with the transition from product-centric offerings to service-based value concepts and product-service bundles as high-value integrated customer solutions to increase their revenues and build sustainable competitive advantages; a phenomenon known as the “servitization” of manufacturing. Nowadays, consistently with the Industry 4.0 paradigm, these companies have therefore started a process of integrating their traditional value offerings with digital services. This recent strategy is known as “Digital Servitization” and consists of developing new services and/or improving existing ones through digital technologies. However, this transformation is challenging, and companies often struggle to achieve their expectations. Thus, this study aims to shed light on the current state of Digital Servitization strategies in the manufacturing sector based on a survey addressed to the top and middle management. The results obtained by the analysis of the data collected from the survey show an increasing trend towards the adoption of digital technologies for enabling innovation and differentiation in service delivery processes.
753
Many organizations are planning to engage in the monetization of data. However, it turns out to be a practical problem where to start and what to build on from the existing capabilities. A maturity model helps assess the maturity of the actual capabilities. At the same time the same model can be used to prescribe a desired target state. The gap between the target state and the actual status helps setting up actions to remedy the situation. This paper proposes a Data Maturity Model (DMMM), the first maturity model for data monetization. It has been scientifically developed bottom up based on literature and extensive expert knowledge and its first release is extensively documented here.
Bd. 690
To monetize the potential of digitalization in times of saturated markets, increased machinery and plant engineering companies are starting to transform the transaction-based business model into a customer- and service-oriented subscription business. Even though subscription offerings can create win-win situations for providers and customers, companies encounter significant difficulties in acquiring customers for this innovative business model. Historically linear acquisition processes focused on transactional product sales impede success. To identify key challenges and targeted coping strategies for customer acquisition we conducted in-depth interviews with 18 subscription managers and sales representatives from seven machinery and plant engineering case studies. In our research we uncovered four challenge dimensions: (1) lack of motivation, (2) missing skills and competences, (3) insufficient customer confidence and (4) transaction-oriented sales approach. Beyond that we derived four appropriate coping strategies (1) steering mechanisms, (2) human resource management, (3) trust building instruments and (4) systematic methodology to address them. These insights highlight the key challenges at the management level for customer acquisition that companies face when trying to initiate and sustain the transition from a purely transactional product and service business to subscription-oriented growth. Furthermore, they provide guidance how to cope with these challenges.
Vol. 689
The adoption of artificial intelligence (AI) technologies in manufacturing companies is challenging, particularly for SMEs that lack the necessary skills to develop and integrate AI-based applications (AI applications) into their existing IT system landscape. To address this challenge, the research project VoBAKI (IGF-Project No.: 22009 N) aims to enable SMEs to identify and close skill gaps related to AI application development and implementation using proper sourcing strategies. This paper presents the interim results from the second phase of the project, which involves identifying the tasks in the lifecycle of AI applications and determining the specific skills required for executing these tasks. The presented results provide a detailed lifecycle including the phases for the development and usage of AI applications, as well as the specific tasks that SMEs must consider when implementing an AI application. These results serve as the foundation for future research regarding the required skills to execute the presented tasks and provide a roadmap for SMEs to close skill gaps and successfully implement AI applications.
488
Influenced by the high dynamic of the markets the optimization of supply chains gains more importance. However, analyzing different procurement strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve the production process. The objective of this research is to evaluate different procurement strategies in a four-stage supply chain. Besides, this research aims to identify main influencing factors on the supply chain’s performance. The performance of the supply chain is measured by means of back orders (backlog). A scenario analysis of different customer demands and a Design of Experiments analysis enhance the significance of the simulation results.
689
The adoption of artificial intelligence (AI) technologies in manufacturing companies is challenging, particularly for SMEs that lack the necessary skills to develop and integrate AI-based applications (AI applications) into their existing IT system landscape. To address this challenge, the research project VoBAKI (IGF-Project No.: 22009 N) aims to enable SMEs to identify and close skill gaps related to AI application development and implementation using proper sourcing strategies. This paper presents the interim results from the second phase of the project, which involves identifying the tasks in the lifecycle of AI applications and determining the specific skills required for executing these tasks. The presented results provide a detailed lifecycle including the phases for the development and usage of AI applications, as well as the specific tasks that SMEs must consider when implementing an AI application. These results serve as the foundation for future research regarding the required skills to execute the presented tasks and provide a roadmap for SMEs to close skill gaps and successfully implement AI applications.
