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Institute
- FIR e. V. an der RWTH Aachen (24) (remove)
Smart Services – die effektive Trias aus Produkt, Service und kundenorientiertem Leistungsversprechen – bieten Chancen für produktionsorientierte Unternehmen eine Differenzierung und neue Marktchancen zu erreichen. Der bislang geringe Einsatz von Smart Services zeigt, dass im produzierenden Gewerbe vielschichtige Herausforderungen bestehen, die Bausteine Produkt, Service und Leistungsversprechen zu nachhaltigen und wettbewerbsfähigen Smart Services zu kombinieren, erfolgreiche Geschäftsmodelle abzuleiten und Organisationen auf das Smart-Service-Geschäft anzupassen. Nur die großen Player schaffen dies eigenständig, der Innovationsstandort Deutschland lebt aber auch von seinen Hidden Champions: Kleinunternehmen und Mittelständlern.
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.
The mechanical and plant engineering industry faces a stagnation in the new machinery market and is relying on innovative business models such as subscription to overcome these. In this business model, individually customized solution packages are offered. The success of these models depends directly on the future success of the customer, making the selection of the right customers crucial. The aim of this paper is to identify the criteria that indicate the suitability of customers for subscription models. While there are individual descriptions of suitability criteria in the existing literature, there is a lack of comprehensive consideration of customer relationship, customer company, and customer market, as the extensive consideration was not necessary in the transactional sale of machines until now. Therefore, in this study, expert interviews are conducted with companies in mechanical and plant engineering that offer subscription models. The results show criteria that are used to evaluate customers in the six main categories of creditworthiness, market potential, benefit potential, feasibility, relationship, and sales effort. In total, 24 criteria can provide insight into the suitability of the customer for a successful subscription relationship. These criteria are intended to develop target systems that meet the requirements of different stakeholders in the customer and thus support the economic viability of these business models.
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.