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Notes
This phenomenon of focus shifting in production is investigated in research under the term “servitization” (Baines & Lightfoot, 2013; Brax & Visintin, 2017). Other synonyms for this are among others “service transition,” “service transformation,” “service strategy,” “service infusion,” “product-service systems,” or sometimes “hybrid offering” (Adrodegari & Saccani, 2017; Brax & Visintin, 2017; Fliess & Lexutt, 2019; Kowalkowski et al., 2017; Paiola & Gebauer, 2020).
While the first type of building new business models internally, in addition to existing business models, is to be regarded as business model innovation, the second type addresses the change of existing business models in the sense of a business model transformation. To circumvent any misunderstanding, the notion has to be made, that there is no uniform interpretation of both these terms (Arnold et al., 2016; Bouwman et al., 2018; Grijalvo Martín et al., 2021). As part of this article both terms will be summarized in the single term business model development.
Gassmann et al. clarify this in the following way: “In the future competition will not be between products and processes, but instead between business models” (Gassmann et al., 2017, p. 5).
A more comprehensive overview of the role of digital technologies in service transformation is provided by Ardolino et al. (2018).
AI describes methods, processes, and technologies that enable IT systems–such as machines, robots, or software systems—to interpret large amounts of data and to learn from this data in order to reproduce or imitate certain human-cognitive abilities (Di Vaio et al., 2020; Lee et al., 2019; Metelskaia et al., 2018; Paschou et al., 2020; VDMA Bayern, 2020). This means that tasks that require visual perception, language or strategic thinking and planning, for example, can be carried out independently and efficiently by machines (Ahlborn et al., 2019; Dowling et al., 2021; Metelskaia et al., 2018; VDMA Bayern, 2020; Keding, 2021).
The quality and relevance of the data play a decisive role here, since possible latent or openly immoral patterns in earlier decisions, for example, in the area of racism or sexism, may later be reproduced by the AI under certain circumstances (Keding, 2021).
Special methods are used for this, such as language understanding or machine or deep learning (Beins et al., 2017).
With reference to the example of anomaly detection, this information could be used, for example, to identify relationships between sensor values and quality assurance results in order to anticipate potential production errors and ideally to arrive at dedicated recommendations for action for machine operation (VDMA Bayern, 2020) This enables tasks that require, for example, visual perception, language, or strategic thinking and planning to be carried out independently and efficiently by machines (Ahlborn et al., 2019; Dowling et al., 2021; Metelskaia et al., 2018; VDMA Bayern, 2020; Keding, 2021).
Artificial intelligence is based on mathematical-statistical models, so-called algorithms (Beins et al., 2017; Joenssen & Müllerleile, 2020). Based on a specific problem and the underlying data model, algorithms are able to autonomously identify different solutions, gain new knowledge, optimize processes, and support decisions (Beins et al., 2017; VDMA Bayern, 2020).
As a result, it can be expected that the future role of people will be to focus more on topics and tasks that require strong judgment, intuition, creativity, flexibility, empathy, and tacit knowledge (Keding, 2021).
The installed base is therefore very important, because as a general rule, the larger it is, the larger is the available database. A large number of products in circulation and a broad base of existing customers make it possible to generate valuable data, which in turn can provide insights for optimizing your own offers and business model (Adrodegari & Saccani, 2017; Baines & Lightfoot, 2013).
Companies that want to offer their customers AI solutions do not necessarily have to develop them in-house (Pfau & Rimpp, 2021). It often makes more sense to use services that are already available on the market and, if necessary, to customize them (Pfau & Rimpp, 2021). In particular, if the AI applications are expected to have only a weak to moderate influence on the business model, outsourcing can be a worthwhile alternative (Pfau & Rimpp, 2021). For example, if a manufacturing enterprise wants to offer predictive maintenance services in addition to its existing business, i.e., the sale of production goods, it must be considered whether the investment in building up the necessary know-how and the necessary technical and organizational infrastructure would be profitable. In addition, it must be ensured that the respective company has the necessary technical resources. AI systems usually place high demands on computing power (Boll-Westermann et al., 2019). This can be remedied, for example, by special platforms that are offered in the cloud, on-premise or as edge computing (Falk et al., 2020).
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Here's another on the rise in 2022: 🔸 Microlearning.
✔️ Bite-sized fragments
✔️ When and where the user needs it
✔️ Often visual or interactive content