The ongoing trend of digitization does not exclude the manufacturing sector which poses challenges for both involved parties: machine manufacturers and producing companies. While larger corporations and certain industries, such as the automotive industry, may be already well advanced in digitization, small and medium-sized enterprises (SMEs) often still face difficulties. For many producing companies, the added value of digitization is unclear and they also have strong security concerns about making their data available for analyses. On the other hand, machine manufacturers face problems in their transformation from hardware manufacturers to service providers (Bitkom Research & Ernst & Young 2018, p. 21; VDMA & McKinsey 2020, p. 22; Vargo & Lusch 2008, p. 254ff). Due to these difficulties and the currently high implementation costs, IIoT (industrial internet of things) platforms have so far been deployed rather sporadically. However, they can offer great potential for optimization, for example of service processes or the overall equipment effectiveness (OEE) and will play an important role in maintaining competitiveness in the future (VDMA & McKinsey 2020, p. 27ff).
For this reason, more and more startups and third-party providers are currently establishing businesses that are trying to solve the challenges and problems of both sides with a wide variety of approaches. Currently, the market is quite opaque, which makes it difficult to compare providers on the market and thus to compete. This thesis is written in cooperation with the Aachen-based startup developing the IIoT platform “United Manufacturing Hub” (UMH; UMH Systems GmbH). Its objective is to set UMH apart from the existing red-ocean market with the development of a blue ocean strategy. By redistributing the development focus to attributes that are most relevant to customers in the market and reducing efforts in less relevant areas, the goal is to create a new, non-competitive market (Kim & Mauborgne 2015, p. 24ff). UMH has set itself the task of making the digital transformation as easy as possible for machine manufacturers and producing companies as their end customers. To do this, it is important to know the needs and problems of the customers and to obtain their assessment of the solution approaches. As a starting point for market analysis, this thesis focuses on machine manufacturers as customers of the platform.
The research question is divided into sub-questions, which together contribute to answering the primary question (Karmasin & Ribing 2017, p. 24f). While the topic is elaborated on the example of UMH, the underlying questions can be generalized and are not sufficiently addressed in the existing literature. The concepts further described in chapter 2 provide useful insights into IIoT, open-source platforms, as well as blue ocean strategies, but there is limited literature on the linkages between those topics (e.g., Frank et al. 2019; p. 341ff; Shafiq et al. 2018, p. 1076ff) and none describing a blue ocean strategy in an IIoT platform context. Therefore, the primary research question (PQ) is:
PQ: Which blue ocean strategy has the best potential to set industry standards and establish an IIoT platform in the manufacturing sector?
Currently, most machine manufacturers rely on in-house developed IIoT platforms (Bender et al. 2020, p. 10f), although using an external platform would reduce duplication costs and provide access to existing applications and customers (Evans & Schmalensee 2008, p. 673). This suggests that currently available external IIoT platforms do not sufficiently cover customer needs. To better understand machine manufacturers’ needs and their motivation, the first sub-question (SQ) is therefore:
SQ1: What functionalities do the manufacturers’ platforms include and how were they implemented? Why have machine manufacturers decided to develop their own platform?
The four actions framework in the blue ocean literature suggests that product attributes need to be raised or created to increase the customer value and create new demand while others are reduced or eliminated to achieve cost leadership (Kim & Mauborgne 2015, p. 51). To assess and extend UMH’s solution approaches, the second sub-question is:
SQ2: 11 What functions or features are currently missing from existing platforms on the market? Which attributes must be raised to fulfill the desired customer benefits?
Finally, making the core of the software stack open source is a relevant part of UMH’s disruptive business model. Open source reduces costs and dependence and promotes among other things value creation. Dedrick and West (2004, p. 5f) found that the perceived reliability of Linux-operated servers was lower than that of servers with a proprietary operating system, which could also be the case for an open-source IIoT platform. To examine the effects of the open-source approach the third sub-question is:
SQ3: How does an open-source approach affect the value curve and how is it perceived by machine manufacturers?
To answer these research questions, this thesis first reviews the state of research on digitization and IIoT, platforms, and technology adoption of a market. Next, UMH and its open-core concept are presented based on an implemented proof of concept at the Digital Capability Center (DCC) Aachen. UMH’s competitors are then clustered into infrastructure providers, proprietary IIoT platforms, and system integrators, for which value curves are generated that show the current focus of the providers on the market. Hypotheses are formulated about the requirements of the IIoT market based on the literature, a conversation with Bender and Lewandowski (2021; authors of the underlying paper Bender et al. 2020), and an existing market research by UMH (2020). The hypotheses facilitate the preparation of the three-part interview guideline, each dedicated to answering one sub-question. Finally, the interviews with eight development managers at machine manufacturing companies are evaluated. Based on the findings, the hypotheses are assessed and the blue ocean strategy for UMH open core and premium is derived, thus answering the primary research question.