Digital Business Models in Industrial Ecosystems

The need to digitally transform is omnipresent in almost every company. Nevertheless, many companies are currently still failing to holistically apply the implications of increasing digitalization to their business model. Thus, this paper aims to analyze drivers, technological elements as well as the success of companies on their way from digitization efforts to a digital business model. This study utilizes a representative longitudinal online survey that covers key stakeholders in logistics and SCM. Our findings show that on the way from digitization (simply transferring analog processes to digital ones) to a digital business model, companies perceive increased opportunities and reduced risks. They expand their focus on cost reductions to new ways of increasing revenues. Technological concepts that contribute to generate a digital twin of the material flow, as well as the usage of platforms/IT services and forecasting methods, are of essential and increasing importance. Companies in the early stages of their way to a digital business model seem to misjudge the potential of concepts like predictive analytics. We finally can show that it is worth taking the long way. The further companies are on their path, the higher is their adaptability to key trends. Our results contribute to the research on digital business models and provide insights for practitioners on how to effectively tread the path to a digital business model.

Disruptive technologies in the context of Industry 4.0, such as the Internet of Things, Big Data Analytics, smart devices, or Artificial Intelligence are developing at a rapid pace with an increasing impact on global value creation. Companies are facing major challenges in maintaining an overview of these developments and linking them to other trends. Thus, the aim of this article is the development of a comprehensive trend management process for the structured collection and processing of Industry 4.0 trend information. The process is divided into the steps identification, analysis and evaluation, processing, and preparation and is supplemented by continuous monitoring and improvement through knowledge management. Special attention is paid to the development of Industry 4.0, which enables an automated collection of information through the application of web and text mining, which can significantly improve the preparation of information.

Even though a digitalized supply chain can be a veritable driver of performance, industry is still doing far too little to unlock the potential that is hidden in a data-driven supply chain. Yet for any company that seizes this opportunity and systematically exploits the wealth of supply-chain data, an enduring competitive advantage beckons. The game-changer is an end-to-end supply chain, based on standardization and data transparency, that permits largely automated, AI-assisted, and highly efficient planning processes. In this respect, supply-chain planning and production scheduling go hand in hand as building blocks of the factory of the future, whose operations are digitalized and highly flexible. There are four fundamental elements for achieving this:

Combining cross-company and company-internal digitalization projects can play a major part in making processes more robust, conserving resources, enhancing flexibility, and making the supply chain future-proof.

Subscription business models provide an important component for monetizing the potential of Industrie 4.0. Subscription business is based on a long-term and participative business relationship between customer and provider. However, only digitalization offers the necessary framework conditions to realize the characteristic recurring and performance-based billing, and to ensure the necessary transparency about the usage phase of products as well as continuous performance improvements in the customer process. Against this background, companies must not only recognize the much-cited potential that lies in the total dedication to the success of individual subscription customers. Rather, the central obstacles must be addressed, examined, and subsequently overcome in a targeted manner in order to successfully establish subscription business models and place them on the market.

Today more than ever, industrial suppliers are facing existential challenges. The ever-faster pace of technological change, the emergence of innovative business models, and the increase of servitization are threatening the very existence of entire industries. In view of these challenges, the question arises as to how established companies in the supplier industry can develop strategies and generate innovations that can ensure the long-term survival of these companies. This article answers this question by examining a current practical case in the rotating machinery market. Using Schaeffler OPTIME, a digital condition monitoring solution, the challenges and benefits of digital business models for traditional industrial suppliers are highlighted. Thus, the article provides a valuable contribution to a better understanding of new digital business models in changing ecosystems. At the same time, the article provides a best practice for those responsible for innovation in industrial companies.

The Corona pandemic has highlighted the necessity for enterprises resilience, i.e. the ability to withstand systemic discontinuities and adapt to new risk environments. Companies can adjust to these challenging circumstances by constantly reinventing themselves. German industry is world-renowned for its engineering of technical products and services. Due to the success of US-American and Chinese consumer-oriented platform-based business models, the question arises if German companies’ approaches to develop industrial business-to-business platforms can enhance their resilience in light of challenges such as the digital transformation, or systemic shocks, such as global trade conflicts and the Corona pandemic. Based on a categorisation of Germany’s B2B landscape into data-centric and transaction-centric platforms, the paper draws on recent quantitative studies to analyse the potential of German B2B platforms for augmented enterprise resilience in industry and industry-related services. The paper showcases that B2B platforms are a cornerstone for implementing Industry 4.0 and thus the digital transformation from a product-centric economy to a model that smartly combines physical engineering with data-based, digital value-added services. At the same time, digital B2B platforms help to minimise CO₂ emissions, to cut back on production and logistics costs and to maximise the efficient utilisation of R&D, production and transport capacities.

Thanks to comprehensive networking possibilities and improved information flow, platforms present themselves as catalysts of the logistics industry’s digital transformation. Providing a (digital) infrastructure enables the value-creating exchange of information, goods, or services between several players, the basis for the emergence of a functioning two-sided market. It is often startups that appear as new players in the digital logistics market, not infrequently from outside the industry and with a pronounced affinity for ICT. Digitization makes it significantly easier for new providers to enter the market, as entry barriers in the form of capital-intensive investments in vehicle fleets or warehouse capacity are eliminated. The market for digital logistics platforms is developing dynamically and offers a heterogeneous range of services that cannot be clearly defined and delimited. In addition, there is no meaningful overview of the provider structure. Platform users are faced with the question of which platform is best suited with its range of services for specific performance characteristics. This paper addresses these challenges and aims to segment the service spectrum of digital logistics platforms. This approach forms the basis for providing shippers, logistics service providers, platform operators, and investors with a market overview of the logistics’ digital platform landscape.

Digital platforms interconnect small and medium-sized enterprises (SMEs) to facilitate their demand-driven collaboration for tendering and manufacture, which calls for changes in their business models. We define what a collaborative business model is in the context of SME manufacturing, layout aviation, and automotive collaborative business models developed during the EU-funded project DIGICOR (2016–2019) and generalize the collaborative business model for the ‘platform of platforms’ to support the development of Industry 4.0 in the European Union. In particular, this model supports SMEs working in ‘lot size of one’ and circular economy manufacturing.

Current developments and trends in the business environment, like digitalization or servitization, are transforming industrial value creation logic and especially business models. Therefore, this study analyzes the impact of data on industrial business models and how newly emerging industrial data-driven business models (IDDBMs) can be systematically clustered. Given the novelty of the research topic, we choose an exploratory study design that consists of two parts. Firstly, we analyze the body of literature on data-driven business models and discuss existing clustering approaches, including elemental classifications and archetype schemes. Using the insights from the analysis of our literature sample, we develop a preliminary conceptual-theoretical classification framework. In a second step, we match, adapt, and enrich this initial framework with empirical data from seven expert interviews, strengthening practical embeddedness and generalizability. The resulting IDDBM cluster framework consists of six clusters for IDDBMs that differ in the manner by which data is transferred to the customers. While business models in the first cluster extensively use data to improve their product portfolio, in the fifth cluster, data is only used to provide a service to the customers. Business models in the sixth cluster, in contrast, generate value with their data alone without any product or service in the process. Thereby, our model expands the well-established goods-to-service continuum towards a goods-service-data continuum. Against the backdrop of the currently proposed service-dominant logic, we propose a data-dominant logic to sensitize industrial companies for the disruption that is about to influence their business models. Therefore, several recommendations are given for corporate practice to adapt to this uprising new business environment. At the same time, our model is suited to function as a research agenda on IDDBMs by channeling and structuring future research efforts into a unified framework.

Data-driven business models are modern ways for innovation in traditional environments. The vision of the Internet of Things (IoT), by making products smart and leveraging the data into services, is a modern paradigm of value creation. A representative case study evolved business models for industrial containers in the automotive industry into a cloud-based data-driven business model. This publication outlines the background for industrial containers and provides insights into the relevant technological architectures. Additionally, the data-driven business model and the expansion of the ecosystem are outlined. Thereby this publication enhances the necessary interdisciplinary understanding of business model research and information science.

Artificial intelligence (AI) is often seen as one of the main enabling technologies behind Industry 4.0, especially within the next decade. In this chapter, we reflect on a frequently neglected type of adoption hurdles, founded in the psychology of the individuals purchasing, developing, and working with those types of systems. Within this work, we integrate both insights from the latest scientific releases on the industrial application of AI as well as our practical experience from that field to a handy six-point list of psychological key success factors. That list is supposed to help both managers and practitioners in the field to ensure the effective deployment of AI within their organizations in the future.

Blockchain presents multiple use cases for supply chain management. As supply chains are increasingly complex and global inter-organizational networks, trust and transparency are key success factors. Blockchain provides a promising solution to address supply chain transparency by enabling product tracing along the supply chain, potentially ensuring product authenticity and provenance as well as ethical production. This chapter focuses on supply chain traceability for luxury goods, where counterfeits present a special challenge. Three case examples in luxury goods illustrate the potential benefits of blockchain.

The implementation of Industry 4.0 brings about potential gains for firms in terms of efficiency, productivity, and the generation of new sources of income. A process that offers considerable opportunities for revenue generation is what is known as servitization. Both features (Industry 4.0 and servitization) are complementary and have critical implications for territories as regards technological upgrading and the diversification of their economy. Here, the extent to which the managerial conditions for transitioning towards Industry 4.0 and, specifically, for adopting digital technologies can influence small manufacturers’ attitudes towards servitization is examined. The empirical study is carried out using both a quantitative approach based on an ordered logit model and a qualitative analysis of 174 manufacturing SMEs in the province of Gipuzkoa (Basque Country, Spain). Preliminary results show that firms’ awareness of external and internal conditions for Industry 4.0 can be used as a lever of servitization. Furthermore, firms with a higher level of digitalization and involved in activities with a medium to a high level of technology intensity were found to have a more favourable attitude towards servitization.

In the realities of Industry 4.0, we can find the main two ways of building business models: developing existing models toward Digital Maturity and building completely new models, different from previous solutions. In the automotive industry, it seems that the search for development toward Digital Maturity, which currently ensures the development of the industry, may have its limitations. The text proposes to consider a business model taken from outside the automotive industry as a development proposal beyond Digital Maturity.

Studying the geographical factors for Industry 4.0 implementations is a novel research line. Only a few studies tried to define the term Geography 4.0, and we are lack empirical evidence of potential regional differences. Besides, the use of digital technologies is a key element and a triggering factor in the business model innovation nowadays. Therefore, we conducted empirical research among 302 Industry 4.0 aware business to business companies in various regions of four Central Eastern European countries to reveal the associations between the use of digital technologies and geographical factors. We found that business model innovation towards Industry 4.0 triggered by the use of digital technologies is more advanced in central and knowledge-intensive regions in the case of high-value activities, which may cause further divergence and not a convergence of regions.