Gerhard Gudergan
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- Business Transformation (8) (remove)
Companies in the manufacturing industry are shifting towards a more service-oriented business model. One major challenge of this transformation is the information exchange between the different stages of the product-service-lifecycle.
We extend the existing body of knowledge by conducting an empirical study in the German manufacturing industry, addressing the cause-effect relationship between 1) information gathering over the product-service-lifecycle, 2) data analytics 3) interpretation and use of new information and 4) distribution of new product related information and the impact of these four aspects on performance.
The analysis reveals five different success factors with a significant impact on innovation and operation excellence. The implications from our research can help to develop new and more practical oriented Lifecycle-Product-Service-System approaches on the one hand. On the other hand it enables companies to focus on activities leading to higher service efficiency. Creating new stimuli will transform their existing business model to a more service-oriented one.
Patterns of Digitization
(2019)
This article describes the results of a survey designed to assess how companies are implementing digital transformation, including the various strategies they employ and the actions they take to achieve large-scale transformations. While a few companies seem to reach front-runner status, the majority seem to lag behind. This phenomenon is a top concern of boardrooms worldwide and motivated the development of this study. To help these organizations, we highlight differentiated strategic principles and characteristics of the companies' design processes digitally mature companies undertake to transform their businesses. These insights should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to enforce this transformation.
The acquisition, processing and analysis of internal and external data is one of the key competitive factors for corporate innovation and competitive advantage. Many firms invest a significant amount of resources to take advantage of advanced analytics methods. Machine learning methods are used to identify patterns in structured and unstructured data and increase predictive capabilities. The related methods are of particular interest when previously undiscovered and unknown structures are discovered in comprehensive data sets in order to more accurately predict the outcome of manufacturing or production processes based on a multitude of parameter settings. So far, this knowledge is often part of the individual or collective knowledge of experts and expert teams, but rarely explicit and therefore not replicable for future applications. On the one hand, it is demonstrated in this paper how different machine learning algorithms have been applied to better predict the output quality in the process industry. On the other hand, it is explained how the application of machine learning methods could contribute to making previously not accessible process knowledge explicit. In order to increase the prognostic accuracy of the model diferrent methods were combined, later on compared and evaluated within an industrial case. In this paper a comprehensive approach to knowledge-based process engineering is being presented.
The almost boundless possibilities of realizing saving potentials and innovations drive manufacturing companies to implement Business Analytics as part of the digitalization roadmap. The increasing research within the field of algorithm design and the wide range of user-friendly tools simplify generating first insights from data also for non-professionals. However, small and medium sized companies struggle implementing Business Analytics company-wide due to the lack of competencies. Especially the customization of a multitude of analytic methods in order to match a superordinate, business-relevant question is not done easily. This paper enables researchers as well as practitioners to close the gap between business relevant questions and algorithms. From a practical point of view, this paper helps shortening the search time for a suitable algorithm. Out of a research perspective, it aims to help positioning new algorithms within a structured framework in order to enhance the communication of algorithms’ capabilities.
Digital Leadership – Which leadership dimensions contribute to digital transformation success?
(2021)
The digital transformation of industry and
society continues to advance. While some companies are
achieving trailblazer status, others are finding it difficult to
manage or even initiate the necessary changes. Top-level leaders
play a central role in these transformational processes, as they
have the opportunity to directly or indirectly influence decisive
variables. In this article, we present the results of interviews
with 13 digital leaders who have successfully implemented the
necessary changes for the digital transformation of their
companies. The results of the interviews provide key dimensions
for leaders to digitally transform their companies.
The rapid developments in information and communication technology enable new bus iness models that are based on digital platforms. Marketplaces such as Amazon or Airbnb have already adapted this business model to connect previously unconnected supply-side and demand-side to conduct a business transaction via a digital platform. Due to Industrie 4.0 and the rapid technological development that comes with it, digital platforms have entered the market within the area of the mechanical engineering. Different platform types exist, such as marketplaces for machine equipment or digital data platforms for connected machines. Although numerous companies claim to offer platform-based bus iness models, they often lack knowledge on individual business model components. To close this gap, this paper structures a variety of existing platforms based on their detail characteristics. Within this paper, existing typologies of digital platforms from other industry areas are analyzed. Case study research ofplatforms within the mechanical engineering is used to adjust these typologies and create a new one for digital platforms within the mechanical engineering.
In the course of the advancing digitalization, new business fields are characterized by a mixture of competition and cooperation of the actors involved. MOORE (1993) postulates that in analogy to natural ecosystems, long-term successful companies also operate in comparable network structures. In this context, there are pronounced controversies about the extent to which there are leading actors in such a business ecosystem and to what extent they can control the entire system. Similarly, it is largely unclear where the boundaries of a business ecosystem actually lie and how meaningful selective boundaries are. Especially the extent of the coopetition proves to be characteristic for the relationship between the involved actors. Therefore, the aim of this research approach is to develop a new approach for the analysis of corporate ecosystems. To ensure applicability, the developed approach was validated in a current case study in the telecommunications industry.
Disruptive innovations confront companies with great challenges. Leading companies are losing their market position to disruptive competitors and are forced to react instantly to defend their position in the market. Companies not only lack knowledge of various strategic options that have been successfully used against disruptive attackers, they also do not know about the effects of these different strategic options on their own company. On the basis of a use case analysis, 30 companies were examined with regard to their strategic reaction on a disruptive attacker. In the evaluation of the use cases, the strategic options were grouped into clusters, from which seven master strategies could be identified. These seven master strategies were then transformed into a regulatory framework, which differentiates between reactive and proactive strategies and classifies them according to their intensity. With the help of the identified master strategies, companies will be able to identify options for action in competition with disruptive attackers, thus giving them greater chances of success in the defense of their market position. In addition, companies can use the master strategies to prepare an emergency strategy even before a disruptive attacker appears on the market, thus significantly minimizing the risk of customer loss.