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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.
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.
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.
In an increasingly changing market environment, the long-term survival of companies depends on their ability to reduce latencies in adapting to new market conditions. One strategy to meet this challenge is the anchoring of data-driven decision making, which leads to an increasing use of advanced information technologies and, subsequently, to an increase in the amount of data stored. The complexity of processing these data spurred the demand for advanced statistical methods and functions called Business Analytics. Companies are, despite all promised benefits, overwhelmed with the implementation of Business Analytics as indicated by a failure rate of 65 to 80 %. This paper provides an empirically validated, multi-dimensional model that takes an integrative look at critical success factors for the implementation
of Business Analytics and based on which management recommendations can be generated. For this purpose, constructs of the model are conceptualized, before a structural equation model is developed. This model is then validated with data from 69 industrial partners in the food industry. It is shown amongst others, that the three success factors top management support, IT infrastructure and system quality are pivotal to increase the company performance.
This paper presents a simulation approach for service production processes on the basis of which an optimal operating point for service systems can be identified. The approach specifically takes into account the characteristics of human behavior. The simulation is based on a system theory approach to the service delivery process. A specific use case of the simulation approach is presented in detail to illustrate how characteristic curves are deduced and an optimal operating point is obtained.
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.
Technologiebasierte Leistungssysteme versetzen den Werkzeugbau am Hochlohnstandort Deutschland in Zukunft in die Lage, nachhaltige Wettbewerbsvorteile zu generieren. Dazu ist es allerdings erforderlich, nicht nur die Technologiebasis in Form von Transponder- und Sensortechnik in das Werkzeug zu integrieren, vielmehr ist es nötig, entsprechende neue Geschäftsmodelle für diese Leistungssysteme zu entwickeln. Außerdem ist sicherzustellen, dass die Geschäftsmodelle auf operativer Ebene auch mit der Technologie harmonieren und die gewonnenen Daten entsprechend in die Auftragsabwicklungsprozesse integriert werden. Der vorliegende Beitrag stellt potenzielle neue Geschäftsmodelle für den Werkzeugbau vor und skizziert einen Ansatz zur operativen Integration der benötigten Informationen in die Geschäftsprozesse.
Der vorliegende Beitrag baut auf den Arbeiten eines Forschungsprojekts auf. Das Forschungsprojekt 'TecPro - Geschäftsmodelle für technologieunterstützte, produktionsnahe Dienstleistungen des Werkzeug- und Formenbaus' wird mit Mitteln des Bundesministeriums für Bildung und Forschung (BMBF) innerhalb des Rahmenkonzepts "Forschung für die Produktion von morgen" (Förderkennzeichen 02PG1095) gefördert und vom Projektträger Forschungszentrum Karlsruhe, Bereich Produktion und Fertigungstechnologien (PTKA-PFT), betreut.
Digital Leadership
(2020)
This article describes digital leadership-specifically character and competency-that differentiate digitally mature organizations from digitally developing organizations. We assess the differentiated actions of leaders of digitally mature organizations and discuss their results. The study is based on Patterns of Digitization survey with insights from 559 decision makers across five geographic regions-America, Europe, Asia, Africa, and Oceania designed to assess how companies are implementing digital transformation, the various strategies they employ, the investments they make, and the actions they take to achieve large-scale institutionalized digital transformations. The insights gleaned from the study should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to catch up.
Digitalization offers enormous opportunities not only to optimize operational processes, but also to redefine creative processes, e.g., in the area of innovation. This is becoming increasingly important in light of the fact that innovation is increasingly taking place in ecosystems, which means that an enormous amount of collaboration must be enabled in distributed and interdisciplinary teams. To be successful in this, innovation teams need easy access to the multitude of methods and assistance in selecting the appropriate method for the specific task. To this end, we propose a classification framework that structures methods from innovation management and service design based on higher-level task areas. The framework was developed and evaluated together with several companies. Results were implemented in the form of a playbook that won the red dot design award. [https://link.springer.com/chapter/10.1007/978-3-030-80840-2_22]