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Digitalization is invading every aspect of our lives and modern technologies are at the helm of much disruptive change in all spheres of life. Hailed as the 4th industrial revolution every company has a mind to understand the implications of the Industry 4.0 suit of technologies and their multiple innovative applications for its operations. In this paper, we explore how the industry 4.0 transformation might affect Small and Medium sized
enterprises in Germany over a 15-year horizon. We focus on SMEs because they play a significant role in ensuring the prosperity of Germany as a global industrial and economic
powerhouse. We develop alternative pictures of the possible futures using the foresight technique of Scenario planning in which the factors that shape the business environment
SMEs and indeed all companies operate in are identified and used to build the most plausible alternative realities. The outcome is four distinct scenarios that reflect the possible growth trajectories regarding the impending transformation for SMEs.
This bachelor thesis deals with the changing qualification requirements caused by In-dustry 4.0 and provides an overview of the current qualifications of employees and the accompanying future training measures to improve their qualifications, using Daimler and its strategies as an example. The results regarding necessary future qualifications are derived from an extensive literature research as well as an employee survey and qualitative expert interviews. Previous studies depict that due to Industry 4.0 and the transformation from internal combustion engines to electrical engines many current job positions are dissolving, but in return new fields of work are being created. Conse-quently, to see what challenges companies and employees will face in the future, the-oretical concepts were described and analyzed. As theoretical basis the resource-based, competence based and knowledge-based view as well as the strategic leader-ship approach were chosen, which explain how companies grant their competitive ad-vantage and future success. Afterwards, the theoretical foundations were applied to the resources, competences, knowledge and leadership styles relevant to Industry 4.0. In addition, the company, which served as the research object, was presented with its business units, departments and strategies. Conclusively, the results state that it is necessary and possible to invest primarily in basic knowledge using further training measures. Additionally, more intensive communication is required for the effi-cient implementation of the company's strategy. Lastly, recommendations regarding training measures to increase competences, limitations and further measures are dis-cussed.
With today’s trends of higher flexibility in production processes and Industry 4.0, there is a heightened demand for flexible sensor solutions. SICK IVP is catering to this demand by providing highly customizable vision sensor systems. However, in the past, customization was mainly done by experts in the vision market and so, vision sensors were primarily sold in a B2B market. Recent development
towards easier to use micro-software solutions, so called apps, enable the end user to develop his own solutions. This allows SICK to market their vision products more directly to the end user, or in other words
in a B2C market.
These trends necessitate new marketing strategies and User Experience Design. Accordingly, this thesis evaluates the current marketing approach for the SICK vision apps, namely, the SICK AppPool and sick.com by mapping customer experience for a specific given task. In-depth empirical research on
customer experience was conducted. Finally, recommendations on a marketing approach for the SICK vision apps, including training, a pricing model and a UX Design concept are given.
Within the 21st century data are the new raw material, is what Ms. Angela Merkel said at the CEBIT conference in 2016. Digitization, what means data-analysis in real-time as well as fast and steady changes in the technological environment, is a key factor nowadays. New processes, new daily tasks and new know-how are needed to survive in a steadily changing world. In succession, companies and private households need to adapt. Otherwise, these will be selected according to Darwin’s theory of evolution. “Survival of the smartest” – as the mantra of today. While some companies are already familiar with the changes introduced by industry 4.0, others are still challenged with catching up industry 3.0. In a smart world it is important to know in which digital maturity status the company is staying and where the company sees itself in the future. But especially for smaller companies the obstacles of realizing industry 4.0 or digitization are defined by high investment costs, a lack of human resources and high requirements in data protection. Therefore, the following thesis is handling this topic specifically within procurement and gives answers to the following questions: What is procurement 4.0? Do the massive changes mean the procurement-endgame? What are the new challenges that procurement has to face and what are the new competencies a purchaser has to adopt? On the basis of a survey the procurement department of the Sto SE & Co. KGaA is classifying the own digital Status quo in the digital capability maturity model. On the basis of the current project “Implementing a Supplier-Relationship-Management and Collaboration system (SRM)” the thesis is answering to the research question: “What approach is suitable to introduce an SRM-and Collaboration tool and how to implement this tool specifically in procurement?”
Cylindrical grinding is an important process in the manufacturing industry. During this process, the problem of grinding burn may appear, which can cause the workpiece to be worthless. In this work, a machine learning neural network approach is used to predict grinding burn based on the process parameters to prevent damage. A small dataset of 21 samples was gathered at a specific machine, grinding always the same element type with different process parameters. Each workpiece got a label from 0 to 3 after the process, indicating the severity of grinding burn. To get a robust neural network model, the dataset has been scaled by augmentation controlled by grinding experts, to generate more samples for training a neural network model. As a result, the model is able to predict the severity of grinding burn in a multiclass classification and it turned out that even with little data, the model performed well.
The rise of digital twins in the manufacturing industry is accompanied by new possibilities, like process automation and condition monitoring, real time simulations and quality and maintenance prediction are just a few advantages which can be realized. This paper takes a novel approach by extracting the fundamental knowledge of a data set from a production process and mapping it to an expert fuzzy rule set. Afterwards, new fundamental augmented data is generated by exploring the feature space of the previously generated fuzzy rule set. At the same time, a high number of artificial neural network (ANN)models with different hyperparameter configurations are created.
The best models are chosen, in line with the idea of survival of the fittest, and improved with the additional training data sets, generated by the fuzzy rule simulation. It is shown that ANN models can be improved by adding fundamental knowledge represented by the discovered fuzzy rules. Those models can represent digitized machines as digital twins. The architecture and effectiveness of the digital twin is evaluated within an industry 4.0 use case.