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The digital transformation of companies is expected to increase the digital interconnection between different companies to develop optimized, customized, hybrid business models. These cross-company business models require secure, reliable, and traceable logging and monitoring of contractually agreed information sharing between machine tools, operators, and service providers. This paper discusses how the major requirements for building hybrid business models can be tackled by the blockchain for building a chain of trust and smart contracts for digitized contracts. A machine maintenance use case is used to discuss the readiness of smart contracts for the automation of workflows defined in contracts. Furthermore, it is shown that the number of failures is significantly improved by using these contracts and a blockchain.
Distributed machine learning algorithms that employ Deep Neural Networks (DNNs) are widely used in Industry 4.0 applications, such as smart manufacturing. The layers of a DNN can be mapped onto different nodes located in the cloud, edge and shop floor for preserving privacy. The quality of the data that is fed into and processed through the DNN is of utmost importance for critical tasks, such as inspection and quality control. Distributed Data Validation Networks (DDVNs) are used to validate the quality of the data. However, they are prone to single points of failure when an attack occurs. This paper proposes QUDOS, an approach that enhances the security of a distributed DNN that is supported by DDVNs using quorums. The proposed approach allows individual nodes that are corrupted due to an attack to be detected or excluded when the DNN produces an output. Metrics such as corruption factor and success probability of an attack are considered for evaluating the security aspects of DNNs. A simulation study demonstrates that if the number of corrupted nodes is less than a given threshold for decision-making in a quorum, the QUDOS approach always prevents attacks. Furthermore, the study shows that increasing the size of the quorum has a better impact on security than increasing the number of layers. One merit of QUDOS is that it enhances the security of DNNs without requiring any modifications to the algorithm and can therefore be applied to other classes of problems.
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.
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.
This study aims to deliver a strategic and systematic analysis about the consumer loans banking business with the integration of industry 4.0. Industry 4.0 itself comprise of a lot of things in regards of advance technology being used in order to create more value to the company. The purpose of this study also determine which components of industry 4.0 are suitable and applicable in the consumer loans banking business. This thesis uses secondary data as a primary resource to provide research objective. The secondary data collected through the use of published journal literature as well as the academic literatures. The research starts by providing the general information and literature about the consumer loans banking business and the industry 4.0 as well. Following by the reason why the consumer loans banking business need the technological advancement of industry 4.0. Next, this thesis also gathers the quantitative data related to financial measurement to provide more understanding the consumer loans banking business. The analysis is carry out by using the three level of environmental analysist segmentation in which commonly use for the industry analysis. The first factor is the PEST analysis, it resembling the external factor of the industry, second is the Porter’s five forces to explaining the operating forces within the industry, the third is the value chain analysis. The findings of this thesis pose important implication for bank consumer loans business stakeholders with the purpose to integrate or related to industry 4.0 technological advancement, providing enough information about the keys factor and analysis behind the industry.