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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.
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.
Currently, a digital transformation is taking place in logistics and manufacturing environments of the Robert Bosch GmbH. This digital transformation consists of several central initiatives, resulting in big impacts on the organizations of the business units. This paper aims to provide an overview of these organizational changes with regard to the role of change management. After identifying the organizational setting at Bosch, the ongoing digital transformation at the business unit Powertrain Solutions is analyzed by focusing on the future collaboration strategy between logistics and manufacturing as well as the organizational transformation along the Value Stream Integrated Processes and IT program. The complexity of the ongoing transformation is narrowed down to the changes in processes and IT-landscapes before elaborating the future interaction between the landscape documentation tools “scout.it” and “Lean IX”. The readiness of plants in preparation for the Bosch Manufacturing and Logistics Platform is determined by creating a dashboard using Microsoft Power BI.
Ahead of the analytical work, the paper considers theoretical insights about the current state of research in terms of the digital development and change management to ensure successful planning, implementation and preservation of future organizational changes at Bosch.
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.
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?”
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.
In the context of Industry 4.0, smart factories use advanced sensing and data analytic technologies to understand and monitor the manufacturing processes. To enhance production efficiency and reliability, statistical Artificial Intelligence (AI) technologies such as machine learning and data mining are used to detect and predict potential anomalies within manufacturing processes. However, due to the heterogeneous nature of industrial data, sometimes the knowledge extracted from industrial data is presented in a complex structure. This brings the semantic gap issue which stands for the lack of interoperability among different manufacturing systems. Furthermore, as the Cyber-Physical Systems (CPS) are becoming more knowledge-intensive, uniform knowledge representation of physical resources and real-time reasoning capabilities for analytic tasks are needed to automate the decision-making processes for these systems. These requirements highlight the potential of using symbolic AI for predictive maintenance.
To automate and facilitate predictive analytics in Industry 4.0, in this paper, we present a novel Knowledge-based System for Predictive Maintenance in Industry 4.0 (KSPMI). KSPMI is developed based on a novel hybrid approach that leverages both statistical and symbolic AI technologies. The hybrid approach involves using statistical AI technologies such as machine learning and chronicle mining (a special type of sequential pattern mining approach) to extract machine degradation models from industrial data. On the other hand, symbolic AI technologies, especially domain ontologies and logic rules, will use the extracted chronicle patterns to query and reason on system input data with rich domain and contextual knowledge. This hybrid approach uses Semantic Web Rule Language (SWRL) rules generated from chronicle patterns together with domain ontologies to perform ontology reasoning, which enables the automatic detection of machinery anomalies and the prediction of future events’ occurrence. KSPMI is evaluated and tested on both real-world and synthetic data sets.