Attribute Identification and Predictive Customisation Using Fuzzy Clustering and Genetic Search for Industry 4.0 Environments

Today´s factory involves more services and customisation. A paradigm shift is towards “Industry 4.0” (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment

Determinants of the Profitability of Islamic Rural Banks During Covid-19 in Indonesia

The Return on Asset (ROA) value of Islamic rural banks (BPRS) keeps decreasing during the Covid-19 pandemic, specifically from 2020 to 2021. The ROA value of Sharia Commercial Bank and Sharia Business Unit also decreased in 2020 but increased in 2021. During the pandemic, many financial institutions were in trouble, but BPRS was still able to survive in the midst of a crisis. This phenomenon attracted some scholars to study the factors that made BPRS survive during a pandemic. This study aims to analyze the effect of internal and external factors on Islamic rural banks’ profitability from the second quarter of 2020 to the first quarter of 2022. The sample used consisted of 134 Islamic rural banks with complete data to be analyzed. This study used panel data regression with ROA as the dependent variable. The result of regression shows that partially, FDR has a positive impact on ROA, whereas NPF and OER hurt ROA. On the other hand, CAR, GDP, and CPI have no impact on ROA. The finding shows that the internal factors of the BPRS have an important role in dealing with crises during a pandemic. The government is expected to support the digital transformation of BPRS in order to increase the efficiency and convenience of BPRS, so the public is attracted to join BPRS

Determinants of the Profitability of Islamic Rural Banks During Covid-19 in Indonesia

The Return on Asset (ROA) value of Islamic rural banks (BPRS) keeps decreasing during the Covid-19 pandemic, specifically from 2020 to 2021. The ROA value of Sharia Commercial Bank and Sharia Business Unit also decreased in 2020 but increased in 2021. During the pandemic, many financial institutions were in trouble, but BPRS was still able to survive in the midst of a crisis. This phenomenon attracted some scholars to study the factors that made BPRS survive during a pandemic. This study aims to analyze the effect of internal and external factors on Islamic rural banks’ profitability from the second quarter of 2020 to the first quarter of 2022. The sample used consisted of 134 Islamic rural banks with complete data to be analyzed. This study used panel data regression with ROA as the dependent variable. The result of regression shows that partially, FDR has a positive impact on ROA, whereas NPF and OER hurt ROA. On the other hand, CAR, GDP, and CPI have no impact on ROA. The finding shows that the internal factors of the BPRS have an important role in dealing with crises during a pandemic. The government is expected to support the digital transformation of BPRS in order to increase the efficiency and convenience of BPRS, so the public is attracted to join BPRS

Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements

Machine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper

Machine Learning on Large Databases: Transforming Hidden Markov Models to SQL Statements

Machine Learning is a research field with substantial relevance for many applications in different areas. Because of technical improvements in sensor technology, its value for real life applications has even increased within the last years. Nowadays, it is possible to gather massive amounts of data at any time with comparatively little costs. While this availability of data could be used to develop complex models, its implementation is often narrowed because of limitations in computing power. In order to overcome performance problems, developers have several options, such as improving their hardware, optimizing their code, or use parallelization techniques like the MapReduce framework. Anyhow, these options might be too cost intensive, not suitable, or even too time expensive to learn and realize. Following the premise that developers usually are not SQL experts we would like to discuss another approach in this paper: using transparent database support for Big Data Analytics. Our aim is to automatically transform Machine Learning algorithms to parallel SQL database systems. In this paper, we especially show how a Hidden Markov Model, given in the analytics language R, can be transformed to a sequence of SQL statements. These SQL statements will be the basis for a (inter-operator and intra-operator) parallel execution on parallel DBMS as a second step of our research, not being part of this paper

DEVELOPMENT OF A CONCEPTUAL FRAMEWORK FOR INTEGRATING INTELLIGENT-PRODUCT STRUCTURES INTO A FLEXIBLE MANUFACTURING SYSTEM

Industrial Engineerin

Customizing the Product Life Cycle and its Management for the Optimal Handling of Digital-Physical Products within the Railway Industry

Competitive products are considered as an essential source of corporate success, which is why their continuous development plays a decisive role. Thereby, diverse product innovation results from the integration of digital solutions in physical products. Such so-called "digital-physical" products are hybrid products, meaning neither purely physical nor purely digital, in which information technology is an integral component and plays a significant role in determining the functionality of the product. Digital-physical products enable companies to use new sales strategies, services and business models along the product life cycle to ultimately increase the product's sales. This creates various opportunities outside of product development, especially for sectors such as the railway industry, whose products are required to have a long service life. At the same time, this affects the management of the product along the life cycle and leads to a change in company-specific processes, concepts and models. Particular attention is paid to product management, which is responsible for controlling and planning the activities of a product. Despite the obvious changes caused by digital-physical products, many companies still use their previous concepts and models like the life cycle model in product management and only adapt them inadequately to the new circumstances. This can result in ineffective and inefficient management of products. Researchers have recognized this importance and are therefore focusing on adapting life cycle concepts for the purely digital world. However, little is known about the discussions around adapting life cycle concepts for the transition between physical and digital worlds, especially outside of product development. Therefore, the aim of this study was to explore the influence of digital-physical products on the market phase of the classic product life cycle model and the product management as well as product managers. Specifically, the product life cycle model within Siemens Mobility GmbH (Business Unit Rail Infrastructure) and its product management were to be adapted to digital-physical products. In this study, the inductive-qualitative research approach was used, and the case study served as the research strategy. The case study was well-suited for this study as it was necessary to delve deep into the subject matter to gain an understanding of the benefits of digital-physical products and to identify and analyze the impact on the traditional product life cycle and product management. Expert interviews and focus groups were used as data collection methods to benefit from the personal experiences of the experts, most of whom work with digital-physical products, and thus generate knowledge together. With the help of these methods, the perspectives and experiences of the experts in this field could be revealed and understood in order to achieve the objectives of the study. As a result, the research has shown that digital-physical products have a significant influence on the classic product life cycle and that a change in the way these products have been handled so far is necessary. In this context, considering the boundary conditions of the railway industry and the aforementioned company, the product life cycle model was expanded to include an integrated digital cycle within the product life cycle and a new role, the digital product manager, was introduced. Furthermore, as a result, the previous product management was organizationally supplemented by a digital portfolio in order to be able to optimally manage digital-physical products. The study thus provides a scientific contribution on the correlation between digital-physical products and the theory of the product life cycle model. The previously missing understanding of how digital-physical products interact within the framework of the product life cycle model as well as its impact on product management was addressed in this thesis. In this way, a contribution was made to the product life cycle theory by mirroring and integrating the nature and characteristics of digital-physical products. The findings and changes resulted in a product life cycle model and product management adapted for digital-physical products. In practice, the study helps to better structure the often-reactive behaviour of a product manager in relation to these products. The orientation towards a second cycle that focuses on the digital part of a product helps with strategic decision-making and roadmap planning in practice