Mining the Automotive Industry: A Network Analysis of Corporate Positioning and Technological Trends

The digital transformation is driving revolutionary innovations and new market entrants threaten established sectors of the economy such as the automotive industry. Following the need for monitoring shifting industries, we present a network-centred analysis of car manufacturer web pages. Solely exploiting publicly-available information, we construct large networks from web pages and hyperlinks. The network properties disclose the internal corporate positioning of the three largest automotive manufacturers, Toyota, Volkswagen and Hyundai with respect to innovative trends and their international outlook. We tag web pages concerned with topics like e-mobility and environment or autonomous driving, and investigate their relevance in the network. Sentiment analysis on individual web pages uncovers a relationship between page linking and use of positive language, particularly with respect to innovative trends. Web pages of the same country domain form clusters of different size in the network that reveal strong correlations with sales market orientation. Our approach maintains the web content's hierarchical structure imposed by the web page networks. It, thus, presents a method to reveal hierarchical structures of unstructured text content obtained from web scraping. It is highly transparent, reproducible and data driven, and could be used to gain complementary insights into innovative strategies of firms and competitive landscapes, which would not be detectable by the analysis of web content alone.Comment: Preprint version to be published in Springer Nature (presented at CompleNet 2020

Towards Predictive Maintenance for Flexible Manufacturing Using FIWARE

Industry 4.0 has shifted the manufacturing related processes from conventional processes within one organization to collaborative processes across different organizations. For example, product design processes, manufacturing processes, and maintenance processes across different factories and enterprises. This complex and competitive collaboration requires the underlying system architecture and platform to be flexible and extensible to support the demands of dynamic collaborations as well as advanced functionalities such as big data analytics. Both operation and condition of the production equipment are critical to the whole manufacturing process. Failures of any machine tools can easily have impact on the subsequent value-added processes of the collaboration. Predictive maintenance provides a detailed examination of the detection, location and diagnosis of faults in related machineries using various analyses. In this context, this paper explores how the FIWARE framework supports predictive maintenance. Specifically, it looks at applying a data driven approach to the Long Short-Term Memory Network (LSTM) model for machine condition and remaining useful life to support predictive maintenance using FIWARE framework in a modular fashion

Requirements Engineering for Servitization in Manufacturing Service Ecosystems (MSEE)

Nowadays, manufacturing enterprises shift to bundle their products with services to satisfy customer needs. This process is called “Servitization”. The European project “Manufacturing SErvice Ecosystem” (MSEE) is developing models supporting Servitization, based on Future Internet architectures and platforms. To allow efficient collaboration for the provision of Product-Service Systems (PSS), the business as well as the ICT environment needs to be adapted. However, stakeholders are typically not aware of all requirements for the transition in the areas of physical resources, organization and IT. This paper presents one of the results of MSEE project: the development of an adequate Requirements Engineering approach

Designing a Privacy Dashboard for a Smart Manufacturing Environment

In smart manufacturing environments sensors are collecting data about work processes. This data likely also contains references to actions of a single worker, which can be considered personal data. Privacy dashboards convey information on what personal data is stored by a system and provide means for users of a system to control what personal data is shared according to their needs. Dashboards put the control over their personal data in the hands of the users. However, to act as a trust building component the dashboard needs to convey or mediate the trade-off between the user's privacy and the benefits of data sharing. This work describes the design process and a elicitation of preliminary requirements for a privacy dashboard that is developed in the context of the the H2020 project HUMAN Manufacturing

Uzbekistan Towards Industry 4.0. Defining the Gaps Between Current Manufacturing Systems and Industry 4.0

Part 3: PLM for Digital Factories and Cyber Physical SystemsInternational audienceWith the advancements in industry technology and applications, many concepts have emerged in manufacturing. Since the term Industry 4.0 was published to highlight a new industrial revolution, many manufacturing organizations and companies in Europe, North and South America are researching on this topic. Even the Industry 4.0 concept is included on government duty, sponsored by national initiatives and research funding. However, developing country like Uzbekistan, with high industrial potential are experiencing a different position and the technology roadmap of accomplishing Industry 4.0 is not clear yet. In the last 20 years, Uzbekistan managed to join the group of lower-middle income countries; the ultimate development goal of the country in the next stage is to reach the development benchmark comparable to the higher-middle income group by 2030. Therefore, this paper aims to depict the current state of manufacturing systems in Uzbekistan and identify the gaps with the Industry 4.0 requirements. The findings of this paper can serve for researches from emerging countries as technological roadmap towards Industry 4.0 paradigm and can assist industrial people in understanding and achieving the requirements of Industry 4.0

PLM Competencies Analysis Based on Industry Demand

Part 10: PLM Maturity, Implementation and AdoptionInternational audienceLast decade recognizes a high job demand, more specialized trainings with very oriented jobs offers. This situation makes hiring and recruitments officers in the difficulty to select and find easily the appropriate candidate as well for candidates to choose the best practices and trainings to find later a respectable position. This work aims to help all actors in the job sector by modeling the Product Lifecycle Management (PLM) competencies and analyzing the demands especially in industry 4.0. First, the enterprises needs, in terms of skills, are identified through various job offers distributed on online media. Job offers are structured according to profile, geolocation and required competencies, etc. Then, the analysis is based on information retrieval and text mining through a statistical measure used to evaluate how important a competence to a job offer in a given collection. This contribution applies the Term Frequency Inverse Document Frequency (TF-IDF) to determine what skills in a corpus of job offers is the most requested in PLM jobs. This contribution addresses more than 1300 job offers, written in French, and posted in France during the period of (2015–2016). The offers cover more than 388 K words, from which 20 types of PLM job titles and 106 terms are related to the job competencies. The obtained results allow us to identify the most requested jobs, skills and classifying jobs and competencies for a better guidance of PLM job actors

Bringing Advanced Analytics to Manufacturing: A Systematic Mapping

Part 5: Variety and Complexity Management in the Era of Industry 4.0International audienceAdvanced analytics has the potential to redefine manufacturing. However, practical implementation is in its infancy. One reason is a lack of management tools that enable decision-makers to choose suitable techniques from advanced analytics for domain-specific problems in manufacturing. This paper uses a systematic mapping review in order to identify seven application areas to which analytics can add substantial value. Each area is then matched with suitable techniques from the field of advanced analytics. The resulting systematic map provides a novel management tool for the purpose of identifying promising analytics projects in manufacturing and thus facilitates decision-making

Intelligent sensing systems – Status of research at KaProm

Within Industrie 4.0 intelligent sensing systems represent an indispensable asset with significant role in enabling shifting from automated to intelligent manufacturing. Instead of being simple transducers, intelligent sensors are able to retrieve useful information from raw signal. They represent systems with integrated computation and communication capabilities, that run sophisticated and real time applicable algorithms and communicate the necessary information to the other elements of the manufacturing facility. In this paper we present the recent research results in the field of intelligent sensing systems that were accomplished at Laboratory for Manufacturing Automation and Laboratory for Robotics and Artificial Intelligence at Department for Production Engineering (KaProm) at Faculty of Mechanical Engineering in Belgrade. Presented systems are intended for application in various manufacturing processes, such as machining, assembly, manipulation, material transport, rubber processing lines. They are based on application of different non-stationary signal processing (Discrete Wavelet Transform, Huang-Hilbert transform) and machine learning and artificial intelligence techniques (Support Vector Machines, Artificial Neural Networks, bio-inspired algorithms, clustering methods, fuzzy inference mechanisms). The most of developed systems are implemented in embedded devices and their real-world applicability is demonstrated