This course concentrates on the Smart Industry concept with an emphasis on solutions to address interoperability problems through an architectural approach. Specifically, after completing this course successfully, the students are capable of:
- explaining the main concepts of Smart Industry: smartness, smart products, smart technologies, smart applications; Artificial Intelligence (AI), Internet-of-Things (I-IoT), and Digital Twins; and their impact on ethics, business strategies, and organizational aspects
- explaining semantic and technical interoperability challenges for developing interconnected smart applications, how to design their associated data, and the role of semantic-based solutions in Industry 5.0 (I5.0) reference architectures
- identifying the suitability, applicability, and benefits of certain types of I5.0 technologies for smartness in a specific business context.
- designing and to developing a small smart application based on I5.0 technologies, and integrating it into the IT landscape of a case study.
- applying and evaluating a small smart application based on I5.0 technologies, and analyzing the impact in the IT landscape of a case study.
Smart Industry is the concept of applying smartness to improve industrial processes through technologies for contextual sensing and data analytics, such as mining events, situations, and trends; which are capabilities covered by Industry 5.0 (I5.0) architectures. I5.0 architectures guide the use of Artificial Intelligence (AI), ubiquity computing, Service-Oriented Architecture (SOA), Cloud Computing, Internet of Things (IoT), autonomous Cyber-Physical Systems (CPS), Digital Twins to develop smart applications that can partly or completely take over the human’s thinking process.|
In Smart Industry Systems (SIS), machines and devices are connected and generate data during the execution of the processes, so the identified insights from these data can be used to understand what is actually happening in the organization and processes can be optimized faster. This creates new opportunities in the whole business value chain, once lead times can be shortened and work can be performed more efficiently.
However, these integrated networks of automation devices (sensors and actuators), services, and enterprise systems bring interoperability challenges to the industry ecosystem. Interoperability is the “ability of two or more systems or components to exchange information and to use the information that has been exchanged” (IEEE, 1990). Therefore, interoperability defines the way of interconnection between sensors, devices, manufacturing systems, and people, including the exchange of products and materials among facilities. In particular, semantic interoperability is the most challenging because it is about the “interpretation of shared data in an unambiguously way, ensuring that the understanding of the information is the same for senders and receivers” (Heiler, 1995).
Establishing automatic semantic interoperability for seamless systems integration is an arduous task. The SIS course gives emphasis on interoperability solutions that enable sustainable and robust distributed Smart Industry Systems. The SIS course covers software engineering for smart application development based on software engineering practices and I5.0 architectures applied in CPSs. For the successful integration of CPSs with society, the sociotechnical dimension of CPS should be addressed (Kant, 2016). So, the SIS course also covers the impact of smartness in ethics, business strategies, and organizational aspects. The SIS course is project-based and the project part is structured according to the Challenge-Based Learning (CBL) approach.
|Knowledge on design science research methodology, architecture of information systems, software engineering and linked data.|
|Master Business Information Technology||Verplicht materiaal|
|5-10 papers that will be selected and made available in the Canvas page before the course starts|
|Zelfstudie geen begeleiding|