Big data reference architecture for industry 4.0including economic and ethical implications

Resumen

Abstract TESEO (common language) The development of technology is getting faster and faster. In 1965 Moore predicted that the computer chip processing power would double every year for the next ten years. However, newer technologies evolve even faster, and recently a new term was born to describe them: “exponential technologies”. The rapid progress in manufacturing, also called "Industry 4.0", is achieved through the combination of exponential technologies and further innovations in several fields, such as manufacturing, big data, and artificial intelligence. Manufacturing companies need support to implement the required software systems to use artificial intelligence in factories. The thesis presents the software architecture "CAAI" as a possible solution, which is especially suited for the challenges of small and medium-sized enterprises. The work examines the economic and ethical implications of those technologies and highlights the benefits but also the challenges for countries, companies and individual workers. The "Industry 4.0 Questionnaire for SMEs" was conducted to gain insights into smaller and medium-sized companies’ requirements and needs. Thus, the new CAAI architecture presents a software design blueprint and provides a set of open-source building blocks to support companies during implementation. Different use cases demonstrate the applicability of the architecture and the following evaluation verifies the functionality of the architecture. Wider adoption of the CAAI architecture will further increase its usefulness as it is an open-source project and users can share their created modules with the community which decreases future implementation effort for all users. Abstract TESEO (technical language) The development of technology is getting faster and faster. In 1965 Moore predicted that the number of transistors in a computer chip and therefore its processing power would double every year for the next ten years. However, newer technologies evolve even faster, and recently a new term was born to describe them: “exponential technologies”. Technologies such as big data, the internet of things, or artificial intelligence double in power or processing speed every year, while their cost halves, often also complementing or catalyzing each other. The rapid progress in Industry 4.0 is achieved through the combination of exponential technologies and further innovations in several fields, e.g., manufacturing, big data, and artificial intelligence. The thesis at hand motivates the need for a Big Data reference architecture to apply Artificial Intelligence in Industry 4.0 and presents a cognitive architecture for artificial intelligence – CAAI – as a possible solution, which is especially suited for the challenges of small and medium-sized enterprises. Initially, the introduction of Artificial Intelligence, Big Data, and Industry 4.0 establishes common ground and explains why the intersection of those exponential technologies holds enormous economic potential. Subsequently, the economic and ethical implications of AI, Big Data, and Industry 4.0 are examined. These technologies show the potential to revolutionize the global economy and our working lives. Therefore, many national research programs and business ventures emerge in a race of economic powers for global competitive advantages, even though the ethical consequences are not yet completely foreseeable. Workers fear the loss of their jobs and the independence of their work. However, employee support is one of the most important criteria for a successful implementation. Therefore an ethical introduction process is presented, which is including and assisting rather than overburdening the employees. Large corporations develop most Industry 4.0 technologies and standards. The “Industry 4.0 Questionnaire for SMEs” was conducted to gain better insight into smaller and medium-sized companies’ requirements and needs. The majority of participants stated, amongst other things, that they call for assistance to formulate an Industry 4.0 strategy and evaluate the available Industry 4.0 technologies. They need standards or best practices, which unfortunately are not yet available. Reference architectures are introduced as they represent best practices for designing a software system and support companies during implementation. An evaluation of the proposed reference architectures for Industry 4.0 shows the possibilities but also the existing shortcomings. Subsequently, the CAAI architecture for the application of AI in I4.0 production systems is presented. The provided set of standard architecture building blocks eases the implementation effort and assists companies in the Industry 4.0 introduction. Different use cases demonstrate the applicability of the architecture and the following evaluation verifies the capabilities and functionality of the CAAI architecture. The resulting modular architecture can be implemented on existing IT resources or through various cloud computing providers. Thus, a small or medium-sized enterprise can use the architecture to optimize production processes even if their budget does not allow them to buy the required hardware outright. Wider adoption of the CAAI architecture will further increase its usefulness as it is an open-source project and users can share their created modules with the community which decreases future implementation effort for all users.