An Ontological Approach to Defining and Systematizing Traceability Terminologies

This paper outlines a structured ontological approach to defining and systematizing numerous product traceability terminologies in novel token-based and traditional enterprise systems (ES). It will aid researchers and manufacturers in regulated industries in defining syntactical and semantic standards for objects and events in the traceability domain. In this paper, a design science research method supports the development of a backward-forward-enterprise ontology (BFEO) artifact that helps to design and manage the complexity of multi-organization enterprise networks. This paper compares and evaluates this ontological artifact against several ontologies, offering further development opportunities. Finally, traceability professionals and various developer communities can adopt and further develop the ontology using simple development tools

A Model-driven Approach for the Description of Blockchain Business Networks

The concept of blockchain technology has gained significant momentum in practice and research in the past few years, as it provides an effective way for addressing the issues of anonymity and traceability in distributed scenarios with multiple parties, which have to exchange information and want to securely collaborate with each other. However, up-to-date, the impact of the structure and setup of business networks on successfully applying blockchain technology, remains largely unexplored. We propose a model-driven approach, combining an ontology and a layer model, that is capable of capturing the properties of existing blockchain-driven business networks. The layers are used to facilitate the comprehensive description of such networks. We also introduce the Blockchain Business Network Ontology (BBO), formalizing the concepts and properties for describing the integral parts of a blockchain network. We show the practical applicability of our work by evaluating and applying it to an available blockchain use case

Trusted Artificial Intelligence in Manufacturing; Trusted Artificial Intelligence in Manufacturing

The successful deployment of AI solutions in manufacturing environments hinges on their security, safety and reliability which becomes more challenging in settings where multiple AI systems (e.g., industrial robots, robotic cells, Deep Neural Networks (DNNs)) interact as atomic systems and with humans. To guarantee the safe and reliable operation of AI systems in the shopfloor, there is a need to address many challenges in the scope of complex, heterogeneous, dynamic and unpredictable environments. Specifically, data reliability, human machine interaction, security, transparency and explainability challenges need to be addressed at the same time. Recent advances in AI research (e.g., in deep neural networks security and explainable AI (XAI) systems), coupled with novel research outcomes in the formal specification and verification of AI systems provide a sound basis for safe and reliable AI deployments in production lines. Moreover, the legal and regulatory dimension of safe and reliable AI solutions in production lines must be considered as well. To address some of the above listed challenges, fifteen European Organizations collaborate in the scope of the STAR project, a research initiative funded by the European Commission in the scope of its H2020 program (Grant Agreement Number: 956573). STAR researches, develops, and validates novel technologies that enable AI systems to acquire knowledge in order to take timely and safe decisions in dynamic and unpredictable environments. Moreover, the project researches and delivers approaches that enable AI systems to confront sophisticated adversaries and to remain robust against security attacks. This book is co-authored by the STAR consortium members and provides a review of technologies, techniques and systems for trusted, ethical, and secure AI in manufacturing. The different chapters of the book cover systems and technologies for industrial data reliability, responsible and transparent artificial intelligence systems, human centered manufacturing systems such as human-centred digital twins, cyber-defence in AI systems, simulated reality systems, human robot collaboration systems, as well as automated mobile robots for manufacturing environments. A variety of cutting-edge AI technologies are employed by these systems including deep neural networks, reinforcement learning systems, and explainable artificial intelligence systems. Furthermore, relevant standards and applicable regulations are discussed. Beyond reviewing state of the art standards and technologies, the book illustrates how the STAR research goes beyond the state of the art, towards enabling and showcasing human-centred technologies in production lines. Emphasis is put on dynamic human in the loop scenarios, where ethical, transparent, and trusted AI systems co-exist with human workers. The book is made available as an open access publication, which could make it broadly and freely available to the AI and smart manufacturing communities

Full Information Product Pricing: An Information Strategy for Harnessing Consumer Choice to Create a More Sustainable World

Research and practice in the information systems (IS) field have been evolving over time, nourishing and promoting the development of applications that transform the relationships of individuals, corporations, and governments. Building on this evolution, we push forward a vision of the potential influence of the IS field into one of the most important problems of our times, an increasingly unsustainable world, which is traditionally considered the product of imperfect markets or market externalities. We describe our work in Full Information Product Pricing (FIPP) and our vision of a FIPP global socio-technical system, I-Choose, as a way to connect consumer choice and values with environmental, social, and economic effects of production and distribution practices. FIPP and I-Choose represent a vision about how information systems research can contribute to interdisciplinary research in supply chains, governance, and market economies to provide consumers with information packages that help them better understand how, where, and by whom the products they buy are produced. We believe that such a system will have important implications for international trade and agreements, for public policy, and for making a more sustainable world

A model and prototype implementation for tracking and tracing agricultural batch products along the food chain

There is an increasing demand of traceability in the food chain, statutory requirements are growing stricter and there is increasing pressure to develop standardized traceability systems. Each event in the chain, like production of transportation, packing, distribution or processing results in a different product which can have its own information associated within the tracing system. From the raw material to the sale of goods, more and more information needs to be gathered and made available. Supplementary information may also be collected at any step, in order to provide data for analysis and optimization of production practices. Using web-based systems for data processing, storage and transfer makes possible a flexible way of information access, networking and usability. In this paper an architectural proposal is presented and the proposed solution is tested by the implementation of a prototype. The software architecture presented makes use of a series of standards than offer new possibilities in traceability control and management. For testing the prototype, information from precision farming together with the information recorded during the transport and delivery was used. The system enables full traceability and it complies with all existing traceability standards