Blockchain technologies allow for multiple organizations, individuals, and software to become part of a decentralized network where they can reach consensus on the state of the system including all data stored. Due to the ability of reaching consensus even among untrusted actors, the idea of decentralized organizations has been proposed, where both the system components as well as their coordination are distributed. Given such a system of distributed actors, the problem of decentralized coordination for following common goals and planning becomes apparent. This paper addresses the decentralized coordination problem by means of a blockchain-based approach that uses conceptual modeling to reach consensus in decentralized organizations. With a unified view on the processes and instances of distributed actors, the aim is a decentralized planning and execution through models. For this purpose, an existing approach for decentralized process modeling and instance tracking is applied and extended with the possibility for actors to form consensus on an organizational level through blockchain transactions.
For supporting the conceptualization and the management of enterprise models in a decentralized manner, this paper introduces an approach based on model versioning and blockchain technologies. The main contribution is twofold, consisting of a., the creation of models for inter-organizational business processes in a decentralized environment, and b., means for tracking process instances using meta-data at run time. Models for business processes, workflows, and instance states are collaboratively created as part of a decentralized architecture. Based on this approach, a hierarchical versioning and modeling approach is employed in order to create and manage public and private models in a transactional fashion. For forming relationships among decentralized participants, semi-formal models linked to a blockchain are suggested. The approach is evaluated with a supply chain use case and demonstrated in an implemented modeling tool.
One of the essential points of food manufacturing in the industry and shelf life of the products is to improve the food traceability system. In recent years, the food traceability mechanism has become one of the emerging blockchain applications in order to improve the anti-counterfeiting area’s quality. Many food manufacturing systems have a low level of readability, scalability, and data accuracy. Similarly, this process is complicated in the supply chain and needs a lot of time for processing. The blockchain system creates a new ontology in the traceability system supply chain to deal with these issues. In this paper, a blockchain machine learning-based food traceability system (BMLFTS) is proposed in order to combine the new extension in blockchain, Machine Learning technology (ML), and fuzzy logic traceability system that is based on the shelf life management system for manipulating perishable food. The blockchain technology in the proposed system has been developed in order to address light-weight, evaporation, warehouse transactions, or shipping time. The blockchain data flow is designed to show the extension of ML at the level of food traceability. Finally, reliable and accurate data are used in a supply chain to improve shelf life.