The lack of transparency and traceability in food supply chains (FSCs) is raising concerns among consumers and stakeholders about food information credibility, food quality, and safety. Insufficient records, a lack of digitalization and standardization of processes, and information exchange are some of the most critical challenges, which can be tackled with disruptive technologies, such as the Internet of Things (IoT), blockchain, and distributed ledger technologies (DLTs). Studies provide evidence that novel technological and sustainable practices in FSCs are necessary. This paper aims to describe current practical applications of DLTs and IoT in FSCs, investigating the challenges of implementation, and potentials for future research directions, thus contributing to achievement of the United Nations’ Sustainable Development Goals (SDGs). Within a systematic literature review, the content of 69 academic publications was analyzed, describing aspects of implementation and measures to address the challenges of scalability, security, and privacy of DLT, and IoT solutions. The challenges of high costs, standardization, regulation, interoperability, and energy consumption of DLT solutions were also classified as highly relevant, but were not widely addressed in literature. The application of DLTs in FSCs can potentially contribute to 6 strategic SDGs, providing synergies and possibilities for more sustainable, traceable, and transparent FSCs.

Software-intensive systems in business information technology (IT) and industrial automation have become increasingly complex due to the need for more flexible system reconfiguration and business and engineering processes. Systems and software-engineering projects depend on the cooperation of experts from heterogeneous engineering domains using tools that were not designed to cooperate seamlessly. Current semantic-engineering-environment integration is often ad hoc and fragile, thereby making the evolution of tools and the reuse of integration solutions across projects unnecessarily inefficient and risky. This paper describes the engineering-knowledge-base (EKB) framework for engineering-environment integration in multidisciplinary engineering projects. The EKB stores explicit engineering knowledge to support access to and management of engineering models across tools and disciplines by providing 1) data integration based on mappings between local and domain-level engineering concepts; 2) transformations between local engineering concepts; and 3) advanced applications built on these foundations, e.g., end-to-end analyses. As a result, experts from different organizations may use their well-known tools and data models and can access data from other tools in their syntax. The research results have been evaluated in the industrial-application domain of software-intensive production-automation systems. The evaluation results indicate an effort-reduction for reuse in new engineering projects and finding defects earlier in the engineering process.

In times of ever-growing system complexity and thus increasing possibilities for errors, high-quality requirements are crucial to prevent design errors in later project phases and to facilitate design verification and validation. To ensure and improve the consistency, completeness and correctness of requirements, formal languages have been introduced as an alternative to using natural language (NL) requirement descriptions. However, in many cases existing NL requirements must be taken into account. The formalization of those requirements by now is a primarily manual task, which therefore is both cumbersome and error-prone. We introduce the tool DODT that semi-automatically transforms NL requirements into semi-formal boilerplate requirements. The transformation builds upon a domain ontology (DO) containing knowledge of the problem domain and upon natural language processing techniques. The tool strongly reduced the required manual effort for the transformation. In addition the quality of the requirements was improved.