A digital twin is a digital replica of a physical entity to which it is remotely connected. A digital twin can provide a rich representation of the corresponding physical entity and enables sophisticated control for various purposes. Although the concept of the digital twin is largely known, designing digital twins based systems has not yet been fully explored. In practice, digital twins can be applied in different ways leading to different architectural designs. To guide the architecture design process, we provide a pattern-oriented approach for architecting digital twin-based systems. To this end, we propose a catalog of digital twin architecture design patterns that can be reused in the broad context of systems engineering. The patterns support the various phases in the systems engineering life cycle process, and are described using a well-defined pattern documentation template. For illustrating the application of digital twin patterns, we adopt a multi-case study approach in the agriculture and food domain.

Logistics in the food and agribusiness industry must address perishable products, unpredictable supply variations and stringent food safety and sustainability requirements. The Internet of Things (IoT) could contribute significantly to solve these challenges because it allows for remotely controlling the location and conditions of shipments and products. This paper develops a reference architecture for IoT-based logistic information systems in agri-food supply chains. It proposes a hybrid solution that combines the IoT and cloud computing. The architecture supports the provision of affordable tailor-made solutions by utilising technology enablers of the European Future Internet programme and by facilitating the reuse of domain-specific functionalities.

Demand-driven supply chains are highly dynamic networks of different participants with different allocations of business processes and different modes of control and coordination. Companies must be able to take part in multiple supply chain configurations concurrently and to switch rapidly to new or adjusted configurations. This imposes stringent demands on information systems and requires a modelling approach that (1) combines high-level models for supply chain design with detailed models for engineering the accompanying information systems and (2) enables rapid instantiation of specific supply chain configurations from a repository of standard building blocks. This article designs a process modelling framework that enhances such an approach. Building on the terminology and process definitions provided by the Supply Chain Operations Reference model, it models supply chain configurations as specific sets of transformations, control systems and coordination mechanisms. The designed modelling framework is applied in a case study in the Dutch flower industry.

Traditional farming has evolved from standalone computing systems to smart farming, driven by advancements in digitalization. This has led to the proliferation of diverse information systems (IS), such as IoT and sensor systems, decision support systems, and farm management information systems (FMISs). These systems often operate in isolation, limiting their overall impact. The integration of IS into connected smart systems is widely addressed as a key driver to tackle these issues. However, it is a complex, multi-faceted issue that is not easily achievable. Previous studies have offered valuable insights, but they often focus on specific cases, such as individual IS and certain integration aspects, lacking a comprehensive overview of various integration dimensions. This systematic review of 74 scientific papers on IS integration addresses this gap by providing an overview of the digital technologies involved, integration levels and types, barriers hindering integration, and available approaches to overcoming these challenges. The findings indicate that integration primarily relies on a point-to-point approach, followed by cloud-based integration. Enterprise service bus, hub-and-spoke, and semantic web approaches are mentioned less frequently but are gaining interest. The study identifies and discusses 27 integration challenges into three main areas: organizational, technological, and data governance-related challenges. Technologies such as blockchain, data spaces, AI, edge computing and microservices, and service-oriented architecture methods are addressed as solutions for data governance and interoperability issues. The insights from the study can help enhance interoperability, leading to data-driven smart farming that increases food production, mitigates climate change, and optimizes resource usage.