Amirali Amiri

Supply Chain Management (SCM) is crucial for business operations, requiring careful planning and optimization to minimize product costs. New technologies like the Internet of Things (IoT) enable real-time monitoring of supply chain performance, helping detect malfunctions and adapt strategies to address issues like rising costs. However, IoT system architects face challenges in mastering diverse technologies and integrating them into SCM. Methodologies like Model-Based Systems Engineering (MBSE) simplify the design process by providing system models and views. In this paper, we propose an MBSE approach for IoT-driven supply chain optimization. Our approach automatically performs optimization based on IoT monitoring data. We illustrate how system designers can adapt our approach to their specific needs. For example, system designers can provide templates for the artifact generation, such as system code or input files for the optimization. We discuss multiple scenarios showcasing the benefits of our approach.

Context: Various patterns of dynamic routing architectures are used in service- and cloud-based environments, including sidecar-based routing, routing through a central entity such as an event store, or architectures with multiple dynamic routers. Objective: Choosing the wrong architecture may severely impact the reliability or performance of a software system. This articles objective is to provide models and empirical evidence to precisely estimate the reliability and performance impacts. Method: We propose an analytical model of request loss for reliability modeling. We studied the accuracy of this models predictions empirically and calculated the error rate in 200 experiment runs, during which we measured the round-trip time performance and created a performance model based on multiple regression analysis. Finally, we systematically analyzed the reliability and performance impacts and trade-offs. Results and Conclusions: The comparison of the empirical data to the reliability models predictions shows a low enough and converging error rate for using the model during system architecting. The predictions of the performance model show that distributed approaches for dynamic data routing have a better performance compared to centralized solutions. Our results provide important new insights on dynamic routing architecture decisions to precisely estimate the trade-off between system reliability and performance.

Mature verification and monitoring approaches, such as complex event processing and model checking, can be applied for checking compliance specifications at design time and runtime. Little is known about the understandability of the different formal and technical languages associated with these approaches. This uncertainty regarding understandability might be a major obstacle for the broad practical adoption of those techniques. This article reports a controlled experiment with 215 participants on the understandability of modeling compliance specifications in representative modeling languages, namely linear temporal logic (LTL), the complex event processing-based event processing language (EPL) and property specification patterns (PSP). The formalizations in PSP were overall more correct. That is, the pattern-based approach provides a higher level of understandability than EPL and LTL. More advanced users, however, seemingly are able to cope equally well with PSP and EPL in modeling compliance specifications.