Supply chain optimization using model-based systems engineering and the Internet of Things

Abstract 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.