SupplyNetPy is an open-source, well-documented Python library designed for modeling and discrete-event simulation of arbitrary multi-echelon supply chain networks. It supports multiple replenishment policies, perishable inventory, node disruptions, and stochastic demand and lead times.
All components are extensible via inheritance. Users can describe a supply chain as a graph with node and link attributes, while the library manages simulation, providing logs and extensive performance reports at both node and network levels.
This paper discusses the motivation, design, key features, and architecture of SupplyNetPy, along with detailed validation results against analytical benchmarks, a commercial tool, and a published case study. A primary motivation behind the development of SupplyNetPy is the programmatic generation and simulation of complex models, enabling design-space exploration, what-if analysis, training data generation, and supply chain digital twins.
Blogger's Review: The launch of SupplyNetPy provides a powerful tool for supply chain management, particularly in modeling and simulating multi-echelon structures, effectively supporting complex decision analysis and optimization. Its open-source nature also offers vast opportunities for research and practice, allowing users to flexibly extend the library's capabilities to meet various real-world needs.