About SupplyNetPy
SupplyNetPy is an open-source Python library designed specifically for modeling, simulating, and analyzing supply chain networks and inventory systems. The library features supply chain-specific components to model arbitrary supply chain networks easily. It is built on Python’s SimPy discrete-event simulation framework and provides a flexible and extensible toolkit for researchers, engineers, and practitioners in operations, logistics, and supply chain management.
Purpose
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Construct detailed supply chain models, including suppliers, manufacturers, distributors, retailers, and demand points.
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Simulate inventory dynamics by modeling stock levels, replenishment cycles, lead times, supplier selection, costs, and disruptions.
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Test and compare inventory replenishment policies and supplier selection strategies.
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Analyze performance through generated logs and computed metrics such as throughput, revenue, stockouts, costs, and profit.
Features
- Modular architecture: Build arbitrarily complex, multi-echelon supply chain networks by assembling built-in components.
- Discrete-event simulation: High-fidelity event-driven simulation powered by SimPy.
- Inventory models: Support for multiple replenishment policies:
- (s, S) replenishment
- (s, S) with safety stock
- Reorder point–quantity (RQ)
- Reorder point–quantity (RQ) with safety stock
- Periodic review (Q, T)
- Periodic review (Q, T) with safety stock
- Flexible lead times: Define deterministic or stochastic lead times and transportation costs.
- Simple API: Build and simulate supply chain models using clear Python code.
- Performance tracking: Automatically generate logs and compute supply chain performance indicators.
Architecture
SupplyNetPy provides core components for supply chain modeling:
- Node classes:
Node,Supplier,Manufacturer,InventoryNode,Demand. - Link: Represents transportation connections between nodes, with configurable cost and lead time
- Inventory: Tracks stock levels and related operations at each node.
- Product and RawMaterial: Define supply chain items.
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InventoryReplenishment: Abstract base for implementing replenishment policies:
- SSReplenishment: order up to max when stock drops below s.
- RQReplenishment: fixed quantity reorder when stock drops below a threshold.
- PeriodicReplenishment: replenish at regular time intervals.
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SupplierSelectionPolicy: Abstract base for implementing supplier selection strategies:
- SelectFirst: Selects the first supplier.
- SelectAvailable: Selects the first available supplier.
- SelectCheapest: Selects the supplier with the lowest transportation cost.
- SelectFastest: Selects the supplier with the shortest lead time.
- Statistics and InfoMixin: Provide built-in tools for summarizing and reporting system behavior and object-specific metrics.
Why SupplyNetPy?
- Open-source and extensible: Designed for researchers, students, and professionals; easy to extend or integrate into larger systems.
- Specialized for supply chain dynamics: Specifically designed and built for supply chain simulation.
- Reproducible and customizable: Enables experimentation with fully configurable models, suppliers behaviours and stochastic elements.
Authors
Tushar Lone
Neha Karanjkar
Lekshmi P