Supply Chain Simulation with SimPy: From Supplier to Customer

Published on February 17, 2026

Supply chains are networks of queues, buffers, and delays. Orders flow one way. Goods flow the other. Simulation reveals where they get stuck. <h2 id="the-supply-chain-model">The Supply Chain Model</h2> Key elements: - Suppliers - Source of materials - Warehouses - Storage buffers - Manufacturing - Transformation - Distribution - Delivery to customers - Demand - Customer orders <h2 id="basic-inventory-model">Basic Inventory Model</h2> <div class="code-block"><pre><code>import simpy import random class Warehouse: def __init__(self, env, name, capacity, reorder_point, order_quantity): self.env = env self.name = name self.inventory = simpy.Container(env, capacity=capacity, init=capacity//2) self.reorder_point = reorder_point self.order_quantity = order_quantity self.orders_placed = 0 self.stockouts = 0 def fulfill_demand(self, quantity): """Try to fulfill demand from inventory.""" if self.inventory.level >= quantity: yield self.inventory.get(quantity) return True else: self.stockouts += 1 return False def receive_shipment(self, quantity): """Receive goods into inventory.""" yield self.inventory.put(quantity) print(f"{self.name}: Received {quantity}, level now {self.inventory.level}") def reorder_check(self, lead_time): """Check and reorder when inventory is low.""" while True: yield self.env.timeout(1) # Check daily if self.inventory.level <= self.reorder_point: self.orders_placed += 1 print(f"{self.name}: Ordering {self.order_quantity} at {self.env.now}") yield self.env.timeout(lead_time) yield from self.receive_shipment(self.order_quantity) </code></pre></div> <h2 id="multi-echelon-supply-chain">Multi-Echelon Supply Chain</h2> <div class="code-block"><pre><code>class SupplyChainNode: def __init__(self, env, name, capacity, upstream=None): self.env = env self.name = name self.inventory = simpy.Container(env, capacity=capacity, init=capacity//2) self.upstream = upstream self.stats = { 'fulfilled': 0, 'stockouts': 0, 'orders_placed': 0 } def request_material(self, quantity, lead_time): """Request material from upstream.""" if self.upstream: # Place order with upstream self.stats['orders_placed'] += 1 yield self.env.timeout(lead_time) success = yield from self.upstream.fulfill_order(quantity) if success: yield self.inventory.put(quantity) else: # We are the source - unlimited supply yield self.env.timeout(lead_time) yield self.inventory.put(quantity) def fulfill_order(self, quantity): """Fulfill order from downstream.""" if self.inventory.level >= quantity: yield self.inventory.get(quantity) self.stats['fulfilled'] += 1 return True else: self.stats['stockouts'] += 1 return False # Create chain: Supplier -> DC -> Store env = simpy.Environment() supplier = SupplyChainNode(env, "Supplier", capacity=1000, upstream=None) dc = SupplyChainNode(env, "DC", capacity=500, upstream=supplier) store = SupplyChainNode(env, "Store", capacity=100, upstream=dc) </code></pre></div> <h2 id="order-fulfilment">Order Fulfilment</h2> <div class="code-block"><pre><code>class OrderManager: def __init__(self, env, warehouse): self.env = env self.warehouse = warehouse self.pending_orders = simpy.Store(env) self.completed_orders = [] def place_order(self, order_id, quantity, customer): order = { 'id': order_id, 'quantity': quantity, 'customer': customer, 'placed_at': self.env.now } yield self.pending_orders.put(order) def process_orders(self): while True: order = yield self.pending_orders.get() # Check inventory if self.warehouse.inventory.level >= order['quantity']: yield self.warehouse.inventory.get(order['quantity']) order['fulfilled_at'] = self.env.now order['status'] = 'fulfilled' else: order['status'] = 'backordered' # Wait for restock then retry yield self.env.timeout(5) continue self.completed_orders.append(order) print(f"Order {order['id']} fulfilled at {self.env.now}") </code></pre></div> <h2 id="transportation-and-delivery">Transportation and Delivery</h2> <div class="code-block"><pre><code>class DeliveryNetwork: def __init__(self, env, num_trucks, warehouse): self.env = env self.trucks = simpy.Resource(env, capacity=num_trucks) self.warehouse = warehouse self.deliveries = [] def delivery(self, order_id, destination, quantity): """Pick, load, deliver.""" arrival = self.env.now # Wait for truck with self.trucks.request() as req: yield req # Pick from warehouse yield self.warehouse.inventory.get(quantity) # Load time yield self.env.timeout(random.uniform(10, 30)) # Transit time (based on destination) transit = destination * 0.5 # 0.5 hours per km yield self.env.timeout(transit) self.deliveries.append({ 'order_id': order_id, 'lead_time': self.env.now - arrival }) # Return journey (empty) yield self.env.timeout(transit) def stats(self): times = [d['lead_time'] for d in self.deliveries] return { 'deliveries': len(times), 'avg_lead_time': sum(times) / len(times) if times else 0 } </code></pre></div> <h2 id="demand-forecasting-impact">Demand Forecasting Impact</h2> <div class="code-block"><pre><code>def demand_generator(env, order_manager, demand_pattern): """Generate customer demand.""" order_id = 0 while True: # Demand varies by day of week day = int(env.now / 24) % 7 daily_demand = demand_pattern[day] quantity = max(1, int(random.gauss(daily_demand, daily_demand * 0.2))) env.process(order_manager.place_order(order_id, quantity, f"Customer")) yield env.timeout(random.expovariate(1/4)) # ~4 hours between orders order_id += 1 # Weekly pattern (Mon-Sun) demand_pattern = [100, 110, 120, 115, 130, 80, 60] </code></pre></div> <h2 id="complete-supply-chain-example">Complete Supply Chain Example</h2> <div class="code-block"><pre><code>import simpy import random import numpy as np class SupplyChainSimulation: def __init__(self, env, config): self.env = env self.config = config # Create nodes self.supplier = simpy.Container(env, capacity=float('inf'), init=float('inf')) self.dc = simpy.Container(env, capacity=config['dc_capacity'], init=config['dc_initial']) self.store = simpy.Container(env, capacity=config['store_capacity'], init=config['store_initial']) self.stats = { 'sales': 0, 'stockouts': 0, 'inventory_levels': [], 'dc_orders': 0, 'supplier_orders': 0 } def customer_demand(self): """Generate customer demand at store.""" while True: # Demand arrives yield self.env.timeout(random.expovariate(self.config['demand_rate'])) demand = random.randint(1, 5) if self.store.level >= demand: yield self.store.get(demand) self.stats['sales'] += demand else: # Partial fulfillment if self.store.level > 0: fulfilled = self.store.level yield self.store.get(fulfilled) self.stats['sales'] += fulfilled self.stats['stockouts'] += 1 def store_reorder(self): """Store reorders from DC.""" while True: yield self.env.timeout(1) # Daily check if self.store.level <= self.config['store_reorder_point']: order_qty = self.config['store_order_qty'] self.stats['dc_orders'] += 1 # Check DC inventory if self.dc.level >= order_qty: yield self.dc.get(order_qty) yield self.env.timeout(self.config['dc_to_store_lead']) yield self.store.put(order_qty) def dc_reorder(self): """DC reorders from supplier.""" while True: yield self.env.timeout(1) if self.dc.level <= self.config['dc_reorder_point']: order_qty = self.config['dc_order_qty'] self.stats['supplier_orders'] += 1 yield self.env.timeout(self.config['supplier_lead_time']) yield self.dc.put(order_qty) def monitor(self, interval=1): """Track inventory levels.""" while True: self.stats['inventory_levels'].append({ 'time': self.env.now, 'store': self.store.level, 'dc': self.dc.level }) yield self.env.timeout(interval) def run(self, duration): self.env.process(self.customer_demand()) self.env.process(self.store_reorder()) self.env.process(self.dc_reorder()) self.env.process(self.monitor()) self.env.run(until=duration) def report(self): print("\n=== Supply Chain Report ===") print(f"Duration: {self.env.now}") print(f"Total sales: {self.stats['sales']}") print(f"Stockouts: {self.stats['stockouts']}") print(f"DC orders: {self.stats['dc_orders']}") print(f"Supplier orders: {self.stats['supplier_orders']}") fill_rate = self.stats['sales'] / (self.stats['sales'] + self.stats['stockouts']) print(f"Fill rate: {fill_rate:.1%}") levels = self.stats['inventory_levels'] store_levels = [l['store'] for l in levels] dc_levels = [l['dc'] for l in levels] print(f"\nAvg store inventory: {np.mean(store_levels):.0f}") print(f"Avg DC inventory: {np.mean(dc_levels):.0f}") # Configuration config = { 'dc_capacity': 500, 'dc_initial': 300, 'dc_reorder_point': 150, 'dc_order_qty': 200, 'store_capacity': 100, 'store_initial': 60, 'store_reorder_point': 30, 'store_order_qty': 50, 'dc_to_store_lead': 2, 'supplier_lead_time': 7, 'demand_rate': 1/0.5 # 2 per day } random.seed(42) env = simpy.Environment() sim = SupplyChainSimulation(env, config) sim.run(duration=90) # 90 days sim.report() </code></pre></div> <h2 id="summary">Summary</h2> Supply chain simulation reveals: - Inventory positioning and levels - Lead time impacts - Bullwhip effect dynamics - Order policy optimisation - Network bottlenecks Supply chains are long queues. Simulate to shorten them. <h2 id="next-steps">Next Steps</h2> <ul> <li><a href="/blog_posts/simpy-warehouse-simulation.html">Warehouse Simulation</a></li> <li><a href="/blog_posts/simpy-manufacturing-simulation.html">Manufacturing Simulation</a></li> <li><a href="/blog_posts/simpy-container-explained.html">Container Explained</a></li> </ul>

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