Supply Chain Simulation Python is the process of simulating a supply chain that is examined as challenging as well as fascinating. Beneficial perceptions based on the effectiveness and efficacy of various supply chain policies and arrangements could be offered while simulating a supply chain in Python. We suggest this instance which instructs you during the procedure of configuring a simple supply chain simulation with Python. Generally, components such as manufacturers, customers, suppliers, and warehouses are encompassed in the simulation:

Step 1: Install Required Libraries

Initially, we must require matplotlib for visualization and numpy for numerical processes:

pip install numpy matplotlib

Step 2: Define the Supply Chain Components

For various elements of supply chain such as Manufacturers, Customers, Suppliers, and Warehouses, we plan to describe classes.

Supply Chain Simulation Code

import numpy as np

import matplotlib.pyplot as plt

class Supplier:

def __init__(self, supply_rate):

self.supply_rate = supply_rate

def supply(self):

return self.supply_rate

class Manufacturer:

def __init__(self, production_rate, supplier):

self.production_rate = production_rate

self.supplier = supplier

self.inventory = 0

def produce(self):

supply = self.supplier.supply()

production = min(supply, self.production_rate)

self.inventory += production

return production

class Warehouse:

def __init__(self, capacity):

self.capacity = capacity

self.inventory = 0

def store(self, products):

space_available = self.capacity – self.inventory

stored = min(products, space_available)

self.inventory += stored

return stored

def ship(self, demand):

shipped = min(demand, self.inventory)

self.inventory -= shipped

return shipped

class Customer:

def __init__(self, demand_rate):

self.demand_rate = demand_rate

def demand(self):

return self.demand_rate

# Simulation parameters

days = 30

supplier_rate = 100

production_rate = 80

warehouse_capacity = 500

customer_demand_rate = 70

# Create supply chain components

supplier = Supplier(supplier_rate)

manufacturer = Manufacturer(production_rate, supplier)

warehouse = Warehouse(warehouse_capacity)

customer = Customer(customer_demand_rate)

# Arrays to store results

supplier_inventory = np.zeros(days)

manufacturer_inventory = np.zeros(days)

warehouse_inventory = np.zeros(days)

customer_demand = np.zeros(days)

customer_fulfilled = np.zeros(days)

# Simulation loop

for day in range(days):

# Supplier supplies raw materials to the manufacturer

supplier_inventory[day] = supplier.supply()

# Manufacturer produces products

produced = manufacturer.produce()

manufacturer_inventory[day] = manufacturer.inventory

# Warehouse stores produced products

stored = warehouse.store(produced)

warehouse_inventory[day] = warehouse.inventory

# Customer demands products

demand = customer.demand()

customer_demand[day] = demand

# Warehouse ships products to the customer

fulfilled = warehouse.ship(demand)

customer_fulfilled[day] = fulfilled

# Plot results

plt.figure(figsize=(12, 8))

plt.subplot(3, 1, 1)

plt.plot(supplier_inventory, label=’Supplier Inventory’)

plt.plot(manufacturer_inventory, label=’Manufacturer Inventory’)

plt.legend()

plt.title(‘Supply Chain Simulation’)

plt.ylabel(‘Inventory Level’)

plt.subplot(3, 1, 2)

plt.plot(warehouse_inventory, label=’Warehouse Inventory’)

plt.legend()

plt.ylabel(‘Inventory Level’)

plt.subplot(3, 1, 3)

plt.plot(customer_demand, label=’Customer Demand’)

plt.plot(customer_fulfilled, label=’Customer Fulfilled’)

plt.legend()

plt.xlabel(‘Day’)

plt.ylabel(‘Products’)

plt.tight_layout()

plt.show()

Description of the Code

1. Supplier Class: Every day, a certain value of raw materials is delivered by the supplier class.
2. Manufacturer Class: With the support of raw materials provided by the supplier, the manufacturer class generates products. Generally, by the production rate and the quantity of raw materials offered, the production is constrained.
3. Warehouse Class: The generated products are conserved. On the basis of the requirement, it sends them to consumers. By the capability of the warehouse, the storage is constrained.
4. Customer Class: Every day, consumer class includes a determined demand rate for specific products.
5. Simulation Loop: For monitoring the inventory levels at every phase of the supply chain and the requirement and satisfaction levels for consumers, it executes the simulation for a certain number of days.
6. Plotting: To plot the inventory levels and customer requirement and satisfaction in a periodic manner, we focus on utilizing matplotlib.

supply chain simulation python projects

In the motive of assisting you in selecting crucial and impactful project topics, an extensive collection of 100 supply chain simulation project topics in Python are offered by us that encompasses different complications, settings, and factors of supply chain management:

Basic Simulations

1. Single Supplier, Single Manufacturer, Single Warehouse, Single Customer
2. Multiple Suppliers, Single Manufacturer
3. Variable Customer Demand
4. Inventory Management Policies
5. Bullwhip Effect Simulation
6. Basic Supply Chain Simulation
7. Single Supplier, Multiple Manufacturers
8. Multiple Warehouses
9. Seasonal Demand Patterns
10. Just-In-Time (JIT) Supply Chain

Advanced Inventory Management

1. Reorder Point (ROP) Simulation
2. Backorder Management
3. ABC Analysis in Inventory Management
4. Consignment Inventory Simulation
5. Multi-Echelon Inventory Management
6. Economic Order Quantity (EOQ) Model
7. Safety Stock Calculation
8. Inventory Turnover Simulation
9. Vendor-Managed Inventory (VMI)
10. Push vs. Pull Inventory Systems

Transportation and Logistics

1. Fleet Management Simulation
2. Routing and Scheduling of Deliveries
3. Cold Chain Management
4. Dynamic Routing Simulation
5. Multi-Modal Transportation Simulation
6. Transportation Lead Time Simulation
7. Transportation Cost Optimization
8. Cross-Docking Simulation
9. Third-Party Logistics (3PL) Simulation
10. Transportation Network Design

Production and Manufacturing

1. Production Scheduling Optimization
2. Lean Manufacturing Simulation
3. Batch Processing Simulation
4. Production Downtime Simulation
5. Simulating Production Delays
6. Capacity Planning Simulation
7. Production Line Balancing
8. Six Sigma in Supply Chain
9. Make-to-Order vs. Make-to-Stock Simulation
10. Assembly Line Simulation

Demand Forecasting and Planning

1. Machine Learning in Demand Forecasting
2. Collaborative Planning, Forecasting, and Replenishment (CPFR)
3. Demand Shaping Simulation
4. Seasonality and Trend Analysis
5. Rolling Forecast Simulation
6. Time Series Forecasting of Demand
7. Scenario Analysis for Demand Planning
8. Demand Sensing Simulation
9. Promotion Planning and Simulation
10. Sales and Operations Planning (S&OP)

Risk Management and Resilience

1. Risk Mitigation Strategies
2. Scenario Planning for Risk Management
3. Inventory Buffers for Risk Management
4. Demand Surge Simulation
5. Natural Disaster Impact Simulation
6. Supply Chain Disruption Simulation
7. Supplier Reliability Simulation
8. Resilient Supply Chain Design
9. Supplier Diversification Strategies
10. Geopolitical Risk Simulation

Sustainability and Green Supply Chain

1. Sustainable Supply Chain Practices
2. Waste Reduction in Supply Chain
3. Renewable Energy in Supply Chain
4. Resource Efficiency in Manufacturing
5. Lifecycle Assessment in Supply Chain
6. Carbon Footprint Simulation
7. Green Logistics Simulation
8. Circular Economy Simulation
9. Sustainable Packaging Simulation
10. Eco-friendly Transportation Simulation

Technology and Innovation

1. Internet of Things (IoT) in Supply Chain
2. Digital Twins for Supply Chain Simulation
3. Augmented Reality (AR) for Warehouse Management
4. Smart Contracts in Supply Chain
5. Big Data Analytics in Supply Chain
6. Blockchain in Supply Chain
7. Artificial Intelligence (AI) in Supply Chain Optimization
8. Robotic Process Automation (RPA)
9. 3D Printing in Supply Chain
10. Predictive Analytics for Supply Chain

Global Supply Chain

1. International Trade Simulation
2. Customs and Compliance Simulation
3. Global Supplier Network Simulation
4. Currency Exchange Rate Impact
5. Export and Import Simulation
6. Global Supply Chain Network Design
7. Tariff and Trade Policy Impact
8. Cross-Border Logistics Simulation
9. Supply Chain Finance Simulation
10. Global Sourcing Strategies

Retail and E-commerce

1. Omni-Channel Supply Chain
2. Warehouse Automation for E-commerce
3. Customer Service Level Optimization
4. Drop Shipping Simulation
5. Click-and-Collect Supply Chain Simulation
6. E-commerce Supply Chain Simulation
7. Last-Mile Delivery Optimization
8. Dynamic Pricing Simulation
9. Return Logistics (Reverse Logistics) Simulation
10. Order Fulfillment Simulation

Including procedures, instance code, concise description, and widespread collection of 100 project concepts, an extensive note on supply chain simulation with Python is provided by us which can be beneficial for you in creating such kinds of projects.

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