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Business Modeling

SimLab provides powerful tools for modeling business scenarios and market dynamics. This guide demonstrates how to use SimLab for various business modeling applications.

Overview

Business modeling involves simulating market conditions, product lifecycles, resource allocation, and other business processes to gain insights and support decision-making. SimLab offers several simulators specifically designed for business applications:

  • Stock Market Simulation: Model stock price fluctuations and market events
  • Resource Fluctuations Simulation: Model commodity price dynamics and supply disruptions
  • Product Popularity Simulation: Model product demand trends and marketing impacts
  • Supply Chain Simulation: Model multi-tier supply chains with inventory management

Example: Market Analysis

Here's an example of using SimLab to analyze different market scenarios:

from sim_lab.core import SimulatorRegistry
import matplotlib.pyplot as plt

# Create simulations for different market conditions
bull_market = SimulatorRegistry.create(
    "StockMarket",
    start_price=100.0,
    days=252,  # Trading days in a year
    volatility=0.01,
    drift=0.001,  # Strong positive drift
    random_seed=42
)

bear_market = SimulatorRegistry.create(
    "StockMarket",
    start_price=100.0,
    days=252,
    volatility=0.02,
    drift=-0.0005,  # Negative drift
    random_seed=42
)

# Run simulations
bull_prices = bull_market.run_simulation()
bear_prices = bear_market.run_simulation()

# Calculate end-of-year returns
bull_return = (bull_prices[-1] / bull_prices[0] - 1) * 100
bear_return = (bear_prices[-1] / bear_prices[0] - 1) * 100

# Plot results
plt.figure(figsize=(12, 6))
plt.plot(bull_prices, 'g-', label=f'Bull Market (+{bull_return:.1f}%)')
plt.plot(bear_prices, 'r-', label=f'Bear Market ({bear_return:.1f}%)')
plt.title('Stock Price Scenarios')
plt.xlabel('Trading Days')
plt.ylabel('Price ($)')
plt.legend()
plt.grid(True)
plt.show()

Example: Product Launch Strategy

SimLab can help assess different product launch strategies:

from sim_lab.core import SimulatorRegistry
import matplotlib.pyplot as plt

# Strategy 1: High initial marketing with promotion
strategy1 = SimulatorRegistry.create(
    "ProductPopularity",
    start_demand=50,
    days=365,
    growth_rate=0.003,
    marketing_impact=0.005,  # High marketing spend
    promotion_day=30,        # Launch promotion after 1 month
    promotion_effectiveness=0.3,
    random_seed=42
)

# Strategy 2: Gradual growth with sustained marketing
strategy2 = SimulatorRegistry.create(
    "ProductPopularity",
    start_demand=50,
    days=365,
    growth_rate=0.005,      # Higher natural growth
    marketing_impact=0.002, # Lower marketing spend
    random_seed=42
)

# Run simulations
demand1 = strategy1.run_simulation()
demand2 = strategy2.run_simulation()

# Calculate total sales for each strategy
total_sales1 = sum(demand1)
total_sales2 = sum(demand2)

# Plot results
plt.figure(figsize=(12, 6))
plt.plot(demand1, 'b-', label=f'High Marketing + Promo (Total: {total_sales1:.0f})')
plt.plot(demand2, 'g-', label=f'Organic Growth (Total: {total_sales2:.0f})')
plt.axvline(x=30, color='r', linestyle='--', label='Promotion')
plt.title('Product Launch Strategies: Demand Comparison')
plt.xlabel('Days')
plt.ylabel('Daily Demand (units)')
plt.legend()
plt.grid(True)
plt.show()

Applications in Business Education

SimLab is particularly useful in business education contexts:

  1. MBA Programs: Students can explore market dynamics and test business strategies
  2. Finance Courses: Simulate trading strategies and market conditions
  3. Marketing Education: Model product lifecycle and marketing campaign impacts
  4. Supply Chain Management: Simulate inventory policies and supply disruptions

Further Resources

For more specific applications, see: