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Supply Chain Optimization

Supply chains are complex causal systems where disruptions cascade through interconnected nodes. This notebook uses Fount's causal discovery to automatically map the dependency structure of your supply network ΓÇö identifying which nodes causally influence others and which disruptions are likely to cascade. You'll learn to simulate disruption scenarios, predict cascading effects, and design resilient inventory policies based on causal rather than correlational risk assessments.

What You'll Accomplish

  • Automatically discover causal dependencies between supply chain nodes from operational data
  • Simulate disruption scenarios (supplier failure, port closure) and predict cascading effects
  • Identify critical bottleneck nodes whose failure causes disproportionate downstream impact
  • Design resilient inventory policies based on causal risk rather than historical averages

Prerequisites

Python 3.8+Fount API keySupply chain operations data with node-level metrics

Code Preview

Supply-Chain-Optimization.py
# Function for nth Fibonacci number 

def Fibonacci(n): 
	if n<0: 
		print("Incorrect input") 
	# First Fibonacci number is 0 
	elif n==1: 
		return 0
	# Second Fibonacci number is 1 
	elif n==2: 
		return 1
	else: 
		return Fibonacci(n-1)+Fibonacci(n-2) 

# Driver Program 

print(Fibonacci(9))

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