import pandas as pd

# Load the data

df = pd.read_csv('supply_chain_data.csv')

# Clean the data

df = df.dropna()

df = df[df['Order Quantity'] > 0]

# Calculate metrics

average_lead_time = df['Lead Time'].mean()

average_order_quantity = df['Order Quantity'].mean()

average_order_processing_time = df['Order Processing Time'].mean()

# Visualize the data

df.plot.scatter(x='Lead Time', y='Order Quantity')

This code loads supply chain data from a CSV file and cleans the data by removing any missing values and orders with zero quantity. It calculates metrics such as the average lead time, order quantity, and processing time. Finally, it visualizes the data by creating a scatter plot of lead time versus order quantity.

This is just how Python can be used for supply chain optimization. The specific code will depend on the data and the specific optimization goals.