Chain

📢 Enhance Supply Chain Insights with Seaborn! 📊🚀

Seaborn is a powerful Python library for advanced data visualization, helping supply chain professionals analyze trends, relationships, and distributions more effectively. Let’s explore some key use cases!

🔹 1. Visualizing Demand Trends with Line Charts
Seaborn makes it easy to create aesthetically appealing trend visualizations for demand forecasting.


import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

data = pd.DataFrame({"Week": ["W1", "W2", "W3", "W4", "W5"], "Demand": [100, 120, 90, 110, 130]})

sns.lineplot(x="Week", y="Demand", data=data, marker="o")
plt.title("Weekly Demand Trend")
plt.show()
💡 Usage: Helps forecast demand fluctuations and identify seasonality.

🔹 2. Inventory Analysis with Bar Charts
Compare stock levels across multiple products with a visually appealing bar chart.


products = ["A", "B", "C", "D", "E"]
stock_levels = [500, 300, 700, 400, 600]
data = pd.DataFrame({"Product": products, "Stock": stock_levels})

sns.barplot(x="Product", y="Stock", data=data, palette="Blues")
plt.title("Inventory Levels by Product")
plt.show()
💡 Application: Helps in monitoring stock levels and avoiding overstocking or stockouts.

🔹 3. Analyzing Supplier Lead Time Variability with Box Plots
Lead time variability impacts inventory planning. A box plot helps detect inconsistencies.


import numpy as np

supplier_data = pd.DataFrame({"Supplier": np.repeat(["A", "B", "C"], 20),
"Lead Time": np.random.normal([10, 12, 9], [2, 3, 1.5], 60)})

sns.boxplot(x="Supplier", y="Lead Time", data=supplier_data, palette="coolwarm")
plt.title("Supplier Lead Time Variability")
plt.show()
💡 Usage: Identifies suppliers with unstable lead times to mitigate risks.

🔹 4. Shipping Cost Distribution with Histograms
A histogram helps visualize cost variations across multiple shipments.

shipping_costs = np.random.normal(500, 100, 200) # Simulated cost data

sns.histplot(shipping_costs, bins=20, kde=True, color="green")
plt.title("Shipping Cost Distribution")
plt.xlabel("Cost ($)")
plt.show()
💡 Benefit: Helps in cost control and vendor selection.

🔹 5. Warehouse Performance Analysis with Heatmaps
A heatmap can help visualize warehouse efficiency across locations.


data = pd.DataFrame({"Warehouse": ["WH1", "WH2", "WH3", "WH4"],
"On-Time Shipments": [90, 85, 75, 95],
"Inventory Accuracy": [88, 92, 80, 90]})

sns.heatmap(data.set_index("Warehouse"), annot=True, cmap="coolwarm")
plt.title("Warehouse Performance Metrics")
plt.show()
💡 Application: Identifies warehouses with performance gaps to improve logistics efficiency.

🔥 Why Use Seaborn in Supply Chain?
✅ Enhances data visualization with advanced styling
✅ Makes trends and relationships easier to analyze
✅ Helps in identifying inefficiencies and anomalies
✅ Works seamlessly with Pandas and Matplotlib


#SupplyChain #Python #Seaborn #Logistics #InventoryManagement #DataVisualization

1 week ago | [YT] | 0