Chain

📢 Visualize Supply Chain Data with Matplotlib! 📊🚀

Matplotlib is a powerful Python library for data visualization, helping supply chain professionals analyze demand trends, inventory levels, transportation costs, and more. Let’s explore how you can use it!

🔹 1. Visualizing Demand Trends Over Time
Tracking demand patterns helps in better forecasting and inventory planning.


import matplotlib.pyplot as plt

weeks = ["Week 1", "Week 2", "Week 3", "Week 4", "Week 5"]
demand = [100, 120, 90, 110, 130]

plt.plot(weeks, demand, marker="o", linestyle="-")
plt.title("Weekly Demand Trend")
plt.xlabel("Weeks")
plt.ylabel("Demand")
plt.grid(True)
plt.show()
💡 Usage: Identifies peak demand periods and trends.

🔹 2. Analyzing Inventory Levels
Compare stock levels across different products.


products = ["A", "B", "C", "D", "E"]
stock_levels = [500, 300, 700, 400, 600]

plt.bar(products, stock_levels, color="skyblue")
plt.title("Inventory Levels by Product")
plt.xlabel("Products")
plt.ylabel("Stock Quantity")
plt.show()
💡 Application: Helps in identifying overstocked or understocked items.

🔹 3. Comparing Shipping Costs Across Vendors
Choosing the best vendor requires analyzing cost variations.


vendors = ["Vendor A", "Vendor B", "Vendor C", "Vendor D"]
shipping_costs = [500, 700, 450, 600]

plt.barh(vendors, shipping_costs, color="lightcoral")
plt.title("Shipping Costs by Vendor")
plt.xlabel("Cost ($)")
plt.ylabel("Vendors")
plt.show()
💡 Benefit: Helps optimize logistics by selecting cost-effective suppliers.

🔹 4. Visualizing Warehouse Utilization
Monitor how efficiently warehouses are being used.


warehouses = ["WH1", "WH2", "WH3", "WH4"]
capacity = [5000, 6000, 7000, 8000]
utilized = [4500, 5500, 6000, 7500]

plt.bar(warehouses, capacity, color="gray", label="Total Capacity")
plt.bar(warehouses, utilized, color="green", label="Utilized Space")
plt.title("Warehouse Space Utilization")
plt.xlabel("Warehouses")
plt.ylabel("Space (Cubic Meters)")
plt.legend()
plt.show()
💡 Usage: Helps in warehouse planning and optimizing storage space.

🔹 5. Analyzing Lead Time Variability
Lead time fluctuations impact supply chain efficiency.


import numpy as np

lead_times = np.random.normal(10, 2, 1000)

plt.hist(lead_times, bins=20, color="purple", alpha=0.7)
plt.title("Lead Time Distribution")
plt.xlabel("Days")
plt.ylabel("Frequency")
plt.show()
💡 Application: Helps in setting realistic reorder points.

🔥 Why Use Matplotlib in Supply Chain?
✅ Helps visualize key supply chain metrics
✅ Enables better decision-making with data-driven insights
✅ Identifies trends, anomalies, and optimization opportunities
✅ Makes reports more engaging and easy to understand


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1 month ago | [YT] | 2