Do you have any udemy course or can u pls suggest how to use puthon with other tools like pandas matlab etc
1 month ago
| 0
Do you have any udemy course or can u pls suggest how to use puthon with other tools like pandas matlab etc
1 month ago
| 0
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
#SupplyChain #Python #Matplotlib #Logistics #InventoryManagement #DataVisualization
1 month ago | [YT] | 2