Oppiskele Visualizing Supply Chain Metrics | Supply Chain Data Analysis

Pyyhkäise näyttääksesi valikon

Visualizing data is essential in supply chain management because it transforms raw numbers into clear, actionable insights. By plotting key metrics like stock levels, order fulfillment rates, and shipment delays, you can quickly identify trends, spot potential issues, and make more informed decisions. For instance, tracking stock levels helps you avoid both overstocking and stockouts, while monitoring order fulfillment rates and shipment delays can highlight bottlenecks or inefficiencies in your supply chain.


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import matplotlib.pyplot as plt
import pandas as pd

# Example inventory DataFrame
inventory = pd.DataFrame({
    "Product": ["Widget A", "Widget B", "Widget C", "Widget D"],
    "Stock": [120, 60, 30, 90]
})

plt.figure(figsize=(8, 5))
plt.bar(inventory["Product"], inventory["Stock"], color="skyblue")
plt.xlabel("Product")
plt.ylabel("Stock Level")
plt.title("Stock Levels per Product")
plt.tight_layout()
plt.show()

When you look at the bar chart of stock levels per product, you can immediately see which products have high or low inventory. For example, a product with a much lower bar than others may be at risk of running out, signaling a need for replenishment. On the other hand, a very tall bar could indicate excess stock, which may tie up capital and storage space. By interpreting these trends, you can prioritize which products need attention and optimize your inventory management strategies.


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import matplotlib.pyplot as plt
import pandas as pd

# Example stock level data over time for Widget A
data = {
    "Date": pd.date_range(start="2024-06-01", periods=6, freq="D"),
    "Stock": [120, 110, 100, 85, 70, 60]
}
stock_over_time = pd.DataFrame(data)

plt.figure(figsize=(8, 5))
plt.plot(stock_over_time["Date"], stock_over_time["Stock"], marker="o")
plt.xlabel("Date")
plt.ylabel("Stock Level")
plt.title("Stock Level Over Time for Widget A")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

1. What type of plot is best for showing stock levels of multiple products at a single point in time?

2. How can visualizing stock trends over time help supply chain managers?

3. Fill in the blank: To plot a bar chart in matplotlib, use plt.____(x, y).

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