Impara Simulating Inventory Levels Over Time | Inventory Management and Demand Forecasting

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Tracking inventory over time is crucial for effective supply chain management. By simulating how inventory levels change daily—due to customer demand and scheduled replenishments—you gain valuable insight into potential stockouts, overstock situations, and the timing of orders. This allows you to make informed decisions about when to reorder products, how much safety stock to keep, and how to optimize storage space.


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# Simulate daily inventory for 30 days
initial_stock = 100
daily_demand = 7
replenishment_quantity = 50
replenishment_interval = 7  # days between replenishments
days = 30

inventory = []
current_stock = initial_stock

for day in range(1, days + 1):
    # Subtract daily demand
    current_stock -= daily_demand
    # Replenish if it's a replenishment day
    if day % replenishment_interval == 0:
        current_stock += replenishment_quantity
    # Prevent negative inventory
    current_stock = max(current_stock, 0)
    inventory.append(current_stock)

print(inventory)

The simulation logic follows a simple but powerful pattern. You start with an initial stock value, then use a loop to represent each day in your simulation period. On each day, you subtract the expected daily demand from the current inventory. If the day matches the replenishment schedule (for example, every 7 days), you add the replenishment quantity to the current stock. At each step, you ensure the inventory does not fall below zero, which reflects a real-world scenario where you cannot ship more than you have in stock. By appending the current inventory value to a list each day, you build a complete record of inventory levels throughout the simulation period.


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import matplotlib.pyplot as plt

days = list(range(1, 31))
plt.figure(figsize=(10,5))
plt.plot(days, inventory, marker='o', linestyle='-', color='b')
plt.title('Simulated Inventory Levels Over 30 Days')
plt.xlabel('Day')
plt.ylabel('Inventory Level')
plt.grid(True)
plt.show()

1. What can inventory simulations help supply chain managers anticipate?

2. How does replenishment frequency affect inventory levels?

3. Fill in the blank: To append a value to a list in Python, use `list.____(value)`.

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Sezione 2. Capitolo 2

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Sezione 2. Capitolo 2

Simulating Inventory Levels Over Time

1. What can inventory simulations help supply chain managers anticipate?

2. How does replenishment frequency affect inventory levels?

3. Fill in the blank: To append a value to a list in Python, use list.____(value).

3. Fill in the blank: To append a value to a list in Python, use `list.____(value)`.