Lära Analyzing Shipment Schedules | Logistics and Transportation Optimization

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Shipment scheduling and tracking play a critical role in logistics by ensuring that goods move efficiently from origin to destination. Accurate schedules help you coordinate resources, reduce idle time, and meet customer expectations. Tracking shipment progress and analyzing historical data allow you to spot patterns, anticipate potential disruptions, and continuously improve your logistics operations.


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import pandas as pd

# Create a DataFrame representing a shipment schedule
shipment_data = {
    'shipment_id': [101, 102, 103, 104],
    'origin': ['Chicago', 'Dallas', 'Atlanta', 'Denver'],
    'destination': ['New York', 'Los Angeles', 'Miami', 'Seattle'],
    'departure': pd.to_datetime([
        '2024-06-01 08:00', 
        '2024-06-01 09:30', 
        '2024-06-01 10:00', 
        '2024-06-01 11:15'
    ]),
    'arrival': pd.to_datetime([
        '2024-06-02 14:00', 
        '2024-06-02 22:00', 
        '2024-06-01 22:30', 
        '2024-06-02 19:45'
    ])
}

shipments = pd.DataFrame(shipment_data)
print(shipments)

Once you have structured shipment data in a DataFrame, you can analyze on-time performance and delays by comparing scheduled and actual times. Calculate each shipment's duration by subtracting the departure time from the arrival time. If you have expected arrival times, you can measure delays by comparing them to actual arrivals. This analysis helps you identify which shipments are consistently late and understand the causes, enabling proactive improvements in your logistics process.


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import matplotlib.pyplot as plt

# Calculate shipment durations in hours
shipments['duration_hrs'] = (shipments['arrival'] - shipments['departure']).dt.total_seconds() / 3600

# Simulate delays (in hours) for demonstration
shipments['expected_arrival'] = shipments['departure'] + pd.to_timedelta([30, 36, 12, 32], unit='h')
shipments['delay_hrs'] = (shipments['arrival'] - shipments['expected_arrival']).dt.total_seconds() / 3600

# Plot shipment durations
plt.figure(figsize=(10, 5))
plt.bar(shipments['shipment_id'], shipments['duration_hrs'], color='skyblue', label='Duration (hrs)')
plt.xlabel('Shipment ID')
plt.ylabel('Duration (hours)')
plt.title('Shipment Durations')
plt.legend()
plt.show()

# Plot shipment delays
plt.figure(figsize=(10, 5))
plt.bar(shipments['shipment_id'], shipments['delay_hrs'], color='salmon', label='Delay (hrs)')
plt.xlabel('Shipment ID')
plt.ylabel('Delay (hours)')
plt.title('Shipment Delays')
plt.legend()
plt.show()

1. What information is essential for analyzing shipment schedules?

2. How can visualizing shipment delays help improve logistics operations?

3. Fill in the blank: To calculate shipment duration, subtract 'departure' from '____'.

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