How Logistics Companies Can Track Shipments with Protocols

In logistics, on-time deliveries are everything. But tracking shipments manually through spreadsheets is slow and inefficient. Shipping companies need real-time insights to optimize routes and reduce delays.

With Protocols, you can automatically process shipment data, detect late deliveries, and optimize performance. This guide will show you how—even if you don’t know how to code.

By integrating MongoDB, shipment data can be stored and accessed in real time, allowing for seamless tracking across different locations.

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The Problem: Manual Shipment Tracking Slows Down Operations

Logistics teams rely on spreadsheets to track shipments, but:
❌ Delivery delays often go unnoticed
❌ Manually updating tracking data takes hours
❌ Inefficient routes increase costs

Time Wasted Without Automation

On average, logistics managers spend 10 hours per week tracking shipments. At $30/hour, that’s $1,200 per month lost in manual work.

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The Solution: Automating Shipment Tracking with Protocols

With Protocols, you can:
✅ Automatically detect late deliveries
✅ Generate real-time delivery performance reports
✅ Optimize shipping routes for efficiency

By storing tracking data in MongoDB, companies can access live shipment updates and analyze trends over time.

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Step 1: Import Shipment Data

Let’s start by loading a dataset containing delivery records.

Example Shipment Data (shipments.xlsx)

Order ID

Destination

Shipped Date

Delivered Date

Status

20001

New York

2025-02-10

2025-02-12

On Time

20002

Chicago

2025-02-10

2025-02-14

Delayed

20003

Los Angeles

2025-02-11

2025-02-13

On Time

20004

Houston

2025-02-12

2025-02-16

Delayed

Now, let’s load this into Python.

import pandas as pd  

# Load shipment data
shipments = pd.read_excel("shipments.xlsx")

# Display first few rows
print(shipments.head())

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Step 2: Identify Late Deliveries

We’ll flag shipments that took longer than expected.

# Calculate delivery duration
shipments["Shipped Date"] = pd.to_datetime(shipments["Shipped Date"])
shipments["Delivered Date"] = pd.to_datetime(shipments["Delivered Date"])
shipments["Delivery Days"] = (shipments["Delivered Date"] - shipments["Shipped Date"]).dt.days

# Flag late deliveries
shipments["Delayed"] = shipments["Delivery Days"] > 2  

print(shipments[["Order ID", "Destination", "Delivery Days", "Delayed"]])

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Step 3: Analyze Route Performance

Let’s find which locations have the most delays.

# Count delays per destination
delays_by_location = shipments[shipments["Delayed"]].groupby("Destination").size().sort_values(ascending=False)

print(delays_by_location)

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Step 4: Generate a Delivery Performance Report

To optimize routes, we need a summary of on-time vs. delayed shipments.

# Count total shipments and delays
performance_summary = shipments.groupby("Status").size()

print(performance_summary)

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Step 5: Export the Shipment Report

Finally, we save the results to an Excel file.

shipments.to_excel("shipment_report.xlsx", index=False)
print("Shipment report saved as shipment_report.xlsx")

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The Result: Faster, More Reliable Shipment Tracking

By automating shipment tracking, logistics companies can:
✅ Save 10 hours per week, worth $1,200/month in wages
✅ Identify late deliveries instantly
✅ Improve delivery efficiency with real-time tracking

With MongoDB, shipment data can be stored for long-term analysis, helping businesses optimize logistics over time.

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I Can Set It Up So You Never Miss a Shipment Update

I specialize in automating logistics data with Python and Pandas. If you need a system that tracks shipments, detects delays, and optimizes routes, I can build it—so your team can focus on getting deliveries out on time.

Let’s make your shipment tracking effortless—contact me today!