Packages and Packets Counting and Dimension Check for an E-commerce Delivery and Supply Chain Company

SOP Features

Company Overview

One of the largest supply chain and logistics companies, processing approximately 22 million packages every month across more than 100 conveyor belts, sought to improve the efficiency and accuracy of their package handling operations.

Problem Statement

The company faced significant issues due to incorrect orientation and alignment of packages, resulting in the rejection of around 1% of packages. The wrong orientation and labels led to substantial time loss, customer dissatisfaction, and operational inefficiencies. Accurate counting of packages on each conveyor belt was also a critical requirement.


The primary objective of the project was to accurately count packages on each conveyor belt and check their orientation and alignment in real-time. The system needed to generate alerts whenever any misalignment or incorrect orientation was detected. Detecting labels accurately on each package was also an objective.

Solution Design

We designed and developed a computer vision solution that monitors each package on the conveyor belt, checks alignments, detects labels on each package, and generates alerts for any deviations from the standard operating procedures (SOPs).

Challenges and Solutions

Label Detection on White Packages:

  • Challenge: Detecting labels on white-colored packages was difficult due to the lack of contrast.
  • Solution: We increased the frame rate of the cameras and applied advanced image processing algorithms to enhance the contrast and visibility of the labels, improving detection accuracy.

Camera Angle for Orientation and Alignment Detection:

  • Challenge: Incorrect camera angles led to improper detection of package orientation and alignment.
  • Solution: We adjusted the camera angles to 90 degrees for optimal visibility and trained the AI model with a larger dataset of images to improve its ability to detect orientation and alignment issues accurately.


The implementation of the computer vision solution was a significant success, achieving high accuracy in package counting, label detection, and orientation detection. The system's real-time monitoring and alert capabilities enhanced operational efficiency, reduced time loss, and improved customer satisfaction by ensuring packages were correctly oriented and aligned.


The project demonstrated the effectiveness of leveraging computer vision and AI to address complex logistical challenges in a high-volume supply chain environment. The successful deployment of this solution underscores the potential for AI-driven automation to transform and optimize supply chain and logistics operations.

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