Cognitive Automation
for Container Booking

Reduces running costs and errors simultaneously.

Description of Problem

There were various time-consuming processes in the Operations Department of the Client’s company. Most of the processes were rules-based, while some required machine-learning (an aspect of artificial intelligence).

  1. Booking of containers when client’s end-customers place orders
    • This process required staff from Client’s Company to log on to carriers’ (mostly shipping lines) web portals to make bookings.
  2. Reports of booking orders/ incoming cargo with detailed information about containers
    • This process required staff from Client’s Company to log on to government’s web portals to get data about cargo.
    • The information had to be formatted in a specific way.
  3. Transferring information from Bills of Lading (Excel) into the desired format and preparing a Manifest, which is a combination of various Bills of Lading.
  4. Extracting information from scans of other Bills of Lading, to extract data and re-formatted into a Manifest. 

Client Overview:

The client is a regional shipping/logistics company that is involved in businesses such as stevedoring, operating vessels (container vessels, bulk carriers, coastal vessels), cargo movement, and jetty maintenance.

Industry:

Logistics

Processes Type:

Container booking/ automated reading of Bills of Lading

How Gleematic Helps

Gleematic was able to complete the whole process of form-filling onto government web-portals and also shipping lines' websites for booking and declarations.

Gleematic was alerted upon departure/arrival of vessels to get information from government portals. Information from web portals was retrieved quickly by clicking through various tabs. The information was then placed into an Excel file and formatted in a specific way to be sent to end customers.

Gleematic was able to transfer data from one Excel file to another of a different format with near-perfect accuracy to create a Manifest. The data in the Manifest was formatted by Gleematic so that the information fits into one page.

Gleematic was trained with about 90 different copies of Bills of Lading (BLs) of a specific format by tagging various fields of data. The purpose was to build a “model” to read this format of BLs. Logic and coding were included to break up strings of text into distinct categories. The “model” was then tested on 10 new copies of BLs of a specific format and achieved an accuracy of about 85%.

Results

neuromorphic-computing

Degree of Robotization: 75% Effort Automated

financial-statement

ROI: 3 Months

Human Error Rate Reduced

tester

Manual Effort Reduction to 75%

real-time

Faster Processing Time: Reduction of 65%

workflow

More Standardization

information-technology

High-Quality Improvement

working-hours

No More Repetitive Administration

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