LOGISTICS AUTOMATION
AND DIAGNOSTIC SOFTWARE

The software solution allows you to identify, diagnose, and solve problems while processing large-scale logistics operations, more than a million per day, such as: sorting, scanning, or sizing equipment, and so on.
ERPLOGISTIC & SUPPLY CHAINSAP

PROJECT
DESCRIPTION

Product company from the USA that specializes in software for the largest Fortune 500 logistics and retail operators.
The software solution allows you to identify, diagnose, and solve problems while processing large-scale logistics operations, more than a million per day, such as: sorting, scanning, or sizing equipment, and so on. The solution allows:

BUSINESS
CONTEXT

Project timeline: STRICT

Selection process: TENDER

The customer company has created its own product that covers the needs for automatic cargo tracking of large logistics companies. The product is wider in functionality, lower in cost, and easily customizable compared to similar systems offered to the market by global operators. 

The main pain points of logistics companies:

Our specialists were involved in the recommendation for the project from its first stage, as previously, they had successful experience with a similar project in a relevant domain with bigger data and the Splunk analytical system, which was used for the development of the first version of the product.

Goals of
the project

We were required to develop a software solution with a wide range of capabilities. Using it, client companies will be able to: quickly receive accurate data on cargo in logistics tunnels, quickly process large amounts of information with subsequent sorting of parcels. The entire array of data is deposited in a secure storage (at the final stage of the project, all information was transferred to cloud servers), which ensures the uninterrupted execution of the client’s software functions. An important part of the solution was the ability to collect and analyze data on loading and breakdowns of hardware and logistics equipment in order to keep them up and running in an ever-increasing operational load.

For the effective implementation of the tasks:

Business goals:

Technical goals:

SOLUTION'S ARCHITECHTURE

CHALLENGES

SECURITY REQUIREMENTS

A LARGE NUMBER OF HUBS AND TUNNELS

ABSENCE OF DIRECT ACCESS TO THE CUSTOMER'S SYSTEM

CLIENT COMPANY HAS WIDE GEOGRAPHY

HIGH PACE OF UPDATING EQUIPMENT

INABILITY TO USE EQUIPMENT

REMOTE INSTALLATION OF SOFTWARE

UPDATING THE STACK FOR ML

OUTCOMES

Technical Outcomes:

Impact on business:

PROJECT TIMELINE

Timing: 2 years

Development of a system for analytics and visualization of large amounts of data:

Timing: 11 months

Development of an algorithm for the support service and its launch:

Timing: 2 years

Expansion of the information collection and analytics system, improvement of the functions of existing applications:

Timing: 2 years

Expansion of the information collection and analytics system, improvement of the functions of existing applications:

TEAM

Engineering team spent 2 month in the production facilities to learn about the production processes and coordinate with engineering team.

Analytical stage:

Project manager, Data analyst

Development stage:

Team lead, Splunk Python developer, AI Python + Java developer, Java developer - 1, Java Script + React.js developer, Front End (interface layout), DevOps – 2 person, QA, AQA

Support stage:

Project manager, Team lead, QA

Technological stack

Linux (Ubuntu), Amazon Linux 2, Windows 7,10, Android, iOS

MongoDB, Amazon Redshift, PostgreSQL 12

Java 11, Kotlin, Swift, JavaScript, Python 3, BASH, Powershell

Spring, Java enterprise server, React, Realm, OpenCV, Tensorflow

Java enterprise server, Tomcat, AWS EC2, AWS S3, AWS Lambda, Amazon SageMaker

Jenkins, Docker, Nginx

AWS EC2, AWS S3, AWS Lambda, Amazon SageMaker

Amazon SageMaker, Tensorflow, PyTorch

SAP ERP 6.0. SCM/CRM/HRM

ISO 25010, ISO/IEC 12119, ISO/IEC 27000

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