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Development of a Web-Based Vessels Market Analytics Platform
  1. case
  2. Development of a Web-Based Vessels Market Analytics Platform

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Development of a Web-Based Vessels Market Analytics Platform

effectivesoft.com
Logistics

Challenges in Manual Vessel Market Analysis

The client relied on manual analysis of the ship market using mathematical models and Excel files, resulting in a time-consuming, error-prone process that required automation and a user-friendly interface for efficient decision-making.

About the Client

Global leader in container shipping with over 88,000 employees across 130 countries

Key Project Goals

  • Automate complex vessel market analysis workflows
  • Develop a user-friendly web-based platform for data visualization and comparison
  • Integrate mathematical models with modern technology stack for accurate forecasting
  • Enable customizable dashboard for cross-module project comparison

Core System Functionalities

  • Market Measures module for assessments, indicators, and benchmarks
  • Vessel Valuation calculator with customizable timeframes and parameters
  • Risk & Opportunity analysis for market condition evaluation
  • Core Data subsystem for real-time vessel information
  • Depreciation tracking with interactive charting tools
  • Investment Analysis for ROI calculations
  • Customizable dashboard with cross-project visualization capabilities

Technology Stack

Python
.NET framework
React.js
MS SQL Server
Matlab
Docker
Azure

System Integrations

  • Matlab mathematical models integration with .NET backend
  • Real-time data feeds for vessel market information

Non-Functional Requirements

  • High-performance data processing for large datasets
  • Scalable architecture for global user base
  • Enterprise-grade security for sensitive shipping data
  • Responsive UI/UX across devices

Expected Business Impact

Streamlined vessel market analysis processes, reduced manual effort by 70%, improved decision-making accuracy through visual analytics, and enabled real-time market responsiveness through automated forecasting models.

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