Nova Poshta: AI-Powered Warehouse Monitoring for Conveyor Systems
Nova Poshta: AI-Powered Warehouse Monitoring for Conveyor Systems
AI/ML
CRM/ERP
IoT
Web Development
About the Task
Nova Poshta needed a real-time system to detect conveyor bottlenecks, balance labor across sectors, and reduce operational delays in its large-scale logistics terminals.
results
Goods jam resolution time cut from minutes to seconds, improving package flow and delivery reliability.
results
Over $4 million in annual savings through optimized labor allocation and faster sorting throughput.
Nova Poshta, a market-leading national delivery service in Ukraine, operates one of the largest logistics terminal networks in Eastern Europe. At the heart of their operations are automated conveyor systems, developed by Dutch firm Vanderlande, that sort and process thousands of packages per hour across multiple warehouse terminals — each the size of two to three football stadiums.
Faced with growing complexity and demand, Nova Poshta turned to Infinity Technologies to develop a real-time monitoring system that would detect bottlenecks, optimize labor distribution, and improve delivery throughput across its national network.
The Business Challenge
Despite the efficiency of their conveyor system, Nova Poshta faced several operational challenges:
Goods jams and bottlenecks were detected manually, often too late to prevent delays.
The workload across conveyor sectors was unbalanced — some site workers were overwhelmed, while others were idle.
There was no centralized system for real-time visual monitoring, predictive alerting, or automated workforce reallocation.
The management team required a digital solution to:
Identify real-time conveyor stress areas
Visually map performance metrics across multiple sectors
Allow managers to reallocate human resources with a tap or click
All of this had to integrate smoothly with existing infrastructure and respect strict performance and latency constraints.
Our Solution
Infinity Technologies built a smart warehouse monitoring application that provides logistics managers with a real-time overview of the conveyor system, complete with predictive stress analytics, visualization of load intensity, and direct communication tools to redirect labor in seconds.
The solution includes:
A real-time conveyor load tracking algorithm that analyzes package flow, scanner alerts, and belt speed
A sector-based visualization engine displaying conveyor belt stress levels in nine color-coded gradients
Automated alerts when thresholds are exceeded in any area
A manager control panel to communicate directly with on-site teams and redirect them as needed
Managers can now monitor goods flow, detect and classify jams, assess operational stress, and issue relocation instructions — all from a single intuitive interface.
Architecture Overview
The system was architected for low-latency, real-time performance and modularity — enabling it to operate across large-scale terminal facilities without introducing operational overhead or requiring invasive hardware changes.
Core Technologies
01
Java 11 + Quarkus
High-performance backend services were developed using Quarkus, a Kubernetes-native Java framework designed for low-memory footprint and rapid boot times. This allowed us to scale the backend horizontally while maintaining sub-second response times.
02
Vue.js (Frontend Framework)
The frontend was built in Vue.js to deliver a highly reactive user interface that updates stress metrics, color-coded maps, and personnel status dashboards in real-time.
03
Native Android and iOS Apps
Managers on the ground use mobile apps for monitoring alerts and sending workforce instructions. Native development ensures the apps are responsive, offline-capable, and integrated with on-device messaging.
04
Corezoid Integration Layer
Used to orchestrate workflow rules, transform events into actionable insights, and simplify real-time decisioning. Corezoid processed events like scanner alerts, belt speed drops, and task acknowledgments from the workforce.
Key Features & Algorithms
01
Transport Stress Detection Algorithm
Continuously monitors load on conveyor sectors, calculating performance against KPIs like throughput, scanner velocity, and operational density. Results are translated into heatmaps updated in real time.
02
Color-Coded Conveyor Visualization Map
Each sector of the terminal is mapped using nine distinct colors — from green (low load) to red (critical overload) — allowing managers to instantly assess stress levels across the facility.
03
Alert and Workforce Allocation Engine
Once a threshold is breached, the system triggers an alert and suggests optimal reallocation. Managers can then issue instructions via the app to move workers from low-load to high-load areas.
04
Reporting and Analytics Dashboard
Historical trends, stress distribution, shift-based performance, and bottleneck frequency are aggregated and visualized for continuous process improvement.
Business Results
Nova Poshta reported dramatic performance gains after deployment of the monitoring solution:
Goods jam resolution time dropped from minutes to seconds
Balanced worker utilization across sectors improved throughput consistency
Operational savings of over $4 million annually (an estimated $350,000 per month), due to reduced downtime, optimized labor allocation, and faster sorting cycles
Improved employee satisfaction and bonus eligibility, as performance-based sorting became more transparent and manageable
Conclusion
Infinity Technologies delivered a real-time warehouse monitoring system that not only enhanced operational efficiency but also empowered management with data-driven decision-making tools. The solution integrated smoothly with Nova Poshta’s complex logistics setup and introduced an AI-driven layer of responsiveness into physical warehouse operations.
By aligning advanced software with real-world logistics infrastructure, we demonstrated how smart automation and visualization can redefine operational performance in high-volume logistics environments.
Would you like this version exported as a sales presentation, impact report, or white-labeled case study for similar logistics prospects?
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