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Volodymyr Koliadin

Volodymyr Koliadin

Volodymyr is a Senior Data Scientist at Grid Dynamics. He joined Grid Dynamics in 2021 and has more than 15 years of experience working on data driven solutions.


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Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products
Article
Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products
Article Optimization of order and inventory sourcing decisions in supply chains with multiple nodes, carriers, shipment options, and products

Order sourcing decisions can be defined as the logistics decisions related to how customer orders and internal inventory requisitions are fulfilled: what nodes (warehouses, distribution centers, stores, etc.) the inventory should be sourced from, what carriers should be used to ship it, what sh...

Building a Predictive Maintenance Solution Using AWS AutoML and No-code Tools
Article
Building a predictive maintenance solution using AWS AutoML and no-code tools
Article Building a predictive maintenance solution using AWS AutoML and no-code tools

Originally published on the AWS Partner Network blog Industrial machine, equipment, and vehicle operators are often faced with the challenge of minimizing maintenance costs under strict constraints related to safety, equipment downtime, and other SLAs. Over-sufficient or preventive maintenan...

Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
Article
Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
Article Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook

Modern manufacturing, transportation, and energy companies routinely operate thousands of machines and perform hundreds of quality checks at different stages of their production and distribution processes. Industrial sensors and IoT devices enable these companies to collect comprehensive real-t...

Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning
Article
Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning
Article Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning

Automated health monitoring, including anomaly/fault detection, is an absolutely necessary attribute of any modern industrial system. Problems of this sort are usually solved through algorithmic processing of data from a great number of physical sensors installed in various equipment. A broad range...

Anomaly detection in industrial applications: solution design methodology
Article
Anomaly detection in industrial applications: Solution design methodology
Article Anomaly detection in industrial applications: Solution design methodology

Modern technical systems cannot operate optimally without special tools for health monitoring, fault detection, and predictive maintenance. This is the case even for relatively simple systems, such as a service elevator. As for more complex industrial systems - like a power plant or an assembly lin...

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