AI and Industrial Manufacturing: A Guide to Optimizing Production Processes with Data and KPIs
White paper AI and Industrial Manufacturing: A Guide to Optimizing Production Processes with Data and KPIs
Zerynth Common Data Model: Zerynth’s model for industrial data management
White paper Zerynth Common Data Model: Zerynth's model for industrial data management Extracting and managing
Digitising the Metalworking Industry with Zerynth’s IoT & AI Platform
The landscape of the Italian metalworking industry is undergoing an unprecedented transformation, but the challenges are numerous: from modernising machinery to efficient data management, including the integration of management systems.
Preventive and Predictive Maintenance in Industry 4.0
In this document you can discover how integrating IoT technologies with solutions for cleaning machinery parts cleaning can revolutionize maintenance practices in Industrial plants.
5 best practices for optimizing production
In this document we will explain how you can optimize your production processes and what are the best practices your company can implement to achieve greater efficiency.
Maintenance 4.0: How AI and Data Driven strategies increase machine performance
This document describes how, by using Artificial Intelligence (AI) and machine learning algorithms, companies can adopt predictive maintenance techniques allowing them to embark on a complete digitization 4.0 journey.
Industry 4.0 – Opportunity for manufacturing companies – White paper
Industry 4.0 Industry 4.0: what it is and why it is an opportunity for manufacturing
Industrial Edge Computing on the Shop Floor
Industry 4.0 Industrial Edge Computing on the Shop Floor How to prepare an architecture
Energy Efficiency Optimization for Industrial Plants
In this document we offer an overview of the process optimization challenges of companies and explain how Zerynth's technologies are able to provide the most suitable tools to reduce energy costs.
IoT technologies in the waste industry
Knowing how to properly manage waste is one of many challenges that companies find themselves facing today as they look for innovative systems capable of automating their disposal processes or waste collection that is produced by municipalities.