Crosser Blog

An important use of machine data is to calculate different KPIs to measure the performance and behaviour of machines or processes. Edge analytics is a perfect tool both for collecting the relevant data and calculating

In this blog we will discuss how you can build a modern historian by combining best-of-breed tools in a flexible and efficient way.

Siemens Mindsphere has taken the lead as the open operating system for Industrial IoT, according to Forrester. The cloud-based platform is built to connect and integrate the entire manufacturing process, to give manufacturers the capability to implement a wide array of solutions

Stockholm/Sinsheim, February 11th, 2020 – Today, Crosser, a leading provider of Intelligent Edge Analytics software for Industrial IoT and Industry 4.0, and GEBHARDT Fördertechnik, knowhow leader in the industry of intralogistics, announced a joint initiative

As part of GEBHARDT’s digital transformation CEO Marco Gebhardt is preparing his company for the future. He calls the next generation of warehouse automation ”Innovation of Things”, based on the term ”Internet of Things” in which physical and virtual objects are interconnected in order to allow them to work together through information and communication techniques.

Stockholm, 12 March, 2019 - Crosser today announced that the firm has partnered with Ericsson, a global leader in 4G/5G cellular network technology, by joining the Ericsson Industry 4.0 partner program.

Industry 4.0 is based on the convergence of OT and IT using modern technologies that are significantly easier to use and faster to deploy in order to extract business value from integrating machine data, shop floor data, enterprise data and cloud applications.

Stockholm/Munich, 20 November, 2019 - Crosser, a leading provider of Intelligent Edge Analytics software for Industrial IoT and Industry 4.0, today announced the opening of a new office in Munich as part of it’s growth strategy the company announced earlier this year.

OSIsoft PI System is one of the most widely deployed solutions for capturing and storing machine data within manufacturing, energy, utilities, pharma, transportation and facility management. 

The use of machine learning (ML) models is becoming more and more common in industrial IoT applications, especially for use cases such as anomaly detection and predictive maintenance. ML models are typically trained in the cloud using large stored datasets and iterative processing on powerful servers.