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A common problem in our industry is data mismatch, data from different sources, to send it to other systems that expects data in another format. These situations are where data transformation and harmonization...

- What is the potential of data in manufacturing?
- Which technologies are supporting the factories of the future?
- How can appropriate solutions be applied and used?

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

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.

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. 

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.

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.