Use Cases / Solutions / Ideas

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

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.

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. 

In this webinar we will introduce you to remote deployment and life-cycle management of ML models running at the edge. Using the Crosser Edge Director to radically simplify the orchestration of edge deployments at scale.

Kepware is one of the leaders in industrial connectivity, by providing drivers for almost any type of machine-level protocol. The main use case is to collect data from machines and deliver it to centralized platforms for analysis - but what if you want to take action on your data already on the factory floor? This is where Crosser comes in.

Using video cameras as sensors in industrial IoT applications is becoming more and more popular. The reason is of course that it opens up for many interesting applications.

Extracting valuable insights out of data collected from machine sensors can be hard, often requiring analyzing data from many sensors in parallel.
Due to the complexity, machine learning (ML) methods are becoming more and more popular to analyze these datasets.