All Customers / Customer Story
The Warwick Manufacturing Group, known as WMG, is an academic department at the University of Warwick, that provides innovative solutions to industry by providing research, education and knowledge transfer in engineering, manufacturing and technology. The group provides degrees for postgraduate students at the University of Warwick campus in England, and at overseas campuses. WMG is seen as an international role model for successful collaboration between academia and the public and private sectors.
Through their Industrial IoT (IIoT or Industry 4.0) research project they plan to publish as a case study, WMG is aiming to exploit the full potential of industrial data and improve visibility, productivity and performance of manufacturing systems considering well-defined KPIs (key performance indicators). While developing this project, WMG decided to employ InfluxData’s TICK Stack to help them get the job done. Throughout their project they have used InfluxDB as their dedicated, time series database, Chronograf for dashboarding and managing other aspects of the Stack as a whole, and Kapacitor for advanced analytics.
WMG had much to say about each tool they implemented in their project. First, they found InfluxDB, the purpose-built time series database, easy to understand with easy, “container-friendly” installation. Additionally, they found InfluxQL, InfluxData’s SQL-like query language for interacting with InfluxDB, flexible and easy to use. Further, they appreciated all of the documentation available regarding the database. As for Chronograf, they appreciated its “out-of-the-box integration” with InfluxDB and Kapacitor. They found that it was modern, responsive, and had a good range of visualization templates. Last but not least, they saw Kapacitor as a “to-the-point rule engine” for processing both batch and stream data with an easy-to-use scripting language. They also reported that Kapacitor was able to easily solve their extract, transform, load (ETL) and integration problems.
Improving industrial IoT monitoring
By collecting sensor metrics from manufacturing systems
Enabling predictive maintenance
Providing better visibility, productivity and performance
Fixed operational issues
Able to stream data and solve their ETL and integration problems