AUGUST 3, 2017
Faster, customizable, end-to-end predictive analytics solutions – that is the main purpose behind PDX, Predictronics’ innovative software platform for collecting and analyzing industrial big data.
Launched Aug. 1, the platform includes data acquisition, model-building and asset-monitoring components that use data to reduce unplanned downtime and increase productivity and product quality.
By using PDX, businesses in nearly every industry segment become equipped with the tools to avoid all-too-common problems such as sudden machine failures, unnecessary maintenance and waste.
The concept behind PDX was conceived by Predictronics’ co-founders long before its launch. In fact, plans to create an industrial predictive analytics software were in place soon after the company started.
In working with clients such as Komatsu and Nippon Steel, the Predictronics team realized many industries faced similar problems. A reconfigurable predictive analytics platform, then, allowed for the faster development of complete solutions that could be customized to various industrial applications.
Before they could implement those industrial solutions, however, PDX’s creators needed to equip businesses with the ability to collect necessary information for analysis and producing impactful results.
The end-product is an adjustable software platform that facilitates model development, displays actionable information and enables businesses in nearly every industry segment to achieve worry-free uptime.
The creation of PDX has already garnered much attention from the automotive and manufacturing industries, among others, with Makino becoming one of its earliest adopters. To meet customers’ demands, Predictronics has grown the company exponentially, more than doubling the number of employees.
Even after its early successes, CTO David Siegel says PDX will continue to innovate in the world of predictive analytics. “PDX will evolve over time to incorporate the latest and most advanced pattern recognition technology. We will also continuously learn from our users to improve our software."