5 Steps to Select a Predictive Analytics Solution

Every industrial organization knows that it’s imperative to move ahead at light speed with predictive analytics. Consequently, new analytics startups and consulting companies are showing up every week.

How does an engineer select the best predictive analytics solution for their operations?

The following advice provides guidance for engineers.

Make sure that “predictive analytics” isn’t buzz hiding risky, unproven software and newbie “experts.”

For example, GE Digital has offered and implemented analytic software solutions and services for more than 15 years, serving industrial organizations around the world across a diverse set of industries.

Work with a partner that you can trust and know will support you for the long term.

Select industrial advanced analytics with a troubleshooting component that enables engineers to rapidly troubleshoot continuous, discrete, or batch manufacturing process performance by mining insight from available manufacturing sensor and production data.

Seamless connectivity, rich visualization, and predictive analytics enable engineers to analyze operating scenarios, quantifying the impact that operational changes will have on key performance metrics and identifying causes for performance variation.

Additionally, to support the full IoT value journey, look for capabilities from simple calculations to predictive machine-learning models to real-time optimization and advanced-control algorithms.

Be sure that the analytics package enables engineers to rapidly develop analytic solutions – supporting improvements in production throughput, yield, quality, and efficiency with significant margins. 

A comprehensive analytic solution-development environment provides visual analytic building blocks to build and test calculations, predictive analytics, and real-time optimization and control solutions with connectivity to real-time and historical data sources and drag-and-drop access to rich functional libraries.  

Plug-and-play connectivity to historical and real-time data sources and automation systems make for faster configuration. Built-in support for data quality makes real-time data cleaning and validation easy.

Confirm that the analytics package can speed deployment with templates for greater efficiency.

Engineers should be able to save analytics solutions as reusable templates for easy deployment to similar assets or process units. Additionally, while the analytics troubleshooting component should enable engineers to find answers faster with analytics-guided data mining and process-performance troubleshooting, the development/configuration capabilities should allow them to more easily capture expert knowledge and best practices into high-value analytic templates for rapid enterprise-wide deployment. 

Focus on analytics solutions developed with engineers – not just data scientists – in mind.

With an analytics package that is accessible to engineers, teams can create a Process Digital Twin for smarter operations.

Visual drag-and-drop analytics accelerate time to value and reduce dependence on data scientists and programmers.  Online demos enable rapid mastery of the software with easy-to-follow demonstrations and guided simulations.

As previously mentioned, a rapid development environment is critical. The best solutions provide rapid wizard-driven data mining for engineers for fast time-to-insight, an easy visual drag-and-drop environment for subject matter experts and engineers, and analytic solution templates without programming for simple calculations, data cleaning, maths, statistics, machine-learning models, real-time optimization, and advanced process control.

Ready to try a predictive analytics solution that meets all of the key requirements? Get a free trial of Proficy CSense and explore how you can optimize operations.