Every industrial organization knows that it is imperative to move forward at lightning speed with predictive analytics. As a result, new solutions, analytics startups, and consulting firms are popping up every week.
How does an engineer select the best predictive analytics solution for his Operations?
The following recommendations of GeDigital they provide guidance for process engineers in the process of selecting the right technologies
1. Not just a buzzword
Make sure “predictive analytics” isn't a buzzword that hides innovative but untested software.
For example, GE Digital has been building and implementing (first for internal use) analytical software solutions and services for more than 15 years, serving industrial organizations around the world in a number of different industries.
Work with a partner you can trust and know that they will support you for a long time.
2. Problem solving skills
Choose advanced industrial analytics with a troubleshooting component that enables engineers to quickly troubleshoot the performance of continuous, discrete or batch manufacturing processes by extracting insights from data directly from production and sensors available at the plant floor ”.
Seamless connectivity, rich visualization, and predictive analytics enable engineers to analyze scenarios in the field, quantifying the impact operational changes will have on key performance metrics, and identifying causes for performance variation. performance.
Additionally, to support your entire IoT journey, look for capabilities in software ranging from simple calculations to predictive machine learning models, real-time optimization, and advanced control algorithms.
3. Development environment
Ensure that the analytical package enables engineers to rapidly develop analytical solutions, supporting improvements in production, OEE, quality and efficiency by significant margins.
A comprehensive analytic solution development environment provides visual analytic building blocks for building and testing 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 real-time and historical data sources and automation systems makes setup faster. Built-in data quality support makes real-time data cleansing and validation easy.
4. Models for rapid development
Confirm that the analytics package can accelerate deployment with templates and dedicated dashboards for greater efficiency.
Engineers should be able to save and reuse analytical solutions for easy distribution to similar resources or process units (other similar machines, lines or plants). Additionally, while the analytical troubleshooting component should enable engineers to find answers faster with analytics-driven data extraction and process performance troubleshooting, the development/configuration capabilities should allow them to More easily capture expert knowledge and best practices into high-value analytical models for rapid enterprise-wide deployment.
5. Focus on engineers, not just data scientists
Focus on analytics solutions built with engineers (and for process engineers) in mind, not just data scientists.
With an analytics package that engineers can use, teams can create a Process Digital Twin for smarter operations.
Analytics and trends that can be created with a few simple drag-and-drops accelerate time-to-value and reduce reliance on data scientists and programmers. Online demos enable rapid mastery of the software with easy-to-follow demonstrations and guided simulations.
As mentioned earlier, a rapid development environment is essential. The best solutions provide rapid guided data mining for engineers for fast time-to-insight, an easy drag-and-drop visual environment for subject matter experts and engineers, and code-free analytic solution models for simple calculations, data cleansing, math, statistics, machine learning models, real-time optimization, and advanced process control.
