This presentation was held at the 2020 NAFEMS UK Conference "Inspiring Innovation through Engineering Simulation". The conference covered topics ranging from traditional FEA and CFD, to new and emerging areas including artificial intelligence, machine learning and EDA.

Resource Abstract

Simulation software delivers your ideas and projects safely to real life. What if there was a way to harness and increase synergistic productivity of your workforce talents and simulation software? Merging machine learning with software usage sorts through copious amounts of unrelated data to establish patterns. Access to reliable real-time analytics is vital for planning on projects regarding streamlining workflow, usage trends forecasting, and allocating resources.

In particular, analysis of engineering software usage requires specialized insights that take into consideration the multitudes of complex product functions and features, as well as the behaviors of engineers and other technical professionals. Precise planning is crucial in determining the cycles when users will need to use applications and in purchases of future licenses. With knowledge of future usage trends, managers can allocate resources with the benefit of foresight: pick the best time for the adoption of new technology; know the optimal time to start a new project; mitigate risks of production delays due to possible denials; and schedule the most suitable time for training.

Through the utilization of a variety of statistical techniques from data mining, predictive modeling, and machine learning, these advanced analytical techniques can analyze vast quantities of historical and current usage data to create forecasts and predictions considering future usage trends. The forecasts produced by this system are highly useful in identifying risks and opportunities that allow the organization to anticipate outcomes based on the data and not on assumptions. Having a glimpse of the future can lead to better decision making, and proactively respond to systemic issues (including data loss), unusual user behavior or any possible risks within the system. The use of machine learning produces more precise forecasts, thus enhancing IT efficiencies and generating savings.

Companies employing engineering software applications need to ensure that they receive maximum value from their license investment. Utilization of advanced analytics can optimize your software usage and productivity. Join us in our presentation as we share case studies of software optimization solutions for enterprises.