Predictive performance engineering for heavy equipment

The heavy equipment industry demands innovation while navigating stricter regulations and the integration of connected technologies. Predictive performance engineering with a digital twin is the key to achieving this.

Siemens' solutions combine heavy equipment simulation, real-world physics and performance prediction continuity to optimize design, predict performance and enable proactive maintenance. Results from heavy equipment leaders using the Siemens predictive performance engineering approach include reduced costs, accelerated development and improved product reliability.

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Predictive performance engineering for heavy equipment

Heavy equipment technology drives design complexity

The exciting advancements in heavy equipment technology – autonomy, electrification and connected worksites – bring a hidden cost: increased design complexity and development times.

Siemens' solutions for predictive performance engineering empower you with a comprehensive digital twin, simplifying the virtual design and verification process. This translates to faster, more cost-effective validation of real-world machine performance.

Unique in its ability to combine the best of virtual and physical worlds through multi-physics modeling, seamless CAE automation, and performance prediction continuity, Siemens equips you to outpace the competition.

Increasing performance engineering in heavy equipment

The challenge for heavy equipment manufacturers is clear: how do you manage ever-growing design complexity while simultaneously improving the engineering process?

Our industry-leading simulation and testing tools enhance engineering accuracy and development speed, enabling the creation of a comprehensive digital twin that reflects the real-world performance of your most intricate machines.

Learn more about Siemens' solutions and unlock the full potential of your heavy equipment performance engineering. Start a free heavy equipment simulation and performance engineering trial today.