As published in Construction Executive, September 26, 2019
By Drew Carruthers, CM Labs Simulations
Drew Carruthers is Product Line Manager for Construction Products at CM Labs Simulations, Montreal, Quebec. Drew Carruthers directs the development of CM Labs’ award-winning Vortex Construction Equipment Simulators. He has extensive experience as a systems engineer and technical trainer, and he closely follows technological trends in the construction industry.
It wasn’t so long ago that technology in construction equipment training meant PowerPoints and videos. But the technology in today’s equipment is highly connected, and the ability to analyze equipment usage in real-time also influences the way training is delivered. Among the biggest influences on construction equipment training today are simulation, automation, data analysis, augmented reality and smartphones.
Fleet management principles of predictive analytics also apply to operator training. In fleet management, GPS and telematics technology can provide feedback on bucket positioning or fill rates to improve duty cycles. In some cases, those operations are partially automated.
This is already having an impact on how progressive training is being delivered. Case in point: Some simulators can teach advanced dozer techniques. For instance, a material spreading exercise teaches operators how to operate in a complex work environment where they must focus on dump truck positioning as well as materials management. Real-time metrics captured by the simulator can indicate areas for improvement, whether in real-time or as part of an after-action review session with the trainer.
Likewise, modern construction equipment is designed with heads-up displays and systems-monitoring controls. Equipment manufacturers are using simulation-based software as a testbed for how equipment can better dialog with operators in the seat. They are using the technology to better understand operator interaction, comfort and safety.
With the advent of big data analysis, modern simulators can assess and predict the impact of individual operator actions on machine efficiency. This information typically provides the shortest, most effective path to skills acquisition. When a simulator’s intrinsically unbiased feedback is considered, the operator training experience becomes measurably more productive.
This is especially true with simulators that give training organizations the ability to “tune” the simulator to indicate which operating characteristics are most important to them. The ability to remove bias and teach skills in a consistent manner is why simulators are already now being used for operator performance or certification exams. In the future, there will likely be training paths that are customized to individual operator learning profiles.