MapleMBSE implementation starts with a foundation of clearer communication. Resource involvement becomes less taxing, and roles intermesh cleanly. A simpler knowledge capture process requires a lower time commitment for gathering knowledge from other roles and disciplines.

In an example of system development for automotive transmissions, Nissan found that using the MapleMBSE tool reduced the time to complete the typical SysML modeling tasks to less than 25% - a four times improvement in productivity across an interdisciplinary team. A streamlined sharing of knowledge from across multiple teams resulted in a more accurate model that everyone had confidence in.

As the project at Nissan gained traction, the teams involved showed greater engagement, working faster and collaborating more effectively across disciplines.

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The MapleMBSE interface is simpler to apply, it gives system engineers more flexibility for making model adjustments, and overall it cuts down knowledge-capture errors.

When you strip out the rework from errors and remove the obstacles created by the misuse of tools and poor communication, a connected team can deliver their system engineering projects much faster and on budget.

For example, a study published by IBM Research showed the use of MapleMBSE resulted in faster task-completion and fewer errors, compared to performing the same tasks using a standard MBSE tool directly. Overall, a 75% reduction in errors was observed when MapleMBSE was used.