Boeing

Boeing Uses Aurora to Manage Aircraft Production

Over fifteen years ago, Boeing first used Aurora to manage aspects of their assembly operations for the Boeing 787 Dreamliner™ airliner. Since then, Boeing’s use of Aurora has grown greatly to more than 60 applications that manage their diverse commercial and defense aircraft production systems.

Boeing uses a version of Aurora, called Aurora-CCPM, which prioritizes production tasks by using the Critical Chain buffer management method to balance resource capacities with manufacturing requirements and constraints. The result is a dynamic assembly schedule that adapts to real-time production variability and enables Boeing to operate as efficiently as possible. In a study conducted by Boeing, Aurora managed resources more efficiently than all other software Boeing could identify, including software that Boeing had developed specifically for managing its own operations and had maintained for almost two decades.

The article ‘Night Moves’ in Boeing Frontiers magazine describes why efficient scheduling of aircraft production is so challenging. It describes the numerous tasks and resources that must be modeled and intelligently optimized, including tooling, scaffolding, people, and physical space, to maximize production efficiency.

Conventional scheduling systems often support only basic constraints such as finish-to-start, start-to-start, finish-to-finish, and start-to-finish. However, Boeing’s scheduling constraints are much more complicated. Without a complete and accurate model of the constraints that schedules must satisfy, simpler systems cannot even determine whether a candidate schedule is valid. By contrast, Aurora enables specification and enforcement of complex constraints, so it can schedule projects that other tools cannot even model.

Aurora combines a variety of scheduling techniques, intelligent conflict resolution, and decision support. Aurora’s scheduling decisions take into account resource requirements, constraints, and domain knowledge. Aurora also encodes attributes of data objects representing tasks, groups of tasks, resources, resource sets, and constraints. Aurora’s built-in and user-supplied decision rules consider the values of these attributes at key scheduling decision points. Aurora’s knowledge-rich approach combines human expertise with intelligent algorithms to generate superior schedules that satisfy complex constraints.

After the schedule is generated, Aurora’s graphical displays show scheduled activities, resource allocations, and temporal relationships among the activities. Using these displays, users can review and edit the schedule directly, easily, and intuitively. Aurora’s analytic capabilities help the scheduling team understand why the software scheduled activities the way it did, helping them focus on the parts of the production plan that could improve the schedule cycle if streamlined.