Production scheduling software, also known as constraint based scheduling helps manufacturers generate realistic and executable production schedules. Some of these applications use genetic algorithms to programmatically arrange production schedules for the best possible outcome based on a number of constraints, which are pre-defined by the user. These rule-based programs cycle through thousands of possibilities, until the most optimal schedule is arrived at which best meets all criteria. Constraint-based scheduling needs accurate data in order to arrive at the best possible scheduling scenario.

The most effective constraint-based scheduling systems require correct routings that reflect steps in the proper order, and definition on whether steps can be parallel or whether they need to be sequential. The amount of thoughtful planning that is required for a successful system to be implemented is one of the largest stopping points in implementing these systems, but the effort certainly pays off.

Users needs to define accurate routings in terms of operation sequence and operation overlap. Resource constraints also need to be accurately documented, along with accurate run and set-up times. Tools like constraint based production scheduling systems should not be thought of as a magical solution, but rather as a tool that needs accurate inputs in order to produce a executable schedules which are understood by it's users.

When using constraint based scheduling within an ERP (enterprise resource planning) system, an ERP system which is well integrated with other aspects of the business will generally also be a powerful tool in terms of production scheduling. A number of variables which reside in non-manufacturing software system functionality affect capacity. A well integrated system will be able to update the other business systems of scheduling operations.

Apart from the immediately apparent capacity management benefits of constraint based scheduling, there are a number of less obvious analytical capabilities. Scheduling functionality typically allows you to conduct predictive analyses (what-if) of what would happen if certain changes are made to an optimized schedule. This type of functionality can also provide predictive analyses on the effect of added capacity in the plant. This enables manufacturers to see if equipment purchases will truly deliver an increase in capacity, or if it will simply result in another bottleneck further downstream in the manufacturing process.