The MRP Assumption Problem
Material Requirements Planning (MRP) was revolutionary when it emerged in the 1960s. It gave manufacturers their first systematic way to calculate what materials they needed and when. But MRP makes a critical assumption that rarely holds true: infinite capacity.
When your MRP system says "start this job on Monday," it has no idea whether your CNC machine is already scheduled for three other jobs. It does not know that your best operator is on vacation. It cannot see that the raw material for Job A is blocking the staging area needed for Job B.
What Happens in Reality
Consider a typical metal fabrication shop with 5 machines and 20 active orders:
- Machine 1 can run Job A or Job B, but not both simultaneously
- Job C requires a setup changeover that takes 2 hours if it follows Job D, but only 15 minutes if it follows Job E
- The quality inspector is shared across all machines and becomes a bottleneck during peak hours
- Material handling creates blocking constraints—finished goods from Station 1 must be moved before Station 2 can output
MRP sees none of this. It generates a "plan" that is impossible to execute on day one.
Enter Constraint Programming
Constraint Programming (CP) takes a fundamentally different approach. Instead of assuming infinite capacity, it explicitly models every constraint:
Resource Constraints: Each machine can only process one job at a time. Each operator can only be in one place.
Temporal Constraints: Job B cannot start until Job A finishes. Setup time depends on the previous job.
Capacity Constraints: The paint booth can hold 4 parts maximum. The oven has a batch size limit.
Logical Constraints: If we run Job X, we cannot run Job Y on the same day (shared tooling).
The CP-SAT Solver
Google's CP-SAT solver—the engine behind GenOpsX scheduling—uses a combination of techniques:
- Constraint propagation: Automatically eliminates impossible options
- Intelligent search: Explores the solution space efficiently
- Optimization: Finds not just any feasible schedule, but the best one
The result? A schedule that is actually executable. One that respects every constraint your shop floor faces.
Real-World Impact
When manufacturers switch from MRP-based scheduling to constraint-based scheduling, they typically see:
- 30-50% reduction in schedule changes after initial publication
- 15-25% improvement in on-time delivery because promises are realistic
- 10-20% increase in throughput by eliminating hidden bottlenecks
The schedule your planner publishes Monday morning is still valid by Friday. That alone transforms how your shop floor operates.
Making the Transition
Moving from MRP to constraint-based scheduling does not require replacing your ERP. MRP still handles material requirements calculation—that is what it is good at. The constraint solver handles sequencing and timing—what MRP was never designed to do.
The key is integration: pull demand from MRP, optimize the sequence with CP-SAT, push the executable schedule back to your shop floor systems. GenOpsX handles this integration out of the box with SAP, Oracle, and other major ERP systems.
Conclusion
MRP answered the question "what do we need?" Constraint-based scheduling answers "how do we actually build it?" Both are necessary. Neither is sufficient alone.
If your planners spend their mornings adjusting schedules that were "optimized" yesterday, the problem is not your planners. The problem is asking MRP to do something it was never designed to do.