- Traditional scheduling software optimises against assumed capacity — AI planning agents optimise against real capacity, updated continuously from actual production performance.
- The biggest planning failures (overtime surprises, missed delivery dates, idle equipment) are almost always caused by plans built on theoretical performance rather than operational reality.
- FactoPlan generates schedules that adapt to real-time production actuals — not plans that are fixed at the start of the week and ignored by Thursday.
The core problem: every production plan is built on assumptions that are wrong by Wednesday
Standard scheduling software (whether inside ERP or a dedicated APS) builds schedules from a bill of materials, routings, and standard times. Standard times are set during implementation, reflect how fast the factory was designed to run, and are almost never updated to reflect how fast it actually runs.
The result: a schedule that looks achievable on Monday is already showing strain by Tuesday afternoon. The planning team is aware of this. They pad schedules, add buffer, and spend significant time each week manually adjusting what the system generated. The system becomes something to work around rather than something that helps.
FactoPlan replaces theoretical standard times with actual performance data — so schedules reflect the factory that exists, not the factory that was originally specified.
Studies across discrete manufacturers find that production plans diverge from actual production by 15-25% by mid-week. AI planning agents that continuously update against actuals reduce this divergence to 5-8% — directly translating into fewer delivery misses and less reactive replanning.
What FactoPlan specifically does
Capacity planning from actuals: Uses real OEE data per machine and product family — not standard times — as the basis for capacity calculations.
Constraint-aware scheduling: Automatically accounts for maintenance windows (from MaintIQ predictions), tooling availability, material constraints, and changeover sequences — without requiring a planner to manually factor these in.
Demand-responsive adjustment: When production runs ahead or behind plan, FactoPlan regenerates the downstream schedule against the updated position — alerting planners to decisions that need to be made, rather than leaving the divergence to compound silently.
Changeover optimisation: Sequences production to minimise changeover time and cleaning cycles — particularly valuable in food and pharma where changeover sequences have compliance dimensions.
Frequently Asked Questions
FactoPlan receives demand signals (planned orders, customer orders) from ERP, generates the optimised detailed schedule, and publishes execution-level work orders back to ERP or MES. It operates as the detailed scheduling layer that ERP production planning modules don’t handle well.
No — it changes what the scheduling team works on. Planners stop spending 60% of their time manually adjusting system-generated schedules and start spending that time on decisions that require human judgment (customer escalations, strategic capacity decisions). The team typically becomes more effective, not smaller.
Related: AI Agents in Manufacturing · AI + ERP · AI + MES