Summary
Advanced Planning and Scheduling (APS) bridges ERP and MES by introducing finite capacity modeling, multi-constraint optimization, and real-time data-driven scheduling. APS operates at two levels: Advanced Planning (AP) for medium-term demand-capacity balancing, and Detailed Scheduling (DS) for shop-floor-level sequencing. Key algorithms include heuristic dispatching rules (SPT, EDD, CR), meta-heuristics (genetic algorithms, simulated annealing, particle swarm optimization), and constraint programming (IBM ILOG CP Optimizer). Implementation follows a four-phase path from basic modeling through rule-based, optimized, to intelligent scheduling.
Key Claims
- APS must simultaneously satisfy equipment, personnel, material, tooling, and changeover constraints while optimizing for objectives like minimum makespan, minimum changeovers, or maximum utilization.
- System integration is critical: APS receives demand/BOM/inventory from ERP and delivers production plans back; it sends dispatch orders to MES and receives real-time progress and exceptions in return.
- Success factors include data quality (process route accuracy >= 95%, standard time deviation ⇐ 10%), organizational alignment, change management, and continuous algorithm tuning.
Connections
- DataWarehouse — APS requires clean, integrated data from ERP, PLM, and MES, all flowing through the warehouse
- DataGovernance — accurate process routes, standard times, and capacity calendars are governance fundamentals
- SparkPerformance — solving large combinatorial optimization problems for scheduling parallels distributed computing challenges