Our manufacturing operation struggled with MRP runs exceeding the 4-hour overnight batch window, causing delayed production schedules and impacting on-time fulfillment rates. We implemented a comprehensive optimization strategy focusing on three key areas. First, we reconfigured MRP run profiles to leverage parallel processing capabilities in SAP HANA, splitting material requirements planning across multiple work processes. Second, we simplified complex calculation views that were creating bottlenecks during planning runs. Third, we established strict batch window monitoring to ensure compliance. Results were impressive: MRP runtime reduced from 5.2 hours to 2.8 hours, giving us comfortable headroom. Planning accuracy improved as we could run more frequent updates without impacting business hours. Our fulfillment rates increased from 87% to 94% within two months of implementation.
We segmented by plant and planning strategy group combinations. Created five parallel MRP profiles that run simultaneously, each handling specific material categories. Fast-moving items process in dedicated profiles with optimized selection parameters. The key was balancing the workload so all profiles complete within similar timeframes, avoiding one long-running profile that delays everything. We used SAP HANA Cockpit’s performance monitoring to identify which material groups were causing the longest processing times, then distributed them across profiles strategically.
The calculation view simplification aspect is critical but often overlooked. Complex views with multiple joins can kill performance during planning runs. What specific changes did you make to your views? Did you denormalize any data structures or implement materialized views?
Excellent results on the parallelism front. How did you approach the MRP profile configuration specifically? Did you segment by planning areas or material groups? We’re seeing similar issues where our nightly MRP runs are pushing into morning operations.
How are you monitoring batch window compliance now? We need something proactive rather than discovering overruns after the fact.
Building on the monitoring suggestion, here’s our complete implementation approach that addressed all three optimization areas systematically.
MRP Profile Parallelization Strategy: We created five parallel MRP run profiles based on planning complexity and volume. Profile 1 handles high-velocity items (planning strategy 40) across all plants. Profile 2 processes make-to-order materials (strategy 20). Profiles 3-4 split standard MRP materials by plant groupings. Profile 5 handles special procurement scenarios. Each profile runs as a separate background job with dedicated work processes. Critical success factor: we load-balanced by analyzing historical runtime data from SM37 and redistributing material assignments until all profiles completed within 15 minutes of each other. This eliminated the bottleneck of waiting for one long-running profile.
Calculation View Optimization: Used HANA Cockpit’s SQL Plan Cache and Expensive Statements trace to identify problematic views. Our biggest gains came from three changes: First, simplified the BOM explosion view by pre-calculating component relationships in a materialized table updated hourly. Second, restructured the availability check view to use column engine aggregation instead of row-based calculations. Third, removed seven unnecessary LEFT OUTER JOINs in the planning parameters view that were pulling reference data we didn’t actually need during MRP runs. We also implemented pruning optimization for plant and material range selections. Each view now has execution plans reviewed quarterly to prevent regression.
Batch Window Compliance Framework: Implemented three-tier monitoring using custom Z-programs integrated with Solution Manager. Tier 1: Real-time progress tracking that samples MRP job statistics every 5 minutes and calculates projected completion time using linear regression on materials processed versus remaining. Tier 2: Alert thresholds at 75% of window (warning) and 90% (critical escalation). Tier 3: Automated corrective actions including dynamic work process allocation and optional profile prioritization. We also established a weekly performance review process analyzing trends, identifying material groups causing slowdowns, and continuously refining profile assignments. The monitoring dashboard displays current runtime, historical averages, and compliance metrics.
Results and Sustainability: Average MRP runtime dropped from 5.2 to 2.8 hours with standard deviation reduced from 45 minutes to 18 minutes, indicating more predictable performance. Zero batch window breaches in eight months since implementation. Planning frequency increased from once daily to twice daily for critical material groups, improving responsiveness. On-time fulfillment improved from 87% to 94% as planners receive accurate data earlier. Key lesson: optimization requires ongoing maintenance. We schedule quarterly performance reviews and maintain a backlog of continuous improvement items. The combination of parallel processing, view optimization, and proactive monitoring created a sustainable high-performance planning environment.
We identified twelve calculation views used during MRP that had redundant joins and suboptimal filter pushdown. Worked with our HANA team to restructure five of the most problematic ones. Denormalized some frequently accessed reference data into planning tables to avoid repeated lookups. Also implemented column store optimizations and removed unnecessary calculated fields that weren’t actually used in planning logic. The view performance analyzer in HANA Cockpit was invaluable for identifying bottlenecks. Some views went from 45-second execution to under 8 seconds.