Wanted to share our experience implementing automated validation for production schedules in DAM 2022. We were dealing with constant MRP and BOM mismatches that caused schedule adherence issues and resulted in material shortages on the floor.
Our previous process relied on manual spot-checks by production planners, which meant errors often weren’t caught until materials were already committed or work orders released. We built an automated validation framework that runs every time a schedule is generated or modified, checking MRP calculations, BOM explosion logic, and material availability against actual inventory.
The results have been significant - we’ve seen a 40% reduction in schedule-related discrepancies and our on-time delivery improved from 78% to 94% over six months. The system now catches issues like phantom BOM components, incorrect yield factors, and timing mismatches before they impact production.
Happy to discuss our implementation approach and the specific validation rules we developed.
This is exactly what we need! We’re struggling with the same MRP accuracy issues. Can you share more details about what validation rules you implemented? Specifically interested in how you handle BOM explosion checks for multi-level assemblies.
Absolutely - reporting was critical to adoption. We built shift-based reports that show validation results by production area, material category, and severity level. Planners get a dashboard at shift start showing any schedule issues that need attention, prioritized by impact to customer orders.
The exception-based review process means planners only focus on the 5-10% of items that failed validation rather than reviewing everything. Each exception includes root cause analysis and suggested corrections, which cut resolution time significantly.
Great question. Performance was definitely a challenge initially. We optimized by implementing incremental validation - only checking what changed rather than revalidating the entire schedule. For full validations, we run them asynchronously during off-peak hours and cache the results.
We also use exception-based validation where possible - instead of checking every line item, we focus on high-risk scenarios like new products, engineering changes, or materials with historical shortages. This reduced validation time to under 3 minutes for typical schedule changes while maintaining accuracy.