Pre-MRP vs Post-MRP: Where Data Quality Issues Hide

What is MRP?

Material Requirements Planning (MRP) is the calculation engine at the heart of manufacturing ERP systems. It takes demand forecasts, current inventory, and production parameters to generate planned orders for materials and production.

The Garbage In, Garbage Out Problem

MRP is only as good as its input data. Feed it incorrect inventory counts, and it will plan for materials you already have. Give it wrong lead times, and orders will arrive late. The challenge is that MRP problems often do not surface until production is disrupted.

Pre-MRP Data Quality

Pre-MRP validation focuses on the inputs to the MRP calculation:

Master Data

Item master - Part numbers, descriptions, units of measure

BOMs - Bill of Materials accuracy, component relationships

Routings - Operation sequences, work centers, labor times

Lead times - Purchasing and manufacturing lead times

Transactional Data

Inventory counts - On-hand quantities, location accuracy

Open orders - Purchase orders, work orders already in progress

Demand - Sales orders, forecasts, interplant transfers

Common Pre-MRP Issues

Duplicate or orphaned item records

Inactive components in active BOMs

Lead times that do not reflect reality

Inventory in wrong locations or statuses

Post-MRP Data Quality

Post-MRP validation focuses on the outputs and their reasonableness:

Planned Order Review

Are planned order quantities reasonable?

Do start dates allow enough lead time?

Are there impossible schedules (past-due dates)?

Capacity Considerations

Can work centers handle the planned load?

Are there obvious bottlenecks?

Exception Analysis

What exceptions did MRP generate?

Are exception counts increasing over time?

Building a Validation Framework

Effective MRP validation is ongoing, not one-time:

1. Define validation rules for each data type 2. Automate checks to run before each MRP run 3. Generate exception reports for items requiring attention 4. Track trends in data quality metrics over time 5. Assign ownership for data quality in each area

Conclusion

MRP failures are expensive—expedited shipping, production delays, customer dissatisfaction. Investing in pre-MRP and post-MRP validation prevents these failures by catching data quality issues before they impact operations.