Successfully submitting device data to UDI databases is only one measure of an effective UDI program. Medical device manufacturers also need to understand whether their submission processes are accurate, timely, efficient, and cost-effective. The right key performance indicators (KPIs) give Executives, Regulatory Affairs, Quality, Master Data, and IT teams a measurable way to identify bottlenecks, improve data quality, control operating costs, and determine whether their UDI processes can scale.
This guide provides a practical KPI framework across the following key areas: submission performance, and resource cost & productivity.
Submission Performance
These KPIs measure whether UDI records are being submitted successfully, accurately, and on time.
1. On-Time Submission Rate
What it measures: The percentage of required UDI submissions or updates completed by the applicable regulatory or internal deadline.
Formula: Submissions Completed On Time ÷ Submissions Due × 100
Why It Matters
This is one of the clearest measures of whether a UDI submission program is keeping pace with regulatory obligations. Tracking it by market or product group can provide additional details about submission delays.
2. First-Pass Acceptance Rate
What it measures: The percentage of submissions accepted without requiring correction and resubmission.
Formula: Submissions Accepted on First Attempt ÷ Total Submissions × 100
Why It Matters
High submission volume does not necessarily indicate good performance if records repeatedly require correction. First-pass acceptance provides a better indication of submission quality and the effectiveness of upstream data validation and governance.
3. Submission Rejection Rate
What it measures: The percentage of submissions that fail or are rejected and require corrective action.
Formula: Rejected Submissions ÷ Total Submissions × 100
Why It Matters
Categorizing rejections makes this metric particularly useful. Manufacturers can track whether failures result from missing data, invalid values, business rules, or technical transmission issues. Recurring errors can then be addressed at their source rather than corrected one record at a time.
4. Mean Time to Resolve Submission Errors
What it measures: The average time required to resolve a failed or rejected submission.
Formula: Total Resolution Time ÷ Number of Resolved Submission Errors
Why It Matters
Some submission errors will occur even in mature programs. Measuring how quickly teams identify, correct, and resubmit them provides an important indication of operational responsiveness.
5. End-to-End Submission Cycle Time
What it measures: The average cycle time required for a device record to be prepared, validated, submitted, and accepted by the regulatory agency.
Breaking the metric into stages can make it more actionable.
For example:
- Average cycle time to aggregate/prepare records
- Average cycle time to validate records (after preparation)
- Average cycle time to submit records (after validation)
- Average cycle time for records to be accepted (after submission)
Why It Matters
Measuring the overall submission process, as well as key steps within it, helps identify process bottlenecks. For example, long preparation or validation times may indicate upstream data issues, while acceptance delays after submission may point to inefficient error-resolution processes.
Resource Cost and Productivity
These KPIs measure the human effort and financial resources required to achieve successful UDI submissions.
1. Submission Effort per Successful Record
What it measures: The average staff time required to prepare, review, validate, submit, monitor, and resolve a UDI record through successful submission.
Formula: Total UDI Staff Hours ÷ Successful Submissions
Why It Matters
If submission volumes increase while effort per successful record remains stable or decreases, the UDI process is demonstrating greater operational scalability.
2. Resource Cost per Successful Submission
What it measures: The average labor cost required to achieve a successful UDI submission.
Formula: Total UDI Submission Labor Cost ÷ Successful Submissions
Why It Matters
Total spending can increase simply because submission volumes increase. Cost per successful submission provides a better measure of whether the underlying operation is becoming more efficient.
3. Training Cost per User
What it measures: The average cost of training personnel involved in the UDI submission process.
Training costs can include formal training, internal trainer time, documentation development, employee training time, and retraining.
Formula: Total UDI Training Cost ÷ Number of Users Trained
Why It Matters
Tracking training costs helps manufacturers understand an important component of the ongoing cost of maintaining and scaling their UDI program.
4. Time to User Proficiency
What it measures: The average time required for a new user to independently perform assigned UDI activities at an acceptable level of quality.
Why It Matters
Faster proficiency can reduce onboarding costs, decrease reliance on specialized individuals, and make the UDI operation easier to scale.
Your UDI Performance Scorecard
These metrics can be brought together into a simple scorecard:
| KPI | Category | Desired Direction |
|---|---|---|
| On-Time Submission Rate | Performance | ↑ |
| First-Pass Acceptance Rate | Performance | ↑ |
| Submission Rejection Rate | Performance | ↓ |
| Mean Time to Resolve Errors | Performance | ↓ |
| Submission Cycle Time | Efficiency | ↓ |
| Effort per Successful Record | Cost & Productivity | ↓ |
| Cost per Successful Submission | Cost & Productivity | ↓ |
| Training Cost per User | Cost & Productivity | ↓ |
| Time to User Proficiency | Cost & Productivity | ↓ |
Establish Your Own Baseline
There is no single benchmark that defines a successful UDI submission program. Product complexity, submission volumes, markets, regulatory requirements, and levels of automation vary significantly between manufacturers.
Instead, establish your current performance and set improvement targets. The following table illustrates an example:
| Metric | Baseline | Target |
|---|---|---|
| On-Time Submission Rate | 91% | 98% |
| First-Pass Acceptance Rate | 84% | 95% |
| Mean Time to Resolve Errors | 4.2 days | <2 days |
| Submission Cycle Time | 6.5 days | <4 days |
Targets should reflect your organization’s regulatory obligations, risk profile, operating model, and historical performance rather than arbitrary industry averages.
Look at Metrics Together
No KPI should be evaluated in isolation. For example, reducing submission cycle time is not necessarily an improvement if rejection rates increase. The objective is not simply to report KPIs. It is to use them to identify where submissions are slowing down, why failures occur, and where process improvements will have the greatest impact.
Key Takeaway
To understand the true performance of a UDI compliance program, manufacturers should evaluate metrics together rather than in isolation. Combining measures of submission reliability, data quality, operational efficiency, and resource costs provides a more complete view of whether UDI processes are compliant, cost-effective, and scalable as submission requirements and volumes evolve.
Conclusion
A strong UDI submission program should become more reliable and efficient as it matures. Achieving that requires manufacturers to move beyond simply monitoring whether submissions are completed, and develop a consistent and measurable view of how the overall process is performing.
Establishing clear KPIs and measuring them against a meaningful baseline gives teams the insight needed to identify improvement opportunities, evaluate the impact of process and technology changes, and make informed decisions as submission requirements and volumes evolve.
Ultimately, effective performance measurement helps turn UDI submission from a compliance activity into a continuously improving and scalable operational capability.
