Robotic process automation (RPA) offers immense potential to transform operations. However, organizations need the right kpis (key performance indicators) to track in order to optimize RPA success.
As an experienced data scientist, I have helped multiple companies implement performance dashboards to accurately measure RPA project impact andcontinually tune automated processes.
This comprehensive guide will equip you with 18 essential rpa kpis to assess across key phases of your RPA program. For each metric, I share data-driven insights on target benchmark levels based on industry research.
KPIs for Determining Initial RPA Project Viability
The first priority is determining positive ROI on your initial RPA investments to validate proof of concept.
1. Return on Investment
ROI measures total economic return by comparing implementation costs to the efficiencies, savings and revenue gains achieved.
Industry benchmarks show top-performing RPA projects delivering ROI of 200% or more. Average returns range from 30% to over 100%, with payback in less than 12 months.
Source: UiPath Business Case Framework Whitepaper
Positive ROI requires careful process selection and technical optimization. But realized returns exceeding 100% validate prioritizing further RPA adoption.
2. Payback Period
Payback period indicates ROI velocity – how fast you recoup RPA investments through efficiency dividends and cost savings.
Leading organizations experience payback on RPA projects in 6-9 months. Average payback periods range from 9 months to over 1 year.
Faster payback times demonstrate automation delivering material productivity boosts across your digital operations. Prioritize automating high-transactions processes first to drive fast returns.
3. Net Present Value (NPV)
NPV helps quantify anticipated future financial gains from RPA by projecting discounted cash flows over a defined time period, usually 3-5 years.
Top quartile RPA programs deliver a striking average NPV of over $14M over 3 years. Positive NPV confirms automation investments will unlock substantial value.
4. Internal Rate of Return
Closely related to NPV, internal rate of return (IRR) reveals the expected compound annual growth rate (CAGR) you can expect on RPA investments.
Average IRR for RPA initiatives is a robust 50%, vastly outpacing general capital project hurdle rates. This signals enormous productivity upside from scaling automation.
KPIs for Tracking Ongoing RPA Performance
Once early returns validate RPA viability, focus shifts to metrics guiding automation expansion and continual optimization.
5. Number of Processes Automated
This tracks how many processes get automated over time. Top quartile RPA adopters automate over 70 processes within 18 months.
Higher totals evidence growing penetration across operations. Though avoid over-automation by first targeting high-impact processes.
6. Task Throughput
Throughput tallies how many transactions or tasks are processed via RPA bots monthly or annually. This workload metric reveals scope of automation.
For example, top performing bots average over 60,000 invoice transactions automated per year. Throughput can scale exponentially with wider RPA adoption.
7. Bots per Employee
This ratio benchmarks bot workforce scale relative to human employees.
Research shows top quartile RPA companies reaching 8 bots per 100 employees within 2 years. The more bots supplementing staff, the greater productivity multiplier from RPA technology.
8. Automation Coverage
What percentage of staff activities within a business function gets addressed via RPA?
For instance, 75% order processing automation coverage means RPA tackles 3 out of every 4 process steps. Top teams expand coverage towards comprehensive automation.
9. Automation Density
Density shows scope of end-to-end process automation by revealing the number of distinct software systems bots interconnect with per process automated.
Instances of high automation density include month-end financial reconciliations spanning 5+ different enterprise applications. This demonstrates complex automation capability.
10. Bot Utilization Rate
Tracks what portion of time bots actively execute automated tasks vs sit dormant. Industry benchmarks show:
- 80%+ utilization – Optimized RPA efficiency
- 60-80% – Typical target range
- <60% – Signals process filtering and bot usage issues
Boosting utilization means mining more value from your automation software licenses.
11. Bot Accuracy Rate
This reveals how often bot-performed processes incur errors requiring fixes or human intervention.
Bot accuracy rates should target 99%+. Common factors impacting accuracy include data quality flaws and suboptimal process logic.
Continuous process monitoring and bot retraining will help sustain maximum accuracy as application changes occur.
12. RPA Support Tickets
Tracks volume of bot-related IT support tickets. Upticks may signal technical obstacles or process adaptation needs limiting automation reliability.
Investigate spikes in support incidents to address the root causes proactively. Don’t just fix individual tickets since similar issues likely will recur.
13. Lifetime Automation Value
Lifetime value estimates total economic benefit accumulated over a bot or process’ lifespan. This factors bots enhancing productivity years into the future.
Bots handling high volume tasks like payroll processing can each drive millions in value over 5-10 years. Tracking lifetime value spotlights automation’s lasting impact.
Advanced KPIs: The Machine Learning Impact
Sophisticated RPA adopters increasingly overlay machine learning for advanced capabilities:
14. Automated Process Mining
Process mining algorithms automatically reverse-engineer workflows by mapping system events logged digitally. This reveals optimization areas otherwise invisible.
KPIs include mining efficiency (time and resources to map processes) and resulting performance uplifts from data-driven enhancements.
15. Intelligent Document Processing
IDP leverages AI for context-aware content analysis so bots can ingest diversified file types like emails or forms. Measuring:
- Document classification accuracy
- Data extraction precision
- Processing cycle time reductions
…demonstrates the power of augmented RPA performance.
16. Anomaly Detection Rate
Machine learning pattern recognition spots anomalies in real-time data feeds. Detecting inconsistencies, outliers or exceptions aids resolving issues proactively before downstream impacts.
For instance, inventory shortfall alerts can trigger just-in-time bot-managed supply replenishments well before stock-outs stall production.
Leading Indicators for Sustaining RPA Excellence
Finally, these predictive metrics indicate whether your environment will fuel ongoing RPA innovation:
17. Employee Satisfaction Score
Positive staff sentiment towards automation, measured via internal surveys, drives RPA success. Declines demand prompt action to address concerns impeding adoption.
18. Automation Training Engagement
From completion rates, assessment scores, and participation levels, employee training KPIs reveal automation-readiness.
Strategic mastery of automation-enabled processes means organizations can continually adapt and expand automation. Training engagement fuels this dynamic culture of optimization.
Conclusion
By mastering this robust set of 18 KPIs, spanning ROI, utilization, innovation and more, you can precisely benchmark and continually upgrade your RPA outcomes.
Automation excellence relies on accurate performance measurement. Using these industry targets as your guideposts positions your organization for transformative gains as RPA increasingly permeates operations.