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Why We Measure Issues per 1,000 Payslips

Jul 16, 2019  | Tag: Analytics

It’s been three months since we launched the Payroll Efficiency Index, the revolutionary approach to global payroll analytics that is changing how multinationals think about payroll. Available exclusively from CloudPay, the PEI leverages anonymized payroll data from more than 2,500 global entities to analyze process efficiency and set benchmarks for payroll performance at the global, regional, and country levels. 

Looking past limited SLAs and output reports, the PEI uses five key efficiency metrics that focus on what can be learned from the actual process of payroll. In addition to helping the payroll department better understand how individual processing factors impact overall payroll operations, this new model enables teams to identify bottlenecks, breakdowns, and opportunities for improvement that would otherwise be missed.  

Here, we take a closer look at the third efficiency metric, the number of Issues per 1,000 Payslips, and what it can reveal about global payroll processing.

What It Is

Issues per 1,000 payslips provides the hard number of total payroll errors identified in data input, data output, and new data for every 1,000 payslips processed in each pay cycle. To calculate this number, we take the total number of data issues divided by the total number of payslips processed, and multiply that result by 1,000. 

For the 2018 calendar year, the global average for issues per 1,000 payslips was 16.72. Countries in the APAC region averaged the lowest number of issues per 1,000 payslips, at just 9.69. The Americas had nearly twice as many issues per 1,000 payslips, averaging 19.63, while EMEA had the highest average count at 21.69.


PEI_Global2019_MetricsDD_1kPayslips


Why It’s Important

By looking at the number of payslips affected by data errors every cycle, companies are able to quantify the consequences of an inefficient payroll process on employees. They get to see exactly how many individuals are receiving incorrect payslips, which can help put the price of ongoing issues and tolerated inefficiencies into perspective. 

This metric is all about data accuracy, and a wide range of factors can impact the numbers, beginning with data management upstream of payroll and continuing through the last adjustment made in the payroll system. Whether your team is implementing big changes or making small tweaks to drive process improvement, keeping an eye on the number of issues per 1,000 payslips can help you gauge effectiveness where it counts. 

The issues per 1,000 payslips KPI provides a concrete, useful snapshot of your processing accuracy before any issues threaten the SLA. Figures for this metric vary widely from company to company, suggesting that this KPI is a good individual indicator of process efficiency. 

For Example: A Singapore-based manufacturing company with around 3,000 employees across the APAC region was receiving an increasing number of complaints about incorrect payslips. A look back at monthly numbers of issues per 1,000 payslips confirmed a slight but steady rise since two new locations became operational six months earlier. Using real-time analysis, the payroll team identified a delay between data entry and payroll lock. Upon exploration, this revealed a need for additional training for key employees in the new locations. 

How To Use It

If you’re a CloudPay customer, you can access these payroll metrics directly in the Analytics tool, where you can not only view your data but also compare it against companies similar in size and location. Payroll benchmarking data for all efficiency metrics is available in the Global 2019 Payroll Efficiency Index. If you’re not a CloudPay customer but are able to measure or even estimate your average issues per 1,000 payslips, you can compare that figure to the average in your countries and regions.

If your issue count per 1,000 payslips is high, work with your payroll team and global payroll provider to identify all possible factors and how to determine which ones are impacting the accuracy of your payroll data at every step of the process.

It’s interesting to note that while many issues in payroll compound as the headcount increases, the number of issues per 1,000 payslips is on average more than five times greater for payrolls with fewer than 50 employees than those with more than 200. Again, this is a very company-specific metric, but that tendency could be due to the fact that larger organizations are more likely to use integrated systems, which can dramatically reduce the number of data issues overall. 

For additional information and country rankings, download the complete PEI report at payrollefficiencyindex.com.

 

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