A Discussion of Key Findings Related to Technology and Analytics

In January of 2019, ScottMadden’s Brad DeMent and Trey Robinson delivered a webinar to discuss the results of the latest finance shared services benchmarking study. The study was designed by ScottMadden and surveys were administered by APQC over four cycles: spring/summer 2014; spring/summer 2015; summer/fall 2016; and spring/summer 2018. The scope of the study covers the following topics for an in-depth analysis of financial shared service centers (SSCs): Delivery model, scope of services, staffing, location, performance, and technologies leveraged.

This article summarizes the practices of top performers regarding technology and analytics. The top performer peer group was a group of organizations in the study that consistently led the way across key cost, staffing, and performance metrics. When it comes to technology and analytics, the study found that the top-performing financial shared service centers (SSCs) outpace the comparison group in piloting and adopting robotic process automation (RPA) and intelligent automation (IA) technologies. Top performers also leverage data and analytics much more deeply and comprehensively across their organizations than the comparison group. As they discussed the traits of top-performing SSCs for technology and analytics, DeMent and Robinson also highlighted some common challenges and misconceptions that hamper greater data and analytics work for SSCs.

Access the complete article series here.

Robotics and Intelligent Automation

Top performing SSCs have made significant progress in adopting RPA. As Figure 1 illustrates, 21 percent have conducted RPA pilots to validate utility for RPA, while another 25 percent have moved beyond this point to put bots into production. “We help companies conduct a lot of RPA pilots, and we can attest that moving from pilot to production is a very big step,” DeMent said. Top-performing SSCs are “successfully thinking through issues like governance, ownership, and other complexities that come with moving from a test environment into an actual production environment.” Only 18 percent of the comparison group, by contrast, have conducted pilots or put bots into production.

Figure 1: RPA Piloting and Production within SSCs

Even as many top performing SSCs adopt and implement RPA, ownership for RPA falls outside of many SSCs. For more than half of respondents, ownership resides within RPA centers of expertise instead, followed by business units (28 percent). Much fewer organizations give ownership of RPA to SSCs (8 percent), and an equal percentage of respondents say ownership falls within IT.

Intelligent Automation (IA) is widely perceived as a useful and valuable tool within SSCs. For example, 54 percent of respondents say IA will help improve business outcomes and reduce risk; 53 percent say it will expand organizational capabilities and improve decision making; and 52 percent say it will improve efficiency and reduce operating costs. Despite this optimism, SSCs have not implemented Intelligent Automation (IA) at the same rate as RPA, DeMent said.

As Figure 2 shows, while 25 percent of top performers are implementing pilot programs, only 17 percent have fully implemented IA applications. The top challenges to adopting artificial intelligence and IA capabilities more broadly include availability of technology (for 31 percent of respondents); difficulty establishing a business case or ROI for IA (29 percent); and availability of skilled resources or technical expertise (29 percent).

Figure 2: Use of IA within SSCs

Lack of organizational buy-in/cultural fit also makes the list of top challenges at 21 percent of respondents, reflecting ongoing anxiety about IA. “There’s a lot of nervousness around IA,” DeMent noted. “People are asking questions like: Am I turning too much decision-making over to a machine? Will these language processors confuse or frustrate people when they don’t get it right? It doesn’t surprise us that we see less implementation and piloting or testing with IA.” While SSCs are adopting some IA applications like optical character recognition, embracing newer IA technologies remains a more daunting prospect for many.

RPA and IA Applications

Figure 3 shows the top processes to which RPA and IA are applied within SSCs. RPA is most effective when applied to routine, transactional, and highly manual processes, so it is no surprise to see AP as a common application for RPA technologies (20 percent), followed by general accounting (15 percent), and financial reporting (14 percent).

Figure 3: For Which Processes Have You Applied RPA/IA Applications?

Applications for IA are roughly parallel: The most common applications are for AP, general accounting, and financial reporting, though in slightly lower numbers than for RPA. The most common purposes of IA adoption are for research, analytics, and reporting (66 percent of organizations); prescriptive solutions (66 percent), and predictive forecasting (65 percent). Robinson expects these percentages to grow as organizations continue to harness big data with analytics to serve customers better. “What we see in the leading SSCs is that they are providing analytics as a service to internal customers, helping drive better performance rather than tracking what they might traditionally manage within a shared services organization,” Robinson said. “Top performers are forward-looking and they are helping their customers.”

ERP Technologies and Analytics

When organizations are establishing an SSC, DeMent said, a common mistake is thinking that the organization needs to move to a single ERP solution. “There’s a surprisingly consistent concern that if I don’t have one ERP I’m not ready to build a shared service operation, and that I should wait until I get all of my ERPs consolidated to start my shared services project. The truth is, most of our clients—even some of the top performers—rarely have one global ERP.”

Figure 4 shows that the majority of top performing SSCs (roughly two-thirds) use more than one ERP throughout their organization, and some use as many as six. Only 34 percent of respondents, by contrast, use only one ERP. For DeMent, these responses “might hint that these leading companies are just not seeing the value of consolidating down to one ERP given the investment and time that it takes to do that. RPA is being used to connect a lot of these ERP systems now, so there are other solutions that could be cheaper and add more value than a big project to consolidate ERPs.” For that reason, having more than one ERP should not stand as a barrier to establishing SSCs.

Figure 4: How Many ERPs for ERP Instances are in Use throughout Your Entire Organization?

When it comes to how SSCs use data within their ERPs, top performers generally leverage more advanced data analytics, DeMent said. Figure 5 shows that among the top performers, 25 percent “are doing a much better job of using data and analytics in a comprehensive method across the organization using a predictive forecast,” while the comparison group does so to a lesser extent.

Figure 5: How Would You Best Characterize the Use of Data Analytics within Your SSC?

There is a significant gap between top performers and the comparison group when it comes to leveraging ERP data for predictive analytics: While 47 percent of top performers utilize ERP data for this purpose, only 21 percent of the comparison group does. The use of ERP data primarily for metrics and reporting is also much higher within the comparison group: 20 percent characterize their use of data and analytics as a tool for metrics and reporting, while only 5 percent of top performers do. In general, “the comparison group has not penetrated to deeper levels of analytics as much as the top performance group,” DeMent said.

Key Takeaways

Data and analysis from the finance shared services benchmarking study show that the top performing SSCs:

  • Run more RPA pilots than the comparison group
  • Successfully resolve complex governance and ownership questions to move RPA from piloting to production
  • Embrace IA to a greater extent—though its use remains more limited than RPA
  • Apply RPA and IA most commonly to routine, transactional processes like AP and general accounting
  • Leverage ERP data in a more comprehensive way to predict future patterns rather than to report past performance

Study Demographics

The study population is robust and diverse, with 468 organizations participating. While 64 percent of participating organizations are from the US and Canada, respondents also included organizations from Europe (26 percent), Asia-Pacific (8 percent), and Central and South America (2 percent). Company size is balanced across the revenue profile, with median revenue of $7.8 billion. Most organizations (89 percent) have been operating for more than three years, and more than half have been operating for longer than five years.

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