They say “you’re biggest competitor is yourself.” In corporate finance, though, it may not pay to look at financial performance in isolated fashion.
In procurement, companies tend to measure their performance against historical, internal data. Common trends include how much companies are spending with suppliers, which suppliers they’re using and where they’re doing business, then comparing those metrics to previous months’ and years’ data.
However, the power of data analytics is growing as more enterprises become familiar with its potential to look ahead, look behind and look both within and outside the organization. For the procurement team, according to Sievo Vice President and Co-founder Sammeli Sammalkorpi, data analytics offers a glimpse into how businesses are performing, not only against themselves, but against their peers, too.
Sievo provides businesses with procurement analytics software. Its most recent rollout is the launch of Dynamic Benchmarking, an initiative that aggregates company data, anonymizes it and allows businesses to see how their key procurement metrics compare against others over time.
“The competitive landscape is harsh,” he recently told PYMNTS in an interview. “The real question procurement people should be asking is whether they are creating a competitive advantage for their stakeholders and owners.”
To answer that, businesses can look at how procurement performance has changed compared to past performance, he said.
While most companies would probably jump at an opportunity to see how they stack up against their competitors, fewer may be eager to share their own data so other firms can compare their performance as well. Indeed, some organizations have been reluctant to provide approval for the type of data collection Sievo needs for its solution, but Sammalkorpi said most are quite willing once they understand that data will be handled in a secure way.
He said a “surprisingly large majority” of companies will share their data, a critical component of providing value through data analytics. Information shared includes data points on topics like supplier payment terms, geographic location, company size, spend as a percentage of total revenue, spend per employee and more.
This type of analytical dive into procurement isn’t new. The Hackett Group calculated that the procurement function is expected to triple its adoption of advanced analytics in the next two to three years, with particular focus on spend analytics, procurement scorecards and market intelligence. Separate analysis from Deloitte suggested that while current adoption of Big Data analytics is relatively low today, procurement professionals expect the technology to have the largest impact on the procurement function moving forward, particularly in the areas of cost optimization, process improvement and management reporting.
These reports focus on how procurement teams are able to take their own organization’s data and analyze it. However, Sammalkorpi said there are more opportunities for Big Data in procurement beyond the internal analysis on which many firms focus. For some companies, that could mean turning away from some of the typical metrics they focus on. When presented with data from other industry players, some companies can even recognize metrics they were unaware of that may need to be improved upon, he explained.
“The benchmarks they see are often not surprising. They are what the clients expected,” he said. “They figured they were good or not-so-good at certain metrics. But, often, clients say they didn’t realize certain metics.”
It’s a case of “you don’t know what you don’t know,” and broadening the data pool beyond a single organization can lift the veil on performance issues.
Other opportunities for procurement-focused data analytics lie in continuous analysis. Sammalkorpi noted that advisors and analytics initiatives will often result in one report to assess performance at a single point in time. Ongoing collection of data provides the capability to look at performance trends over time. In Sievo’s case, that means monthly reports, providing what Sammalkorpi described as a “dynamic” versus “static” analytics solution. This characteristic also means companies can adjust the parameters and focal points of their analysis: perhaps examining payment terms with a supplier based in northern Europe, for example.
“This helps nail down an actionable level of detail,” he explained.
As the data pool grows, more opportunities will present themselves. Like other companies operating in the data analytics space, Sievo may have an opportunity to share this information with other third parties, though Sammalkorpi said this isn’t currently in the pipeline for the firm. Instead, Sievo is examining other ways to deploy its Dynamic Benchmarking strategy, either by expanding this model into other areas of the enterprise, such as accounting or treasury, or by enhancing analytics capabilities with technologies like artificial intelligence (AI) or machine learning (ML).
“We see a lot of other uses for the data, not only for benchmarking, but also, for example, supplier sourcing,” he said. “In the next few years, it will be more prevalent in procurement and other areas as well, as there is more and more data. Things like machine learning will help us and our clients make sense of their expanding data.”