Big data opportunity in benchmarking

For the procurement function, new ways to analyse extremely large data sets bring new dimensions to performance management.

“Procurement performance measurements that look at factors like how much has been saved on the budget on a year-to-year basis have always been around,” says Sammeli Sammalkorpi, co-founder of Sievo, a global leader in procurement analytics.

“These internal-facing metrics are useful, but they fail to address the fundamental question for procurement functions: are we creating a competitive advantage and helping the wider company to compete in the market?”

Traditional procurement measurement solutions have relied on third-party benchmarking that typically use interview data to establish benchmarks, which often means benchmarks are out of date and too high to be actionable. In contrast, big data tools are able to provide a radical level of transparency on how firms are performing against their peers, based on realised transactional spend data.

Big data-driven benchmarking can be based on millions and millions of anonymised transactions, totalling more than $1 trillion-worth of spending data. This enables companies to tailor the benchmark to fit their industry closely and ensure the results are as relevant as possible.

For example, the chief procurement officer of a food manufacturer can compare their business against other firms in the same region, with the same kind of characteristics. They can drill down to comparisons of spending as a percentage of revenue or per employee across key categories, such as logistics, marketing or packaging.

“Not only can they get the big picture, which is very helpful, but they can also read down to the details and see performance in a number of different internal categories, so it will be easy to understand both the areas where procurement is doing well, as well as pockets that need attention,” says Mr Sammalkorpi.

A central part of the big data opportunity in benchmarking is the transition from backward-looking descriptive analytics to forward-looking prescriptive analytics. The rise of advanced analytics technologies is enabling firms to process data in real time to unlock actionable insights, allowing companies to move away from focusing on weekly or monthly procurement measurement cycles and automatically update predictions.

The large amounts of data now available to be processed by big data tools allows for the creation of different scenarios to understand what will happen in an uncertain business environment. You don’t have to just consider one potential scenario, while it is important for businesses to test different scenarios to prepare for a variety of outcomes.

Firms that don’t leverage their data will be at a clear disadvantage over those embracing procurement analytics solutions. By partnering with an experienced leader in procurement analytics, such as Sievo, businesses can turn procurement data into increased profits.

“Data is the new oil. Therefore, for a company to stay competitive, they need to be able to exploit the data assets they have. There’s three steps in this process. First of all, you need to be able to acquire the data. Secondly, you need to be able to process the data and thirdly, you need to take action on the findings,” says Mr Sammalkorpi.

Sievo have developed a powerful procurement benchmarking solution based on real procurement spending and big data, which offers unrivalled insight into procurement statistics. By working closely with your internal data sources and connecting to valuable external data sources, Sievo opens up a new level of procurement measurement.

“We anonymise and categorise to give meaningful comparisons across different peer groups or industries and apply our artificial intelligence-based analytics to make sense out of all your data and to give recommendations,” says Mr Sammalkorpi.

“Big data is giving procurement ways to benchmark performance in far more detail against peers or industries, shifting the conversation from ‘how much have we paid?’ to ‘how much should we be paying?’” he concludes.

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