The Quick Guide to Procurement Analytics
Procurement analytics offer the key to long-term, sustainable growth, according to research by a number of consulting firms. EY found that with the right metrics, organizations can transform procurement from a traditional cost-management activity to a competitive advantage. Likewise, Deloitte’s 2021 survey of chief procurement officers found that most view analytics as the technological activity with the most impact on business.
Procurement analytics is a broad term with deep implications. This quick guide will break down the different procurement analytics that procurement teams can use to optimize spending, as well as tools that provide advanced procurement analytics.
What is procurement analytics?
Procurement analytics is the practice of collecting and analyzing data from different sources, classifying this data, and developing insights that can help optimize the organization’s sourcing and buying processes.
Procurement analytics generally fall into one of four categories. These categories are:
- Descriptive analytics: data is analyzed to describe what happened over a specific historical period
- Diagnostic analytics: data is analyzed to describe why something happened over a specific historical period
- Predictive analytics: data is analyzed to anticipate future procurement performance
- Prescriptive analytics: predictive models are further analyzed to aid in strategic decision making
Procurement teams have long used descriptive and diagnostic analytics to answer questions such as, “How can we save on procurement this year as compared to last year?” However, today’s e-procurement tools allow procurement teams to proactively address areas like tail spend on a daily basis.
The success of advanced procurement analytics depends on the data: is there enough of it, and is it quality data? Procurement analytics uses data from a number of internal and external sources. Internal sources include data from enterprise plannings systems (ERPs), accounting data, supplier data, and historical financial records. External sources of data include public information — commodity prices, third-party proprietary sources, supplier industry codes, credit ratings, and more.
All this data has become invaluable — for teams that know how to harness it. The focus for most procurement professionals is now on predictive analytics: the ability to simulate different business scenarios and plan procurement strategies based on the most likely outcomes.
Why are procurement analytics important?
As McKinsey puts it, “information is power.”
Procurement analytics can use historical data to negotiate better pricing with suppliers. More advanced procurement analytics can help organizations gain insight to improve vendor segmentation, manage tail spend, gain cost savings, and maximize annual purchasing strategies.
Many organizations rely on advanced analytics to help reduce tail spend. At Fairmarkit, we’ve found that procurement teams have an average 30% difference between the prices they’ve paid for the exact same product over different purchase times. Procurement analytics allow Fairmarkit to automatically cluster and bucket groups of purchase by risk, illuminating where tail spend is eroding an organization’s bottom line. With the right data, we’ve found companies can save as much as 12% on their tail spend.
Crucially, today’s procurement analytics solutions do the heavy lifting. Machine learning is at the forefront of technological advancements in countless industries — especially procurement — enabling teams to handle greater spend with the same bandwidth. Without e-procurement solutions, the sheer volume of data makes effectively using predictive analytics untenable.
Procurement analytics solutions
As McKinsey outlines, procurement analytics solutions are the only way to effectively gain insight from procurement data.
“Procurement functions generate more data than any one employee can track and manage,” wrote the consulting firm. “At one midsize manufacturing company with approximately $2 billion in annual revenue, for example, procurement had data on more than 20,000 transactions for a single category, each with four to five statistically significant drivers of price.”
Procurement analytics depends on clean, accurate data to begin automating the sourcing process and getting results. Ideally, a procurement analytics solution takes insights derived from data and puts it to use. For instance, Fairmarkit uses an organization’s historical data to give buyers recommendations while structuring those insights to be measured and reported. With every bid, Fairmarkit’s recommendation engine improves the information for next time and helps you improve your process.
These predictive and prescriptive analytics save time and manpower, allowing procurement teams to focus on other critical processes. However, for organizations that are tracking procurement analytics manually, here are a few must-know KPIs.
- Spend under management
- Total cost of ownership
- Cost avoidance
- Average payment terms
- Number of suppliers
- Direct vs. indirect spend