6 Ways Procurement Analytics Makes Data Work for You

6 Ways Procurement Analytics Makes Data Work for You

  • Many procurement organizations are yet to fully leverage data analytics
  • Predictive and prescriptive analytics help optimize sourcing and spend
  • Advanced solutions provide dynamic insights into internal and external variables 
June 03, 2021 | Procurement Software Blogs

Procurement organizations are today swimming in datasets, thanks to rapid digitization. And the volumes are only increasing every day.

But are they fully utilizing all these datasets?

Have they invested in technologies that analyze this data and mine useful insights?

The answer, for many teams, is still “not yet”.

The result? This useful data is not utilized in a way that can make a meaningful business impact.

The power of procurement analytics

Having lots of data is a good thing — but it is the procurement organization’s analytical capability that makes a difference.

Analytics unlocks the true value of large amounts of structured and unstructured data (internal as well as external) to get the desired impact on the enterprise’s bottom line.

What is procurement analytics and how does it work?

Procurement analytics involves collecting and analyzing data from different sources, classifying them and displaying the insights on a dashboard that can be accessed easily by stakeholders.

The need for analytics originated when businesses wished to get a consolidated view of procurement spend. However, procurement today is about much more than saving money.

Over a period, the scope of procurement has extended beyond managing suppliers and cutting costs to making strategies and guiding future decision-making.

With digital technologies, advanced analytics as well as cross-functional visibility, procurement has a huge opportunity to optimize processes and strategies.

Nearly 62% of chief procurement officers believe predictive analytics has the potential to disrupt or create competitive advantage for the organization, as it is a powerful tool for uncovering data-driven insights.

Predictive and prescriptive analytics

Traditional analytics solutions rely heavily on past trends and historical data to model scenarios and predict outcomes.

Bur today, procurement teams cannot settle merely for estimations and historical data. Instead, they want to exactly know how events will unfold and what they can expect.

Predictive analytics leverages statistical tools to understand cause-and-effect relationships between variables. It helps simulate different business scenarios to support planning or to predict the most likely business outcomes based on actual conditions. It is increasingly used in procurement for counter fraud analytics, invoice analytics or to predict spending patterns.

The more mature prescriptive analytics helps a business shift focus from “what will happen if” to “what you can do to meet a goal”. It recommends one or more possible courses of action and enables a business to assess possible outcomes based on their actions.  

What are the benefits of data analytics for procurement?

Smart procurement teams today leverage advanced procurement analytical tools powered by artificial intelligence and machine learning to make better decisions, reduce operational spend and enhance savings. They also use analytics to assess and evaluate suppliers and carry out compliance checks.

Here are 6 ways procurement leverages data analytics to streamline buying processes:

 ⚬ Simplifies procurement planning:

The introduction of data analytics in procurement can help optimize the planning process. For instance, analytics can minimize cost and risk and eliminate human bias while allocating purchase orders to vendors.

 ⚬ Enhances savings and value generation opportunities:

Advanced analytics can derive useful insights from internal spend data and the impact of external economic and market drivers in real time. This combination of internal and external insights helps create deeper partnerships between business stakeholders seeking to strategically analyze disparate sources of data.

 ⚬ Eases strategic sourcing:

Analytics can also be used in strategic sourcing to implement data-based business strategies. For instance, it can help identify the best times to run sourcing events and requests for proposal. It can also determine which suppliers to include in sourcing projects by providing rich information about suppliers’ quality and risk positions. Also, it can help forecast the cost of a material and future spend.

 ⚬ Improves efficiency:

Advanced analytics aids in the effective execution of different activities across source-to-pay processes. Appropriate tools and models can draw insights from custom dashboards. Further, the use of AI can lead to guided execution.

 ⚬ Improves supplier performance evaluation:

Analytics enables procurement teams to look at suppliers’ past performance data and current market pricing. This can help adopt a data-driven approach to award supplier contracts. Such an approach is likely to be more profitable and rewarding than simply going by lowest prices, biases or existing supplier relationships. Prescriptive analytics enables risk scoring and performance scoring of vendors.

 ⚬ Boosts compliance:

Predictive analytics can improve compliance by streamlining requisitioning and invoicing processes. It can monitor and track compliance across all suppliers. For instance, you can assess whether your company is using the deals that it has with preferred suppliers. Again, you can determine if there is any deviation from the purchase order policy compliance.

4 Key questions to ask before choosing analytics technology

There is also a word of caution here. Bad data can lead to bad decision-making.

You must ensure, at the outset, that procurement data being collected is accurate and validated. This is where latest technology and analytics come into play.

But how do you choose the right analytics solution that can generate meaningful insights from data and enable effective decision-making?

Mudit Kumar, vice president of consulting at GEP, stresses the need to distinguish between information overload and insights generated through robust analytics.

He lists the below key questions to consider while evaluating an advanced analytics solution:

  • Can the system generate actionable insights or simply provides a visually appealing data aggregation?
  • Can it generate dynamic insights across internal and external market variables?
  • Does it increase end-to-end visibility across the value chain?
  • Are reports oriented toward future outlook and business decisions or using tools and dashboards to repackage historical reporting?

Advanced analytics must be an integral part of the sourcing and business strategy. It helps tie together internal data sources, such as spend and contract data and data related to supplier relationship management, with external data sources, such as supplier databases. In this way, it helps derive real value from the datasets to streamline procurement processes.

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