With the rise of data-driven solutions in supply chain management, procurement analytics has become critical in business operations. It’s transforming procurement from obsolete manual practices to an automated process driven by procure-to-pay software.
Procurement data analysis not only helps keep track of an organization’s spending patterns—it also helps make informed, strategic decisions for business growth. As a result, companies can reduce costs, mitigate upcoming challenges, and improve operational efficiency.
Here’s a breakdown of procurement analytics, its types, functions, and role in business operations.
Defining Procurement Analytics
In a nutshell, procurement analytics is used to turn data from an organization’s past purchasing patterns into actionable insights to improve future strategy and decision-making. Also referred to as procurement or purchasing analysis, the process involves data gathering from numerous internal and external sources and analyzing said data.
The final takeaways are presented in accessible formats, such as graphs, charts, and intuitive dashboards. Procurement analysis doesn’t end with visualization—the analyzed data is then leveraged by dedicated teams to identify future market trends, minimize risks, and build sustainable strategies.
Types of Procurement Analysis
Companies can choose from various types of purchasing analysis based on their specific needs and challenges. Whether the goal is to assess historical data or identify the reason behind any procurement oversight, analytics helps find the solution. Key types of procurement data analysis include descriptive, diagnostic, predictive, and prescriptive analytics.
Descriptive Analytics
The role of descriptive analytics is to examine historical data and describe past trends and patterns in the organization. Descriptive procurement analysis might show that the company spent more on office supplies during the holiday season or has consistently worked with suppliers who delay deliveries. Subsequently, they can save costs on office supplies by purchasing them in bulk before the holidays and switching to a different, more reliable vendor. These insights help guide the organization’s decision-making, making the operational processes more effective and lowering the expenditure of resources.
Diagnostic Analytics
Diagnostic analytics, on the other hand, focuses more on the reason why certain past events or trends happened. True to its name, this type of procurement data analysis is key to understanding the root cause of the procurement failure. For example, if the organization notices that its product has significantly dropped in quality, it can use diagnostic analytics together with other tools to analyze supplier performance. Once the purchasing analysis helps identify the issue (e.g., the supplier’s quality standards have decreased), stakeholders can work on a solution.
Predictive Analytics
Predictive analytics leverages the collected procurement data to identify potential business trends, forecast upcoming risks, and predict performance in purchasing analytics. The company can use the patterns from previous years and anticipate any challenges that they’ve already faced in the past. For example, if predictive analytics has shown that the store has previously run out of a popular product, they can avoid this by stocking up in advance. This type of purchasing analysis not only helps the company make informed decisions but also mitigates risks.
Prescriptive Analytics
Using information from other types of purchasing analytics, prescriptive analytics suggests potential solutions and courses of action for the business. The process is mainly done with AI and machine-learning tools. For example, the prescriptive analytics model leverages historical data and supplier performance metrics to recommend optimal sourcing strategies and contract terms. This is especially helpful in procurement opportunity analysis when you need to evaluate supplier performance and lead times to determine strategies for long-term vendor relationships.
KPIs & Metrics in Procurement Analysis
Key performance indicators (KPIs) for purchase analysis help businesses measure their performance, cost efficiency, and supplier effectiveness with clear, quantifiable data. Although KPIs depend on the needs of the organization, some of the key metrics include:
- Spend vs. Budget compares spending history with the allocated budget to determine whether the purchases fit within spending limits. The goal is not to save costs but to stay consistent with the given financial resources. Companies use procurement cost analysis to examine indirect and direct costs and determine areas for optimization.
- Spend under management is the amount of spending managed directly by the organization’s procurement team.
- Supplier lead time measures the time it takes for the supplier to deliver the product after receiving the purchase order.
- Variance factors, such as price, quantity, and lead time variance, refer to any differences between the planned and actual outcomes of the procurement process.
Key Steps in Procurement Analytics
Analyzing and identifying purchasing patterns and spending management practices is crucial in today’s market landscape. But what’s even more important is approaching procurement analysis with a clear strategy in mind.
While it may be tempting to dump your entire database into procurement analytics software and try to resolve issues on the fly, this method rarely proves fruitful. Procurement analytics usually involves several key steps, including data collection, data cleansing and organization, analysis, visualization, and reporting.
1. Identifying Objectives & Gathering Data
The groundwork of effective purchasing analysis lies in the objectives defined at the very beginning of the process. No matter the purpose—whether it’s to reduce costs, monitor supplier performance, or build a long-term strategy—it’s important to identify the key areas to focus on.
Some of the factors businesses take into account during this stage include their available and needed resources, current KPI metrics, and potential risks. Understanding the priorities for the business operations ensures more precise and actionable results in procurement analytics.
The data-gathering process in purchasing analytics involves extracting data from various resources into a centralized database. The organization’s data sources can be both internal and external, with both data assets being equally useful.
Internal data sources come from within the organization and include:
- Enterprise resource planning (ERP) systems
- Invoice and purchase management software
- Accounting software
- Customer relationship management (CRM) & vendor relationship management (VRM) platforms
- Procure-to-pay software
- Internal correspondence, sales reports, and financial records
External data sources include assets outside the organization that are available either publicly or at any of the third-party resources. They include:
- Supplier information
- Industry codes and reports
- Public databases
- Third-party financial data, such as reports on currency and costs
2. Cleansing & Organizing the Data
While gathering as much data as possible is certainly helpful for an in-depth purchasing analysis, some dirty data might hinder the results. Dirty data refers to faulty, incomplete information, which can contain duplicates, be broken, or outdated. Procurement specialists work to ensure that data is accurate across different sources, avoiding inconsistent formats, date mismatches, or small errors in reports.
Classification is another important step in analytics for procurement, involving separating data into clearly defined categories. It’s extremely helpful if you plan to focus on a specific area, like sourcing in sourcing analytics. For example, you can group suppliers by risk factor and profitability with the Kraljic matrix or prioritize data with an 80/20 rule.
3. Purchase Analysis
By using objectives clearly defined in step one as a starting point, carry out procurement data analysis, which is arguably the most vital part of the process. What you want to focus on entirely depends on your needs. Some of the steps you can take during purchasing analysis include but are not limited to:
- Spend analysis examines past spending to determine purchasing patterns and gain insights into cost-saving opportunities.
- Procurement cost analysis focuses on individual procurement costs, including direct and indirect expenses, total cost of ownership, and performing a cost-benefit analysis. For example, the company might discover that importing certain materials requires more costs than using domestic suppliers. After performing procurement cost analysis, the organization may decide to decrease expenses by switching to a different vendor.
- Supplier analysis narrows down the broader scope of sourcing analytics and evaluates each supplier by key factors, such as their performance, delivery times, reliability, and cost-efficiency.
- Supplier diversity analysis allows businesses to have a clear overview of minority-owned, women-owned, and other diverse suppliers in their supply chain.
- Procurement opportunity analysis involves analyzing the market competition and customer demand to make strategic decisions for business growth.
- Contract analysis involves examining legal obligations in any business relationship.
4. Visualization & Reporting
To report your findings to any stakeholders, utilize procurement software to create a graphical representation of the data. A study by Karin Eberhard in Management Review Quarterly found that data visualization significantly improves the quality of decision-making in strategic management. Some of the visual formats include pie charts, line charts, diagrams, geospatial maps, and many other techniques, all of which can be compiled into an intuitive dashboard. For example, visualization helps highlight areas for growth in procurement opportunity analysis.
Leveraging Analytics for Procurement
By analyzing past and current data, procurement analytics can be essential in strategic growth across a variety of industries and departments inside the organization. They help procurement teams gain crucial insights into purchasing data, which can then be used to tackle one of the business needs. From market research to supply chain management, purchasing analytics have various advantages, such as:
- Prioritization of resources
- Improving sourcing, supply chain management, and risk management
- Optimizing logistics and delivery planning
- Gaining a helicopter view of procurement processes
Procurement analysis helps businesses take control of their purchasing cycle operations, cover any gaps and inconsistencies in customer demand and supplier relationships, as well as keep track of their spending patterns. Using data-driven procurement analytics software helps procurement teams perform purchase analysis efficiently, save resources, and gain predictive insights.
