1. What is procurement analytics?

Procurement analytics: Complete guide for 2024

As we sail into 2024, the power of procurement analytics continues to transform how businesses manage and strategize their purchasing decisions.

With companies reporting up to a 15% reduction in costs through advanced analytics, the stakes are high, and the rewards are even higher. Imagine diving deep into data and surfacing with insights that not only save money but also enhance efficiency and supplier relationships. 

This complete guide will decode the complexities of procurement analytics, offering you the latest strategies and tools to turn data into your most valuable ally.

Main takeaways from this article:

  • Understand the role and importance of procurement analytics in modern businesses.

  • Explore the different types of procurement and financial data and how you can utilize each to make informed decisions.

  • Discover internal and external data sources for procurement analytics and learn how to use them effectively.

  • Determine key procurement metrics to monitor for optimal performance and cost savings.

  • Learn how to implement procurement analytics software in your organization and how GelatoConnect can substantially aid this process.

What is procurement analytics?

Procurement analytics involves analyzing purchasing data to improve the efficiency, effectiveness, and transparency of procurement processes. It uses statistical methods and predictive models to identify cost-saving opportunities, optimize supplier management, and forecast purchasing trends, helping organizations make informed decisions about their procurement strategies.

How analytics fits into each stage of the process

Just as each stage of the procurement process requires careful planning by the procurement department, it also involves the implementation of analytics at each stage. 

Here's why: 

  • Supplier evaluation: Predictive analytics can help identify potential suppliers, gauge their reliability, estimate their performance based on historical data, and mitigate risks tied to supplier selection and contract negotiations.

  • Price optimization: Spend analytics can spot spending trends and price fluctuations, underpinning effective cost-saving strategies while ensuring quality isn't compromised.

  • Demand forecasting: By analyzing past purchasing patterns and market trends, predictive analytics can accurately predict future demand, helping to streamline procurement scheduling and inventory management.

  • Contract management: Descriptive analytics of previous contract data help improve terms, mitigate risks, and drive better contract compliance.

  • Risk management: Diagnostic analytics can help identify patterns and potential risks, providing crucial insights into strategic decision-making processes and enabling proactive mitigation measures.

Procurement data analysis: Advantages and opportunities

Analyzing procurement data

There are numerous advantages and exciting opportunities to unlock with procurement data analysis. Leveraging this data can genuinely transform your procurement processes. 

Let's explore some key benefits: 

  1. Optimized spending: Comprehensive data analysis can help you identify and capitalize on cost-saving opportunities. Strategic sourcing decisions become effortless and cost-effective as you better understand your spending patterns.

  2. Improved supplier performance: Procurement analytics gives insights into supplier performance metrics, empowering enterprises to choose reliable and efficient suppliers. This results in enhanced relationships, better negotiation, and improved performance.

  3. Increased efficiency: Data analytics in procurement allows the procurement team to help organizations streamline their operations, reduce manual tasks, and simplify processes, boosting team productivity and increasing organizational efficiency.

  4. Better risk management: Predicting potential risks and bottlenecks enables organizations to prepare for and mitigate these risks, ensuring smoother operations.

  5. Deeper market insights: Procurement analytics help procurement teams understand market trends and business scenarios. This knowledge can guide procurement decisions and inform them about what, when, and whom to buy from.

Types of procurement analytics

Different procurement analytics

You've likely heard the buzzwords - predictive, prescriptive, and descriptive. But what do they mean in the context of procurement? 

Let's unravel the critical types of procurement analytics and how they revolutionize decision-making processes in various industries. 

1. Descriptive analytics

Descriptive analytics is fundamental to procurement analytics. With its focus on past performance, this subset of analytics can be invaluable for businesses looking to understand their historical purchasing patterns and supplier performance. 

Here's why:

  • Historical data interpretation: Descriptive analytics dives into the details of past procurement data, allowing for an in-depth understanding of previous purchasing habits, vendor performance, and past procurement strategies.

  • Real-time insight generation: By giving a comprehensive view of past and present data, descriptive analytics can offer near real-time insights that allow businesses to track critical metrics and the immediate performance of suppliers and partners.

  • Trend identification: Descriptive analytics evaluates past data to illuminate patterns and trends, aiding businesses in predicting future behavior and events based on their history.

  • Benchmark establishment: Descriptive analytics helps businesses establish benchmarks based on historical performance. These benchmarks serve as a standard when measuring future strategies and goals.

  • Progress tracking: Descriptive analytics compares the business's current performance against established benchmarks, and this can lead to more proactive and efficient procurement processes.

  • Data visualization: Businesses can present their analyzed data in easily understandable charts and diagrams, enhancing the accessibility of insights derived from descriptive analytics.

2. Diagnostic analytics

Diagnostic analytics serves as the bridge between raw data and actionable insights. By diving into historical data, this type of analytics helps us understand why a particular event occurred in the past. 

Let's explore this:

  • Uses: Experts use diagnostic analytics for post-event analysis. Suppose you've determined through descriptive analytics that there's a drop in sales or an unexpected surge in logistic expenses. Diagnostic analytics will help you dig into the why.

  • Techniques: This type of data analysis majorly employs techniques like data discovery, data mining, correlations, and regression analysis.

  • Value: Diagnostic analytics add value by providing context around data, making it easier to identify patterns and relationships, thus revealing the causes of specific outcomes.

  • Limitations: Although incredibly effective at diagnosing past events, it has limited predictive capabilities. It can explain why an event happened but can't predict if it will happen again.

  • Application: In procurement, diagnostic analytics could help understand a sudden change in a supplier's performance or analyze a sudden hike in procurement costs.

3. Predictive analytics

Let's now focus on predictive analytics, a critical type of procurement analytics. Predictive analytics, leveraging both current and historical data, transforms raw data into valuable insights about potential future events. 

It is integral for uncovering new opportunities, mitigating future risks, and making strategic decisions. Here's how:

  • Uncovering risks and opportunities: By utilizing existing patterns in the data, predictive analytics helps enterprises identify potential risks and opportunities, driving insightful decision-making.

  • Problem-solving: With its capability to uncover hidden trends and patterns, predictive analytics is essential for managing complex issues and exploring uncharted opportunities.

  • Forecasting: Predictive analytics enables organizations to prepare for the future by projecting market trends, customer behavior, and other vital factors affecting procurement activities.

  • Efficiency: Predictive analytics enable accurate forecasts, which aid organizations in operating more efficiently, reducing risk, and optimizing resources.

  • Customer management: Predictive analytics tools can also analyze customer responses and aid in formulating strategies to retain and grow a profitable consumer base.

  • Manufacturing: In a manufacturing context, predictive analytics helps anticipate machinery breakdowns, avoid significant damages, and facilitate timely maintenance.

4. Prescriptive analytics

Prescriptive analytics in the procurement process uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. This advanced analytics form takes the insights from predictive analytics one step further. 

Here are its key benefits:

  • Actionable insights: Prescriptive analytics provides actionable insights based on the predictions. It suggests a specific course of action to achieve the desired outcome.

  • Improved decision-making: Prescriptive analytics aids in improved decision-making by offering a recommended course of action. It enables procurement professionals to act with a greater degree of certainty.

  • Dynamic adaption: Prescriptive analytics uses machine learning to adapt to changing situations, meaning recommendations keep pace with real-time market changes or supply chain conditions.

  • Risk reduction: Prescriptive analytics can model various scenarios considering constraints and uncertainties. This process helps identify and mitigate risks before they affect procurement operations.

  • Optimized strategies: Prescriptive analytics assists in strategic planning by suggesting the best routes or actions to take, ensuring you maximize procurement strategies for success.

Procurement data sources

Sourcing procurement data

One of the hallmarks of successful procurement analytics is sourcing from a comprehensive and varied data pool, which means relying on internal data and exploring external data sources. Combining both can provide crucial insights that lead to smarter procurement decisions. 

Internal data sources

This is the information collected within your organization. Examples include purchase orders, supplier contracts, invoices, and receipt data. These sources can be instrumental in analyzing spending patterns, supplier performance, and other procurement metrics.

External data sources

Complementing your internal data with external data essentially broadens your perspective. External data sources may involve market trends, supplier audits, third-party databases, and macroeconomic indicators, which can help predict external market events that might impact procurement, such as price fluctuations, supply chain disruptions, or supplier risks.

Key procurement metrics to monitor

Understanding and monitoring key metrics is crucial to any procurement analytics strategy. Let's delve into the critical procurement metrics you should keep an eye on to ensure efficiency and cost-effectiveness in your operations: 

  1. Spend under management: This metric shows the portion of your organization's spend that is managed actively. Increasing this percentage could lead to significant cost savings.

  2. Supplier performance scorecard: It's a comprehensive assessment of your suppliers' performance, giving insight into quality, delivery speed, compliance, and more.

  3. Contract compliance rate: This reveals the percentage of purchases made under your contracts. It indicates your procurement team's adherence to guidelines and procedures.

  4. Procurement return on investment (ROI): It's a measure of cost savings from procurement activities relative to the cost of those activities. This metric highlights the effectiveness of your procurement strategy.

  5. Purchase order cycle time: This measures how long it takes to create a purchase order until the receipt of goods. Shorter cycle times contribute to more efficient operations and faster response rates.

How to implement procurement analytics step-by-step:

Implementing analytics strategies

Implementing procurement analytics in your operations requires careful planning and strategic execution. Remember that every organization is unique, and you should tailor the solution to your specific objectives and circumstances. 

That said, let's walk you through an introductory step-by-step approach to embedding analytics in your procurement processes: 

1. Define your objectives

Before diving into the nitty-gritty of procurement analytics, it’s crucial to establish clear, actionable objectives. What do you hope to achieve through analytics? Is it to streamline your procurement operations, enhance the accuracy of your spending forecasting, or perhaps strengthen your supplier relationships? 

Identifying specific goals, such as reducing procurement cycle times by 20% or achieving a 15% cost reduction within a fiscal year, will guide the focus of your analytics efforts.

2. Identify critical procurement metrics

These measurable values will help you track progress toward your defined objectives. Key metrics include contract compliance rate, savings achieved, on-time delivery performance, and supplier lead time.

3. Gather and consolidate relevant data

This data can come from various internal and external sources, such as purchase orders, supplier performance records, and market trends. Integrating these data sources enables a holistic view and deeper insights.

4. Choose the right analytics tools

Different types of analytics are suited for various tasks: descriptive analytics for understanding historical data, diagnostic analytics to determine causes of past events, predictive analytics for forecasting future outcomes, and prescriptive analytics for suggesting actions to achieve desired results.

5. Implement analytics into procurement processes

Incorporating the selected analytics tools into the procurement cycle makes the most sense for your organization. Deploying these tools at proper stages can result in improved decision-making and cost savings.

6. Monitor and adjust

After implementing procurement analytics, it’s essential to maintain an ongoing review system. This involves regularly monitoring the outcomes of your analytics initiatives against your set objectives. Are you seeing the expected improvements in efficiency and cost savings? Use analytics to track performance and identify areas where the process may not be delivering as expected. 

This continuous feedback loop allows for real-time adjustments and refinements. For example, if the data reveals that supplier lead times are not improving, you might need to delve deeper into supplier selection criteria or contract terms.

Enhance your procurement strategy with GelatoConnect

GelatoConnect is the strategic solution for your procurement challenges. Our robust analytical tools offer an intelligent approach to data management, allowing comprehensive insights into your spending patterns, supplier performance, and market trends.

With GelatoConnect, you can streamline your procurement process, identify cost savings, and make data-driven decisions. 

Sign up for GelatoConnect today and experience the power of procurement analytics!


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