The Power of Data Analytics in Your Procurement Strategy

Procurement analytics is the process of gathering and examining procurement data to generate numeric insights for data-based business decisions. This approach is not just about figures and spreadsheets but more about leveraging information to create a clear picture of procurement in business, helping to guide actions and strategy.

McKinsey has reported that businesses can enhance their financial stability and cost management by leveraging data to allocate funds more wisely and effectively. This practice not only clarifies and speeds up decision-making processes but also equips those in charge of the supply chain with the essential information to determine when and how to take action.

In this article, we will explore the fundamental ways procurement analytics can be implemented within your business structure. Our goal is to empower you and your team to take control of your procurement strategy, leveraging the data at your fingertips to understand where you’ve been and where you’re heading.

Here are the topics that we will cover: 

1. Benefits of Data Analytics in Procurement 
2. Types of Procurement Analytics 
3. Examples of Analytics in Procurement 
   

Benefits of Data Analytics in Procurement 

Leveraging analytics and dedicated server technology has become an integral aspect of modern business operations, as it helps organisations worldwide use data effectively for their decision-making.

The integration of data analytics in your procurement process comes with its own host of benefits, including:

1. Improved Forecasting for Budgeting and Financial Planning: Numerical insights allow for a more refined understanding of your spending patterns and demand cycles. When you gather enough data on your past performance and market trends, it will be easier to forecast your future needs more accurately. On top of that, procurement data ensures that your budget management is aligned with actual requirements, reducing unnecessary expenditures and optimising your whole purchase process.
   

2. Numerical Insights on Quality and Performance: Quality is at the forefront of top-tier procurement, and reliable analytics sheds light on both the performance of your suppliers and the operations within your company, enabling you to refine your processes. This is achieved by pinpointing areas requiring enhancement and monitoring the positive effects of the changes made. Over time, this translates into more efficient procurement practices and increased customer satisfaction. Furthermore, another valuable benefit of relying on analytics is the ability to perform comparative analysis. This method will allow you to measure your procurement efficiency against industry benchmarks or compare it across various units, categories, or facilities within your company. Having a clear view of your position will help you create attainable goals and outline the best route to achieve them.
   
   
3. Improved Risk Management: The past years have taught us that supply chain disruptions can happen at any moment. That being said, procurement analytics enables you to identify potential risks and weak links in your procurement systems at an early stage, which will you allow you to devise strategies to mitigate risks and manage potential disruptions efficiently. For example, if your supplier in a particular region is prone to delays, alternative arrangements can be made in advance.
   
   
4. Spotting Opportunities for Consolidation. One of the lesser-discussed but highly valuable aspects of procurement analytics is the ability to discover opportunities for consolidation. When you start taking into account more data points, including data purchase, supplier relationships, and contract analytics, you can identify areas ripe for streamlined collaboration. For instance, you might detect overlapping efforts or redundancies that could be merged, thus reducing complexity and cutting costs. This consolidation can also lead to greater purchasing power with suppliers, potentially resulting in more favourable terms and conditions.

Types of Procurement Analytics 

Data, in all its variety, requires different approaches. In this section, we’ll walk you through the distinct kinds of analytics tailored to different facets of procurement, each fulfilling a specific role:

1. Descriptive Analytics: Where procurement data is analysed to describe what has happened in the past. Descriptive analytics is the foundational level of procurement analytics, offering a clear view of past procurement performance. This includes basic data like total spend, supplier performance, and contract analytics. For example, you might use descriptive analytics to understand your company’s spending patterns over the previous fiscal year.
   
   
2. Diagnostic Analytics: Taking it a step further, diagnostic analytics interprets procurement data to understand why something has happened in the past. This analysis delves into the underlying causes and relationships within the data. If descriptive analytics tells you that a particular supplier’s performance dropped, diagnostic analytics can help pinpoint why that happened, such as a change in contract terms or market conditions.
   

3. Predictive Analytics: Where trends and patterns in data are used to forecast future procurement performance. This approach offers predictions about future outcomes by leveraging your historical data and statistical algorithms. If you’ve noticed a seasonal trend in the purchase of certain materials, predictive analytics can help forecast the demand for those materials in the upcoming season, allowing for better inventory management.
   
   
4. Prescriptive Analytics: Perhaps the most advanced form of procurement analytics, it doesn’t just predict future outcomes but provides recommendations for ways to handle potential future scenarios. This type of analytics aids decision-making by suggesting specific actions. For example, if predictive analytics forecasts a shortage of a key component, prescriptive analytics might recommend alternative suppliers or suggest a change in production schedules to mitigate the risk.

Examples of Analytics in Procurement 

Understanding the concept of procurement analytics is just the beginning; putting it into practice requires knowing how it manifests in various aspects of your procurement process. 

Here are some of the ways you can leverage analytics in your procurement:

• Spend Analytics: This approach involves the collection, cleansing, and analysis of spending data. Spend analytics will offer in-depth insights into your spending patterns and supplier performance, allowing for better negotiation and strategic sourcing. For example, identifying a trend of increasing costs with a specific supplier might lead to a renegotiation of contract terms.
  
  
• Spend Forecasting: This approach utilises historical data to predict future spending patterns, enabling you to budget effectively and manage your cash flow. For example, when you know what expenses are on the horizon, you can make informed financial decisions, such as allocating funds for new investments.
  
  
• Demand Forecasting: This approach involves predicting the quantity and timing of items required, based on various factors like seasonality and market trends. Among others, accurate demand forecasting supports inventory management, reduces carrying costs, and minimises stockouts, helping you to maintain service levels with customers.
  
  
• Purchase Order and Payment Term Analytics: This approach examines purchase orders and payment terms to identify trends, compliance, and efficiency. Understanding these aspects can lead you to improved supplier relationships and cash management. For instance, extending payment terms with a reliable supplier could free up cash for other urgent needs.
  
  
• Supplier Analytics: This approach assesses and monitors supplier performance, including quality, reliability, and pricing. This is essential for managing supplier relationships and ensuring they align with your business goals (e.g. identifying a supplier’s consistent delays can trigger a search for alternatives).
  
  
• Sustainability Analytics: This approach focuses on monitoring and evaluating sustainability practices within your supply chain, important for ensuring compliance with environmental regulations and meeting corporate social responsibility goals. For example, tracking a supplier’s carbon footprint is key for a company committed to green initiatives. If you’d like to read more on what metrics to track to quantify sustainability in your procurement read our blog post: Your Guide to Green Procurement: What Sustainability Metrics to Track.
  
  
• Contract Analytics: This approach involves the systematic analysis of contracts to extract insights and manage obligations, which will help you ensure that both parties are meeting their contractual obligations, reducing legal risks, and fostering healthy business relationships.
  
  
• Savings Lifecycle Analytics: This approach analyses the entire lifecycle of savings within your procurement, from identification to implementation by tracking savings at each stage. Savings lifecycle analytics will help you identify savings and ensure that your procurement performance is aligned with overall business objectives.
  
  

Conclusion

Whether it’s understanding past trends through descriptive analytics, making predictions about future performance, or fostering decision-making with numerical insights, the shift towards a more data-driven approach in procurement has become a pathway to higher efficiency, better compliance, improved relationships with suppliers, and ultimately, a more robust bottom line.