Supply Chain

The Role of Spend Analysis in Supply Chain Management

Supply Chain Management (SCM) is the centralised management of all processes involved in transforming raw materials and components into final products. Efficient supply chain management is vital for a successful business.

In recent years SCM has faced several challenges like economic slowdowns, rising prices, logistics constraints, and more. Data analytics in SCM has emerged as an effective tool for coping with different challenges. Data analytics in SCM enables businesses to optimise operations, minimise costs, and boost customer experience. 

Procurement, which is a key function of the supply chain, is an area where a business can optimise costs with spend analytics. In recent years spend analytics in procurement has gained a lot of traction. This is because it helps reduce costs, enhance efficiency, improve supplier relationships, etc.

“Spend analysis” and “spend analytics” are two phrases that are often used interchangeably about cost optimisation in procurement. However, there is a subtle difference between the two.

What is spend analysis in procurement?

Spend analysis is the practice of analysing procurement spending to identify and extract valuable insights to optimise costs. These insights help businesses identify savings opportunities, mitigate risks, and optimise their buying power.

What is spend analytics in procurement?

Spend analytics is essentially the process of collecting, categorising, cleaning, and analysing all the spend data with the help of software.

Spend analytics helps businesses understand their expenses and spend based on patterns that are derived from transactional data. It helps businesses identify cost-saving opportunities from purchase-related data. It provides deep insights into purchase trends, supply management, and procurement processes. 

The spend analytics process goes through four stages:

Data collection: Gathering data from multiple sources to get a complete overview of an organisation’s spending.

Data cleansing: This step entails detailed scrutiny of data to eliminate inaccuracies like duplicate entries, missing data, etc.

Data analysis: Techniques like data visualisation, statistical analysis, etc., are used to analyse the cleansed data. The data is analysed to identify trends, patterns, anomalies, etc.

Data reporting: Results of data analysis are communicated to various stakeholders through reports, presentations, real-time dashboards, etc.

Why does a business need spend analytics in procurement?

A business can reduce its costs only by analysing its spending patterns. Hence, a spend analysis module is a must for every manufacturing business.

Spend analytics provides insights into material, inventory, raw materials, etc., procured for running business processes.

With spend analytics businesses gain a better understanding of the business processes based on which procurement teams can optimise processes to enhance profitability.

An analysis of business spending helps mitigate supplier and procurement risks. It enables the business to change its suppliers/vendors without impacting the production process thereby reducing supplier/vendor dependency.
Often spend analysis focuses only on high-value purchases. And, low-value purchases like those required for the smooth running of the business may go unnoticed. Such expenses may cause financial leaks and must be addressed. Spend analytics provides insights based on category-level analysis to optimise spend management.

Spend analytics in procurement helps businesses strengthen their relationships with suppliers by identifying the best suppliers.

Spend analysis in procurement was done manually for decades. However, technological advancements have introduced new and effective tools that streamline the process and provide better outcomes.

Spend analytics technology

Artificial Intelligence (AI)--powered spend analytics tools are being deployed by businesses to automate and enhance the spend analysis process. The software collates, cleanses, categorises, and analyses procurement data from multiple sources including financial and ERP systems across geographies and business entities.

AI-based spend analysis automates tasks like manual data entry and categorisation thereby reducing human errors and accelerating the process.

Gathering data from disparate sources for spend analysis is a tough task. AI tools can extract data from invoices, POs, expense reports, etc. These tools process and analyse this data to provide a comprehensive view of the organisation's spending.

ML (Machine Learning) algorithms detect patterns and connections in data and provide insights into spending, supplier performance, compliance issues, etc.

ML algorithms can forecast future spending trends. Based on these trends and analysis, procurement teams can identify cost-saving opportunities and supplier risks. 

AI tools monitor spending activities and provide insights in real-time. Hence, businesses can detect and address issues promptly.

The spending data of an organisation becomes complicated with the growth of the business. AI-based spend analysis can scale easily to manage increasing volumes of data while ensuring performance and accuracy.

In conclusion

Spend analytics in procurement helps businesses achieve financial efficiency by providing actionable insights into spending patterns, supplier performance, and potential cost-saving opportunities. Spend analysis in procurement helps businesses optimise their operations, achieve strategic objectives, and enhance supplier relationships.

How can Infosys BPM help?

Infosys BPM offers TradeEdge, an enterprise-level AI spend analytics tool that automates spend analytics in procurement. TradeEdge is an AI-based data management system that facilitates spend classification, opportunity identification, and other features to overcome business challenges.