The systematic classification or grouping of goods, services, or materials that an organisation procures from external suppliers is certainly not a new trend. It has been a fundamental practice in procurement and supply chain management for many years. Categorising spend and supplier relationships has long been recognized as a best practice to optimise procurement operations and drive value for organisations.

Indeed the procurement landscape has become more complex and organisations now deal with a wider range of goods, services, and suppliers, often across global markets. This complexity necessitates a more nuanced approach to categorization to effectively manage the diverse procurement needs and optimise supplier relationships.

Mindsets have also changed:

Procurement has evolved beyond a transactional function focused solely on cost reduction. Companies now recognize the value that procurement can bring to their overall business objectives. Categorising spend allows them to identify areas such as supplier innovation, sustainability, or risk management, leading to more strategic and value-driven procurement practices.

But all these developments have been made possible by technological advances based on data analysis. Modern tools such as spend analysis, supplier management, contract management, and sourcing can analyse large volumes of procurement data more effectively. 

On top of that, they can also identify in real time patterns, trends, and outliers in spend, supplier performance, and market dynamics, enabling more agile decision-making and proactive management of sourcing categories. Some advanced platforms can even leverage predictive analytics to forecast demand, market trends, and supplier performance. This provides organisations with a serious advantage on those competitors who rely on traditional or reactive procurement approaches.

If you need help implementing a modern approach to purchasing category management, contact me. Together, we'll see which solution is best suited to your organisation's size, available budget, data availability and specific needs.


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