Five Key Challenges of Intelligent Category Management and Direct Materials

Organizing the purchase of goods and services, including component parts and finished products, into categories can improve efficiency, cut costs, help identify risks and maximize innovation. But it can also feel like a gargantuan task.  Add to that the growing demand for insights on sustainability and forced labor risks, analyzing supplier performance across multiple ERPs, and keeping track of owed discounts, and it’s no wonder category managers wish for more hours in the day.

Nevertheless, the benefits of managing these tasks efficiently and intelligently are potentially game-changing, and artificial intelligence (AI) presents a powerful set of tools to get on top of it all. 

Here are five crucial areas of difficulty, and how AI can play an innovative role in helping handle them.

1 Lack of Visibility Across the Category Management Lifecycle

Category management isn’t the end in itself; the point is to make what you’re managing into something to sell to customers. And that requires cross-team visibility. Category managers require visibility in terms of spend, first and foremost, but also market intelligence, price volatility, and so on. Then, when an order is placed, they need to know whether orders are received on time, at the right quality and quantity, as well as whether the appropriate prices and discounts are being applied. The complexity is astonishing, and spreadsheets just don’t cut it, not least because it’s important to share all this information, in real time, with other divisions — production, marketing, accounts payable, and more. It’s critical to generate a collaborative environment where everyone has the same version of the truth. AI offers powerful, real-time tools to do so, because they categorize and aggregate spend, providing one source of truth that facilitates collaboration.

2 Absence of Real-Time Market Insights

Say the price of copper moves up 3%. You should be able