2 key ways big data can improve procurement

Tips on using big data for procurement

When it comes to delivering on efficiency, today’s procurement professionals face more challenges than ever before. Between shifting regional and global demand, deglobalization trends, diverse input markets and manifold layers of regulation to navigate, the job has never been more dynamic.

To exacerbate the situation, surveys suggest that many of today’s corporate executives expect procurement organizations to “do more with less,” cutting staff sizes and budgets while simultaneously expecting higher returns.

The challenge presented is not impossible to overcome. In many organizations, procurement professionals collect big data and it’s here where the “more with less” model can work.

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Here are a few tips to do more with your big data collection for procurement.

Identify Your Inputs

Procurement professionals may find themselves in silos against engineering and design teams, despite that the two so closely rely on one another’s success. A common pattern consists of a new product designed by R&D with little procurement collaboration, which in turn requires certain inputs that the procurement team may struggle to acquire.

Using big data, procurement professionals can talk about their inputs on a system-level basis. More than having part numbers and product names, the professional now has performance metrics related to the part and can single out its role mathematically in the system. Having this information on hand fosters conversation with other business units and breaks down silos.

Occasionally, substitute inputs can be used where the input is (or is likely to become) too difficult or infeasible to procure. Including procurement in product design and engineering discussions via big data analytics can foster relationships. In turn, these relationships will prevent the kind of disaster creation that operating in silos may bring about.

See How Others Identify Your Inputs

The phraseology one company uses to describe a product can differ from that of another. This is especially true for companies making new products, where the proper terminology for the thing being produced has yet to pan out in industry.

As the procurement professional comparatively analyzes the cost of a certain part or product, it would be wise to first consider all the different names, part numbers, and other identifiers it might go by.

Sometimes, products are close substitutes to one another, but not exactly alike. As inputs phase out and new ones are introduced to market, understanding equivalents is key to avoiding deprecated material costs and maintaining an ever-improving supply chain.

With big data, one can systematically determine the key nomenclature used for certain products and work from there to shop for the right items. Google Shopping, in this sense, is a big data purveyor, able to generate comparable product prices across the web despite product information often being unique between sellers.

Whether it be in the company’s ERP system, the internal requisition program, or vendor management software, understanding inputs from a higher level than one’s own organization grants a surprisingly competitive edge to the business.

Big data can be used in surprising ways by procurement professionals seeking new efficiencies. As these workers tighten belts and dive into ever-advancing technologies to improve their businesses, understanding the big picture behind big data can put issues into better context.