Power of Data Analytics in Procurement
Information is power for the procurement department. Insights drawn from historical data on product pricing and vendors can strengthen the buyer’s negotiating position and drive better pricing. However, currently there are only a handful of procurement functions that are making the best use of large amounts of data they generate.
In line with our previous blog on Supply Chain Management, Procurement Analysis is one of the major functions guiding an enterprise towards cost reduction and management. Compared to traditional pricing models, statistical models and advanced analytics can help procurement departments to achieve cost savings of 3 to 8 percent.
Procurement functions generate humongous amounts of data- something which is difficult for a single employee to track and manage. A lot of potentially valuable insights present in this data gets lost along the way in manual analysis. Take for example- a mid sized manufacturing company. Over the course of a year, this company’s procurement department generates more than 25000 transactions for a single category. These transactions hide a lot of significant drivers of price. If not analyzed properly, the company manages to lose a great deal of money on procurement alone.
Enter Data Analytics tools, and it becomes dramatically easy to simplify the available data for the purchasers to handle. How does it happen?
Rubikon Labs uses advanced analytics algorithms to recognize patterns in complex data sets allowing procurement analysts to query all their data, determine the statistically significant drivers of price, and cluster the data according to those drivers. The resulting clusters represent a set of purchases without significant differences in cost drivers and thus reveal the real differences in vendor performance.
A major advantage is that unlike people, advanced-analytics systems don’t bias their decisions based on gut feeling, or place undue weight on outliers in the data. The systems also enable the testing of thousands of permutations very quickly to determine which statistical clusters fit the data best.