Advanced analytics wrings more value from purchase data—for better negotiation tactics, vendor management, and purchasing strategy.

For procurement departments, information is power. Insights gleaned from historical data on product pricing and vendors can strengthen the buyer’s negotiating position and drive better pricing. Few procurement functions are currently making the best use of the huge amounts of data they generate, however.

Why does that matter? Our experience suggests that statistical models and advanced analytics can help procurement departments achieve cost savings of 3 to 8 percent, compared to traditional pricing models. By employing robust data analytics, procurement managers can uncover new insights from data to use in negotiations, vendor segmentation and performance management, and annual purchasing strategy.

Getting more value from procurement data

Procurement functions generate more data than any one employee can track and manage. At one midsize manufacturing company with approximately $2 billion in annual revenue, for example, procurement had data on more than 20,000 transactions for a single category, each with four to five statistically significant drivers of price.

But the models used by most procurement functions dramatically simplify the available data to make it easier for purchasers to handle. A lot of potentially valuable insights get lost along the way. Take the cost curve. This widely used modeling approach provides an overview of the average price paid to range of vendors over a year. The model is appealingly simple, but averaging prices can obscure the most critical aspects of vendor performance. For, say, an agricultural product with significant seasonal price variation, the highest-price vendor may be the only one that can supply the product only during the high-cost season, giving it no incentive to match others’ prices.

The advantages of advanced analytics

Advanced-analytics techniques use 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 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 crucial 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. Data models also enable the testing of thousands of permutations very quickly to determine which statistical clusters fit the data best.

Three use cases for advanced analytics

Three areas—negotiation, vendor segmentation, and planning—reinforce the value that a statistical approach powered by advanced analytics can deliver.

The first step in successful negotiations is to prepare a fact base with information on previous purchases. One manufacturer illustrates the complexity of this task. It spends hundreds of millions of dollars on raw materials through tens of thousands of transactions with hundreds of vendors. Pricing is affected by multiple factors and fluctuates throughout the year, so the manufacturer uses an advanced-analytics algorithm to group historical purchases into statistically significant clusters. This information can then easily be displayed in any format already familiar to the user—typically a spreadsheet. In the first month of using the improved fact-based negotiation approach, the manufacturer’s procurement function was able to achieve an 8 percent reduction in its cluster prices just by driving the highest purchase prices down toward the average.
Vendor segmentation and management is a relationship-oriented endeavor. As such, it is particularly vulnerable to the many biases that affect human interaction. While the personal nature of the relationship remains important, conclusions about vendor performance should be based on data, rather than feelings. Since advanced analytics is especially useful in isolating vendor performance within a cluster, it can help eliminate biases from the evaluation.

Advanced analytics can be particularly helpful in analyzing purchasing data to support a comprehensive sourcing strategy. Modeling can also inform inventory-carrying decisions. For instance, the chemical manufacturer’s data could show that it pays a 10 percent premium on spot purchases when its safety stocks are depleted. The procurement team can then make a data-driven decision about whether to pay the carrying cost for additional inventory, or pay a premium price for spot purchases.

But what does it take to integrate advanced analytics into procurement operations?

The good news is that almost every organization already has the necessary resources and capabilities. If procurement has a record of historical purchases, transitioning from heuristic approximations to data-driven analytics can be accomplished in a few months with targeted efforts in three key areas.

  • People – Buyers function as the tip of the spear. They don’t need to be data scientists or know how to code algorithms; they just need a computer to view the output of advanced-analytics models. A few days of workshops to explain topics such as statistical clustering and how to incorporate them into negotiations can enable the whole procurement function to grasp the fundamentals.

  • Processes – Procurement functions have access to all of the data they need through existing systems, so processes don’t need to be adjusted to facilitate data entry or aggregation. While performing the initial analysis requires expert knowledge (professional guidance is recommended), the algorithm only needs to be run one time. After that, new data can be easily plugged into the assigned clusters that feed the tools on an ongoing basis.

  • Technology – In many cases, no additional investments in technology will be needed to support advanced analytics. The database to consolidate and house the purchasing data does not need to be fancy. To run the analysis, the database’s minimum requirement is a record of historical purchases and data to model cost drivers (for example, price, date, volume, and vendor). Most organizations use some form of ERP software that already offers the required capabilities. The output of the analysis can be used in spreadsheets or other formats that teams are already using.

For deeper insights into purchasing data and trends, we find that advanced analytics-based systems beat traditional models every time, and the capabilities require little added investment. In most cases, all it takes is a change in mind-set. Since the potential savings from better-informed negotiations are so substantial, procurement functions have no excuse for delay.