Your procurement team is central to the success of the entire company. It’s responsible for balancing costs, delivering returns, and managing relationships throughout the supply chain. To maintain these varied, and sometimes opposing goals, the procurement department must have access to data that can be used to make smart decisions quickly and effectively in an ever-changing environment.
Procurement departments are often pressed to become more efficient and to streamline processes while limiting costly errors and controlling direct and indirect spend. The Deloitte 2017 CPO survey found that 79% of procurement leaders listed cost reduction as a primary focus for their department. The same survey also showed that data analytics are expected to have the largest impact of any technological advancement for 65% of CPOs surveyed.
The application of data analytics to traditional procurement activities can help to transform the activities of the department, helping to streamline activities and increase efficiency in areas such as spend analytics and risk management, forecasting surges in demand, and all aspects of supplier relationship management from contract to post-transaction evaluations.
Big data can be used to transform the way that procurement conducts spend analytics. Historically, procurement teams needed to deal with multiple databases consisting of structured and unstructured data. Best of breed procurement teams need to be able to consolidate and analyze this data in one place. These insights can enable teams to combine expected changes in supply and demand with real-world environmental factors to create dynamic and scalable pricing models.
The application of big data analytics allows a procurement leader to pull together such diverse data sets including invoice unit, price variance and fulfillment, supplier and buyer information, benchmark price, and tax information into a single, comprehensive analysis. This allows procurement to seek out opportunities to reduce spend, directly affecting the bottom line of the enterprise.
For example, with the application of a spend analytics program to its $200 million annual procurement budget, PPG Industries was able to bring 95% of indirect spend under central visibility and control. The company also achieved a 90% supplier reduction and 10% hard dollar savings in overall costs.
Furthermore, CPO participation in mitigating risk to the enterprise nearly doubled from 2013-2015, and that number is expected to continue to grow. Advanced data analytics can help procurement departments to make the best spend decisions by incorporating risk analysis into the decision-making process. By synthesizing data related to pricing and compliance risk, geographical risk, and preventative measures, procurement teams can better anticipate future problems in their supply chain.
The ability to accurately forecast surges in demand is essential to efficient procurement activities. The procurement department that is unprepared for a change in demand will be unable to take advantage of the best prices available and may put a strain on supplier relationships struggling to meet short-term requirements. According to a study by Accenture, the use of data analytics increased the accuracy of demand forecasting by over 55%, leading to increased contract negotiation power for the enterprise, along with decreased chances of running low on stock during unexpected demand surges.
Surges in demand could result from cyclical factors, and occur on a fairly predictable basis. For example, retail stores expect a surge in demand on the day after Thanksgiving, signaling the beginning of the holiday shopping season. These companies ensure that in-store and online stock availability is managed in preparation for this predictable, recurring surge in demand. Last year, Wal-Mart increased online inventory by over 50% in anticipation of Black Friday.
On the other hand, an unexpected surge in demand for butter in the UK has been attributed to a combination of factors, including a series of scientific studies on the health benefits of butter versus margarine, and a trend toward home baking.
Data analytics can be used to tie together recurring and seemingly disparate environmental factors to increase accurate demand forecasting to the benefit of the procurement department and the enterprise as a whole.
Supplier relationship management
Data analytics can help a procurement team to conduct in-depth and comprehensive vendor evaluations, taking into account disparate elements such as on-time delivery, quality of goods and services, and cost. With a well-organized analytics system, vendors can be evaluated and ranked on all relevant aspects of their services and compared to one another, in order to find the most effective vendor solutions. This may include vendor consolidation or changing the level of open market transactions.
Procurement can also use advanced analytics for effective contract management, optimizing discounts and forecasting liabilities. Within the first year after implementing a data analytics program, Owens Corning was able to use information garnered from the system to negotiate over $2 million in savings gained from consolidating vendors, driving contract compliance, and standardizing terms and conditions in vendor contracts.
Using existing data to achieve better prices, faster order fulfillment, and automated processing can help a procurement team to control both direct and indirect spend, adding real assets to the bottom line of the enterprise.