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. In Deloitte’s 2020 CPO Flash Survey, procurement leaders identified cost management as their top priority right now, commanding nearly 8x more focus in day-to-day operations. This aligns with additional survey findings, showing that two-thirds of organizations are planning to pursue cost reduction strategies post-COVID-19, compared to just one-third pre-COVID-19.
“In a world in which uncertainty and disruption will likely remain a constant, procurement leaders will need to be even more resourceful to help their organizations manage cash, while limiting supply disruptions.” (Deloitte 2020 CPO Flash Survey)
The application of data analytics to traditional procurement activities can help to streamline and increase efficiency in areas such as spend analytics, forecasting surges in demand, and all aspects of supplier relationship management from contract to post-transaction evaluations.
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.
Supplier visibility, however, remains one of the main challenges for supply chains in 2020. In the Deloitte CPO 2020 Flash Survey, only 50% of procurement leaders surveyed had high or very high visibility into their tier 1 suppliers, while 90% of organizations rated their visibility into their extended supply networks as moderate to very low.
That might be one of the reasons why enhancing supply management capabilities, as well as adopting and investing in advanced technology, emerged as the two primary themes among the survey respondents.
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 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. Surges in demand could result from cyclical factors, and occur on a fairly predictable basis. Companies ensure that in-store and online stock availability is managed in preparation for predictable, recurring surges in demand.
But surges (or plunges) in demand don’t always stick to the calendar. In 2020, COVID-19 has not only disrupted millions of supply chains but has also changed consumer behavior, forcing procurement leaders to quickly get better visibility into the most critical parts of their supply chains.
As hospitals adjusted their capacity of medical equipment for spikes in infections and mortality rates, producers of electronic components like Avnet had to react fast. Using real-time data in their analytics and intelligent automation, they’ve been able to make same-day adjustments and prioritize inventory deliveries for medical and healthcare customers (such as components for ventilators and respirators) over non-essential consumer goods.
Data analytics can be used to tie together both recurring and unexpected environmental factors to increase accurate demand forecasting to the benefit of the procurement department and the enterprise as a whole.
Data analytics can also 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.
Southard Jones is Celonis’ VP, Product Marketing. Prior to Celonis, Southard held various executive product and marketing roles at enterprise software companies in the Business Intelligence, Analytics, and Data Science market, including Domino Data Lab, Birst, Right 90, and Siebel Analytics.
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