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3 Ways Data Analytics Can Empower Your Procurement Team
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.
How procurement data analytics drives efficiency
Procurement departments are pressed to become more efficient and streamline processes while limiting costly errors and controlling direct and indirect spend. In Deloitte’s 2023 CPO Flash Survey, procurement leaders identified cost management as their most-cited “strong priority” right now, especially in light of their highest organizational risk: inflation.
“Any CPO who thinks cost competitiveness can be parked while they ride the new wave of exciting priorities is in for a shock. It’s less a case of “or” and more a case of “in addition to” as uncertain markets lumber along in the general path of stagnation.” (Deloitte 2023 CPO Flash Survey)
The application of data analytics to traditional procurement activities can help transform areas such as spend analytics, forecasting surges in demand, and all aspects of supplier relationship management from contract to post-transaction evaluations.
Read: What is Procurement? The process and KPIs that matter
1. Transform the way you conduct spend analytics
Historically, procurement teams dealt with multiple databases containing structured and unstructured data. Best-of-breed procurement teams need to be able to consolidate and analyze this process data in one place, with systems that are connected. 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 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.
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 saving in overall costs.
Supplier visibility, however, remains one of the main challenges for supply chains. A 2022 McKinsey survey reported that 45% of respondents either have no visibility into their upstream supply chain or can see only as far as their first-tier suppliers.
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.
2. Increase accuracy in your demand forecasting
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. Companies try to 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 not only disrupted millions of supply chains but 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 procurement analytics and intelligent automation, they were 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.
Procurement data analytics can be used to tie together both recurring and unexpected environmental factors to increase accurate demand-forecasting, benefiting the procurement department and the enterprise as a whole.
3. Strengthen your supplier-relationship management
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 procurement analytics system, vendors can be comparatively evaluated and ranked on all relevant aspects of their services, in order to find the most effective solutions. This may include vendor consolidation or changing the level of open market transactions.
Teams can also use advanced procurement analytics for effective contract management, optimizing discounts, and forecasting liabilities. Since working with Celonis Process Intelligence, Avnet has seen €10-15M value annually from making such supply-chain processes run more efficiently, on top of strategic-level benefits including compliance.
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.
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