Supply chain teams have long served as orchestrators between internal employees, budget owners, and supplier partners. That means communicating demand, negotiating deals, managing costs, and locking in allocation, especially during times of volatility. The difference now is that rather than just measuring the quality of supplier deals based on performance, cost, or relationships, enterprises are increasingly paying attention to the role of speed.
The recent short market for computer memory highlights the critical value of speed, but the costs of missing allocation were even higher during the pandemic-era chip shortage. From 2020-2023, some automakers facing chip shortages adopted a tactic called “building shy” because they missed out on scarce allocation, costing billions.
Speed matters most in a short market. Imagine a popular pizzeria that’s running out of ingredients – the line stretches around the block, and the people at the end of the line won’t get their slices. Supply chain decision speed influences where you stand in the line, and whether you get your pizza. And sometimes, when the line grows the costs start to go up.
In the pre-pandemic world, when prices were more stable and supply more abundant, the leverage in dealmaking was often on the buyer’s side. The structure of a traditional request for quote (RFQ) process in standard sourcing configurations assumes that there is relatively abundant supply, and that suppliers will naturally compete for deals. That assumption can no longer be taken for granted. And the time to run this process itself has a higher cost.
To enable faster decision speeds, supply chain teams are turning to a new model called predictive procurement. With predictive procurement, teams make offers to their suppliers instead of vice versa, short-cutting the traditional back-and-forth. Here are three emerging changes across people, process, and technology that enable the predictive procurement approach.
The VUCA world and the shift in supply chain posture from resilience to agility
Whether it’s the on-again off-again Iran war, tariffs and counter-tariffs, or extreme weather events, markets are increasingly reactive to disruption events. This isn’t just about doing the same work faster, it’s about re-designing processes for a world where disruption is the norm.
Traditionally, the debate was between efficiency and resiliency. The most efficient supply chains had the fewest number of suppliers, were single-threaded and were exacting in terms of their operational requirements, such as lead time, order quantity, and shipping terms. These supply chains were highly performant and low-cost but extremely brittle, since they contained numerous single points of failure. In contrast, resilient supply chains were dual- and multi-source from Day 1. But they often required more decision-making, higher costs, and fewer volume-based discounts. Even multi-sourcing wasn’t a guaranteed downside protection against disruption. Three suppliers who operate as distributors might all use the same manufacturer, or might all depend upon the same shipping lane. The same event can still put a supply chain into reactive fire-fighting mode.
That’s why the new focus has moved from the efficiency and resiliency binary to agility. In the new VUCA world (volatile, uncertain, complex, and ambiguous), disruptions will happen and will impact suppliers in ways we cannot predict. The question is: how long does it take to run an end-to-end sourcing process to find new best value supply when we’re up against a clock? How can a dramatically faster process become a source of agility? How can we avoid trade-offs between cost and speed?
Moving from backward-looking data to truly predictive procurement intelligence
Procurement teams have access to a wealth of internal data. It’s not always clean, informative, or relevant. Not to mention, most of this data is about things that happened in the past. And past performance is not indicative of future results.
Most procurement intelligence has traditionally included historical reporting from Purchase Orders, invoices, contracts, and category benchmarking. It often references a Spend Cube with a set of pivots based on cost centers, that help procurement identify relevant trends in spend.
This use of backward-looking internal data was sufficient when quarter-over-quarter changes in costs were relatively flat. Procurement’s focus was more on compliance than on value-creation.
However, this data is not useful as a baseline or benchmark when prices can spike significantly in a short period of time. It is analytically descriptive, not analytically prescriptive. And it would be a mistake to confuse either of these analytic capabilities as truly predictive of the cost of a given item, service, or logistic unit from a given supplier in a given geography.
Today, the sourcing cycles that procurement teams live in cannot rely solely on backward-looking internal data. Procurement intelligence has to be forward-looking. Not because live signals are inherently more reliable than historical baselines, but because the market window for any given decision has collapsed by so much.
McKinsey estimates procurement functions use less than 20% of the data available to them to support decision-making, which speaks to the gap between the intelligence procurement is already generating and what the enterprise is structured to act on. That means integrating external data sources, but it also means looking at the recency of the data as a filter on its relevance and its ability to be consumed as context for a given dealmaking decision.
For procurement, the predictive advantage unlocks a higher floor on consistency and a higher ceiling on capacity
Within large enterprise supply chains, procurement teams do a lot more jobs than they used to. Execution, data, supplier and stakeholder management are all full-time jobs. Asking procurement to operate as both an execution function and an intelligence function used to be aspirational framing. The market tempo has made it an operational requirement.
Workload surveys continue to reflect this pressure. The Hackett Group's 2026 Procurement Agenda and Key Issues Study projects procurement workloads rising 8% in 2026 against a 0.9% headcount decline and 0.4% budget contraction, a 9% productivity gap that likely won’t be closed through process optimization alone.
McKinsey research has documented that procurement teams now manage roughly 50% more spend than they did five years ago, on equal or smaller headcount, against shorter market windows. Spreadsheets and ERP exports were never designed to absorb the volume of decisions this dual mandate requires, nor to surface the live signals that distinguish a proactive and strategic procurement function from a reactive and transactional one.
Historical baselines explain what has happened. Live supplier behavior explains what is happening. Combined and modeled, they begin to explain what is likely to happen next, which is where the predictive advantage lives.
When this intelligence is working, supplier deals can be timed against market windows, alternative suppliers are pre-vetted against existing favorites, and cost signals reach planning fast enough to inform decisions as they are happening. Every number you need lives at your fingertips, and it’s served to you proactively.
When it's absent, the enterprise stays a price-taker, absorbing every shock delivered by the market. When procurement leads with predictive data, the next shock can become an opportunity for agility and value-creation. Procurement teams armed with the predictive advantage never waste a good crisis. And even when the worst happens, supply chain leaders are prepared to act quickly when it does.
