In 2025, procurement leaders are moving beyond simple cost-cutting measures to a more strategic approach focused on cost transparency. Today, the conversation is less about low prices and more about visibility and control. With tools like value analysis and should-cost modeling, organizations can optimize supplier negotiations, uncover hidden cost opportunities, and make informed decisions that deliver long-term value and operational improvements.
Cost Intelligence as a Strategic Asset
While the pandemic and inflationary pressures broke the old low-cost sourcing model, they also forced a shift in how organizations define value. Today, procurement teams are asking tough questions: Is this component over-engineered? Can we swap packaging without sacrificing protection? Are we paying for brand-name parts where generics will do?
That’s where value analysis comes in. By reassessing specifications and processes through a cost-versus-performance lens, organizations are identifying smart ways to lower total cost without undermining reliability, safety, or sustainability.
A standout example comes from ESCATEC, which supported the scale-up of cold chain data loggers critical to Pfizer vaccine delivery during the COVID-19 pandemic. As part of a rigorous review of second-generation temperature monitoring designs, ESCATEC played a pivotal role in ensuring product reliability under extreme logistics demands.
At peak capacity, its facility automated the production of 170,000 units per month for Controlant, whose cold chain-as-a-service technology enabled the successful global distribution of billions of vaccines with a reported 99.99% delivery success rate.
But value analysis only works when it’s anchored in data. Rather than relying on supplier quotes or historical pricing, procurement teams are using should-cost to build fact-based estimates grounded in labor, material, logistics, and overhead costs. The insights can validate pricing, strengthen negotiation positions, or justify internal trade-offs.
Artificial Intelligence applications can help large enterprises sort through thousands of products and examine potential cost opportunities previously unnoticed in product catalogs or pricing sheets. Cross-functional teamwork and analysis will give procurement leaders insights into detailed cost drivers to create an objective view of expected price points and provide information for supplier negotiations and operations improvements.
A prime example comes from American Airlines, which used a should-cost model to develop bid quotes for full-truckload, point-to-point transportation services tied to its $1 billion inventory of maintenance equipment and inflight service items. Each year, the airline issued about 500 RFQs and quotes varied by as much as 200 percent. Using analytics software, the procurement team built a cost structure model for each shipping route and negotiated prices based on the should-cost assessment.
Smarter Trade-Offs Start with Better Visibility
Models and analysis are only useful when viewed in full context. In 2025, every decision carries downstream implications and that’s why leaders are using should-cost and value analysis not in isolation, but as part of integrated scenario planning.
Moreover, the most effective teams are shifting the conversation from “how much does it cost?” to “what are we getting for the cost and where’s the waste?” That reframing helps avoid the trap of viewing suppliers purely as cost centers and instead sees them as partners in continuous improvement. Engaging suppliers early, sharing cost models and collaborating on material swaps or process changes, often yields better outcomes than adversarial negotiations.
It’s also crucial to recognize that not every dollar saved is worth the risk. A marginally cheaper part that drives a 5% increase in return rates can wipe out savings in warranty claims and brand damage. Strategic leaders are weighing short-term savings against long-term value erosion, a balance that requires both analytical rigor and operational maturity.
The Rise of Predictive and AI-Powered Costing
As procurement matures into a data-centric discipline, artificial intelligence is unlocking new frontiers in predictive costing and design-to-value modeling. By analyzing historical spend data, supply chain patterns, and real-time market inputs, AI tools can anticipate cost fluctuations, flag anomalies, and even suggest design alternatives that optimize both cost and performance.
In advanced setups, AI-powered systems are triggering automated sourcing events when market conditions hit pre-defined thresholds, enabling teams to act faster and more strategically. These technologies are turning procurement into a proactive, insight-driven function capable of influencing product design, supplier collaboration, and go-to-market timing.
A Moment to Lead, Not Just to Cut
Should-cost modeling and value analysis are proving to be essential components of modern procurement strategies not because they offer shortcuts to cost savings, but because they bring structure and transparency to complex decisions. When augmented by AI and predictive analytics, these tools empower procurement leaders with real-time intelligence and future-forward insights. The result is not just smarter sourcing but a more resilient, collaborative, and value-focused supply chain.
