Industry Insights
For every euro invested in cost engineering, companies see returns between 10 and 100 times their investment. They're patterns that Severin Heimrath, Managing Director at AWS Cost Engineering & Innovation Partner, has observed consistently throughout his 15-year career leading cost engineering projects for global manufacturing organizations.
Yet despite this proven return, most manufacturing organizations leave massive savings on the table. The reason is that they treat cost engineering as a procurement negotiation tool instead of a product development discipline.
In Episode 5 of the Beyond Cost podcast, host Sasan Hashemi talks with Severin Heimrath about the hard truths of what actually drives cost engineering results and why timing is everything.
The 80% Problem: When Good Companies Start Too Late
Here's the uncomfortable reality: 80-90% of the cost engineering work AWS Cost Engineering & Innovation Partner performs happens on products that are already designed and in production. At that stage, the opportunity for meaningful optimization has largely disappeared.
Once a product reaches production, the constraints multiply. Changes require reinvestment in tooling. The product is already in market, so modifications risk customer perception. Engineering decisions, material selections, and supplier relationships have already locked in the cost structure.
The irony is sharp: companies invest in cost analysis precisely when it can deliver the least value.
When cost engineering begins during product development, teams can make fact-based decisions about which features justify premium costs and which can be optimized without compromising value. By the time a part reaches the negotiation table, those choices are essentially permanent.
From Gut Feeling to Data: The Evolution of Cost Engineering
The cost engineering field has matured dramatically over the past 15 years. When Severin Heimrath started at AWS Cost Engineering & Innovation Partner in 2010, the approach was closer to cost estimation than engineering. The material database contained perhaps 10-15 basic categories: steel sheet, steel rod, and a few plastic variants.
Today, AWS Cost Engineering & Innovation Partner maintains roughly 800 different machines in their database and over a million material data points. But as Mr. Heimrath notes: "At some point, we decided it's not about the number of machines in your database. It's about the quality of data behind them."
This evolution reflects a fundamental shift in methodology. Modern cost analysis doesn't rely on rough approximations like price-per-kilogram. Instead, it builds detailed process models.
For a milled part, for example, cost engineers now analyze which specific machine is appropriate, break down each individual work step, calculate cycle times for each operation, and identify the right tooling. This granular approach reveals the actual cost drivers within a process (information that enables targeted optimization rather than broad negotiations).
The 10-100x Return: Not a Sales Pitch, Just Math
The ROI conversation in cost engineering often sounds too good to be true. But the numbers tell a consistent story across industries and project types. Severin Heimrath puts it simply: "Between 10 and 100 times return. These are typical factors we see consistently."
These returns vary based on annual volume, lot size, and what analysis was done previously. The pattern holds across industries beyond automotive, where cost engineering is already well-established.
The multiplier effect comes from cost engineering's role as what Mr. Heimrath calls a "catalyzer." It creates the foundation for fact-based decisions across departments.
Consider a typical scenario: engineering specifies a tight tolerance because it "feels important." Procurement negotiates based on market benchmarks. Neither party has visibility into what that tolerance actually costs to produce or whether it's technically necessary.
Cost engineering changes the conversation. Instead of defending positions, teams can discuss trade-offs: "We have a requirement of 0.5 millimeter tolerance. Is it not? Like, can we not go to two millimeters or three millimeters?" The discussion shifts from confrontation to collaboration once everyone understands the cost implications.
This explains why the ROI can be so high. Cost engineering doesn't just optimize individual parts. It enables better decisions across the entire organization.
Two Blockers Every Leader Should Recognize
Despite the proven returns, Severin Heimrath identifies two persistent challenges that prevent organizations from capturing full value.
1. Embed Cost Engineering Into Company DNA, Not Budget Cycles
Cost engineering can't be an occasional activity triggered by budget pressure. It requires clear processes that everyone understands and follows. Mr. Heimrath warns, "If you start with excuses, then there's always a reason why not to do it, because time is constrained." Organizations that achieve the best results treat cost analysis as a standard part of product development, not an optional add-on when problems emerge.
2. Align Your Data Before Analysis Begins
Companies often operate with fragmented databases across departments. When cost engineering service providers arrive with their own data, the first challenge becomes reconciling which numbers to trust.
The objective is to align on reliable inputs that enable consistent decision-making across the organization, not to impose external data.
AI Will Scale the Work, Not Replace the Expertise
Artificial intelligence is reshaping cost engineering, but not in the way most people expect. Severin Heimrath sees AI as a tool for horizontal scaling rather than replacement of human expertise.
Manufacturing organizations typically manage thousands of different components. Many of these fall into what's known as C-category parts: lower-value, high-volume components that collectively represent significant spend but individually don't justify extensive manual analysis. Think fasteners, connectors, standard brackets, or commodity materials that appear across multiple product lines.
For these tail C parts, AI can accelerate the analysis dramatically. Mr. Heimrath envisions systems that automatically process thousands of parts, generate should-cost estimates, and even trigger supplier outreach when disconnects appear.
This frees cost engineers to focus on complex, high-value analyses where human expertise remains essential.
As AI handles routine analysis, the cost engineer's role shifts toward communication and cross-functional collaboration. Severin Heimrath emphasizes that successful cost engineering increasingly depends on networking skills within organizations and the ability to translate technical findings into business decisions.
Start Early: The One Action That Changes Everything
For manufacturing leaders evaluating cost engineering, Mr. Heimrath's final recommendation is direct: start early in the process.
Early means calling in cost engineering weeks before a negotiation, not the day before. It means beginning analysis during product development, not after production starts. It means building the capability before the pressure hits, not scrambling when margins compress.
The technology exists. The methodologies are proven. The returns are documented. The only variable is timing, and that's entirely within a leader's control.
Listen to the Full Episode
Listen to the full episode to hear more about how cost engineering has evolved, where AI will make the biggest impact, and why cross-functional collaboration determines success.
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