Richard Hanzlik
MATERIALS TO MARKETS

Richard Hanzlik Materials to Markets

From Lab Bench
to Boardroom.

Advanced materials don't go straight from discovery to market. The challenge is turning technical insight into something you can manufacture and sell.

Let's think together reach out

01
Technical Depth
The physics of materials at scale is not the same as the physics in the lab. Most business cases don't account for this.
02
Product Definition
A material that works is not the same as a product. Defining the difference is harder than it sounds.
03
Commercial Strategy
The companies that win don't always have the best technology. They have the clearest story about the problem they solve.
04
Market Growth
Customer discovery done well changes everything. Customer discovery done badly is just confirmation bias with a travel budget.

Engineering without market context is just an experiment

How I Think

Most technology companies misunderstand their problems.

The failure modes are consistent and predictable. Companies build features when they should be building solutions. They assume customer understanding that doesn't exist. They treat manufacturing scale-up as an engineering afterthought.

Common assumptions

  • We know what customers want
  • Features equal products
  • Technical excellence sells itself
  • Scale-up is an engineering problem, not a business problem
  • Customer discovery can happen after the build
  • Market adoption follows quality

What's actually true

  • Customer discovery is almost always shallow and companies rarely know it
  • Materials scale-up introduces complexity that can destroy a business case
  • Tribal manufacturing knowledge is a hidden competitive asset
  • The best technical products fail commercially for non-technical reasons
  • Most products are solutions in search of a problem, not the reverse
  • The gap between lab performance and customer value is rarely linear

Frameworks & Methodology

Three models for thinking about materials commercialization.

Conceptual tools built from pattern recognition across product development cycles, manufacturing transitions, and commercial launches in advanced materials.

01
Lab-to-Market Framework
The six-stage model from customer problem to market adoption with explicit identification of the two failure points where most technical products die.
Customer Problem ← Failure Point 1
Technical Discovery
Structured Experimentation
Product Definition
Manufacturing Scale ← Failure Point 2
Market Adoption
02
Materials Commercialization Lifecycle
A model showing how commercial risk evolves across the product lifecycle from high-uncertainty discovery through constrained manufacturing and eventual market maturity.
Scientific Discovery (high uncertainty)
Applied Research (narrowing hypotheses)
Development Scale (complexity spike)
Pilot Production (cost reality)
Commercial Launch (market test)
Market Maturity (margin compression)
03
Lab vs. Manufacturing Complexity Gap
A diagnostic model showing the divergence between controlled lab conditions and production reality used to identify where technical assumptions break down before capital is committed.
Lab: controlled variables, small batch
← Gap grows here
Pilot: process sensitivity revealed
← Business case rebuilt (or not)
Production: tribal knowledge critical
Scale: new failure modes emerge

Essays

Essays on materials, markets, and innovation.

Thoughts on Material Science, Industry misconceptions, Innovation, and the commercialization of advanced materials.

Full-Stack Capability

From the lab bench to the boardroom I do it all.

Most organizations have deep technical talent or strong commercial instinct. The challenge is connecting them. That connection translating material science into market strategy is the work I find most interesting.

01
Build Technical Products from Concept to Market
From application identification through product definition, validation, and launch. Understanding what a technology can do and shaping that into something a market will pay for.
02
Translate Materials Science into Commercial Value
Reading a characterization curve and knowing what it means for a customer's process. Connecting technical capability to business outcome in language that moves decisions.
03
Discover Real Customer Problems
Most companies think they understand customer problems. Few actually do. Rigorous discovery before building anything is the difference between a product and a feature.
04
Build Business Cases for Advanced Technologies
Structuring arguments with financial rigor, technical credibility, and strategic clarity for executives, boards, and customers. Internally and externally.
05
Launch and Scale Materials-Driven Products
Moving from lab success to commercial reality. Understanding the manufacturing complexity, cost structure, and GTM motion required to build durable revenue.
06
Competitive Positioning and Market Strategy
Understanding the competitive landscape not just commercially but technically. Knowing where a product is genuinely differentiated and where the claim doesn't hold up.

Industry Perspective

Where advanced materials are Heading.

The industries I've spent time in are technically rich and commercially underserved. Characterization tools have advanced dramatically. Commercial thinking has not kept pace.

Rubber & Elastomers
The most data-rich segment in polymer testing yet most manufacturers still make decisions with surprisingly thin process understanding. Viscoelastic behavior across production conditions remains undercharacterized in most facilities.
Plastics & Polymers
As sustainability pressures reshape formulations, rheological characterization becomes commercially critical not just for QC, but for understanding how recycled content changes processing behavior at scale.
Composites & Prepreg
Aerospace composites have rigorous process specs but often lack real-time characterization at the rate and sensitivity modern cure monitoring demands. The intersection of speed and precision remains a genuine open problem.
Biomaterials
Biocompatibility and mechanical performance are necessary but not sufficient. Commercializing biomaterials requires navigating regulatory, clinical, and economic constraints simultaneously a challenge that is rarely technical in nature.
"

The advanced materials industries have more measurement capability than they've ever had. The constraint isn't data — it's the ability to connect what the data says to a decision a business should make."

Questions I'm Exploring

The problems worth thinking carefully about.

The questions that don't have clean answers yet but shape how I think about the future of advanced materials development and commercialization.

01
How can materials experimentation be systematically accelerated without sacrificing the rigor that makes results meaningful?
The tension between speed and validity in materials R&D is rarely resolved well. Most acceleration strategies sacrifice one for the other.
02
Process knowledge in advanced manufacturing lives in people, not systems. When those people leave, so does the knowledge. There is no good solution to this yet.
How can companies quantify and preserve manufacturing knowledge before it walks out the door?
03
How can characterization and testing tools be redesigned to shorten development cycles rather than just measure outcomes?
Most testing tools are designed to confirm results, not guide decisions. The tools that change this would reshape materials development entirely.
04
How can advanced materials reach market faster without the scale-up failures that destroy the business case before commercialization begins?
Scale-up failure is predictable, but the industry still treats it as a surprise. Better frameworks not just better chemistry are part of the answer.

Field Notes

What you learn building materials technologies at scale.

Five case studies from real commercialization work. Problems that didn't have obvious answers, and what actually moved them forward.

01
Replacing Legacy Testing Methods
Why incumbent testing methods persist long after better alternatives exist. And how repositioning the new method as additive rather than a replacement changed the commercial outcome.
02
Diagnosing Composite Cure Failures
Composite cure failures in aerospace prepreg are rarely caused by what teams initially suspect. How a structured root cause process cut diagnosis time and rebuilt confidence in the testing protocol.
03
Launching Advanced Materials Instruments
What changes when you introduce a new characterization instrument into a market where customers have established workflows and deeply held beliefs about measurement. And what it takes to move them anyway.
04
Elastomer Troubleshooting Systems
Building structured diagnostic frameworks for rubber compound failures. How moving from reactive troubleshooting to systematic process understanding reduced repeat failures and shortened the feedback loop.
05
Modernizing Installed Technical Platforms
The business case for upgrading a technical platform isn't just about new capability. How surfacing the hidden cost of staying put became the actual argument that moved the decision.

Experience

You start to recognize patterns when you've seen enough plants and enough problems.

Product Development
Engineers almost always optimize for measurement precision. Customers usually need measurement speed. The two are more in tension than they appear, and most product roadmaps never resolve it.
Customer Discovery
The defining challenge in polymer manufacturing is rarely the measurement itself. It's understanding which process variables actually drive the quality outcome. Most facilities have strong opinions about this. Fewer can back them up.
Commercial Strategy
The companies that win in technical markets aren't always the ones with the best technology. They're the ones that defined the problem in a way competitors hadn't, and built everything around that definition.
Business Cases
The most effective business cases aren't about ROI formulas. They connect a specific problem with a quantified cost to a specific capability that solves it with less risk than doing nothing. That's a different argument than most teams make.
Scale-up & Complexity
The hidden cost in scale-up is almost always the tribal knowledge that doesn't travel with the technology. What an experienced process engineer knows intuitively about a formulation often isn't captured anywhere. You find out what you lost when something goes wrong at production volume.

The Foundation

Where technical insight becomes commercial reality.

Every technical product follows a similar path. Most teams understand the stages. That’s not where things break. Failure happens in two predictable places. And it usually happens quietly, long before anyone calls it out..

Customer Problem
Is the problem real, painful, and underserved?
Failure Point 1
▼
Technical Discovery
What does the science actually allow?
▼
Structured Experimentation
Systematic validation with real constraints
▼
Product Definition
Specs, positioning, and commercial architecture
▼
Manufacturing Scale
Lab behavior vs. production reality
Failure Point 2
▼
Market Adoption
Revenue, trust, and sustained growth

Why most approaches miss the point

Every stage in this model is teachable. The tragedy is that most companies understand the stages and still fail because they treat discovery as a checkbox rather than a discipline, and treat scale-up as a technical problem rather than a business risk.

Failure Point 1: Companies consistently start with technology rather than a validated customer problem. What they believe about the customer is often a projection not evidence.

The scale-up illusion

A material that behaves beautifully at lab scale may behave entirely differently at production volume. Viscosity shifts. Cure behavior changes. Processing windows narrow. These aren't edge cases they're physics.

What makes this particularly dangerous is that the economics of the business case were built on lab data. By the time scale-up complexity reveals itself, significant capital has already been committed.

Failure Point 2: Scale-up complexity is routinely underestimated and the business case is rarely rebuilt to account for it.

What changes when you account for both

The framework isn't about avoiding failure. It's about knowing where failure is most likely, and investing disproportionately in those two stages before the rest of the process begins.

AI Integration

Anyone can vibe code a Claude tool.
Knowing what to build is the hard part.

The barrier to building AI tools dropped to nearly zero. That means the differentiator isn't the code. It's the industry knowledge that goes into the design. Getting the right questions, the right logic, the right output structure. That's what 15 years in materials commercialization buys you.

Sales Intelligence
01
Sales Pipeline Tracking
Anyone can connect a CRM to an AI and call it intelligent. The hard part is knowing which signals actually predict a deal closing in technical sales, where the evaluation cycle is long, the stakeholders are technical, and "interested" looks identical to "stalling." That pattern recognition comes from the industry, not the model.
Pipeline analysis · Deal velocity · Technical buyer signals
Knowledge Systems
02
Expert Knowledge Agents
Building a knowledge agent is straightforward. Building one that answers like a process engineer, that knows the difference between a lab result and a production claim, that understands why a rheology curve matters in that specific context, requires someone who has actually stood in a plant and made those calls. The AI is the interface. The knowledge is the intelligence.
Formulation knowledge · Process expertise · Technical Q&A
Customer Value
03
Custom Value Tools
Not a generic ROI calculator with your logo on it. A tool built around the specific variables that drive value in your customer's operation. Their yield loss, their test cycle time, their scale-up risk. Getting those variables right is a materials problem, not a software problem. The code is the easy half.
Process-specific modeling · Customer-facing outputs · Built to close deals
Data Navigation
04
Technical Data Systems
The question isn't whether you can query your characterization data. It's whether the system understands what the data means. Which variables co-vary, which results are outliers versus signals, which patterns show up three batches before a failure. Building that requires someone who has used the data to make real decisions, not just store it.
Characterization data · Pattern recognition · Decision-ready outputs
Content
05
Technical Content at Scale
AI can write an application note in 30 seconds. AI that understands cure kinetics, knows why that test condition matters for that substrate, and frames the result the way a process engineer would trust it, that takes a different input. The prompt is only as good as the knowledge behind it.
Application notes · Product documentation · Industry-accurate output
Workflow
06
Lab to Launch, AI-Accelerated
The R&D to commercial handoff fails in predictable ways. Customer discovery findings that never reach the product team. Test data that lives in one person's inbox. Launch decisions made without the scale-up team in the room. Building workflows that fix these requires knowing where they break. That's a commercialization problem first and an AI problem second.
R&D to commercial handoff · Discovery to decision · Launch without the gaps

"The tools on this site took a weekend to build. The 15 years that made them useful took longer."

On building AI tools that actually know the material

Recognize a problem worth working on together?

Whether you've got a commercialization challenge, a product that hasn't found its market, or a scale-up that's revealing new complexity, I'm happy to have a conversation about how to improve.

Start a Conversation → Or try a tool first
22 Technical Publications · Rubber · Polymers · Composites · Biomaterials · Rheology · Process Characterization · Chemical Engineering + MBA

Research & Writing

Published Work

Flagship Papers Peer-Reviewed & Major Conferences 5 papers
SAMPE 2025 Impact of Prepreg Cure Conditions on Product Properties Using Rheological Glass Transition Analysis U. Yilmazoglu, R. Hanzlik
ResearchGate →
SAMPE 2024 Troubleshooting Common Prepreg Cure Failure Modes with Rheological Measurements U. Yilmazoglu, R. Hanzlik
DOI →
ANTEC 2024 Determination of Glass Transition Temperature in Various Polypropylene Grades Utilizing Rheometry Under Sub-Zero Conditions U. Yilmazoglu, R. Hanzlik
Gated
Wood Science and Technology, 2022 An Experimental Study on Pore Structural Changes of Ultrasonic Treated Korean Paulownia E.-S. Jang, C.-W. Kang, R. Hanzlik
DOI →
SAMPE 2020 Optimization of Cure Cycles Using an Encapsulated Sample Rheometer (ESR) R. Hanzlik, H. Pawlowski, L. Dorworth
PDF →
Industry Authority Technical Articles 5 articles
Rubber News, 2023 Effects of Different Recycled Rubber Powders on the Rheology of Rubber Compounds J. Dick, R. Hanzlik, J. Gialamas
PDF →
Rubber News, 2022 Measurement of Wet and Winter Traction with Torsional Dynamic Rheometer R. Hanzlik
PDF →
Rubber & Plastics News, 2021 Correlating Cure Kinetics and Physical Properties with Accelerator Variations in a Model SBR Compound R. Hanzlik
Article →
Rubber & Plastics News, 2021 Studying RPA ASTM Methods for Detecting Compound Quality Differences J. Dick, R. Hanzlik
Article →
Rubber & Plastics News, 2020 Measuring Silica Dispersion Quality in Rubber Mixing J. Dick, R. Hanzlik
Article →
Conference & Additional Technical Publications 6 publications
ACS Rubber Division, 2021 Most Effective Viscoelastic Properties from the Rubber Process Analyzer for Measuring Factory Quality of Mix J. Dick, R. Hanzlik
Conf. Paper
Rubber Tire Digest, 2021 Cure Kinetics: A Comparison of Cure Packages R. Hanzlik
Article →
ACS Rubber Division, 2020 Comparative Advantages of Different RPA ASTM Methods for Detecting Rubber Compound Quality Differences J. Dick, R. Hanzlik
Conf. Paper
ACS Rubber Division, 2019 Molecular Characterization of Elastomers Measured with Modern Rheological Tools H. Pawlowski, R. Hanzlik
Conf. Paper
SLT Caucho, 2019 Cure Kinetics as a Compounder's Tool R. Hanzlik
Article →
Alpha Technologies How to Improve Product Quality Using Six Sigma Practices R. Hanzlik
Webinar →

Final reflection

Recognize a problem worth working on together?

If you're working on a commercialization problem that sits at the boundary of technical and commercial, whether that's a scale-up that's revealing new complexity, a product that hasn't found its market, or a business case that won't close, that's the type of problem I find most useful to work through.

Start a conversation. reach out

Field Notes / 01

Field Note 01 Testing Methods

Replacing Legacy Testing Methods

Overview

Incumbent testing methods in materials manufacturing persist long after technically superior alternatives exist. This isn't inertia in the pejorative sense it's a rational response to switching costs that are rarely made explicit. This investigation explores why displacement is so difficult and what conditions actually enable it.

The Problem

In developing advanced characterization instruments, a persistent challenge was understanding why technically superior products failed to displace legacy tools that customers openly acknowledged were inferior. The answer was almost never the technology itself.

The real barriers were institutional. Testing results are embedded in specifications, customer contracts, and internal quality standards developed over decades. Changing the test means renegotiating all of those a cost that rarely appears in any business case for the new instrument.

Approach

The productive reframe was to stop competing with the legacy method and start asking: what problems does the legacy method create that customers have learned to live with? Those hidden costs unplanned downtime, slow feedback loops, poor process correlation became the commercial argument.

Rather than positioning as a replacement, the more effective strategy positioned the new instrument as additive: providing insight the legacy system couldn't, without requiring the customer to abandon specifications they'd spent years developing.

Displacement Dynamics Legacy vs. New Method
Legacy Method
→
Entrenched in specs, contracts, QC
New Method
→
Additive value first, displacement second

Lessons Learned

Technical superiority is necessary but not sufficient for displacement. The commercial argument must account for switching costs that are invisible to the seller but very real to the buyer. The fastest path to adoption is often a period of parallel operation where the new method proves its value alongside the legacy system before asking anyone to abandon it.

The organizations that successfully adopted new testing methods shared one trait: they had someone who understood both the technical argument and the institutional switching cost. That bridge role is rarely defined on anyone's org chart.

Field Notes / 02

Field Note 02 Composites

Diagnosing Composite Cure Failures

Overview

Composite cure failures in aerospace prepreg manufacturing are rarely caused by what engineering teams initially suspect. This investigation examines root cause identification in cure process failures and the redesign of testing protocols to catch failure conditions earlier in the development cycle.

The Problem

In working with composite manufacturers, a recurring pattern emerged: cure failures were diagnosed by examining the failed part, which revealed effects rather than causes. The process variables that actually drove the failure resin advancement state, moisture content, thermal uniformity were rarely characterized at the incoming material stage.

The cost of this diagnostic gap was substantial. Scrap rates, rework, and schedule delays were treated as production problems when they were fundamentally characterization problems failures that predictive testing could have flagged before layup began.

Approach

The intervention was to move characterization upstream. Rather than testing after cure failure, the approach characterized incoming prepreg material using dynamic mechanical analysis to assess resin state and processing window before any laminate was built.

This required building a correlation between rheological properties measurable on incoming material and the cure quality achievable under production conditions a correlation that didn't previously exist in standardized form for the materials in question.

Cure Diagnosis Reactive vs. Predictive
Reactive (legacy)
→
Test after failure → find effects, not causes
Predictive (new)
→
Characterize incoming material → prevent failure

Lessons Learned

In composite manufacturing, the most valuable testing often happens before anything is built. Incoming material characterization particularly resin state assessment is systematically underinvested relative to its impact on process yield.

The organizational barrier is that incoming QC and process engineering are typically separate functions. The correlation between incoming material properties and cure outcomes requires someone who can speak both languages fluently and that person is rarely in the room when either testing protocol is designed.

Field Notes / 03

Field Note 03 Commercialization

Launching Advanced Materials Instruments

Overview

Introducing a new characterization instrument into a mature materials testing market requires navigating something more complex than a technical sale. Customers have established workflows, trained operators, calibrated expectations, and most importantly deeply held beliefs about what a measurement should look like.

The Problem

In launching advanced materials instruments, the initial assumption was that technical differentiation would drive adoption. The instrument measured more, measured faster, and correlated better with process outcomes. The market response was underwhelming not because customers disagreed with the technical claims, but because the technical claims answered the wrong question.

Customers weren't asking "which instrument is more capable?" They were asking "what would change about how I work if I adopted this?" That question demands a different answer than technical specification comparison.

Approach

The commercial repositioning shifted from capability communication to workflow integration. Rather than leading with what the instrument could measure, the approach led with the specific process decisions the instrument enabled decisions that were currently being made with lower-quality information.

Early adopters were identified not by size or technical sophistication, but by the presence of a specific internal problem: manufacturers with high scrap rates or unexplained process variability who had already exhausted their existing diagnostic toolkit. Their pain was acute enough to justify the switching cost.

Adoption Enablers Instrument Launch
Wrong framing
→
"Our instrument measures more accurately"
Right framing
→
"Here's the decision you can make now that you couldn't before"

Lessons Learned

In mature technical markets, the barrier to adoption is almost never skepticism about the technology. It's the inability to articulate what changes about a customer's process if they adopt it. Closing that gap requires deep familiarity with the customer's workflow not just the instrument's specification sheet.

The most effective salespeople in technical instrument markets aren't the ones who know the product best. They're the ones who can have a credible conversation about the customer's process and then connect that conversation to the product's specific capability.

Field Notes / 04

Field Note 04 Elastomers

Elastomer Troubleshooting Systems

Overview

Rubber compound failures in manufacturing are expensive, disruptive, and poorly understood. Most elastomer troubleshooting is reactive and experience-dependent effective when the right expert is available, unreliable when they aren't. This investigation explores the development of structured diagnostic frameworks that move elastomer manufacturing toward systematic process understanding.

The Problem

In working with elastomer manufacturers, the diagnostic process for compound failures was almost universally based on pattern recognition accumulated by experienced technicians over years. This knowledge was effective but fragile concentrated in individuals, undocumented, and not transferable at the rate the industry required.

When those individuals were unavailable whether due to turnover, scale, or shift coverage the diagnostic quality dropped significantly. The same failure would take three hours to diagnose one week and three days the next, depending entirely on who was on the floor.

Approach

The intervention was to extract the logic from expert troubleshooters and encode it in structured diagnostic protocols essentially making the expert's mental model accessible to less experienced operators. This required extended time with experienced technicians, observing not just what they concluded but how they reasoned.

The resulting framework organized compound failure symptoms by category, mapped each symptom to plausible root causes, and specified which characterization tests would differentiate between those causes moving from reactive symptom management to structured root cause identification.

Elastomer Diagnostic Flow
Failure Symptom
→
Category Classification
Category Classification
→
Root Cause Hypotheses
Root Cause Hypotheses
→
Targeted Testing Protocol

Lessons Learned

The most valuable output of this work wasn't the diagnostic framework itself it was the process of building it. Making expert knowledge explicit forced a level of rigor that even experienced technicians hadn't previously applied to their own reasoning. Several long-held assumptions about compound behavior didn't survive the formalization process.

Structured troubleshooting isn't a replacement for expertise it's a mechanism for making expertise available at scale. The goal is a floor, not a ceiling: ensuring that even a less experienced operator can reach an adequate diagnostic conclusion, while freeing experts to work on problems that genuinely require their judgment.

Field Notes / 05

Field Note 05 Platform Modernization

Modernizing Installed Technical Platforms

Overview

The business case for upgrading a technical platform is rarely about the new capability alone. It's about overcoming the institutional resistance created by sunk costs, retraining requirements, and the deep integration of legacy systems into quality infrastructure that has been built around them for years.

The Problem

In launching modernized versions of established testing platforms, the most common objection wasn't price and it wasn't technical capability it was specification continuity. Customers had built their quality systems around the outputs of the legacy instrument. Changing the instrument meant validating that the new system produced equivalent results, which required time, resources, and a tolerance for uncertainty that most quality managers preferred to avoid.

The paradox was that the new platform was meaningfully better but "better" was a liability when customers needed "equivalent." The metric the customer cared about wasn't performance. It was comparability.

Approach

The key insight was to lead the migration with correlation data rather than capability data. Rather than demonstrating what the new platform could do, the focus shifted to demonstrating that it produced results statistically equivalent to the legacy system within the tolerances customers already accepted.

Once equivalence was established, new capabilities could be introduced as additional value rather than as arguments for why the old system was inadequate. This sequencing changed the customer's frame from "I have to change my specs" to "I can keep my specs and gain additional insight."

Platform Migration Sequencing Strategy
Step 1
→
Establish equivalence to legacy (correlation study)
Step 2
→
Validate within existing spec tolerances
Step 3
→
Introduce new capabilities as additive value

Lessons Learned

Platform modernization is a change management problem with a technical wrapper. The technical argument is necessary but insufficient. The change management argument which addresses specification continuity, retraining burden, and institutional risk is what actually moves purchasing decisions.

The organizations that modernized successfully did so because they had an internal champion who understood both the technical equivalence argument and the organizational risk argument and who could translate between the engineering team's confidence in the new system and the quality manager's mandate to protect specification integrity.

·

Marketing Discovery
Product Business Case
Sales Evaluator