About ChainLytix

Built by someone
who's done this.

ChainLytix was founded on a simple insight: the gap between agentic AI promise and supply chain reality is best diagnosed by someone who has operated inside both worlds — not just studied them from the outside.

Brad Rogers — Founder, ChainLytix
Brad Rogers
Director, Supply Planning · Doctoral Researcher
Active Director-level leader at a Fortune 50 consumer goods company
Doctoral researcher — agentic AI readiness in supply chain
Matrix validated against 30+ real AI deployments
Provisional patent filed on the readiness methodology
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Why ChainLytix exists.

After years leading supply planning at scale, I kept watching the same pattern play out: a vendor would propose an agentic AI solution, leadership would get excited, the pilot would begin — and 12 months later, the project would be quietly shelved. Not because the technology failed. Because nobody had systematically asked whether the supply chain was ready for it.

I started asking that question in my doctoral research. I built a readiness matrix — a 13-dimension framework for evaluating whether a specific supply chain process is genuinely ready for autonomous AI. Then I validated it against 30+ real deployments. The results were consistent: readiness predicts success more reliably than any other factor, including technology quality.

ChainLytix exists to put that framework to work. To give supply chain leaders a rigorous, evidence-backed answer to the question they're all asking privately: "Where do we actually start?"

🏭
18+ Years
Fortune 50 Supply Chain Leadership
Progressed to Director of Supply Planning, managing large-scale operations, cross-functional AI evaluations, and enterprise technology decisions.
📚
Doctoral Research
Agentic AI Readiness in Supply Chain
Developed and validated the 13-dimension readiness matrix. Applied logistic and ordinal regression to confirm predictive validity of readiness scores against deployment outcomes.
🔬
30+ Deployments
Real-World Validation Dataset
The matrix was tested against actual agentic AI deployments across supply chain functions — not surveys. What works in production, what stalls, and why.
⚖️
2025
Provisional Patent Filed
The readiness methodology is protected — ensuring it remains independent, proprietary, and vendor-neutral.
🚀
2026
ChainLytix Founded
Launched to bring the validated methodology to supply chain leaders and AI vendors who need rigorous, independent readiness assessment.

What makes the
matrix different.

Most AI readiness frameworks are checklists. The ChainLytix matrix is a validated predictive model — built from research and tested against real outcomes.

13
Dimensions Scored Per Use Case

Data quality, process standardization, decision frequency, exception rate, integration complexity, governance maturity, workforce readiness, and six more — each scored 1–5 against deployment-validated benchmarks.

15
SCOR-DS Processes Mapped

The matrix maps to the SCOR Digital Standard — the cross-industry framework maintained by ASCM. Every assessment produces process-level scores aligned to the industry's most rigorous supply chain taxonomy.

30+
Deployments Validated Against

Dimension weights and scoring thresholds calibrated against real agentic AI deployments across manufacturing, CPG, logistics, and high-tech — not theoretical frameworks.

30+
Real deployments in validation dataset
13
Dimensions in the readiness matrix
15
SCOR-DS processes evaluated
1
Independent, vendor-neutral methodology

The core finding: readiness score is the single strongest predictor of deployment success — more predictive than technology quality, vendor experience, or budget size. Organizations above the readiness threshold succeed at significantly higher rates. Those that don't, stall — regardless of how good the technology is.

Why we don't recommend
vendors — ever.

🔒
No vendor alignment means honest assessment

ChainLytix has no commercial relationships with supply chain technology vendors. Your readiness score reflects your actual readiness — not which vendor is paying for a referral.

📐
Methodology-first, not technology-first

We start with your supply chain process, score it against the matrix, and only then help you think about which technology class would be appropriate.

🎓
Academic rigor as a differentiator

The matrix was built to withstand academic scrutiny — peer review, regression analysis, real-world validation. A significantly higher bar than frameworks assembled from industry reports and pattern matching.

🏭
Practitioner perspective, not analyst perspective

There's a meaningful difference between someone who has evaluated AI vendors as a buyer inside a Fortune 50 supply chain, and someone who has written about AI from the outside.

Start with a conversation.

Take the free readiness check or book a 30-minute call to talk through your specific situation.