The Research

Empirical evidence.
Operational reality.

ChainLytix diagnostics are derived from statistical analysis of 202 real supply chain AI deployments. Hierarchical logistic regression — validated by a doctoral committee — identifies which variables actually predict success vs. failure.

Research Methodology

Four phases of
evidence building.

The TCM (Trait-Constraint-Model) diagnostic framework was built through a rigorous four-phase research program — not assembled from vendor whitepapers or analyst opinions.

Phase 1
PRISMA Systematic Review
Approximately 3,000 initial records across six peer-reviewed databases (Scopus, Web of Science, IEEE Xplore, AIS eLibrary, ACM Digital Library, INFORMS). Four-stage screening produced ~200 high-quality papers spanning 2019-2025.
Phase 2
Cross-Domain Pattern Sweep
60 high-credibility sources across Finance, Healthcare, and IT Operations. Extracted universal adoption patterns and anti-patterns that translate to supply chain — the "dark side" variables no vendor talks about.
Phase 3
TCM Framework Construction
Built the 15×14 Task-Readiness Matrix using CMM-anchored scoring rubrics. 15 SCOR-aligned supply chain tasks scored across 14 readiness dimensions — traits, constraints, and process physics.
Phase 4
Statistical Validation
Hierarchical logistic regression on 202 real deployment cases. Five anti-pattern variables added to the model lifted R² from .142 to .634 and accuracy to 85.6%. External triangulation against MIT, McKinsey, Gartner, and Deloitte data.
By the Numbers

Research at a glance.

202
real-world supply chain AI deployments analyzed
85.6%
classification accuracy of the prediction model
.634
Nagelkerke R² — variance in outcomes explained
~200
peer-reviewed papers in the scholarly corpus
15
SCOR-aligned tasks in the readiness matrix
14
readiness dimensions per task (traits + constraints)
5
anti-pattern variables with p<.01 significance
6
databases used in systematic review (PRISMA 2020)
Key Findings

What the data shows.

Five findings from the 202-case dataset that directly inform every ChainLytix diagnostic.

41%
Deployments fail or stall — and the reasons are predictable
58.9% of supply chain AI deployments succeed. The rest fail, stall, or degrade. But the failure isn't random — four organizational anti-patterns predict the outcome before a single line of code is deployed.
8.5×
Multi-Agent Systems outperform all other architectures
MAS deployments succeed at 78.6% vs. 48.5% for other approaches (OR = 8.5, p = .029). Architecture choice is a statistically significant predictor — not just a design preference.
−98%
Trust Deficit is the single biggest deployment killer
When organizational trust in AI systems is low, deployment success odds drop by 98% (OR = 0.02, p < .001). This is not a technology problem — it's an organizational readiness problem the diagnostic specifically screens for.
346%
Anti-patterns explain 346% more variance than technical variables alone
R² jumped from .142 (technical model) to .634 (full model with anti-patterns) — a 346% improvement. The organizational variables, not the technical ones, are what actually predict success.
59→78%
Deployment stage matters — later stages succeed more often
Success rates climb from 59.1% at pilot/PoC stage to 77.8% at scaled production, reflecting organizational learning. The diagnostic tells you whether you're ready to advance to the next stage.
The Researcher

Built by someone who
runs supply chains.

Brad Rogers — Founder, ChainLytix
Brad Rogers
Founder, ChainLytix · Doctoral Researcher · Director, Supply Planning

ChainLytix was built because the gap between AI vendor promises and supply chain operational reality kept producing the same outcome — stalled deployments, wasted budget, and organizational skepticism that made the next attempt harder. The diagnostic framework exists to close that gap with evidence instead of guesswork.

18 years Fortune 50 supply chain experience — PepsiCo, Frito-Lay
Active Director-level supply chain leader managing real operations
Doctoral researcher — TCM Framework, 202-case deployment dataset
Guest Lecturer, Baylor University — Supply Chain AI Readiness
Provisional patent holder — AI readiness diagnostic methodology
APICS CSCP certified supply chain professional
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