The Research
Built on evidence, not opinions.
ChainLytix diagnostics are derived from hierarchical logistic regression across 202 real supply chain AI deployments — validated by a doctoral committee, not assembled from whitepapers.
Four phases of evidence building.
PHASE 01
Systematic Literature Review
~3,000 initial records across six peer-reviewed databases. PRISMA 2020 four-stage screening produced ~200 high-quality papers spanning 2019–2025.
PHASE 02
Cross-Domain Pattern Sweep
60 high-credibility sources across Finance, Healthcare, and IT Operations. Extracted universal adoption patterns and the organizational anti-patterns no vendor talks about.
PHASE 03
Framework Construction
Built the 15×14 Task-Readiness Matrix. 15 SCOR-aligned supply chain tasks scored across 14 readiness dimensions using CMM-anchored rubrics.
PHASE 04
Statistical Validation
Hierarchical logistic regression on 202 real cases. Anti-pattern variables lifted R² from .142 to .634 and classification accuracy to 85.6%.
202
Real deployments analyzed
85.6%
Prediction accuracy
.634
Nagelkerke R²
~200
Papers in scholarly corpus
15
SCOR-aligned tasks
What the data shows.
41%
Deployments fail or stall — for predictable reasons
58.9% succeed. The rest fail, stall, or degrade. Four organizational anti-patterns predict the outcome before deployment begins.
8.5×
Multi-Agent Systems outperform all other architectures
MAS deployments succeed at 78.6% vs. 48.5% for other approaches. Architecture choice is a statistically significant predictor (p = .029).
−98%
Trust Deficit is the biggest deployment killer
When organizational trust in AI is low, success odds drop by 98%. This is a people problem, not a technology problem.
346%
Anti-patterns explain far more than technical variables
R² jumped from .142 to .634 when organizational variables were added. The non-technical factors are what actually predict outcomes.
Brad Rogers
Founder, ChainLytix · Doctoral Researcher
ChainLytix exists because the gap between AI vendor promises and supply chain operational reality keeps producing the same outcome — stalled deployments, wasted budget, and organizational skepticism that makes the next attempt harder.
18 years Fortune 50 supply chain — PepsiCo, Frito-Lay
Active Director-level supply chain leader
Doctoral researcher — 202-case deployment dataset
Guest Lecturer, Baylor University
Provisional patent — AI readiness diagnostic methodology
See what the data says about your readiness.
Five minutes. Five supply chain domains. Built on evidence.