4 in 10 supply chain AI deployments fail. Know before you spend.
The ChainLytix SCOR-DS Diagnostic is the only readiness assessment built directly from 202 real-world supply chain AI deployments — and it predicts deployment outcomes with 85.6% accuracy.
Built on doctoral research. Validated by logistic regression. Not a vendor quiz.
Across 202 analyzed deployments, the same failure patterns appear consistently — and they're predictable well before a deployment starts.
41%
Deployments fail or stall
Nearly half of all supply chain AI projects never reach their intended outcome. Most organizations only discover this after committing 6–18 months of budget and resources.
78.6%
MAS architecture success rate
Multi-Agent Systems outperform all other architectures — succeeding at nearly 8.5× the odds of other approaches. Architecture choice is a statistically significant predictor (p = .029).
63.4%
Variance explained by anti-patterns
When organizational anti-patterns are measured, the model explains 63.4% of deployment outcomes — jumping from just 14.2% with technical variables alone.
⚠ Anti-patterns detected in the dataset — each dramatically reduces success odds
−98%
Trust Deficit success odds reduction
−96%
Legacy IT Debt success odds reduction
−86%
Regulatory Risk success odds reduction
−78%
Privacy Risk success odds reduction
p<.001
Statistical significance of all four anti-patterns
The diagnostic measures the same variables that predict deployment success in the 202-case research dataset — applied to your specific supply chain.
01
Answer 20 questions across SCOR domains
Each question maps to a specific dimension from the research — data quality, process standardization, technology trust, organizational readiness. No generic survey questions.
02
Get scored against the deployment dataset
Your answers are scored using the same framework used to analyze 202 real deployments. Domain scores reveal exactly where readiness gaps will cause implementation friction.
03
Anti-pattern screening flags hidden risks
Trust Deficit, Legacy IT Debt, Regulatory Risk, and Privacy Risk are screened — the four anti-patterns that research shows reduce success odds by 78–98%.
04
Receive a prioritized action roadmap
Know which AI use cases to pursue first, which to prepare for, and which to defer — with specific actions to close the gaps that would cause a deployment to fail.
Sample Diagnostic Output
68%
Overall Readiness · Moderate
SOURCE
82%
PLAN
55%
MAKE
78%
DELIVER
70%
RETURN
38%
Anti-patterns detected
Legacy IT Debt — reduces success odds 96%
Trust Deficit — reduces success odds 98%
Who Uses ChainLytix
Two audiences. One diagnostic platform.
The same research dataset serves both sides of the supply chain AI equation.
Enterprise Leaders
Supply Chain Executives
VP Supply Chain, COO, CSCO at $500M–$5B manufacturers, CPG companies, and logistics providers evaluating or running AI initiatives.
Know which AI use cases will succeed before you commit budget
Identify the exact gaps causing vendor pilots to stall
Build a defensible, evidence-backed AI investment roadmap
Avoid the organizational anti-patterns that kill 41% of deployments
ChainLytix diagnostics aren't built from analyst opinions or vendor surveys. They're derived from statistical analysis of 202 real supply chain AI deployments using logistic regression validated by a doctoral committee.
202
real-world deployments in the research dataset
85.6%
model prediction accuracy for deployment outcomes
63.4%
variance in outcomes explained (Nagelkerke R²)
5
anti-pattern variables with p<.001 significance
🎓
Doctoral-Level Research Foundation
The diagnostic framework was developed and validated through doctoral research — not assembled from whitepapers. Hierarchical logistic regression confirmed which variables actually predict deployment success vs. failure.
📊
Statistically Validated Predictions
Four anti-pattern variables are significant at p<.001 or better. Multi-Agent System architecture predicts success at 8.5× the odds of other approaches. These aren't best guesses — they're regression-validated findings.
🏭
Built by a Fortune 50 Supply Chain Leader
Developed by an active Director-level supply chain leader at a Fortune 50 CPG company with 18 years of operational experience — not a consultant who has never run a distribution center.
Start in 5 minutes · No cost
Find out if your supply chain is ready for AI.
Take the free SCOR-DS Readiness Check. Get domain-level scores, anti-pattern flags, and prioritized recommendations — built on 202 real deployments.
