ERP Delay Risk

Repository: github.com/Tmgilliam/erp-ai-delay-risk

:::warning Honest status

This page describes a public portfolio repository exploring ML architecture for operational delay-risk signals. It is not a claim of live production deployment, client engagement, or verified business outcomes unless explicitly documented in that repository's releases and evidence artifacts.

Verify implementation status in the repository README, tags, and CI—not from this summary alone.

:::

Problem

Distribution and fulfillment operations need early signals when orders or lines are likely to miss promise dates—so planners can intervene, reprioritize, or communicate proactively.

Environment

Portfolio-safe context only: ERP-adjacent order and fulfillment signals represented with synthetic or sanitized samples in the public repository. No named client, employer, or private warehouse telemetry.

Constraints

• Labels must align to operational truth (promise vs. actual), not convenience proxies
• Feature timestamps must respect causality (no leakage from future events)
• Coverage must be inspectable across product, channel, and location segments
• Humans remain the authority when the model disagrees with experienced planners
• Public artifacts must never include private ERP extracts

Architecture

ERP-adjacent sources (sanitized / synthetic in public repo)
           |
   Feature engineering pipeline
           |
   Train / evaluate / register model
           |
     +-----+-----+
     |           |
Batch scoring   Real-time scoring API
     |           |
Ops report      App / agent consumer (authorized)
           |
   Human review + override + evidence log

Public repo code may implement subsets of this reference—treat each component as present only if evidenced in repo.

Architectural components (target vocabulary)

Component	Purpose
Feature store or feature scripts	Reproducible transforms with version pins
Training pipeline	Repeatable splits, metrics, artifact output
Evaluation report	Precision/recall or ranking metrics with segment cuts
Model registry (logical)	Version, data snapshot ID, metrics, limitations
Batch scoring job	Scheduled risk scores for planning dashboards
Scoring API	Low-latency inference with schema validation
Executive / ops report	Summarized risk bands—not raw model dumps

Data and labeling discipline

Portfolio-safe practices reflected in this architecture:

• Labels derived from promise date vs. actual ship/complete—with explicit grace rules
• Features restricted to information available at decision time
• Exclusions documented (partial shipments, manual overrides, data gaps)
• Synthetic or sanitized data in public artifacts—never private ERP extracts

Delivery modes

Batch

Suitable for daily planner workflows and email/BI consumption. Emphasizes throughput and audit snapshots.

Real-time (API)

Suitable for cart or order-entry assist only with human oversight and fallback when features are missing.

Both modes require schema validation, model version headers, and structured error responses.

Tradeoffs

Choice	Benefit	Cost
Batch scoring first	Audit snapshots, planner-friendly cadence	Less useful for cart-time assist
Real-time API later	Low-latency assist for authorized apps	Feature completeness and fallback complexity
Human override + evidence log	Recoverability and trust	Throughput and workflow design cost
Synthetic/sanitized public data	Privacy-safe portfolio proof	Not a substitute for production validation

Alternatives considered

• Rules-only delay flags — faster to ship, weaker generalization across segments
• Black-box vendor scoring — less inspectable features, harder human override story
• Full autonomous replanning — rejected; risk scores inform humans, they do not decide

Outcome

Public portfolio status only: architecture and code evidence live in the repository. No production ROI, client outcome, or deployment claim is asserted on this page. Treat outcomes as evidence-gated—present only when releases, tests, and docs in the repo support them.

Lessons learned

• Label and causality discipline matter more than model novelty for operational trust
• Segment-weak coverage is a first-class failure mode, not a footnote
• Readiness and data quality gates (AI Readiness Diagnostic, ERP Data Quality) reduce wasted ML spend
• Portfolio pages must separate intended architecture from implemented evidence

Evaluation honesty

Public portfolio standards require:

• Metrics reported on held-out data with time-aware splits where applicable
• Segment analysis (product family, region, carrier) to expose weak coverage
• Documented limitations and known failure modes
• No implied ROI unless computed from public, reproducible sample data labeled as illustrative

What this repository is not

• Not proof of deployment inside any named client or employer environment
• Not a substitute for ERP data-quality remediation
• Not an autonomous decision engine—risk scores inform humans
• Not using private BBIOS, Nova, or confidential operational telemetry

Relationship to other portfolio work

• ERP Data Quality — prerequisites for features and labels
• AI Readiness Diagnostic — readiness gate before implementation spend
• Deployment and Observability — when API or batch jobs exist in repo

Next steps for reviewers

1. Clone the repository and read the README limitation section
2. Run documented tests or sample pipeline if provided
3. Check release tags and open issues for deployment status
4. Contrast this architecture page with what is actually implemented

External link

• ERP AI Delay Risk on GitHub