AI/ML Systems
End-to-end ML system patterns for operational intelligence—feature engineering, evaluation, delivery, and honest status labeling.
End-to-end ML system patterns for operational intelligence—feature engineering, evaluation, delivery, and honest status labeling.
How doctoral research on ERP implementation strategies in manufacturing SMEs informs enterprise architecture, AI readiness, and governed operational systems.
Foundations for trustworthy ERP data—master data discipline, transaction hygiene, and evidence for exceptions before analytics or AI.
Public portfolio ML architecture for operational delay-risk prediction—capabilities, boundaries, and honest deployment status.
Sanitized architecture and operating patterns for mid-market ERP modernization—without client, employer, or confidential implementation details.
Sanitized FIFO and inventory costing patterns for mid-market ERP—layer integrity, valuation alignment, and reconciliation discipline.
Sanitized warehouse and supply-chain patterns for mid-market ERP—barcode discipline, transaction timing, and location integrity.