🔗 Live Platform & Citation Resource Link Live Platform https://fahimkazmi911.github.io/NPAORF-Platform/ White Paper (Zenodo) DOI: 10.5281/zenodo.19310969 Cite this repository See CITATION.cff

The Problem This Solves As of Q1 2026, the American Society of Health-System Pharmacists (ASHP) documents 323 active drug shortages imposing an estimated 900ドル million annual burden on U.S. healthcare systems. FDA Essential Medicines Supply Chain analysis attributes 40% of these shortages directly to manufacturing equipment failures — a mechanistic, predictable, and therefore preventable root cause. Executive Order 14017 ("America's Supply Chains", February 24, 2021) formally identified pharmaceutical manufacturing equipment reliability as a national security vulnerability requiring systemic federal intervention. NPAORF is the open-source technical instrument designed to operationalize that mandate at the facility level.

Why Existing Solutions Fail No currently available commercial platform simultaneously satisfies pharmaceutical manufacturing's requirements: Platform HW-Agnostic GxP Native 21 CFR Pt 11 Pharma ML Shortage Link Open Access GE Predix ✗ ✗ ✗ ✗ ✗ ✗ Siemens MindSphere ✗ ✗ ✗ ✗ ✗ ✗ PerkinElmer Signals ✗ Partial Partial Partial ✗ ✗ IBM Maximo APM ✗ ✗ ✗ ✗ ✗ ✗ Aspentech Mtell Partial ✗ ✗ ✗ ✗ ✗ NPAORF (this work) ✓ ✓ ✓ ✓ ✓ ✓

Raw Sensor Telemetry (1–100 Hz)
 ↓
 ┌─────────────────────────────────────────┐
 │ Feature Engineering Pipeline │
 │ Time-domain (40+) │ FFT (50+) │ DWT (40+) │
 └─────────────────────────────────────────┘
 ↓
 ┌─────────────────────────────────────────┐
 │ Kazmi MethodologyTM Ensemble │
 │ XGBoost (0.55) + LSTM (0.45) │
 └─────────────────────────────────────────┘
 ↓
 ┌─────────────────────────────────────────┐
 │ SHAP Attribution → Explainable Alert │
 └─────────────────────────────────────────┘
 ↓
 ┌─────────────────────────────────────────┐
 │ 21 CFR Part 11 SHA-256 Audit Trail │
 └─────────────────────────────────────────┘

Why XGBoost + LSTM? Non-stationarity: Pharmaceutical sensor data shifts with batch recipes and maintenance events. LSTM's gated memory cells handle this without preprocessing. Failure sparsity: Failures comprise <0.1% of sensor records. XGBoost with SMOTE augmentation handles extreme class imbalance far more robustly than end-to-end deep learning. Interpretability: GxP frameworks require explainable predictions. XGBoost's SHAP attribution generates per-prediction feature importance scores that satisfy 21 CFR Part 11 audit requirements. Feature Engineering Rationale Domain Features Rationale Time-domain 40+ Statistical moments, rate-of-change, RMS, crest factor FFT (frequency) 50+ Fault-characteristic frequencies (BPFI/BPFO), gear mesh, motor harmonics DWT (time-frequency) 40+ Transient fault signatures localized in time — inaccessible to FFT alone Prototype Validation Results Metric Value Notes Binary Classification Accuracy 94.7% 5-fold time-series cross-validation Average Prediction Lead Time 14.2 days Mean time before simulated failure False Positive Rate 2.3% 20% held-out test partition F1 Score (failure class) 0.943 Harmonic mean of precision/recall Inference Latency <50 ms Consumer-grade hardware Validated on simulated pharmaceutical sensor data using published reliability profiles. See Appendix C of the white paper for full simulation methodology disclosure.

Platform Features (v4.0) National Surveillance Dashboard — Geographic shortage concentration mapping across 6 U.S. manufacturing states with live categorical and risk distribution analysis Universal Equipment Configuration — Hardware-agnostic vendor selection across Bioreactors (Thermo Fisher, Sartorius, Cytiva, Eppendorf), Chromatography (Agilent, Waters, Shimadzu), ULT Freezers (PHCbi, Eppendorf), and Filling Lines (Bosch/Syntegon, IMA Life) Economic Impact Calculator — Interactive deployment scenario modeling with FDA/ASHP 2026 baseline parameters 21 CFR Part 11 Audit Log — SHA-256 cryptographically chained event log with ALCOA+ data integrity and electronic signature workflows FDA/EMA AI Credibility Framework — Built-in compliance guide aligned with EMA/83337/2023

Tech Stack Frontend: Vanilla JavaScript (ES6+), HTML5, CSS3 UI Framework: Tailwind CSS Charts: Chart.js v4.4.1 Icons: Lucide Compliance: SHA-256 cryptographic audit trails (native browser implementation) Deployment: GitHub Pages (static, no backend required)

Supported Equipment Vendors Category Vendors Bioreactors Thermo Fisher HyPerformaTM SUB, Sartorius BIOSTAT STR®, Cytiva XcellerexTM XDR, Eppendorf BioFlo 320, Applikon my-Control Chromatography Agilent 1260 Infinity II, Waters ACQUITY UPLC, Shimadzu Nexera, Cytiva ÄKTA pure ULT Freezers Thermo Fisher TSX Series, PHCbi VIP ECO, Eppendorf CryoCube Filling Lines Bosch/Syntegon, IMA Life, Bausch+Ströbel, Groninger

GET https://fahimkazmi911.github.io/NPAORF-Platform/api/shortage-data.json

Returns structured shortage data based on ASHP/FDA January 2026 baseline. See /api/ directory for schema documentation.

Kazmi, S.F.A. (2026). NPAORF: A Hardware-Agnostic Infrastructure for Mitigating 
2026 Drug Shortages via GxP-Compliant Predictive Maintenance. Technical White Paper 
v2.0. Zenodo. https://doi.org/10.5281/zenodo.19310968

For citing this repository specifically, see CITATION.cff.

Current Status & Roadmap Current: Functional prototype — web-based demonstration interface with simulated sensor data. Next milestones: [ ] Live sensor network integration at pilot pharmaceutical facility [ ] IQ/OQ/PQ validation under 21 CFR Part 11 [ ] Peer-reviewed publication of live-deployment validation results [ ] Federal pilot deployment discussions (FDA/HHS/DHS CISA)

Collaboration & Contact The author invites collaboration with: Federal agencies (FDA, HHS, DHS CISA) for pilot deployment within Essential Medicines manufacturing infrastructure Pharmaceutical manufacturers seeking facility-level predictive maintenance implementation Equipment vendors interested in Kazmi MethodologyTM integration partnerships Academic institutions for co-validation studies and peer-reviewed publication

Author Syed Fahim Abbas Kazmi, M.S. Lead Architect & Principal Investigator, NPAORF M.S. Business Analytics, Temple University (4.0 GPA) 5+ years pharmaceutical R&D asset optimization — GlaxoSmithKline global operations ORCID: https://orcid.org/0009-0000-5075-9638

License This work is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). You are free to share and adapt this work for any purpose, provided appropriate credit is given, a link to the license is provided, and any changes are indicated.