GovSignal-Connect (The Readiness Protocol)

Autonomous Procurement Signals for Legacy ERPs via Federal Data Feeds

Reference Implementation for the Technical White Paper: "The Readiness Protocol: Autonomous Capital Synchronization for Critical Infrastructure Supply Chains" (2026).

1. Overview

GovSignal-Connect is a technical proof-of-concept demonstrating how autonomous agents can bridge the "Inventory Lag" gap in critical industries. It serves as the "Smart Overlay" architecture described in the Readiness Protocol research.

Legacy ERP systems (SAP, Oracle) are historically reactive—optimizing inventory based on past consumption. In high-volatility sectors like Defense and Semiconductors, this reactivity leads to supply shortages during rapid policy shifts. This project implements the Strategic Procurement Agent ("The Scout"), a predictive layer that monitors unstructured government data streams (SAM.gov, Federal Register) to trigger pre-emptive capital release.

2. Simulation Results & Validation

To rigorously validate the "Readiness Protocol," we conducted a series of Monte Carlo simulations (N=1000) comparing the Agentic Overlay against industry-standard baselines. These tests confirm the protocol's superiority in high-uncertainty environments.

2.1 Latency Reduction (V1)

Goal: Quantify the speed advantage of signal-based triggering.

• Result: The protocol reduced effective lead times for critical assets from 12 months to 3 months (75% reduction).
• Metric: Average Days to Delivery.
• 

2.2 Baseline Superiority (V2)

Goal: Compare against robust inventory policies: Legacy ERP, (s, S) Policy, and a Perfect Forecast Oracle.

• Result: While the Readiness Protocol (Policy B) incurs higher holding costs (~$3.6M) due to strategic buffers, it achieves a 98.9% Service Level with half the backorder duration (11.7 days vs 22.4 days) of the Legacy Policy.
• Insight: It closely approximates the "Perfect Oracle's" availability curve (100%) in a way that reactive policies (s, S) cannot.
• 

2.3 Signal Sensitivity "Break-even" (V3)

Goal: Determine the required reliability of the Scout Agent (False Alarm analysis).

• Result: The Readiness Protocol becomes cheaper than the Legacy Policy once the Scout Agent's Signal Precision exceeds 0.77.
• Implication: If the AI agent is correct >77% of the time, the "Resilience Premium" pays for itself through avoided stockout penalties.
• 

2.4 Agent Component Validation (V4)

Goal: Isolate the value of individual agents via Ablation Study.

• Result: The "Full System" (Scout + Inventory + Credit Agents) achieved the highest Capital Utilization Efficiency (5.15x).
• Comparison:
  • Full System: 5.15x (Best Balance)
  • No Credit Constraint: 4.35x (Inefficient Overstocking)
  • Heuristic Only: 3.76x (Inflexible)
• 

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3. How to Reproduce Results

This repository contains the exact simulation scripts used to generate the data for the technical report.

Prerequisites:

• Python 3.10+
• uv package manager (recommended) or standard pip.

Step 1: Setup Environment

scripts\setup_env.bat

Step 2: Run the Monte Carlo Simulation To generate the graphs and calculating the ROI/Latency metrics:

python readiness_simulation.py

Outputs will be saved to the output/ directory.

Step 3: Run the Live Scout Agent To run the NLP surveillance agent against live/mock federal data:

scripts\run_scout.bat

4. System Architecture

GovSignal-Connect operates as a Distributed Service Architecture.

4.1 System Context Diagram

graph TD
    subgraph External_Data [External Data Environment]
        SAM[SAM.gov]
        FR[Federal Register]
        CHIPS[CHIPS.gov Funding Portal]
    end

    subgraph GovSignal [GovSignal-Connect - The Smart Overlay]
        Scout[The Scout - External Signal]
        Inventory[Inventory Module - Stock Analysis]
        Credit[Credit Module - Capital Release]
    end

    subgraph Enterprise [Enterprise Core]
        ERP[Legacy ERP - SAP S/4HANA / Oracle]
        SCM[Supply Chain Planning]
    end

    SAM --> Scout
    FR --> Scout
    CHIPS --> Scout
    
    Scout -- Standardized Demand Signal --> Inventory
    Inventory -- Stock Low Alert --> Credit
    Inventory -- Stock Healthy --> Scout
    
    Credit -- Authorization Token --> ERP
    ERP -- Purchase Order Created --> SCM

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4.2 Data Flow

The system follows a strict unidirectional data flow to ensure signal integrity:

1. Ingestion (The Scout): The Scout continuously polls unstructured federal feeds (SAM.gov, Federal Register, 20+ State Sources).
2. Normalization: Raw text is converted into a Standard Signal JSON payload.
3. Internal Logic: The standardized signal is ingested by the ERP via REST or IDoc interfaces.

5. Use Cases

Case A: Semiconductor Supply Chain

• Trigger: The Federal Register publishes a "CHIPS Act Funding Opportunity."
• Action: The Scout predicts demand for High-Vacuum Chambers and signals the ERP to "Release Capital Hold," securing inventory before market saturation.

Case B: Defense Industrial Base

• Trigger: SAM.gov lists a "DoD Solicitation" for "Electronic Warfare Readiness."
• Action: Immediate recommendation to increase stock levels for TWT Amplifiers.

6. Documentation

Category	Document	Description
Core	Problem Statement	Academic definition of the inventory lag problem.
Methods	NLP Methodology	Mathematical definition of the KDA scoring algorithm.
Security	Threat Model	Assessment of adversarial risks (poisoning, DoS).
Sim Data	Simulation Report	Full breakdown of the Monte Carlo analysis.

📄 Technical Report & Citation

The architectural framework and simulation results for this system are detailed in our Technical Report, released via the Zenodo Open Science Repository.

Read the full report: The Readiness Protocol: Autonomous Capital Synchronization for Critical Infrastructure Supply Chains

If you use this codebase in your research or deployment, please cite:

Thatikonda, R. K., & Donepudi, S. (2026). The Readiness Protocol: Autonomous Capital Synchronization for Critical Infrastructure Supply Chains. Zenodo. https://doi.org/10.5281/zenodo.18293591