Build a National Resilience & Supply Chain Stability Platform with Cortex AI

Snowflake for Developers/Guides/Build a National Resilience & Supply Chain Stability Platform with Cortex AI

Quickstart

Build a National Resilience & Supply Chain Stability Platform with Cortex AI

Marya Mufti (@maryamufti18)

Gain visibility into critical supply chains, model disruption scenarios, and coordinate response across agencies to strengthen national infrastructure resilience.

Overview

Government supply chains span suppliers, sub-tier manufacturers, logistics corridors, and partner agencies. A disruption at a single facility can affect multiple critical programs. This guide builds a reference platform for Pacific Shield Logistics Command, a fictional federal coordination unit. All data in this tutorial is synthetic.

What You'll Learn

Organize supply chain data in Snowflake.
Build governed views that highlight supplier concentration, shipment delays, and quality incidents.
Model facility disruptions and generate analyst-facing explanations with Cortex AI.
Prepare a supplier-risk modeling workflow with Snowpark ML.
Explore ways to expose governed data through a Cortex Agent and Streamlit.

What You'll Build

A supply chain data foundation.
A unified operating-picture view and a facility-disruption scenario view.
Sample data for programs, suppliers, procurement, logistics, telemetry, and quality incidents.
An optional Snowpark ML training workflow.
A Streamlit dashboard example.

What You'll Need

A Snowflake account and permission to create databases, schemas, tables, views, and a warehouse.
Access to the Cortex AI, Snowpark ML, Cortex Agents, and Streamlit features you choose to use. Availability varies by account, region, and edition.
Familiarity with SQL and basic Python.
A role authorized to create and use the objects in this guide. Use least-privilege roles in production.

Setup and Data Foundation

Create a database, schema, and warehouse for the demo. Run the SQL in a Snowflake worksheet using a role with the required privileges.

CREATE DATABASE IF NOT EXISTS PACIFIC_SHIELD_DEMO;
CREATE SCHEMA IF NOT EXISTS PACIFIC_SHIELD_DEMO.SUPPLY_CHAIN;

CREATE WAREHOUSE IF NOT EXISTS NEXUS_WH
  WAREHOUSE_SIZE = 'MEDIUM'
  AUTO_SUSPEND = 60
  AUTO_RESUME = TRUE
  INITIALLY_SUSPENDED = TRUE;

USE DATABASE PACIFIC_SHIELD_DEMO;
USE SCHEMA SUPPLY_CHAIN;
USE WAREHOUSE NEXUS_WH;

CREATE STAGE IF NOT EXISTS AUDIT_DOCUMENTS
  ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE');

Create the Core Tables

These tables represent the core entities used in the demo. In a production deployment, replace the synthetic data with governed ingestion pipelines from authorized source systems.

CREATE OR REPLACE TABLE PROGRAMS (
  PROGRAM_ID VARCHAR(20) PRIMARY KEY,
  PROGRAM_NAME VARCHAR(200),
  SECTOR VARCHAR(30),
  AGENCY VARCHAR(200),
  CRITICALITY VARCHAR(20)
);

CREATE OR REPLACE TABLE SUPPLIERS (
  SUPPLIER_ID VARCHAR(20) PRIMARY KEY,
  SUPPLIER_NAME VARCHAR(200),
  COUNTRY VARCHAR(100),
  REGION VARCHAR(100),
  SECTOR VARCHAR(30),
  TIER NUMBER(1),
  IS_SINGLE_SOURCE BOOLEAN,
  RISK_SCORE NUMBER(5,1),
  LAST_INCIDENT_DATE DATE
);

CREATE OR REPLACE TABLE BILL_OF_MATERIALS (
  BOM_ID VARCHAR(20) PRIMARY KEY,
  PROGRAM_ID VARCHAR(20),
  COMPONENT_ID VARCHAR(40),
  COMPONENT_NAME VARCHAR(200),
  SUPPLIER_ID VARCHAR(20),
  FACILITY_ID VARCHAR(20),
  FACILITY_COUNTRY VARCHAR(100),
  CRITICALITY VARCHAR(20),
  LEAD_TIME_DAYS NUMBER(5,0),
  ALT_SUPPLIER_ID VARCHAR(20),
  ALT_LEAD_TIME_DAYS NUMBER(5,0),
  ALT_CAPACITY_PCT NUMBER(5,1)
);

CREATE OR REPLACE TABLE PROCUREMENT_RECORDS (
  PROCUREMENT_ID VARCHAR(20) PRIMARY KEY,
  PROGRAM_ID VARCHAR(20),
  SUPPLIER_ID VARCHAR(20),
  COMPONENT_ID VARCHAR(40),
  ORDER_DATE DATE,
  CONTRACT_VALUE NUMBER(14,2),
  QUANTITY NUMBER(12,0),
  PROCUREMENT_STATUS VARCHAR(30)
);

CREATE OR REPLACE TABLE LOGISTICS_EVENTS (
  SHIPMENT_ID VARCHAR(20) PRIMARY KEY,
  PROCUREMENT_ID VARCHAR(20),
  SUPPLIER_ID VARCHAR(20),
  ORIGIN_REGION VARCHAR(100),
  DESTINATION_REGION VARCHAR(100),
  STATUS VARCHAR(20),
  EXPECTED_DATE DATE,
  ACTUAL_DATE DATE,
  DELAY_DAYS NUMBER(5,0)
);

CREATE OR REPLACE TABLE IOT_SENSOR_READINGS (
  READING_ID VARCHAR(20) PRIMARY KEY,
  SHIPMENT_ID VARCHAR(20),
  READING_TS TIMESTAMP_NTZ,
  TEMPERATURE_C NUMBER(5,2),
  HUMIDITY_PCT NUMBER(5,2),
  SENSOR_STATUS VARCHAR(20)
);

CREATE OR REPLACE TABLE QUALITY_INCIDENTS (
  INCIDENT_ID VARCHAR(20) PRIMARY KEY,
  SUPPLIER_ID VARCHAR(20),
  COMPONENT_ID VARCHAR(40),
  INCIDENT_DATE DATE,
  SEVERITY VARCHAR(20),
  DESCRIPTION VARCHAR(500),
  RESOLUTION_STATUS VARCHAR(30)
);

CREATE OR REPLACE TABLE MARKETPLACE_RISK_FEED (
  FEED_ID VARCHAR(20) PRIMARY KEY,
  REGION VARCHAR(100),
  RISK_TYPE VARCHAR(30),
  RISK_SCORE NUMBER(5,1),
  FEED_DATE DATE,
  DESCRIPTION VARCHAR(500)
);

CREATE OR REPLACE TABLE SUPPLIER_FAILURE_LABELS (
  SUPPLIER_ID VARCHAR(20) PRIMARY KEY,
  FAILED_WITHIN_90D BOOLEAN
);

Insert Sample Data

The following sample rows are illustrative and intentionally small so you can follow the joins and scenario logic. They do not represent real suppliers, contracts, or government programs.

INSERT INTO PROGRAMS VALUES
  ('PRG-DEF-01', 'Secure Communications Modernization', 'DEFENSE', 'Agency North', 'HIGH'),
  ('PRG-ENE-01', 'Grid Resilience Initiative', 'ENERGY', 'Agency East', 'HIGH'),
  ('PRG-MED-01', 'Emergency Medical Readiness', 'MEDICAL', 'Agency Health', 'HIGH'),
  ('PRG-DEF-02', 'Advanced Sensor Program', 'DEFENSE', 'Agency North', 'MEDIUM');

INSERT INTO SUPPLIERS VALUES
  ('SUP-001', 'Northstar Components', 'United States', 'North America', 'DEFENSE', 1, TRUE, 82, '2026-08-14'),
  ('SUP-002', 'Pacific Micro Devices', 'Taiwan', 'East Asia', 'ELECTRONICS', 2, TRUE, 91, '2026-09-02'),
  ('SUP-003', 'Cedar Medical Supply', 'United States', 'North America', 'MEDICAL', 1, FALSE, 35, NULL),
  ('SUP-004', 'Blue River Logistics', 'United States', 'North America', 'LOGISTICS', 2, FALSE, 48, NULL),
  ('SUP-005', 'Atlas Alternate Manufacturing', 'Japan', 'East Asia', 'ELECTRONICS', 2, FALSE, 29, NULL);

INSERT INTO BILL_OF_MATERIALS VALUES
  ('BOM-001', 'PRG-DEF-01', 'COMP-CPU-01', 'Secure compute module', 'SUP-002', 'TW-FAB-01', 'Taiwan', 'HIGH', 21, 'SUP-005', 35, 60),
  ('BOM-002', 'PRG-ENE-01', 'COMP-CPU-01', 'Grid controller compute module', 'SUP-002', 'TW-FAB-01', 'Taiwan', 'HIGH', 21, 'SUP-005', 35, 60),
  ('BOM-003', 'PRG-MED-01', 'COMP-SENS-01', 'Cold-chain sensor', 'SUP-001', 'US-FAC-02', 'United States', 'HIGH', 10, 'SUP-005', 15, 80),
  ('BOM-004', 'PRG-DEF-02', 'COMP-CPU-01', 'Advanced sensor compute module', 'SUP-002', 'TW-FAB-01', 'Taiwan', 'HIGH', 21, 'SUP-005', 35, 60),
  ('BOM-005', 'PRG-MED-01', 'COMP-MED-01', 'Sterile medical kit', 'SUP-003', 'US-FAC-03', 'United States', 'HIGH', 7, 'SUP-001', 12, 50);

INSERT INTO PROCUREMENT_RECORDS VALUES
  ('PO-001', 'PRG-DEF-01', 'SUP-002', 'COMP-CPU-01', '2026-08-01', 250000, 1000, 'OPEN'),
  ('PO-002', 'PRG-ENE-01', 'SUP-002', 'COMP-CPU-01', '2026-08-05', 180000, 700, 'OPEN'),
  ('PO-003', 'PRG-MED-01', 'SUP-003', 'COMP-MED-01', '2026-08-10', 45000, 500, 'FULFILLED'),
  ('PO-004', 'PRG-DEF-02', 'SUP-002', 'COMP-CPU-01', '2026-08-12', 125000, 400, 'OPEN');

INSERT INTO LOGISTICS_EVENTS VALUES
  ('SHIP-001', 'PO-001', 'SUP-002', 'East Asia', 'North America', 'DELAYED', '2026-09-01', '2026-09-08', 7),
  ('SHIP-002', 'PO-002', 'SUP-002', 'East Asia', 'North America', 'AT_RISK', '2026-09-04', NULL, 3),
  ('SHIP-003', 'PO-003', 'SUP-003', 'North America', 'North America', 'DELIVERED', '2026-09-06', '2026-09-06', 0),
  ('SHIP-004', 'PO-004', 'SUP-002', 'East Asia', 'North America', 'DELAYED', '2026-09-10', '2026-09-15', 5);

INSERT INTO IOT_SENSOR_READINGS VALUES
  ('READ-001', 'SHIP-001', '2026-09-08 10:00:00', 8.4, 55, 'WARNING'),
  ('READ-002', 'SHIP-001', '2026-09-08 11:00:00', 9.1, 58, 'WARNING'),
  ('READ-003', 'SHIP-003', '2026-09-06 12:00:00', 4.2, 45, 'NORMAL');

INSERT INTO QUALITY_INCIDENTS VALUES
  ('QI-001', 'SUP-002', 'COMP-CPU-01', DATEADD(day, -12, CURRENT_DATE()), 'HIGH', 'Component inspection failure', 'OPEN'),
  ('QI-002', 'SUP-002', 'COMP-CPU-01', DATEADD(day, -42, CURRENT_DATE()), 'MEDIUM', 'Batch documentation discrepancy', 'UNDER_REVIEW'),
  ('QI-003', 'SUP-003', 'COMP-MED-01', DATEADD(day, -110, CURRENT_DATE()), 'LOW', 'Packaging label correction', 'CLOSED');

INSERT INTO MARKETPLACE_RISK_FEED VALUES
  ('RISK-001', 'East Asia', 'GEOPOLITICAL', 78, CURRENT_DATE(), 'Illustrative regional shipping and export-control uncertainty.'),
  ('RISK-002', 'North America', 'WEATHER', 42, CURRENT_DATE(), 'Illustrative weather disruption risk.'),
  ('RISK-003', 'East Asia', 'COMMODITY', 69, CURRENT_DATE(), 'Illustrative semiconductor material price volatility.');

INSERT INTO SUPPLIER_FAILURE_LABELS VALUES
  ('SUP-001', FALSE),
  ('SUP-002', TRUE),
  ('SUP-003', FALSE),
  ('SUP-004', FALSE),
  ('SUP-005', FALSE);

Verify the Data

SELECT 'PROGRAMS' AS TABLE_NAME, COUNT(*) AS ROW_COUNT FROM PROGRAMS
UNION ALL SELECT 'SUPPLIERS', COUNT(*) FROM SUPPLIERS
UNION ALL SELECT 'BILL_OF_MATERIALS', COUNT(*) FROM BILL_OF_MATERIALS
UNION ALL SELECT 'PROCUREMENT_RECORDS', COUNT(*) FROM PROCUREMENT_RECORDS
UNION ALL SELECT 'LOGISTICS_EVENTS', COUNT(*) FROM LOGISTICS_EVENTS
UNION ALL SELECT 'IOT_SENSOR_READINGS', COUNT(*) FROM IOT_SENSOR_READINGS
UNION ALL SELECT 'QUALITY_INCIDENTS', COUNT(*) FROM QUALITY_INCIDENTS;

Unified Operating Picture (Governed Views)

Planners need a single place to see supplier concentration, delayed shipments, and recent quality signals. Aggregate each fact table before joining it to avoid multiplying counts when a supplier has multiple shipments and incidents.

CREATE OR REPLACE VIEW VW_UNIFIED_OPERATING_PICTURE AS
WITH bom_agg AS (
  SELECT SUPPLIER_ID,
         COUNT(DISTINCT CASE WHEN CRITICALITY = 'HIGH' THEN PROGRAM_ID END) AS CRITICAL_PROGRAMS
  FROM BILL_OF_MATERIALS
  GROUP BY SUPPLIER_ID
),
logistics_agg AS (
  SELECT SUPPLIER_ID,
         COUNT(DISTINCT CASE WHEN STATUS IN ('DELAYED', 'AT_RISK') THEN SHIPMENT_ID END) AS DELAYED_SHIPMENTS
  FROM LOGISTICS_EVENTS
  GROUP BY SUPPLIER_ID
),
quality_agg AS (
  SELECT SUPPLIER_ID,
         COUNT(DISTINCT CASE WHEN INCIDENT_DATE >= DATEADD(day, -90, CURRENT_DATE()) THEN INCIDENT_ID END) AS INCIDENTS_90D
  FROM QUALITY_INCIDENTS
  GROUP BY SUPPLIER_ID
)
SELECT s.SUPPLIER_ID,
       s.SUPPLIER_NAME,
       s.SECTOR,
       s.TIER,
       s.IS_SINGLE_SOURCE,
       s.RISK_SCORE,
       CASE WHEN s.IS_SINGLE_SOURCE THEN COALESCE(b.CRITICAL_PROGRAMS, 0) ELSE 0 END AS SINGLE_SOURCE_CRITICAL_PROGRAMS,
       COALESCE(l.DELAYED_SHIPMENTS, 0) AS DELAYED_OR_AT_RISK_SHIPMENTS,
       COALESCE(q.INCIDENTS_90D, 0) AS QUALITY_INCIDENTS_LAST_90_DAYS
FROM SUPPLIERS s
LEFT JOIN bom_agg b ON b.SUPPLIER_ID = s.SUPPLIER_ID
LEFT JOIN logistics_agg l ON l.SUPPLIER_ID = s.SUPPLIER_ID
LEFT JOIN quality_agg q ON q.SUPPLIER_ID = s.SUPPLIER_ID;

Query the view:

SELECT *
FROM VW_UNIFIED_OPERATING_PICTURE
ORDER BY RISK_SCORE DESC;

For continuously refreshed data, you can use a Dynamic Table. Dynamic Table support and refresh behavior depend on the query and Snowflake account configuration; validate this statement in your environment before using it.

CREATE OR REPLACE DYNAMIC TABLE DT_UNIFIED_OPERATING_PICTURE
  TARGET_LAG = '1 hour'
  WAREHOUSE = NEXUS_WH
AS
SELECT * FROM VW_UNIFIED_OPERATING_PICTURE;

Disruption Scenario Simulation (Cortex AI)

The scenario view traces each component at a facility to the programs that depend on it and compares normal lead time with the alternative supplier's lead time.

CREATE OR REPLACE VIEW VW_DISRUPTION_SCENARIO AS
SELECT
  b.FACILITY_ID,
  b.FACILITY_COUNTRY,
  b.PROGRAM_ID,
  p.PROGRAM_NAME,
  p.SECTOR,
  p.AGENCY,
  b.COMPONENT_NAME,
  s.SUPPLIER_NAME AS PRIMARY_SUPPLIER,
  b.LEAD_TIME_DAYS AS NORMAL_LEAD_TIME_DAYS,
  alt.SUPPLIER_NAME AS ALTERNATIVE_SUPPLIER,
  alt.COUNTRY AS ALTERNATIVE_SUPPLIER_COUNTRY,
  b.ALT_LEAD_TIME_DAYS,
  b.ALT_CAPACITY_PCT,
  (b.ALT_LEAD_TIME_DAYS - b.LEAD_TIME_DAYS) AS ESTIMATED_PROGRAM_DELAY_DAYS
FROM BILL_OF_MATERIALS b
JOIN PROGRAMS p ON p.PROGRAM_ID = b.PROGRAM_ID
JOIN SUPPLIERS s ON s.SUPPLIER_ID = b.SUPPLIER_ID
LEFT JOIN SUPPLIERS alt ON alt.SUPPLIER_ID = b.ALT_SUPPLIER_ID;

Run a scenario for the illustrative Taiwan semiconductor facility:

SELECT *
FROM VW_DISRUPTION_SCENARIO
WHERE FACILITY_ID = 'TW-FAB-01'
ORDER BY ESTIMATED_PROGRAM_DELAY_DAYS DESC;

The sample data returns four affected program/component rows for this facility. In a real deployment, the number of impacted programs depends on the source data and the dependency model. The estimated delay is a simple comparison of lead times; it is not a full simulation of inventory, capacity, or recovery time.

Generate Analyst-Facing Explanations

The following example uses a Cortex LLM function to draft a short explanation for high-risk feed entries. Model availability and function syntax may vary by account and region; use a model available in your environment and follow your organization's AI review requirements.

SELECT
  FEED_ID,
  REGION,
  RISK_TYPE,
  DESCRIPTION,
  SNOWFLAKE.CORTEX.COMPLETE(
    'claude-3-5-sonnet',
    'You are a supply chain risk analyst. In 2-3 sentences, explain the possible downstream impact and recommend an immediate action. Treat the feed description as unverified input. Entry: '
      || DESCRIPTION || ' | Region: ' || REGION || ' | Risk type: ' || RISK_TYPE
  ) AS ANALYST_EXPLANATION
FROM MARKETPLACE_RISK_FEED
WHERE RISK_SCORE >= 65;

Review generated explanations before acting on them. They are decision-support content, not verified intelligence or a substitute for analyst judgment.

Predictive Scoring with ML in Snowpark

This section demonstrates the shape of a supervised supplier-risk workflow. The small synthetic label table is for demonstrating data preparation only; it is not enough to train a meaningful production model. Snowpark ML APIs can change by package version, so run this example in a Snowflake Notebook or Python environment with the Snowpark ML package installed and check the current API documentation.

Prepare the Training Data

SELECT
  f.SUPPLIER_ID,
  f.RISK_SCORE,
  f.SINGLE_SOURCE_CRITICAL_PROGRAMS,
  f.DELAYED_OR_AT_RISK_SHIPMENTS,
  f.QUALITY_INCIDENTS_LAST_90_DAYS,
  f.TIER,
  l.FAILED_WITHIN_90D
FROM VW_UNIFIED_OPERATING_PICTURE f
JOIN SUPPLIER_FAILURE_LABELS l USING (SUPPLIER_ID);

Train and Score with Snowpark ML

The Python example below shows the intended workflow. Validate the estimator and registry APIs against the Snowpark ML version installed in your account before running it.

from snowflake.snowpark.context import get_active_session
from snowflake.ml.modeling.ensemble import GradientBoostingClassifier

session = get_active_session()

feature_columns = [
    "RISK_SCORE",
    "SINGLE_SOURCE_CRITICAL_PROGRAMS",
    "DELAYED_OR_AT_RISK_SHIPMENTS",
    "QUALITY_INCIDENTS_LAST_90_DAYS",
    "TIER",
]

training_df = (
    session.table("VW_UNIFIED_OPERATING_PICTURE")
    .join(session.table("SUPPLIER_FAILURE_LABELS"), on="SUPPLIER_ID")
    .select(
        "SUPPLIER_ID",
        *feature_columns,
        "FAILED_WITHIN_90D",
    )
)

# This sample dataset is intentionally tiny. Use a larger, representative
# labeled history and a holdout set before evaluating a real model.
model = GradientBoostingClassifier(
    input_cols=feature_columns,
    label_cols=["FAILED_WITHIN_90D"],
    output_cols=["PREDICTION"],
)
model.fit(training_df)

scored_df = model.predict(training_df)
scored_df.write.save_as_table(
    "SUPPLIER_FAILURE_PREDICTIONS",
    mode="overwrite",
)

This is a starting point, not a validated risk model. For production, define a time-aware train/test split, assess class imbalance and model quality, document features, monitor drift, and register versioned models using the current Snowpark ML Model Registry API.

Investigation Agent (Cortex Agent and CoWork)

A Cortex Agent can help users ask natural-language questions over governed supply chain data. Agent creation syntax and availability can vary; use the current Cortex Agents documentation for the exact specification supported by your account.

Explore Questions the Agent Should Answer

Which suppliers have the highest risk scores?
Which suppliers are single-source for high-criticality programs?
Which shipments are delayed or at risk?
Which programs depend on a particular facility?

Grant Access Carefully

After creating and configuring an agent in your account, grant access only to the roles that need it. The exact grant syntax depends on the object and privileges supported by the current agent implementation. Do not grant access to PUBLIC for sensitive supply-chain data.

In CoWork / Snowflake Intelligence, users can ask questions such as:

"Which suppliers have the highest risk scores?"
"Which programs depend on facility TW-FAB-01?"
"What quality incidents have been reported in the last 90 days?"

Validate answers against the underlying governed views. Natural-language responses should not be treated as authoritative without checking the source data.

Streamlit Application

The following minimal Streamlit-in-Snowflake example displays the facility scenario. Create a Streamlit app in the PACIFIC_SHIELD_DEMO.SUPPLY_CHAIN schema, select the warehouse NEXUS_WH, and paste this code into the app editor.

import streamlit as st
from snowflake.snowpark.context import get_active_session

session = get_active_session()

st.title("Pacific Shield Situational Awareness")
st.caption("Illustrative supply-chain scenario explorer")

facilities = session.sql("""
    SELECT DISTINCT FACILITY_ID, FACILITY_COUNTRY
    FROM PACIFIC_SHIELD_DEMO.SUPPLY_CHAIN.BILL_OF_MATERIALS
    ORDER BY FACILITY_ID
""").to_pandas()

if facilities.empty:
    st.info("No facilities are available in the demo data.")
else:
    selected = st.selectbox(
        "Facility to evaluate",
        facilities["FACILITY_ID"].tolist(),
    )

    scenario_df = session.sql(
        """
        SELECT *
        FROM PACIFIC_SHIELD_DEMO.SUPPLY_CHAIN.VW_DISRUPTION_SCENARIO
        WHERE FACILITY_ID = ?
        ORDER BY ESTIMATED_PROGRAM_DELAY_DAYS DESC
        """,
        params=[selected],
    ).to_pandas()

    st.metric("Affected program rows", len(scenario_df))
    st.dataframe(scenario_df, use_container_width=True)

The app uses the active Snowflake session and queries the governed scenario view. Configure Streamlit privileges and data access according to your organization's security model. This minimal example is a foundation for adding KPI tiles, charts, risk-feed summaries, and approved investigation workflows.

Best Practices

Use least privilege. Separate setup, data engineering, analyst, and sharing roles. Avoid using ACCOUNTADMIN for routine application use.
Protect sensitive data. Apply appropriate masking, row-access policies, and secure views before sharing information across agencies.
Validate the source data. Monitor freshness, completeness, and duplicate records in procurement, logistics, and quality feeds.
Tune refresh cadence to business need. Set Dynamic Table target lag based on how quickly data must be available, and monitor refresh behavior and compute cost.
Validate AI output. Review generated explanations, keep evidence links where possible, and require analyst review before taking action.
Evaluate ML honestly. Use representative historical labels, holdout data, documented metrics, and monitoring before relying on predictions.
Govern sharing. Publish only approved datasets and audit access and usage.
Start with a pilot. Validate the dependency model and workflow for one supply chain before expanding to more programs and agencies.

Clean Up

The commands below permanently delete the demo database and all objects contained within it. Run them only when you no longer need the tutorial data. The warehouse is a separate account-level object and is dropped separately.

DROP DATABASE IF EXISTS PACIFIC_SHIELD_DEMO;
DROP WAREHOUSE IF EXISTS NEXUS_WH;

Conclusion And Resources

This reference guide demonstrates how Snowflake can bring supply-chain entities into a governed data foundation, identify potential supplier and shipment risks, explore facility disruption scenarios, and provide a foundation for AI-assisted analysis. The included data is synthetic; adapt schemas, policies, model features, and operational controls to your environment.

Useful documentation:

Updated Oct 22, 2026

This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances