Master Architecture: Risk-Adjusted Supply Chain Dashboard

Global Food Procurement: Operations & Portfolio Architecture

Strictly adhering to the Tatsuyuki Negoro corporate strategy framework, aligning target customers, value propositions, and profit models into a cohesive data ecosystem.

Efficient Frontier (Risk vs. Yield)

Scatter plot mapping SKU gross margin against total operational volatility.

Inventory Duration Risk (Perishability)

Burn-down tracking of active lot shelf-life to prevent default via spoilage.

I. Technical Architecture & Data Pipelines

Component Specification & Execution Protocol
Data Ingestion Executed natively on a Windows environment. Automated Python scripts utilize Playwright/Selenium to scrape freight forwarder portals and supplier catalogs.
Middleware API A lightweight Node.js/Express layer receives the scraped JSON payloads from the Windows machines and translates them into parameterized SQL queries to prevent injection attacks.
Document Storage All unstructured background materials (e.g., customs declarations, health certificates, sanitary invoices) are strictly processed, stored in an AWS S3 bucket, and linked in the database strictly as PDFs.
Central Ledger PostgreSQL database acting as the ultimate source of truth, enforcing foreign key constraints between the PDF metadata, supplier entities, and active inventory lots.

II. Standard Operating Procedures (SOP): Sourcing & Underwriting

To maintain systemic MECE compliance, the procurement of new Western food products must follow a strict, trigger-based workflow.

Step Action Data Output
1. Initiation The initial input for the automated evaluation pipeline must begin exclusively with The Website of the company that is the seller (the Western manufacturer). The system scrapes this origin for SKU catalogs and MOQ specifications. Draft Product Schema
2. Document Verification Procure sanitary and origin certificates from the supplier. Digitally stamp and store these files strictly as PDFs linked to the supplier's unique ID in the database. Validated PDFs
3. Volatility Assessment Calculate expected $\sigma_L$ (Lead Variance) based on historical shipping data from the origin port (e.g., Genoa or Barcelona) to Keelung/Kaohsiung. Risk Metric Generated
4. Portfolio Decision Compute the Sharpe Ratio Equivalent. If the score exceeds the baseline threshold, generate an automated Purchase Order via the UI interface. Executed PO

Active Logistics Pipeline (The Trade Ledger)

PO Ref Supplier / Origin Asset (SKU) Est. Margin Status Action
PO-1042 Agricola (ESP) Olive Oil (500ml) 24.5% Customs Cleared Route to B2B Channel
PO-1043 Roma Foods (ITA) Cured Meat (1kg) 38.2% Delayed (Port) Review Duration Risk