Building a Real-Time Supply Chain Synchronization System

Supply chain disruptions that take days to surface in ERP systems can halt production lines in hours. RisingWave, a PostgreSQL-compatible streaming database, enables real-time synchronization across supplier shipment events, warehouse inventory levels, and production schedules—giving supply chain teams the visibility to respond to disruptions before they become line stoppages.

Why Real-Time Supply Chain Synchronization Matters

Modern manufacturing supply chains are multi-tier, global, and event-driven. A supplier shipment delay, a customs hold, or a material quality rejection anywhere in the chain propagates downstream with compounding effects. The problem is that most supply chain visibility systems are built on batch data flows:

• ERP systems update inventory levels via end-of-day batch jobs, so a warehouse shortage may not surface until the next morning—after overnight production has consumed the last units.
• Supplier shipment events arrive via EDI or email, processed in batches, creating hours of latency between a shipment delay and the planner knowing about it.
• Production schedule adherence is measured in daily capacity reviews, not continuously against live inventory and supplier commitments.
• Cross-tier visibility requires correlating events from Tier 1 and Tier 2 suppliers, warehouse receipts, and production consumption—data that lives in different systems updated at different frequencies.

Real-time supply chain synchronization means every shipment event, inventory transaction, and production consumption is correlated as it happens, and disruption signals reach planners in seconds rather than hours.

How Streaming SQL Solves This

RisingWave ingests multiple concurrent supply chain event streams—supplier ASNs (Advance Ship Notices), warehouse receiving transactions, inventory adjustments, and production material consumption—and maintains materialized views that show synchronized supply-demand state across all streams simultaneously.

Key capabilities:

• Multi-stream joins: correlate supplier shipment events with expected delivery schedules and warehouse receipts
• Inventory coverage views: compute days-of-supply per SKU continuously as consumption and receipt events arrive
• Shortage prediction: detect coverage falling below safety stock thresholds and project stockout timing
• Supplier performance tracking: rolling on-time delivery rates per supplier updated with each shipment event

Building the System

Step 1: Data Source

CREATE SOURCE supplier_shipment_events (
    event_id           VARCHAR,
    asn_id             VARCHAR,
    supplier_id        VARCHAR,
    po_line_id         VARCHAR,
    part_number        VARCHAR,
    event_time         TIMESTAMPTZ,
    event_type         VARCHAR,  -- 'SHIPMENT_CONFIRMED','IN_TRANSIT','CUSTOMS_HOLD','DELIVERED','REJECTED'
    quantity           INT,
    committed_date     DATE,
    actual_date        DATE,
    warehouse_id       VARCHAR,
    carrier_id         VARCHAR,
    tracking_number    VARCHAR
)
WITH (
    connector = 'kafka',
    topic = 'supply_chain.shipments',
    properties.bootstrap.server = 'kafka:9092',
    scan.startup.mode = 'latest'
) FORMAT PLAIN ENCODE JSON;

CREATE SOURCE inventory_transactions (
    txn_id             VARCHAR,
    warehouse_id       VARCHAR,
    part_number        VARCHAR,
    event_time         TIMESTAMPTZ,
    txn_type           VARCHAR,  -- 'RECEIPT','CONSUMPTION','ADJUSTMENT','HOLD','RETURN'
    quantity_change    INT,      -- positive for additions, negative for removals
    balance_after      INT,
    lot_id             VARCHAR,
    work_order_id      VARCHAR,
    reason_code        VARCHAR
)
WITH (
    connector = 'kafka',
    topic = 'supply_chain.inventory',
    properties.bootstrap.server = 'kafka:9092',
    scan.startup.mode = 'latest'
) FORMAT PLAIN ENCODE JSON;

Step 2: Real-Time View

Compute current inventory balance and days-of-supply per part per warehouse, updated with every inventory transaction:

CREATE MATERIALIZED VIEW inventory_coverage AS
SELECT
    warehouse_id,
    part_number,
    window_start,
    window_end,
    SUM(quantity_change)                                         AS net_inventory_change,
    MAX(balance_after)                                          AS current_balance,
    SUM(ABS(quantity_change)) FILTER (WHERE txn_type = 'CONSUMPTION') AS consumed_qty,
    SUM(quantity_change) FILTER (WHERE txn_type = 'RECEIPT')    AS received_qty,
    COUNT(*) FILTER (WHERE txn_type = 'HOLD')                   AS hold_count,
    -- Average daily consumption from this window
    CASE WHEN COUNT(*) FILTER (WHERE txn_type = 'CONSUMPTION') > 0
         THEN SUM(ABS(quantity_change)) FILTER (WHERE txn_type = 'CONSUMPTION') /
              NULLIF(EXTRACT(EPOCH FROM (MAX(event_time) - MIN(event_time))) / 86400.0, 0)
         ELSE 0
    END AS avg_daily_consumption
FROM TUMBLE(
    inventory_transactions,
    event_time,
    INTERVAL '24 hours'
)
GROUP BY warehouse_id, part_number, window_start, window_end;

Track supplier on-time delivery performance with a rolling 30-day window:

CREATE MATERIALIZED VIEW supplier_otd_performance AS
SELECT
    supplier_id,
    window_start,
    window_end,
    COUNT(*) FILTER (WHERE event_type = 'DELIVERED')            AS total_deliveries,
    COUNT(*) FILTER (WHERE event_type = 'DELIVERED' AND actual_date <= committed_date) AS on_time_deliveries,
    COUNT(*) FILTER (WHERE event_type = 'CUSTOMS_HOLD')         AS customs_holds,
    COUNT(*) FILTER (WHERE event_type = 'REJECTED')             AS rejections,
    COUNT(*) FILTER (WHERE event_type = 'DELIVERED' AND actual_date <= committed_date)::FLOAT /
        NULLIF(COUNT(*) FILTER (WHERE event_type = 'DELIVERED'), 0) AS otd_rate,
    AVG(actual_date - committed_date) FILTER (WHERE event_type = 'DELIVERED') AS avg_days_late
FROM TUMBLE(
    supplier_shipment_events,
    event_time,
    INTERVAL '30 days'
)
GROUP BY supplier_id, window_start, window_end;

Step 3: Alerts

Detect inventory coverage shortfalls and in-transit disruptions, routing alerts to supply chain planners:

CREATE MATERIALIZED VIEW supply_chain_alerts AS
SELECT
    ic.warehouse_id,
    ic.part_number,
    ic.current_balance,
    ic.avg_daily_consumption,
    CASE WHEN ic.avg_daily_consumption > 0
         THEN ic.current_balance / ic.avg_daily_consumption
         ELSE 999
    END AS days_of_supply,
    ic.hold_count,
    CASE
        WHEN ic.hold_count > 0 AND ic.current_balance < ic.avg_daily_consumption * 3
            THEN 'CRITICAL_HOLD_LOW_STOCK'
        WHEN ic.current_balance < ic.avg_daily_consumption * 2
            THEN 'STOCKOUT_RISK_48H'
        WHEN ic.current_balance < ic.avg_daily_consumption * 5
            THEN 'LOW_COVERAGE_WARNING'
        ELSE 'ADEQUATE'
    END AS alert_type
FROM inventory_coverage ic
WHERE ic.current_balance < ic.avg_daily_consumption * 5
   OR ic.hold_count > 0;

CREATE SINK supply_chain_alerts_sink
FROM supply_chain_alerts
WITH (
    connector = 'kafka',
    topic = 'supply_chain.alerts',
    properties.bootstrap.server = 'kafka:9092'
) FORMAT PLAIN ENCODE JSON;

Comparison Table

FAQ

Q: How does RisingWave handle supply chain events from multiple ERP systems across global plants? Each ERP system publishes its events to a separate Kafka topic, or all events flow through a standardized event schema to a shared topic. RisingWave sources can be configured per topic and merged in views using UNION or JOIN operations, enabling a unified real-time view across geographies.

Q: Can I use RisingWave to track in-transit inventory from Tier 2 suppliers? Yes. If Tier 2 supplier events are available via EDI-to-Kafka pipelines or third-party logistics (3PL) tracking APIs, they can be ingested as additional sources in RisingWave. Views that correlate Tier 1 and Tier 2 shipment events provide multi-tier in-transit visibility.

Q: How do I account for safety stock policies that differ per part and per warehouse? Create a reference table for safety stock levels per part and warehouse, loaded as a batch source in RisingWave. Your alert view joins the inventory coverage view against this table to use per-part safety stock thresholds instead of global constants.

Key Takeaways

• Real-time supply chain synchronization closes the gap between when disruptions occur and when planners can respond, reducing the risk of production line stoppages due to material shortages.
• RisingWave's multi-stream joins correlate supplier shipment events, inventory transactions, and production consumption simultaneously in SQL, without custom integration code.
• Days-of-supply computed continuously from live consumption and balance data provides a more accurate shortage signal than static reorder points calculated in overnight batch jobs.
• Streaming SQL over supply chain event streams enables supplier OTD performance tracking, inventory coverage monitoring, and disruption alerting within a single system accessible to any PostgreSQL-compatible tool.

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