Logistics & Supply Chain Management

Vantage centralizes supply chain data from ERPs, warehouse management systems, e-commerce platforms, and carrier APIs into a unified control tower. Build workflows that monitor inventory levels, detect shipment exceptions, forecast demand, and model supply disruption scenarios — all feeding real-time dashboards.

Automate Replenishment with AI-Driven Demand Sensing

Calculate optimal reorder points based on real-time inventory, historical demand, and AI-predicted demand shifts.

Scenario: A multi-warehouse distribution company needs to maintain optimal stock levels across 5 distribution centers, avoiding both stockouts and excess inventory carrying costs.

Workflow Steps:

1. Schedule Trigger — Run twice daily (6 AM, 6 PM)
2. Database Query (PostgreSQL) — Pull current inventory positions across all warehouses: SKU, location, on-hand, allocated, in-transit, quarantined
3. Shopify — Get Inventory Levels — Sync real-time inventory from e-commerce channels
4. Database Query (MSSQL) — Pull historical demand: daily unit sales by SKU for the last 365 days
5. Union — Merge warehouse and channel inventory into a single dataset
6. Aggregation — Roll up by SKU across all locations:
   • Total on-hand, total allocated, total available
   • Trailing 7/30/90-day average daily demand
   • Standard deviation of daily demand
7. Computed Column — Calculate replenishment parameters:
   • Safety stock: Z_score * stddev_demand * sqrt(lead_time_days) (Z=1.65 for 95% service level)
   • Reorder point: (avg_daily_demand * lead_time_days) + safety_stock
   • Days of supply: available_inventory / avg_daily_demand
   • Economic order quantity: sqrt((2 * annual_demand * order_cost) / holding_cost_per_unit)
8. AI Enrichment — Adjust demand projections based on:
   • Upcoming promotional calendar events
   • Seasonal patterns (holiday ramp, summer slowdown)
   • Market signals (competitor stockouts, price changes)
   • Weather impact on demand categories
9. Filter — Flag SKUs where available inventory ≤ reorder point
10. Multi-Conditional — Route by urgency:
    • Days of supply < 3 (critical) → Send Email to procurement director with auto-generated PO draft + Dashboard Output (Metric Tile flashes red) + Send Message (Slack #supply-chain-urgent)
    • Days of supply 3–7 (warning) → Dashboard Output (List Tile — review queue) + Send Message (Slack #procurement)
    • Days of supply > 7 (healthy) → Dashboard Output only
11. Dashboard Output — Populate:
    • Forecast Tile — 30/60/90-day demand projection by product category with confidence intervals
    • Scenario Planner Tile — Model: "What if lead times increase 50%?" or "What if demand spikes 30% for holiday?"
    • Map Tile — Warehouse locations with inventory health color-coding
    • Table Tile — SKU-level inventory detail with reorder status
    • Metric Tile — Inventory turns, stockout count, fill rate %
    • Comparison Tile — Inventory levels this month vs. last month by category

Key Nodes: Schedule Trigger, Database Query (PostgreSQL, MSSQL), Shopify Get Inventory Levels, Union, Aggregation, Computed Column, AI Enrichment, Filter, Multi-Conditional, Send Email, Send Message, Dashboard Output

Catch Shipment Exceptions and Scorecard Carriers

Monitor active shipments, detect delays, alert logistics coordinators, and build carrier performance scorecards.

Scenario: A logistics team manages 500+ active shipments daily across 12 carriers. They need to know about delays within 30 minutes and track carrier reliability for contract negotiations.

Workflow Steps:

1. Schedule Trigger — Run every 30 minutes
2. Database Query (PostgreSQL) — Pull active shipment records: shipment ID, carrier, origin, destination, pickup date/time, planned delivery date/time, current status, last milestone timestamp
3. Computed Column — Calculate delay metrics:
   • Transit time variance: actual_milestone_time - planned_milestone_time at each checkpoint
   • Projected delivery: estimated arrival based on current position and remaining distance
   • Delivery risk: HIGH (projected late > 4 hours), MEDIUM (1–4 hours), LOW (on time)
4. Filter — Shipments with delivery risk = HIGH or MEDIUM
5. Geocode — Resolve last-known carrier GPS coordinates or checkpoint city to map coordinates
6. Multi-Conditional — Route by risk:
   • HIGH (> 4 hours late) → Send Email to customer service + Send Message (Teams) to logistics coordinator + Dashboard Output (Event Monitor Tile — critical)
   • MEDIUM (1–4 hours late) → Send Message (Slack #logistics) + Dashboard Output (Event Feed Tile)
7. Aggregation — Calculate carrier performance metrics (rolling 30 days):
   • On-time delivery rate
   • Average transit time vs. quoted transit time
   • Damage claim rate
   • Cost per shipment by lane
8. Sort — Rank carriers by on-time delivery rate
9. AI Enrichment — Generate carrier recommendations: "Carrier A has 94% on-time rate on the NY→LA lane at $2,450/load. Carrier B has 87% at $2,200. Net cost of Carrier B delays (expediting, customer credits) averages $380/occurrence, making Carrier A $130 cheaper per shipment on a total-cost basis."
10. Write CSV — Export exception log for weekly carrier review meeting
11. Dashboard Output — Populate:
    • Map Tile — Active shipment locations with delay risk color-coding
    • Event Monitor Tile — Real-time delay alerts
    • Event Feed Tile — Shipment milestone updates
    • Table Tile — Carrier scorecard: on-time %, avg transit, cost/shipment, damage rate
    • Bar Tile — Delay frequency by carrier
    • Comparison Tile — Carrier performance this month vs. last quarter
    • Timeline Tile — Shipment journey: pickup → checkpoints → delivery

Key Nodes: Schedule Trigger, Database Query, Computed Column, Filter, Geocode, Multi-Conditional, Aggregation, Sort, AI Enrichment, Write CSV, Send Email, Send Message, Dashboard Output

Monitor Warehouse Operations and Fulfillment Velocity

Track warehouse throughput, picking accuracy, and fulfillment SLAs in real time.

Scenario: A warehouse operations manager needs to monitor hourly productivity across receiving, picking, packing, and shipping — and intervene when throughput drops below target.

Workflow Steps:

1. Schedule Trigger — Run every hour
2. Database Query (PostgreSQL) — Pull warehouse operations data:
   • Orders received, picked, packed, shipped per hour per worker
   • Pick accuracy: correct items / total items picked
   • Cycle time by operation: receiving, putaway, picking, packing, shipping
3. Database Query (MSSQL) — Pull order backlog: pending orders by priority and SLA deadline
4. Aggregation — Calculate hourly KPIs:
   • Units per hour (UPH) by operation and by worker
   • Orders fulfilled vs. SLA target
   • Picking error rate
   • Dock-to-stock time (receiving)
5. Computed Column — Calculate SLA compliance:
   • Same-day orders: % shipped within 4 hours of receipt
   • Next-day orders: % shipped within 24 hours
   • Time remaining to SLA breach for pending orders
6. Filter — Alert conditions:
   • UPH < 80% of target
   • Picking error rate > 1%
   • Orders approaching SLA breach (< 1 hour remaining)
7. Multi-Conditional — Route by alert type:
   • SLA breach imminent → Send Message (Slack) to warehouse supervisor with order list + Dashboard Output (Stat Tile — SLA countdown)
   • Throughput below target → Send Message (Teams) to operations manager
   • Picking errors → Dashboard Output (Event Feed Tile with worker and order details)
8. Dashboard Output — Populate:
   • Metric Tile — UPH, SLA compliance %, error rate (each with sparkline)
   • Bar Tile — Throughput by operation (receiving, picking, packing, shipping)
   • Gantt Tile — Order lifecycle: received → picked → packed → shipped
   • Pivot Tile — Worker × Operation productivity matrix
   • Line Tile — Hourly throughput trend
   • Stat Tile — Orders pending, orders at risk, orders completed today

Key Nodes: Schedule Trigger, Database Query (PostgreSQL, MSSQL), Aggregation, Computed Column, Filter, Multi-Conditional, Send Message, Dashboard Output

Detect Supply Chain Disruptions Before They Cascade

Monitor external risk factors that could disrupt your supply chain and model mitigation scenarios.

Scenario: A global supply chain team wants early warning of port closures, weather events, supplier financial distress, and geopolitical disruptions that could impact their supply lines.

Workflow Steps:

1. Schedule Trigger — Run every 2 hours
2. GKG Search — Monitor the Global Knowledge Graph for supply chain disruption signals: port closures, strikes, natural disasters, trade policy changes
3. Web Scraper — Pull maritime tracking data, weather advisories, and government trade bulletins
4. Database Query (PostgreSQL) — Pull your supplier master: supplier name, country, criticality tier, lead time, alternate sources
5. AI Enrichment — For each external event, assess supply chain impact:
   • Affected geography: which of your suppliers are in the affected region?
   • Affected commodity: which materials could be disrupted?
   • Estimated duration: how long will the disruption last?
   • Mitigation options: alternate suppliers, safety stock coverage, expedited shipping
6. Multi-Conditional — Route by impact:
   • Critical supplier in affected area → Send Email to VP Supply Chain + Send Message (Slack #supply-chain-risk) with impact assessment + Dashboard Output (Event Monitor Tile)
   • Non-critical supplier → Dashboard Output (Event Feed Tile for awareness)
7. Dashboard Output — Populate:
   • Map Tile — Global supplier locations with risk event overlay
   • Event Monitor Tile — Active disruption alerts
   • Scenario Planner Tile — Model: "If Port of Shanghai closes for 2 weeks, which SKUs are affected and what is the revenue impact?"
   • Table Tile — Supplier risk matrix: criticality, location risk, financial risk, alternate availability
   • Forecast Tile — Projected inventory impact under disruption scenario
8. Write PDF — Generate weekly supply chain risk briefing for executive team

Key Nodes: Schedule Trigger, GKG Search, Web Scraper, Database Query, AI Enrichment, Multi-Conditional, Write PDF, Send Email, Send Message, Dashboard Output

Example Dashboard: Supply Chain Control Tower

Build this dashboard to give your supply chain team end-to-end visibility from procurement through delivery.

Row 1 — Supply Chain Pulse

Row 2 — Shipments & Geography

Row 3 — Inventory & Demand

Row 4 — Warehouse & Carriers

Row 5 — Risk & Planning

Getting Started

To build your supply chain control tower:

1. Connect your systems — Add your WMS, ERP, and Shopify databases under Integrations
2. Start with inventory visibility — Build a Schedule Trigger → Database Query → Aggregation → Dashboard Output workflow
3. Add demand forecasting — Use AI Enrichment and Forecast Tiles with 12+ months of historical data
4. Monitor shipments — Add carrier milestone tracking with Geocode and Map Tile
5. Model scenarios — Use the Scenario Planner Tile to stress-test your supply chain assumptions