---
name: supply-chain-visibility-platform-ba505cdf7fee
description: End-to-end supply chain visibility skill providing real-time tracking and control tower capabilities
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
  - WebFetch
metadata:
  specialization: logistics
  domain: business
  category: analytics
  priority: medium
graph:
  domains: [domain:logistics]
  skillAreas: [skill-area:procurement-management, skill-area:data-analytics, skill-area:change-management-ops]
  roles: [role:supply-chain-analyst, role:operations-analyst, role:procurement-manager]
---

# Supply Chain Visibility Platform

## Overview

The Supply Chain Visibility Platform provides end-to-end supply chain visibility with real-time tracking and control tower capabilities. It integrates data from multiple tiers of the supply chain to provide comprehensive visibility, exception management, and predictive analytics for proactive supply chain management.

## Capabilities

- **Multi-Tier Supplier Visibility**: Track materials and products across multiple tiers of the supply chain
- **In-Transit Inventory Tracking**: Monitor inventory in transit across all modes and carriers
- **Exception Management and Alerting**: Detect exceptions and trigger appropriate alerts and escalations
- **Predictive ETA Modeling**: Predict arrival times using historical data and real-time conditions
- **Risk Event Monitoring**: Monitor for supply chain risk events including weather, geopolitical, and supplier issues
- **Performance Dashboards**: Provide comprehensive visibility dashboards for supply chain performance
- **Collaboration Portal**: Enable collaboration between supply chain partners through shared visibility

## Tools and Libraries

- Visibility Platforms (Project44, FourKites)
- IoT Integration
- Event Streaming (Kafka)
- Risk Monitoring Services

## Used By Processes

- Shipment Tracking and Visibility
- Distribution Network Optimization
- Demand Forecasting

## Usage

```yaml
skill: supply-chain-visibility-platform
inputs:
  tracking_scope:
    purchase_orders: ["PO-2026-001", "PO-2026-002"]
    shipments: ["SHP-2026-101", "SHP-2026-102"]
    visibility_depth: "multi_tier"
  alert_configuration:
    delay_threshold_hours: 4
    risk_categories: ["weather", "port_congestion", "supplier_issue"]
    notification_channels: ["email", "dashboard", "slack"]
  dashboard_parameters:
    time_range_days: 30
    key_lanes: ["Shanghai-LA", "Vietnam-NYC", "Mexico-Chicago"]
outputs:
  visibility_summary:
    total_shipments_tracked: 156
    in_transit: 42
    at_risk: 5
    delivered_on_time_percent: 94.2
  shipment_details:
    - shipment_id: "SHP-2026-101"
      origin: "Shanghai, China"
      destination: "Los Angeles, CA"
      mode: "ocean"
      carrier: "COSCO"
      vessel: "COSCO Shipping Universe"
      current_location: "Pacific Ocean - 500nm from LA"
      status: "in_transit"
      original_eta: "2026-01-28"
      current_eta: "2026-01-27"
      eta_confidence: 92
      milestones:
        - event: "vessel_departure"
          location: "Shanghai Port"
          timestamp: "2026-01-10T14:00:00Z"
        - event: "transhipment"
          location: "Busan, Korea"
          timestamp: "2026-01-13T08:00:00Z"
  risk_alerts:
    - alert_id: "ALT-001"
      type: "port_congestion"
      location: "Los Angeles Port"
      severity: "medium"
      affected_shipments: ["SHP-2026-101", "SHP-2026-105"]
      impact: "Potential 1-2 day delay"
      recommended_action: "Monitor closely, prepare for potential dray carrier rebooking"
  supplier_visibility:
    - supplier: "Acme Manufacturing"
      tier: 1
      active_pos: 5
      on_time_rate: 91
      risk_score: "low"
```

## Integration Points

- Transportation Management Systems (TMS)
- Enterprise Resource Planning (ERP)
- Supplier Portals
- Risk Intelligence Services
- IoT/GPS Tracking Devices

## Performance Metrics

- Visibility coverage percentage
- ETA accuracy
- Exception detection rate
- Alert response time
- Partner adoption rate


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Marketing, Operations, Sales
Roles: Business Development, Data Analyst, Marketing Analyst, Operations Manager, Supply Chain Manager
Specialties: Specialty not specified
Tools: Fourkites, Kafka, Project44
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 1f21218a853967e66c1d239e2c36e26cb32285f808fff7102c6702154b1e2492