Supply Chain Delay Monitor: n8n Workflow Reference

Complex supply chains generate a constant stream of shipment updates, carrier exception messages, and supplier notifications. Manually reviewing these signals to detect delays is inefficient and often too slow to prevent downstream impact. This reference-style guide describes an n8n workflow template, Supply Chain Delay Monitor, that automates delay detection and classification using embeddings, a vector database, and an LLM-based agent, with results logged into Google Sheets for operational use.

The documentation below is organized for technical and operations engineers who want a clear view of the workflow architecture, node configuration, and customization options.

1. Workflow Overview

The Supply Chain Delay Monitor is an end-to-end n8n workflow that:

• Receives real-time supply chain events via a Webhook node.
• Splits unstructured messages into embedding-ready chunks.
• Generates vector embeddings using an embeddings provider such as Cohere.
• Persists embeddings and metadata in a vector store backed by Supabase.
• Queries historical incidents for semantic similarity and context.
• Uses an AI Agent powered by OpenAI (or another LLM) to analyze and classify the event.
• Writes a structured log entry to Google Sheets for tracking and follow-up.

This pipeline is designed for:

• Automated detection and prioritization of shipment delays and exceptions.
• Pattern analysis across historical incidents using semantic search.
• Generating recommended remediation actions for operations teams.
• Creating a searchable audit trail in a spreadsheet-based log.

2. High-Level Architecture

The workflow combines n8n nodes with several external services:

• n8n – Orchestration, node execution, and workflow logic.
• Cohere (or equivalent) – Embeddings provider for converting text into vectors.
• Supabase – Vector database and storage layer for embeddings and metadata.
• OpenAI (or another LLM) – Language model behind the AI Agent for reasoning and classification.
• Google Sheets – Operational log and simple reporting surface.

End-to-end flow:

1. Webhook receives POST events from TMS/EDI or tracking APIs.
2. Splitter divides long carrier messages or reports into chunks.
3. Embeddings node generates a vector for each text chunk.
4. Insert + Vector Store (Supabase) persists vectors with shipment metadata.
5. Query searches the vector index for similar historical events.
6. Tool + Memory prepare contextual data and maintain short-term state for the Agent.
7. Chat/Agent uses an LLM to classify severity, suggest actions, and summarize.
8. Google Sheets appends a structured record of the analysis.

3. Node-by-Node Breakdown

3.1 Webhook Node (Event Ingestion)

Role: Entry point for all supply chain events.

Typical configuration:

• HTTP Method: POST
• Authentication: Depending on your environment, use a token, header-based auth, or IP restrictions.
• Expected payload fields:
  • shipment_id
  • timestamp
  • location or checkpoint
  • status (e.g., delayed, in transit, delivered)
  • notes or other unstructured text (carrier messages, exception descriptions)

Data flow: The raw JSON body from the POST request is passed to downstream nodes. The unstructured text field is the primary input for embeddings, while structured fields become metadata in the vector store and in the final log.

Security considerations:

• Use authentication tokens or signed payloads to prevent spoofed events.
• Optionally validate a known header or secret before processing.
• Restrict the Webhook URL using IP allowlists where possible.

3.2 Splitter Node (Text Chunking)

Role: Converts long or composite carrier messages into smaller text segments suitable for embedding.

Typical parameters:

• Chunk size: ~400 characters.
• Overlap: ~40 characters between adjacent chunks.

This approach:

• Preserves local context across chunks through overlap.
• Keeps each unit within common embedding model limits.
• Improves recall when querying for similar incidents, since each chunk can be matched independently.

Edge cases:

• Very short messages may pass through unchanged with a single chunk.
• If the payload is missing or empty, consider adding a basic check or guard node before embedding to avoid unnecessary API calls.

3.3 Embeddings Node (Cohere or Equivalent)

Role: Generates numeric vector representations for each text chunk.

Provider: Cohere is used in the template, but any compatible embeddings provider can be substituted with equivalent configuration.

Configuration highlights:

• Set the embeddings model according to your provider’s recommended model for semantic search.
• Map the chunked text field from the Splitter node as the input text.
• Ensure the Cohere (or other provider) API key is stored as an n8n credential and not hardcoded.

Output: Each input chunk produces a vector (array of floats) plus any additional metadata the node returns. These vectors are then associated with shipment-level metadata in the next step.

Error handling:

• Monitor for rate limit or transient network errors and configure retries where appropriate.
• For failed embedding calls, you may choose to skip the record, log the failure, or route it to a separate error-handling branch.

3.4 Insert + Vector Store Node (Supabase)

Role: Persists embeddings and associated metadata in a vector database backed by Supabase.

Key configuration elements:

• Index name: Use a dedicated index such as supply_chain_delay_monitor to keep this use case isolated.
• Vector field: Map the embeddings output vector to the appropriate column or field expected by your Supabase vector extension.
• Metadata: Persist fields like:
  • shipment_id
  • carrier (if available)
  • location
  • timestamp
  • status
  • Original text chunk or a reference to it

Benefits:

• Fast nearest-neighbor queries for similar incidents.
• Rich filtering using structured metadata (for example filter by carrier, lane, or time window).

Index management notes:

• Use meaningful index names so you can manage multiple workflows or use cases in the same Supabase project.
• Periodically prune outdated or low-value embeddings to control storage and query cost.

3.5 Query + Tool Nodes (Context Retrieval)

Role: Retrieve semantically similar historical events and expose them as a “tool” to the AI Agent.

Query node configuration:

• Use the newly generated embedding as the query vector.
• Target the same index used for insertion, for example supply_chain_delay_monitor.
• Optionally apply filters based on metadata (for example same carrier or similar route) if your Supabase setup supports it.
• Limit the number of neighbors returned to a manageable number for the LLM (for example top N matches).

Tool node role:

• Wraps the query results in a format that the Agent node can consume as an external “tool” or data source.
• Provides the LLM with summarized context about similar historical incidents, including prior classifications or remediation steps if you store them.

This pattern allows the Agent to reason over both the current event and a curated set of past events retrieved via vector search.

3.6 Memory Node (Conversation State)

Role: Maintain short-term conversational or workflow context across multiple related events.

Usage:

• Stores recent interactions and Agent outputs so subsequent events can reference them.
• Helps avoid repetitive or redundant actions when multiple similar alerts arrive in a short timeframe.

Typical configuration:

• Use a buffer or similar memory strategy compatible with the Agent node.
• Limit memory size to avoid unnecessary token usage when interacting with the LLM.

3.7 Chat / Agent Node (LLM Analysis)

Role: Central reasoning component that combines the current event, retrieved historical context, and memory to classify and recommend actions.

Provider: OpenAI is used in the template, but any LLM supported by n8n can be configured as long as it integrates with the Agent node.

Typical responsibilities of the Agent:

• Classify delay severity, for example:
  • Minor delay
  • Moderate issue
  • Critical disruption
• Identify likely root causes based on historical similarity.
• Recommend next steps, such as:
  • Contact carrier
  • Expedite alternate routing
  • Escalate to supplier management
• Generate a concise, structured summary suitable for logging.

Prompt design considerations:

• Define explicit classification labels and severity thresholds.
• Specify what the Agent should output (for example JSON with fields like severity, recommended_action, summary).
• Clarify how to use tool results from the vector store and how to interpret memory content.

Error and rate-limit handling:

• Respect the LLM provider’s rate limits, especially for high-volume event streams.
• Consider a fallback strategy, such as default classifications or delayed retries, when the LLM is unavailable.

3.8 Google Sheets Node (Operational Logging)

Role: Persist the Agent’s output and key event attributes as a structured record in a Google Sheet.

Typical output columns:

• Shipment ID
• Timestamp
• Location
• Status
• Severity classification
• Recommended action
• Short rationale or summary

Configuration details:

• Use an n8n Google Sheets credential to securely connect to your spreadsheet.
• Configure the node to append a new row for each processed event.
• Ensure column ordering in Sheets matches the mapping in the node configuration.

This log becomes a simple but effective audit trail and a data source for downstream reporting or dashboards.

4. Configuration Notes & Best Practices

4.1 Webhook Security

• Use tokens, signed payloads, or custom headers to validate request origin.
• Combine network-level controls (IP whitelisting) with application-level checks where possible.
• Log incoming requests with minimal identifying information, such as a payload hash, to support debugging without exposing full PII.

4.2 Chunk Sizing and Embedding Strategy

• Start with 300 to 500 characters per chunk with a small overlap (for example 40 characters) to balance context and cost.
• Adjust chunk size based on the typical length and complexity of carrier messages in your environment.
• Batch embedding calls where possible to reduce API overhead and respect provider rate limits.

4.3 Metadata Design

• Always store shipment-level identifiers and timestamps alongside vectors to enable precise filtering.
• Include carrier, route, or lane information if available to support more targeted similarity queries.
• Use consistent naming conventions for metadata fields to simplify future queries and analytics.

4.4 Index Management in Supabase

• Use descriptive index names such as supply_chain_delay_monitor for clarity.
• Periodically remove stale or low-value records to keep query performance predictable and costs controlled.
• Consider segmenting indexes by business unit or region if you anticipate very large volumes.

4.5 Prompt Engineering for the Agent

• Define a clear schema for outputs, including severity levels and action templates.
• Document how severity maps to operational processes (for example critical events trigger paging, minor events are logged only).
• Iteratively refine the prompt based on real-world outputs and feedback from operations teams.

4.6 Rate Limits and Batching

• Monitor usage against Cohere and OpenAI quotas to avoid service interruptions.
• Implement batching where compatible with your event latency requirements to reduce per-event overhead.
• For extremely high volumes, consider sampling or prioritization strategies so the most critical events are analyzed first.

5. Use Cases & Practical Scenarios

The Supply Chain Delay Monitor workflow supports a range of operational scenarios:

• Automated exception classification: Ingest carrier EDI exceptions and categorize them by severity, enabling teams to focus on critical incidents first.
• Recurring delay detection: Identify patterns such as specific suppliers, routes, or ports that repeatedly cause delays by querying similar historical incidents.
• Remediation guidance: Generate recommended next steps and optionally feed them into downstream ticketing or case management systems.
• Reporting and audits: Use the Google Sheets log as a lightweight source of truth for weekly dashboards and leadership reviews.

6. Security, Compliance & Cost Considerations

6.1 Data Protection

• Ensure data is encrypted in transit and at rest. Supabase provides encryption capabilities; configure TLS for all external calls.
• Mask or hash customer-identifying fields before storing them in embeddings or logs if PII constraints apply.
• Carefully review what fields are included in text passed to the LLM, especially if you handle sensitive customer or shipment data.

6.2 Access Control & Secrets Management

• Store all provider keys (Cohere, OpenAI, Supabase, Google Sheets) as environment-level secrets in n8n.
• Limit who can access n8n credentials and audit changes to workflow configuration.

6.3 Cost Monitoring

• Track embedding and LLM usage over time to understand cost drivers.
• Use caching strategies for repeated, similar queries where appropriate.
• Optimize chunk sizes and reduce unnecessary calls for low-value events.

7. Monitoring & Observ