Imagine being able to streamline your supply chain analytics with just a few clicks and minimal coding. As someone who’s spent years as a Supply Chain Data Scientist, I’ve worked with various frameworks, like LangChain and LangGraph, to build AI-driven solutions using Python.
About a year ago, I dove into LangChain to create an AI agent that functioned as a Supply Chain Control Tower. While it was effective, it demanded a deep understanding of Python, making it quite complex.
But then, the low-code platform n8n came along and changed the game. It allows you to create similar solutions with ease, often with just a few clicks. In this article, I’ll show you how you can use n8n to automate your supply chain analytics workflows effortlessly.
Getting Started with n8n
My first project using n8n was for a client who needed a Control Tower with a chat interface. Control Towers are great at integrating dashboards and reports with Warehouse and Transport Management Systems to keep track of supply chain events.
In the past, I connected a Control Tower to an AI agent using LangChain, which involved processing user requests, generating SQL queries, and formulating responses. Over time, I optimized this solution, but it remained a bit of a puzzle.
The n8n Advantage
Enter n8n, an open-source workflow automation tool that makes it easy to connect apps, APIs, and AI model frameworks. It simplifies workflow creation through pre-built nodes, taking the complexity out of the setup.
For example, you can build an AI-Powered Email Parser with just four nodes to process emails and extract data, all without needing to write complex code.
Empowering Teams
With a team of consultants who aren’t Python experts, n8n has been a game-changer. They can now adapt and maintain workflows with just a bit of training.
The AI Supply Chain Control Tower workflow, while more complex than the email parser, is still far simpler than its Python counterpart. It includes two sub-workflows, featuring a chat interface and an AI agent.
The AI agent node connects a language model, manages conversations, and generates SQL queries. A secondary sub-workflow cleans these queries and executes them, efficiently returning results.
This setup doesn’t require Python skills, making it accessible for my consultants to manage directly, and it delivers results that are on par with Python-based solutions.
Python’s Continued Role
While n8n simplifies automation, Python remains invaluable for comprehensive analytics, serving as a perfect complement to low-code platforms.
n8n’s ease of connectivity enhances analytics products, allowing seamless integration with services like Slack and Google Sheets.
If you’re starting your journey with n8n, there’s a wealth of templates and tutorials available to guide and inspire you.
