A starting point for anyone who wants to understand what AI can really do in supply chain.
If you work in supply chain, you know the scene: a clear problem, a conceptually simple solution, but months of waiting through software selection, budget approval, implementation. Meanwhile the problem sits there, and Excel doesn't cut it anymore.
supplylab was born from that frustration. Today, with domain expertise and AI used well, useful tools can be built in days — not quarters. No vendors, no enterprise project, no waiting for anyone.
Every tool here is designed to be serious enough to reflect real-world complexity and accessible enough that anyone can start in minutes. No subscriptions. No paywalls. No sales funnel. Just tools worth using, explained well enough that you actually understand what's happening under the hood.
AI isn't a buzzword here — it's the engine.
Every tool on supplylab uses AI to do something that would otherwise take hours of manual work: reading weak signals, synthesizing fragmented information, generating output you can act on immediately.
The goal isn't to replace judgment — it's to give you better information, faster, so your judgment lands in the right place.
supplylab is also proof of a bigger point: serious tools can be built quickly, by one person with domain expertise and AI. Not long ago, this would have seemed unthinkable. Today, it's simply a fact.
The tools on supplylab aren't finished products ready for production. They're working demonstrations of how AI and domain expertise can solve real supply chain problems. Each tool is a practical example — you can use it, explore it, understand how it's built. The goal isn't to hand you the definitive software. It's to show you it's possible, and that the distance between "I have a problem" and "I have a tool that tackles it" is much shorter than you think.
Interactive real-time map of global supply chain risk. Active alerts on disrupted corridors, geopolitical signals, AI-generated mitigation actions. Global score 0–100, continuously updated.
Use case: how can AI continuously monitor global supply chain risk without manual intervention?
Upload your demand data, compare forecasting models from Naive to Holt-Winters, run backtests, find out which model works best on your data.
Use case: how do different forecasting models compare on the same data, and how do you pick the right one?
supplylab is a one-person project, but it remains an open space. If you have an idea, a skill, or just curiosity — you're welcome here. Whether you're a developer, a data scientist, or a supply chain professional, there's room to contribute and exchange ideas.
supplylab is a living project. New tools get added when there's something worth building. Feedback is always welcome.
For new tools and updates on supplylab, you can follow me on LinkedIn.