Who's behind supplylab

It happened too many times: a clear problem on the table, a solution conceptually within reach, but weeks — sometimes months — of waiting. Software selection. Budget approval. Implementation. Customization. Testing. The problem, meanwhile, just sat there, and the only available answer was a spreadsheet held together with tape.

More than a skills problem, it was often a problem of access to the right tools.

Then something changed. With tools like Claude Code and Codex, building useful software became radically faster. Not perfect. Not enterprise-grade on the first try. But usable, testable, improvable — in days, not quarters. And most importantly: within reach of anyone who understands the problem, not just those who write code for a living.

Some commercial products are excellent, and in complex environments they're irreplaceable. The point is different: there's a huge range of problems that professionals can solve on their own, faster than they think, if they have the right conceptual tools and know how to use AI.

The goal of supplylab is to demonstrate concretely — not just in words — what becomes possible when domain expertise and AI come together. Each tool is a practical demonstration: not a finished product, but working proof that the problem can be tackled, with those means, in that timeframe. To share what works. And maybe convince someone else to try.

Fifteen years in supply chain, across three industries that work in very different ways: pharmaceuticals (regulatory constraints, GMP, serialization), consumer goods (high volumes, thin margins, speed), luxury (volatile demand, global multi-brand supply chains).

I've done demand planning, production planning, S&OP, ERP implementations, team management, product launches, due diligence. I've worked with SAP, with Python, with spreadsheets that should never have existed.

The full detail is on LinkedIn. What matters here is the point: every tool on supplylab comes from problems I've seen firsthand, not case studies read in a book.

I've just completed an Executive MBA at POLIMI Graduate School of Management. The capstone project — THRU GENIE — combines Goldratt's Theory of Constraints with Large Language Models to help manufacturing SMEs identify their production bottlenecks. A research project, not a commercial one, born to explore an idea: that classical operations theory, combined with AI, can produce concrete results without large budgets or external consultancies.

supplylab comes from the same spirit. I build it alongside my day job — evenings, weekends, in-between moments. No profit motive, no business model behind it. Every tool comes from a real problem I've encountered or been told about: it's not a product to sell, it's a working example to explore. An open portfolio of what happens when you combine operational experience and AI.