# SCENARIO-X Studio

Source: https://dinand.com/experiments/scenario-x/
Author: Dinand Tinholt

> An interactive workbench for designing multi-product supply-chain experiments and inspecting where average performance hides a weak tail.

The question behind the system

> A portfolio can look healthy in aggregate while one product, scenario or shared constraint is already failing.

What it is designed to do

- Design product portfolios, disruptions and policy grids
- Compare cost-service frontiers and weakest-SKU outcomes
- Diagnose binding constraints and export experiment artifacts

Interface: Single-page analytical workbench

Engine: Parallel Python simulation

Outputs: CSV + JSON artifacts

The public boundary

## What this page does—and does not—open.

The interface depends on a local simulation service and persisted run workspace. The public site provides an introduction; a deployed instance can be linked separately when its service boundary is ready.

Separate application

No public deployment is linked yet. The introduction remains available without exposing a private service or local data.

[Read the resilience study ](https://dinand.com/publications/inventory-under-uncertainty/)

Continue the thread

## A next room to enter.

[Choose another path ](https://dinand.com/)

[System profile · Working research product

### Assay Bench

A workbench for measuring the quality and checking cost of AI outputs when there is no simple answer key.

](https://dinand.com/experiments/assay-bench/)[System profile · Paper-only research prototype

### Personal Quant Agent

A research system that scans public market data, tests a small strategy library, applies explicit risk controls and records simulated decisions in a paper portfolio.

](https://dinand.com/experiments/personal-quant-agent/)[System profile · Working research product

### Pakhuis

A seventeenth-century Amsterdam canal warehouse, built in simulation, where two robots count stock and a reviewer decides what reaches the record.

](https://dinand.com/experiments/pakhuis/)

[All experiments ](https://dinand.com/experiments/)[Back home ](https://dinand.com/)

---
Site guide for AI agents: https://dinand.com/llms.txt