Supply Chain Cost of Time Readiness Agent

Supply chains are not just pricing freight right now. They are pricing time.

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I built this demo to show how an AI agent could help an operator read that timing risk at the purchase-order level.

Try it here: https://cost-of-time-in-supply-chains-simul.vercel.app

The demo works like a simple operating agent. It takes the different clocks around an order family and turns them into a decision readout:

• freight pressure
• fuel exposure
• tariff risk
• compliance readiness
• supplier timing
• demand cover
• margin buffer

The agent does not try to predict the exact future.

It helps answer a more practical question:

What action should this order trigger now?

Book now. Wait. Split. Re-source. Hold.

The demo also lets you compare three scenarios:

The premium works Early movement protects service, demand holds, and the paid timing option was worth it.

The air pocket arrives June pull-forward borrowed from July and August, inventory ages, and cash becomes the binding clock.

Policy and fuel keep squeezing Tariff uncertainty, fuel pass-through, and documentation gaps continue while booking windows shorten.

The reason this matters is that most companies already track these inputs, but they track them separately.

Logistics watches rates. Trade watches deadlines. Procurement watches suppliers. Finance watches margin. Sales watches the customer date.

An agentic workflow can bring those clocks together and ask:

Which orders are exposed? Which deadline matters first? Which action should be triggered? Who needs to own the next decision?

That is the point of the demo. Not perfect forecasting.

A better operating rhythm for deciding which clock to pay for, which one to wait on, and which one to stop before it becomes margin, inventory, or service risk.