Working prototype · public data only · Mattel supply-to-margin intelligence

Three supply signals that move Mattel's margin before the P&L prints.

The supply decisions you make this quarter set next year's gross margin. Inventory, sourcing, and freight each commit months before the margin shows up in earnings. Each tool reads Mattel's own public filings and reports one operator number, with the lead time to still act on it.

Built on real SEC filings and public indices, not a forecast you have to trust. Three reads, one operating question each: am I building too much, sourcing too concentrated, or shipping too exposed?

The three reads

Pick a signal.

How to read them

Each tool is calibrated to Mattel, not a peer average. A single off-the-shelf trip-wire would already be in the literature; the work is tuning each signal to Mattel's own operating history, so the score means something for this business.

Each leads with the verdict. Open a tool and the recommended read is on screen. The scenarios are one click, the controls optional. Built on the operator-legible pattern, not a dashboard to configure.

Public data, every number traced. SEC 10-K and earnings disclosures, the HTS/Section-301 tariff schedule, and public freight indices (Drewry, Freightos). Where a private input is needed, it is shown as a clearly labelled, calibrated estimate, never a number the model claims to know.

The lead time is the asset. Inventory, sourcing, and freight all commit before the margin event. Reading them early turns a markdown or a tariff hit into a decision you can still make.

Prototype suite prepared for Mattel · data: SEC EDGAR (Mattel 10-K, earnings 8-K/calls), USITC HTS + Section-301 schedule, Drewry World Container Index / Freightos Baltic Index. Synthetic inputs are visibly labelled and calibrated to cited anchors. Jeff Pinto, data science / ML / operations analytics · github.com/bigbrownjeff.

Preview · Mattel supply-intelligence suite · v0.1