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SupplyGraph.ai — Entity-linked supply-chain risk graph with quantified shock propagation: a supplier-to-end-producer dependency graph over the semiconductor complex, an event feed of discrete occurrences (supplier price increases, raw-material shortages) each stamped with a date and an affected party, and per-event propagation output giving the directed path plus latency-to-impact in days for each hop and a total time-to-margin-impact. Under it sits a daily commodity and process-material price series quoted in CNY per kilogram.. Small team, high-intensity data operation: a roughly four-person…
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Entity-linked supply-chain risk graph with quantified shock propagation: a supplier-to-end-producer dependency graph over the semiconductor complex, an event feed of discrete occurrences (supplier price increases, raw-material shortages) each stamped with a date and an affected party, and per-event propagation output giving the directed path plus latency-to-impact in days for each hop and a total time-to-margin-impact. Under it sits a daily commodity and process-material price series quoted in CNY per kilogram.
Scale Four proprietary databases refreshed continuouslyCoverage Information Technology · FinancialsAsset class Equities · Commodities
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SupplyGraph.ai — Entity-linked supply-chain risk graph with quantified shock propagation: a supplier-to-end-producer dependency graph over the semiconductor complex, an event feed of discrete occurrences (supplier price increases, raw-material shortages) each stamped with a date and an affected party, and per-event propagation output giving the directed path plus latency-to-impact in days for each hop and a total time-to-margin-impact. Under it sits a daily commodity and process-material price series quoted in CNY per kilogram.. Small team, high-intensity data operation: a roughly four-person…
SupplyGraph.ai offers (Alternative, Supply Chain, Sentiment) — Four proprietary databases refreshed continuously, at least two commodity price series at daily resolution in CNY per kilogram, a per-company event archive with date-stamped urls (this record dated 2026-06-22), and a dependency graph spanning upstream material producers, intermediate component makers and device manufacturers; Request further research on node and edge counts, which the site does not print.
Earnings and margin-miss prediction by mapping dated upstream cost shocks onto a named issuer with a known lag; alerts and position-risk screening when an upstream supplier raises price; input-cost inflation nowcasting for semiconductor and electronics coverage; supplier-substitution and second-source diligence; training or grounding data for agent workflows that answer which companies are exposed to an event and when; backtesting event-to-earnings propagation models
Coverage spans APAC, US, Japan; Semiconductors, Financial Exchanges & Data; alternative, supply_chain, sentiment; equities, commodities.
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