DependMap maps the multi-tier dependencies beneath your ERP's view, and turns a disruption into a costed, explainable impact brief in minutes.
SAP, Oracle and Infor can run powerful what-if simulations — copy a bill of materials, reschedule, model a supplier change. But that takes real configuration expertise, it's a manual exercise per scenario, and it works on the chain you hold directly: your tier-one suppliers. The exposure that catches firms out lives deeper — the sub-tier supplier two or three levels down that several of your "diversified" tier-ones quietly share, and that never shows up as a single point of failure in the BOM. DependMap maps that multi-tier structure and runs the cascade across it automatically. Complementary to a strong ERP — not a replacement for one.
An ERP what-if shows the tier-one impact. But mapping the dependencies below that — which sub-tier supplier several products quietly share — still means tracing across BOMs, contracts and inventory logs by hand. By the time that picture is clear, it's often already out of date.
A validated dependency graph answers the question directly: which products stop, what revenue is at risk, which suppliers offer a way out — with the reasoning shown, not asserted.
It ingests your bills of materials, supplier records and contracts and constructs a model of how every product traces down through sub-assemblies and raw materials to the suppliers and regions behind them — checked for consistency, with unsupported data flagged rather than guessed.
A monitor watches for disruption signals; a mapper traverses the graph to find every affected product and quantify the revenue at risk; alternatives and recovery timelines are surfaced where they exist.
The output is a plain-English impact assessment — affected products ranked by financial exposure, recovery options, and a clear confidence note — ready for a Chief Supply Chain Officer or CFO to act on.
DependMap takes standard ERP exports as they come. No deep integration to begin, no long procurement cycle — and because the pipeline is built for messy real-world data, you don't need to clean or reformat anything first.
Bills of materials, supplier master and volumes — straight from your ERP.
→Missing fields, inconsistent names and prose contracts are repaired where there's evidence, flagged where there isn't.
→The dependency graph is built and disruption scenarios are costed across your portfolio.
→A clear, explainable impact assessment you can share and act on.
Scheduled, read-only connectors to SAP, Oracle and Infor are a natural next step for teams that want the graph kept current automatically — and the whole system can run entirely on-premise for sensitive or export-controlled supply chains.
The dependency graph is checked for structural consistency on every build. The figures rest on a model that has been machine-verified, not eyeballed.
Every inference shows its working — this country was read from that contract clause, this supplier link from that delivery record. Nothing is asserted without a source.
Data the system can't support is quarantined with its provenance, not quietly patched. Where figures rest on estimates, it says so.
We tested the core principle against a real event: the March 2021 fire at Renesas's Naka semiconductor plant, which rippled through the global automotive industry. The test was designed to be blind — the analysis never knew a fire was coming.
We built a representative automotive-semiconductor network from publicly known relationships — six chipmakers, six tier-one suppliers, eight carmakers — using only who-supplies-whom. No market-share figures, no hint that any node was larger or more fragile than another. Then we ranked every node by structural systemic risk, and only afterwards revealed which one caught fire.
One tier-3 node, no direct carmaker relationships, still reaches every OEM in the network through the tier-one suppliers it feeds. That reachability is what the structural ranking captured — before the fire was revealed.
With every market-share figure removed, pure network position still put Renesas at the top of the systemic-risk ranking. Its place in the graph — feeding the widest set of tier-one suppliers, which between them reach every carmaker — is what set it apart. When the fire struck that exact node, the disruption spread along the path the structure had already drawn, reaching Toyota, Nissan, Honda, Ford, GM and Volkswagen through suppliers they never knew they shared.
That is the whole principle behind DependMap: the shape of a supply network tells you where a disruption will travel, before it travels there.
Method note: this is a capability demonstration on a representative network reconstructed from public sources, not a claim to have modelled Renesas's actual contract book. The relationships are illustrative of the documented structure of the automotive-semiconductor supply chain; the test shows that a structural analysis, run blind to the outcome, identifies the node that in fact cascaded. It demonstrates the method's logic against a known case — it does not claim to have predicted the fire itself.
The first scenario is on us. Share a single product line's data and we'll build its dependency map and show you a disruption you may not currently have visibility of.
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