SCIRISK
Risk Capital for Supply Chains
11 followers
Risk Capital for Supply Chains
11 followers
SCIRISK helps boards and supply chain leaders price supply chain risk in financial terms, turning resilience from rhetoric into a number you can act on. Unlike spend-based heatmaps or black-box scores, ESCRC models your full multi-tier network with Monte Carlo VaR/ES, cites every parameter, discloses regional correlation, and stamps each run with a seed for exact reproducibility.
Love that the seed-based reproducibility is baked in, that alone sets you apart from the black-box scoring tools I've tried. One thing that would make it stickier for my team: a side-by-side scenario view where we can toggle a tariff shock or climate disruption against a baseline run and instantly see the delta in VaR and ES by tier. Right now exporting two runs and diffing in Excel feels clunky for what should be a core workflow.
@enaypbab thanks for your feedback. Scenario comparison is already avilable as a placeholder in the tool. We have added the geopolitical and macroeconomic shocks to our dev roadmap. In the next release they will be integrated in the core workflow. Stay tuned!
A really specific and useful angle here. One thing I'd love to see is a way to export the seeded Monte Carlo runs as raw CSVs so we can plug them into our own internal risk models without re-running anything. Would make audit handoff way smoother.
@anlmaz0sy8 thanks for pointing that out. Exporting seeded MC runs as a CSV is an easy one to implement and makes the audit track more defendible. You can already export the VaR, ES and several other metrics to a CSV file, see panel below results. We will add it to the dev roadmap and include the requested feature in the next release.
One thing that would make this way more useful for my board is a simple "what changed since last quarter" view that highlights which tier-2 or tier-3 suppliers moved the VaR the most, ideally exportable as a one-page PDF we can drop straight into the risk committee deck.
@demet46p6 thanks for bringing it up. You csn actually save your current network and use the compare function to track risk changes over time. We are adding the report generation to the dev roadmap right now. Thanks!
The Monte Carlo VaR approach actually breaks down our multi-tier network in a way that's defensible to the board, and I appreciate that every parameter cites its source. Surprised how much time the reproducibility seed saves during our quarterly reviews.
@ardazo5v thanks for reviewing the MC VaR results. Indeed, reproducibility and explainability of the reported risk figures was one of our main objectives.
Took a quick look and the Monte Carlo VaR output actually ties back to each parameter source, which is rarer than it should be in this space.
@semihdolaner thanks for reviewing the model's VaR Output, that's right. The MC simulation engine went through a rigorous independent model review.
The seed-stamped reproducibility is a really sharp touch. Most risk tools hand you a number and a shrug, so seeing every run tied to a citable seed and explicit correlation assumptions makes this feel like something built by people who've actually been burned by a model they couldn't defend.
@ikranure11220 thanks for reviewing our tool. Indeed, we've seen how models break many times in our more than 20 years' model development practice.