Resilient.

From best-mean plans to policies that hold under uncertainty.

Plans break. Policies hold. Every committed decision gets a resilience score before it commits — the percentage of plausible futures in which it remains the right call. Tail cost gets priced into the objective, not noticed in the post-mortem.

What this changes on your P&L
−11 to −18%Working capital
+4.2 ppTier-A service
−73%Under-protected nodes
EXPECTED
— SERIES 004 —
Resilient: plans that hold, even when the world moves.
01Current State

Best-mean plans break the moment reality drifts.

Enterprise Resource Planning (ERP) carries a single lead-time number per part. Reality carries a distribution — 9 days when the supplier is on rhythm, 31 days when the port is congested. The plan averages the two and protects neither.

Risk modeling, where it exists at all, is binary: did the scenario happen, yes or no. The dollarized probability curve — what is the cost of this shock, weighted by the likelihood it materializes? — never enters the math.

Best-mean optimisers maximise expected outcome and leave the tail outside the objective. The tail materialises once a year on average, and the post-mortem that follows tends to read like the post-mortem from three years ago, because the architecture that produced it has not changed.

Lead time · static vs learned
ERP STATIC · 14d
LEARNED · 9d → 31d
P50 · 13d
P75 · 19d
P90 · 26d
02Business Impact

What it costs.

Safety Stock · Defensive Bloat
$11M

Working capital trapped in uniform safety stock that nobody can defend on a balance-sheet review.

Service · Tier-A Breaches
4.2pp

Strategic-account OTIF breached during constrained periods. Future revenue silently shifts elsewhere.

Risk · Mitigation Guesswork
0 of 7

Named risks with a dollarized probability curve. Mitigation premium decisions made on gut, not ROI.

Tail Cost · Never Priced
P95

The worst-case scenarios that materialize once a year are never represented in the planning objective.

03VYAN Capability

What we bring.

  1. 01Distribution-driven inputs

    Every driver — lead time, scrap, yield, demand, cost — is a learned shape from history, not a value.

  2. 02Iteration ensemble

    Thousands of futures sampled by Monte Carlo or Latin Hypercube. The plan sees the full uncertainty cone before it commits.

  3. 03Outcome constraints

    A service floor, a margin floor, and a carbon ceiling each become either hard or soft bounds on the enterprise objective, depending on how the policy is configured.

  4. 04Resilient EVA + CVaR

    VYAN's objective combines Resilient Economic Value Add () with a Conditional Value at Risk () tail penalty — mean expected enterprise value minus a dollar penalty on the worst-tail outcomes. The objective explicitly trades expected outcome against tail fragility.

  5. 05Decision Resilience Scoring

    Every committed decision carries a forward score: in what percentage of plausible futures does this decision remain the right call?

  6. 06Named risk events with probability curves

    Port closures, supplier failures, demand pull-ins — each carries a probability distribution and dollarized impact.

04Business Value Chain

How it lands on the P&L.

Capability
Distribution-driven inputs + iteration ensemble + outcome constraints + Resilient EVA + Decision Resilience Scoring.
Process Change
Stress-testing happens before commit, not in the post-mortem after the shock has cost you. Mitigation premiums are sized against the dollarized probability curve of each named risk, so the question is what premium is justified at the current probability rather than whether the risk feels worth covering.
Financial Outcome
Working capital −11–18% from policy-sized buffers. Tier-A service +4.2 pp. Under-protected nodes cut by 73%.
Stress-testing belongs before commit, not after the shock has already cost you.
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