Falcon Defense & Aerospace / Supply Chain Vulnerability Findings

Executive Summary

Six findings prioritized by funded spend at risk. See long-form memo for the full narrative and recommended actions.

Finding 01 HIGH

Tier 3 small-business suppliers handle 74% of parts spend ($1.01T of $1.36T, 5 years)

172 active small-business vendors · avg OTD 79% · 0% DCAA-approved

What the data shows

Summed across all years, Tier 3 captures $1.007T of purchase order value vs $259B for Tier 1 primes and $94B for Tier 2. Despite holding only 10 prime-OEM relationships, T1's per-supplier spend is 17× higher than T3. The small-business layer is load-bearing.

So what

Any concentrated disruption — a supply shock, a quality incident, a capacity ceiling — at the Tier 3 layer hits 3/4 of ARES's parts pipeline. The lack of DCAA approval at Tier 3 means none of this spend is easily pass-through on cost-type contracts.

Finding 02 HIGH

Mission Critical parts are 30% sole-source — 144 parts with single points of failure

Flight Safety close behind at 29% sole-source

What the data shows

Of 479 Mission Critical parts, 144 are sole-source (30.1%). Flight Safety parts (634 total) show 29.0% sole-source. Routine and Operational parts cluster around 24–28%. There is no "criticality premium" driving dual-sourcing on the most mission-essential hardware.

So what

The highest-criticality categories should show the lowest sole-source share — they show the opposite. Each of the 144 Mission Critical sole-source parts is a program-stoppable failure mode. Dual-sourcing even 20% of these would materially de-risk platform deliveries.

Finding 03 HIGH

Zero Tier 3 suppliers are DCAA-approved (172 of 172 unapproved)

$1.01T flows through non-DCAA-compliant accounting systems

What the data shows

100% of Tier 1 suppliers (10/10) and 50% of Tier 2 (8/16) are DCAA-approved. In Tier 3, approval drops to 0%. This means the 172 small businesses handling the bulk of parts spend lack the accounting systems needed to pass through costs on cost-type government contracts.

So what

ARES is absorbing the administrative cost burden of T3 vendor audits internally. A DCAA-approval push targeting the top-spend T3 vendors (15-20 suppliers representing ~30% of T3 spend) would shift this burden back and open cost-type contract eligibility.

Finding 04 MED

Quality-OTD correlation is flat below 4.5 rating — step-function at the top

Suppliers rated 1.0-4.5 all cluster at ~79-80% OTD; 4.5+ jumps to 96%

What the data shows

Binning active suppliers by quality rating: 0-3.0 = 79.7% OTD, 3.0-3.5 = 79.7%, 3.5-4.0 = 80.0%, 4.0-4.5 = 80.3%, 4.5+ = 96.0%. The first four bins are statistically indistinguishable. Only the top 9 suppliers (rating 4.5+) deliver meaningfully different performance.

So what

"Raising a supplier from 3.5 to 4.0" probably won't yield OTD gains. The real leverage is identifying suppliers with structural improvement potential to reach 4.5+. This changes the supplier development framework from gradual coaching to targeted elevation.

Finding 05 MED

95 active suppliers are single points of failure (low OTD + sole-source)

48% of the active supplier base carries concentrated supply risk

What the data shows

Counting active suppliers with ON_TIME_DELIVERY_PCT < 80% that also supply at least one IS_SOLE_SOURCE part: 95 of 198 active suppliers match the profile. These vendors combine below-threshold OTD with single-source exclusivity.

So what

A focused dual-sourcing program on just these 95 vendors would reclassify roughly half the active portfolio's risk profile. Prioritize by (a) spend, (b) part criticality, and (c) whether a qualified alternate exists in DIM_PARTS with the same NSN/CAGE.

Finding 06 INFO

Lead time variability is uniform across tiers (~4.7 days deviation)

Tier performance differentiation comes from OTD & quality, not lead-time noise

What the data shows

Average absolute deviation from promised date: Tier 1 = 4.78 days, Tier 2 = 4.69 days, Tier 3 = 4.73 days. Average lead times cluster tightly at 186-188 days. There's essentially no tier difference in lead-time predictability.

So what

Don't build risk frameworks around lead-time volatility — it's a commodity baseline across all tiers. The differentiated levers are OTD hit rate (yes/no), quality defects, DCAA approval status, and sole-source concentration. Lead-time scheduling can treat all tiers as equivalent.

Method. AutoExplore ran 20 directed-mode hypotheses against FACT_PARTS_SUPPLY, DIM_SUPPLIER, and DIM_PARTS. Findings surfaced with p-value < 0.05 or effect size >10% of portfolio mean. Nulls (hypotheses that did NOT find signal) are logged in autoexplore-journal.md. See the long-form memo for prioritized recommended actions and the "what we didn't find" section. Synthetic demo data.