Why “People-Only” Compliance Can’t Scale (and What to Do Instead)

TL;DR

Modern manufacturers juggle tens of thousands of parts, hundreds or thousands of suppliers, and a flood of evolving regulations. A people-only compliance model collapses under this scale: you’d need unlimited capacity, the costs don’t pencil out, and manual processes introduce latency and error. The answer isn’t replacing experts—it’s redesigning the operating model so automation, analytics, and human judgment work together.

The uncomfortable truth: humans can’t keep up with the scale

• The scale problem. Even a single product line can touch thousands of parts and hundreds of suppliers. In autos, for example, an OEM might have ~250 tier-one suppliers—but 18,000 across all tiers; aerospace averages ~12,000 across tiers. That’s the real workload surface area your team must govern. (McKinsey & Company)

• The cost problem. U.S. manufacturers already spend ~$350B annually complying with federal regulations—costs that rise with every new rule and every new market entered. For smaller firms, the burden per employee is especially heavy. (NAM)

• The change-velocity problem. Requirements (e.g., REACH registration thresholds, dossier checks, and restriction proposals) keep moving—creating a permanent re-validation loop for your data and documents. (Environment, National Law Review)

When you multiply that scale × cost × change velocity, a people-only model inevitably falls behind.

Why people alone break at scale (8 failure modes)

1. Capacity math doesn’t work.
   Imagine 30,000 parts and 2 supplier documents per part (declaration + test): that’s 60,000 docs. At just 10 minutes per doc, you’re at 600,000 minutes = 10,000 hours ≈ 5.6 FTE-years of effort—before re-checks, translations, or customer-specific certificates.

2. Labor costs don’t pencil out.
   Compliance spend already consumes hundreds of billions. Scaling headcount linearly with parts/suppliers is the most expensive path—and it’s the least resilient in high-change periods. (NAM)

3. Error rates compound across fields, forms, and handoffs.
   Manual data entry error rates in research and operations typically land around ~1%–3% (and higher without double entry). Across hundreds of thousands of captured fields, that becomes thousands of defects that must be found and fixed. (PMC, Oxford Academic, Invensis)

4. Latency kills launches.
   Supplier outreach alone can eat cycles. Example: 500 suppliers × 5 nudges × 5 minutes per touch = 12,500 minutes = ~208 hours (~1.2 FTE-months) per campaign. Repeat for each framework, refresh, or product family.

5. Every format, every language, every portal.
   Declarations arrive as PDFs, spreadsheets, scans, and screenshots—in English, German, Mandarin, etc. Humans can handle the variety—slowly. Automation handles it consistently.

6. Moving targets: re-validation never ends.
   Certificates expire; a part changes; new rules land; audits request new evidence. REACH alone sees ongoing checks and decisions that force registrants to update dossiers—today’s “done” becomes tomorrow’s “rework.” (National Law Review)

7. Fragmentation creates blind spots.
   When sales, quality, procurement, and compliance each track their own copies, you get duplication, version drift, and missed renewals—especially across time zones and sites.

8. Key-person risk.
   Tribal knowledge—how a specific auditor interprets clause X, which supplier requires translation Y—often lives in inboxes. People change roles; your audit history should not.

A better operating model: automate the grunt work, elevate the experts

This isn’t about replacing experts. It’s about moving humans to the highest-leverage decisions and letting machines handle the repetitive, error-prone, and time-sensitive work.

Core principles:

1. Single source of truth for readiness. Centralize parts, suppliers, frameworks, documents, and statuses so every function works off one reality (and your PLM/ERP stays in sync).

2. Automated intake & validation. Parse BOMs; ingest vendor docs; validate content against the right rules (e.g., REACH, RoHS, PFAS) with auditable checks—24/7.

3. Continuous change monitoring. Track regulatory updates and expiring evidence so renewals and impact analyses are triggered automatically (not discovered during a customer escalation). (Environment)

4. Risk-based prioritization (not FIFO). Use imputed risk—the probability a part/product fails a framework—to triage what to chase first. High-risk parts (e.g., PFAS-prone coatings, seals) get early attention; low-risk metals can wait.

5. Multilingual supplier orchestration. Standardize campaigns, SLAs, and reminders; measure response times; auto-nudge in the supplier’s language; escalate by risk.

6. Human-in-the-loop governance. Specialists resolve exceptions, approve borderline cases, and own auditor-facing interpretations—with full traceability.

Quick math: why automation changes the curve

Document processing.

• 60,000 docs × 10 minutes = 10,000 hours (people-only).

• If automation cuts reading/entry by 80%, human time falls to 2,000 hours (review, exceptions, audits) and frees experts for higher-value work.

Supplier campaigns.

• 500 suppliers × 5 touches × 5 minutes = 208 hours per wave.

• Standard templates + scheduled nudges + portal intake slash handling time while improving response consistency and audit trail.

Error containment.

• At 1% manual entry error across 300,000 captured fields (e.g., 60,000 docs × 5 fields), you’d expect ~3,000 defects. Preventing/flagging errors up front is far cheaper than fixing them downstream—a real-world reflection of the “1-10-100” cost pattern. (PMC, Oxford Academic, Magellan Solutions)

The executive takeaway

• People are essential—just not as the primary engine. Use them where judgment matters most.

• At scale, manual equals fragile. Costs rise, errors accumulate, launches slip.

• Automate to de-risk and accelerate. With a centralized system, continuous monitoring, and risk-based prioritization, you move faster and reduce exposure.

Leaders who modernize their compliance operating model will see the shift we all want: from bottleneck to business accelerator.