🇧🇷Brazil

Perda de Capacidade Produtiva por Falta de Peças Críticas

2 verified sources

Definition

Search results explicitly cite 'production downtime due to lack of elements for maintenance' as a known cost driver in Brazilian spare parts inventory management. The mining company case study classified parts using both ABC (relevance) and XYZ (criticality) methods to separate high-impact items. Without forecasting, critical parts unavailability cascades into facility shutdowns.

Key Findings

  • Financial Impact: Estimated R$5,000–R$50,000+ per production-day halt (mining/construction machinery dependent on continuous operation); documented case: mining company assessed average inter-demand intervals (ADI) and coefficient of variation (CV²) to identify high-risk stockout scenarios
  • Frequency: Episodic but preventable with forecasting; typically 2-5 unplanned halts per year per facility without forecasting
  • Root Cause: Intermittent or erratic demand patterns in spare parts misclassified as stable demand; manual reorder point calculations fail to account for lead time variability; absence of automated alerts when critical-part inventory drops below safe threshold

Why This Matters

Pitch: Brazilian OEMs lose production capacity worth thousands of R$ daily when critical parts go unstocked. Real-time demand forecasting (Time Series Analysis, Croston's method) ensures availability of high-criticality components, preventing unplanned downtime.

Affected Stakeholders

Operations Managers, Maintenance Planners, Plant Directors, Supply Chain Managers

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Financial Impact

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Current Workarounds

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Methodology & Sources

Data collected via OSINT from regulatory filings, industry audits, and verified case studies.

Evidence Sources:

Related Business Risks

Desperdício em Estoque de Peças de Reposição por Previsão Inadequada

R$942,676.38 annual waste per company (without forecasting method applied); typical range estimated at 15-25% of annual spare parts budget in mining/construction sectors

Decisões de Compra Inadequadas por Falta de Previsão de Demanda Precisa

R$942,676.38 documented annual loss from poor forecast selection; estimated 20–35% cost premium on rush/expedited freight (typical Brazil logistics markup); average 40–60 hours/month of manual reorder point recalculation in mid-sized OEM operations

Sobrecusto por Atrasos em Componentes de Longo Prazo de Entrega

Estimated: 2-8% of COGS annually; typical 200-300 machinery unit manufacturer = R$ 400,000-1,200,000/year in excess expediting, overtime, and storage costs. Manual demand forecasting delays = 40-60 hours/month admin overhead.

Perda de Capacidade por Engarrafamento em Componentes de Longo Prazo

Estimated: 5-15% capacity loss = 200-600 idle machine units/year per manufacturer. Revenue loss at R$ 5,000-50,000/unit = R$ 1,000,000-30,000,000 annually depending on manufacturer size. Idle labor: 20-40 hours/week × 30-50 employees when line stops.

Risco de Penalidades por Inconsistência em Documentação NF-e / NFC-e em Procurement

Hard penalty: R$ 5,000-50,000 per audit finding. Soft cost: 20-40 hours/month manual invoice reconciliation and SEFAZ re-submission. Estimated annual exposure: R$ 50,000-500,000 depending on audit frequency.

Rejeição de Nota Fiscal Eletrônica e Multas SEFAZ

Estimated: R$ 500–2,500 per rejected invoice (penalty + manual rework hours); typical loss = 2–5 rejected invoices/month = R$ 1,000–12,500/month or R$ 12,000–150,000/year

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