🇧🇷Brazil

Desperdício em Estoque de Peças de Reposição por Previsão Inadequada

1 verified sources

Definition

A major Brazilian mining company (70+ years in market, 6,000 BR employees) with 12,723 spare parts in inventory demonstrated that without demand forecasting, annual spare parts spending reached R$942,676.38. Implementation of Moving Average forecasting method reduced this expenditure significantly. This represents pure waste from overstocking, obsolescence, and emergency rush orders.

Key Findings

  • Financial Impact: 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
  • Frequency: Annual recurring loss
  • Root Cause: Decision error: Reliance on manual inventory management and reactive purchasing instead of predictive demand forecasting; lack of visibility into spare parts lifecycle and consumption patterns

Why This Matters

Pitch: Brazilian machinery OEMs waste up to R$942,676 annually on poorly forecasted spare parts inventory. Implementation of Moving Average or exponential smoothing methods reduces this waste by identifying actual demand patterns.

Affected Stakeholders

Inventory Managers, Procurement Officers, Finance Controllers, Production Planners, OEM Supply Chain Directors

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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

Perda de Capacidade Produtiva por Falta de Peças Críticas

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

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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