🇧🇷Brazil

Decisões de Compra Inadequadas por Falta de Previsão de Demanda Precisa

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Definition

The Brazilian mining company case study applied three forecasting methods (Simple Moving Average, Weighted Average, Exponential Moving Average) and calculated Mean Absolute Deviation (MAD) forecast error to validate which model reduced unnecessary spending. Result: R$942,676.38 annual spend without method vs. lower spend with correct method. Decision errors compound: wrong forecasts → wrong orders → rush expedites → inventory shrinkage → compliance/audit issues.

Key Findings

  • Financial Impact: 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
  • Frequency: Continuous (monthly purchasing decisions vulnerable to forecast error)
  • Root Cause: Absence of quantitative demand forecasting models; reliance on manual judgment or static reorder points; lack of cross-departmental data integration (sales/maintenance demand not linked to procurement decisions); tools (ERP/CMMS) not configured to auto-forecast

Why This Matters

Pitch: Brazilian OEMs using manual purchasing waste 15–30% of spare parts budget on wrong-item buys and rush orders. Time series forecasting (Moving Average, ARIMA, or Machine Learning) improves decision accuracy, cutting waste and reducing procurement lead times.

Affected Stakeholders

Procurement Managers, Buyers, Supply Chain Directors, Finance/CFO (budget variance), Maintenance Supervisors (demand input)

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Financial Impact

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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

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

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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