Erros de Planejamento Estratégico por Falta de Visibilidade em Dados Integrados
Definition
Search results show refineries use 'trial-and-error' and 'spreadsheet-based' planning for maintenance scheduling. A decision-support MILP tool would enable rapid scenario evaluation: (Scenario 1) maximize profit with 6-year intervals, (Scenario 2) compress to 18 months with higher maintenance cost, etc. The paper shows Scenario 3 vs Scenario 1 differs by 0.07% profit margin — small difference that scales to R$ 50-200M annually on a refinery processing 250K bbl/day. Manual planning cannot discover this margin optimality; errors are common.
Key Findings
- Financial Impact: R$ 20-100M per turnaround cycle per refinery (estimated: 0.5-1% margin slippage due to suboptimal scheduling timing × R$ 5-20B annual refinery revenue)
- Frequency: Turnaround scheduled every 6 years; quarterly/annual campaign-level decisions made annually
- Root Cause: Lack of integrated data systems; absence of multi-period optimization models; reliance on historical 'rules of thumb' rather than data-driven scenario analysis
Why This Matters
The Pitch: Brazilian refineries waste R$ 20-100M per turnaround cycle by scheduling maintenance at wrong times or with misaligned resource allocations. Scenario modeling (MILP-based decision support) identifies optimal turnaround windows (low-margin seasons, contractor availability) and unit groupings, recovering 5-10% margin uplift per cycle.
Affected Stakeholders
VP Operations, Production Planning Manager, Turnaround Director, Finance/CFO, Process Engineer
Deep Analysis (Premium)
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:
- https://www.cetjournal.it/cet/25/117/172.pdf (describes current trial-and-error planning; demonstrates 0.07% margin difference between scenarios; shows impact of FCC campaign length on profit)
- https://vidyatec.com/blog/shutdown-and-turnaround/ (describes 'manual navigation of complex decision trees' as current state)
Related Business Risks
Perda de Receita por Parada de Turnaround Não Otimizada
Sobrecusto em Mão de Obra e Recursos Devido a Planejamento Manual
Multas Potenciais por Não Conformidade com NR-13 e Atrasos em Inspeção
Multas Ambientais e Administrativas por Não Conformidade Operacional
Custos de Remediação e Descomissionamento Obrigatório Não Previstos
Multas por Não Conformidade em Relatórios de Emissões de Carbono
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