A Time-Series Forecasting Model for Yield Improvement in Nigeria's Industrial Machinery Fleets: A Policy Analysis for Strategic Maintenance Optimisation

{ "background": "Persistent low yield from industrial machinery fleets in Nigeria's manufacturing and construction sectors represents a critical constraint on economic development. Current maintenance policies are largely reactive, leading to excessive downtime and capital inefficiency. A shift towards predictive, data-driven policy is required. ", "purpose and objectives": "This policy analysis evaluates a novel time-series forecasting model designed to measure and improve machinery yield. The objective is to provide a methodological framework for strategic maintenance optimisation, enabling evidence-based policy formulation for fleet management. ", "methodology": "The analysis employs a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, formalised as \ (B) \ (Bˢ) \ᵈ\Ds Yt = \ (B) \ (Bˢ) \ₜ, applied to historical operational availability and output data from a representative sample of machinery fleets. Model diagnostics include analysis of robust standard errors to assess parameter stability. ", "findings": "The forecasting model demonstrates a statistically significant predictive capability for machinery failure windows, with a lead time sufficient for proactive intervention. Application of the model to simulated policy scenarios indicates a potential yield improvement of 18-24% through optimised maintenance scheduling, contingent on data quality and institutional adoption. ", "conclusion": "The integration of time-series forecasting into maintenance policy presents a viable pathway for substantial yield gains. The model provides a quantitative basis for moving beyond schedule-based maintenance regimes, though its efficacy is dependent on systematic data collection and workforce upskilling. ", "recommendations": "Policymakers should mandate the standardised collection of machinery performance data. A pilot programme for model implementation in state-owned enterprises is advised. Investment in training for predictive maintenance analytics is essential for long-term sustainability. ", "key words": "Predictive maintenance, SARIMA modelling, industrial policy, asset management, operational research", "contribution statement": "This paper provides the first applied framework integrating SARIMA forecasting directly into national industrial maintenance policy for Nigeria, demonstrating a concrete methodology to translate operational data into