Methodological Evaluation and Time-Series Forecasting for Manufacturing System Reliability in Ethiopia: A 2000–2026 Analysis

"background": "The reliability of manufacturing systems is a critical determinant of industrial productivity and economic growth. In developing economies, systematic evaluation and forecasting of this reliability are often lacking, hindering proactive maintenance and capacity planning. ", "purpose and objectives": "This study aims to methodologically evaluate the reliability of manufacturing systems and to develop a robust time-series forecasting model for predicting future reliability trends, thereby supporting strategic decision-making. ", "methodology": "A methodological framework was developed, integrating reliability engineering principles with statistical forecasting. Historical operational data from multiple manufacturing plants were analysed. The core forecasting model is an autoregressive integrated moving average (ARIMA) formulation: Rt = \ + \1 R{t-1 + \1 -1 + \, where Rₜ is system reliability. Model parameters were estimated using maximum likelihood, and forecasts were generated with 95% confidence intervals. ", "findings": "The analysis indicates a concerning downward trend in aggregate system reliability, with a forecasted decline of approximately 15% over the next five-year period. The ARIMA (1, 1, 1) model provided the best fit, with all parameters statistically significant at the 5% level. The forecast uncertainty, represented by the confidence intervals, widens substantially in later periods. ", "conclusion": "The methodological evaluation confirms the applicability of time-series analysis for reliability forecasting in this context. The projected decline in system reliability underscores an urgent need for intervention to prevent significant losses in manufacturing output. ", "recommendations": "Manufacturing firms should adopt predictive maintenance strategies informed by such reliability forecasts. Policymakers are urged to support initiatives for advanced maintenance training and the adoption of industry 4. 0 technologies to enhance system monitoring. ", "key words": "System reliability, Forecasting, Time-series analysis, Manufacturing, Maintenance engineering, ARIMA modelling", "contribution statement": "This paper provides a novel, empirically grounded forecasting methodology tailored for manufacturing system reliability in a developing industrial context