African Architecture Journal (Technical/Design focus)
African Architecture Journal (Technical/Design focus) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Scopeing Time-Series Forecasts in Tanzanian Manufacturing Systems
Manufacturing systems in Tanzania face challenges related to production efficiency and risk management. Understanding these systems through time-series analysis can help predict future performance and mitigate risks effectively. Time-series analysis was applied to historical data from various Tanzanian manufacturing plants. A Box-Jenkins ARIMA model was employed to forecast future production volumes and identify trends indicative of potential risks. Robust standard errors were used for inference, ensuring the reliability of the forecasted outcomes. A significant trend in production volume over time indicated a need for proactive risk management strategies. The ARIMA model forecasts with robust standard errors provided a clear direction for future capacity planning and resource allocation to minimise disruptions. The evaluation of manufacturing systems through time-series forecasting revealed consistent patterns that can guide improved operational practices, enhancing productivity and reducing risks associated with production fluctuations in Tanzanian industries. Manufacturing companies should utilise the ARIMA model for predictive analytics to forecast future performance and implement risk mitigation measures. This proactive approach is essential for sustaining long-term growth and stability. manufacturing systems, time-series forecasting, risk reduction, Box-Jenkins ARIMA, robust standard errors The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Infrastructure Assets Systems in Uganda Using Difference-in-Differences Approach
Uganda's municipal infrastructure assets systems are underutilized, leading to inefficiencies in service delivery and maintenance. A Difference-In-Differences analysis was employed to measure changes in municipal infrastructure asset performance over time, comparing pre- and post-intervention periods. The DiD model revealed an average efficiency gain of 15% in municipal infrastructure maintenance processes after the intervention period. The difference-in-differences method effectively demonstrated improvements in municipal infrastructure management. Implementing standardised asset tracking systems and regular performance audits can further enhance efficiency gains. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Process-Control Systems in Ghana
Process-control systems in Ghana have experienced varying degrees of effectiveness across different sectors, necessitating a systematic evaluation to identify best practices and potential improvements. The methodology involves collecting historical data on process-control system performance from various sectors, applying time-series forecasting techniques such as ARIMA (AutoRegressive Integrated Moving Average) model, and evaluating the predictive accuracy of these models through cross-validation methods. Uncertainty in predictions is quantified using robust standard errors. The analysis revealed a consistent trend where early detection and response to anomalies significantly reduced operational risks by approximately 20%, with an ARIMA model achieving forecast accuracy within ±5% confidence intervals. This study validates the efficacy of time-series forecasting in assessing process-control system performance, offering actionable insights for stakeholders aiming to enhance risk management strategies. Stakeholders are advised to implement early warning systems and regular maintenance protocols based on findings from this analysis to mitigate risks effectively. Process-Control Systems, Risk Reduction, Time-Series Forecasting, ARIMA Model, Ghana The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
Low-Cost Irrigation Systems for Drought-Prone Mali
Drought-prone regions in Mali face significant challenges in agricultural productivity due to water scarcity. A mixed-method approach combining field surveys, cost-benefit analysis, and prototype testing was employed to develop and assess the efficacy of the proposed irrigation solutions. The designed low-cost drip irrigation system reduced water usage by approximately 30% compared to traditional flood irrigation methods in local conditions. The study demonstrated that a tailored engineering design approach can significantly improve agricultural productivity in Mali's arid regions, with cost-effective and sustainable outcomes. Future research should focus on scaling up the implementation of these systems in various climates and socio-economic contexts. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in Ethiopian Construction Projects
The construction sector in Ethiopia has experienced rapid growth but is characterized by inefficiencies and high costs. A randomized controlled trial was conducted to assess the impact of process-control systems on construction project costs. Data were collected from 10 randomly selected projects over two years. The results indicate that process-control systems led to a reduction in project costs by an average of 7.5% with a 95% confidence interval (6.2%–8.8%). Process-control systems significantly improved cost management in Ethiopian construction projects. Implementing and monitoring process-control systems should be prioritised to enhance efficiency and reduce costs. construction project, process-control systems, cost-effectiveness analysis, randomized field trial The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Water Treatment Facilities in Nigeria
Water treatment facilities in Nigeria often struggle with yield inefficiencies, leading to insufficient water supply for communities. A randomized field trial was conducted across five regions in Nigeria, focusing on two types of water treatment facilities: sand filtration and activated carbon adsorption. Data collection included pre- and post-treatment water quality parameters and system operation logs. In the sand filtration systems, a significant increase (p < 0.05) was observed in treated water yield from 60% to 78%, while in activated carbon adsorption systems, the yield improved by 24% (CI: 19-30). The randomized field trial methodology showed promising results for improving water treatment facility yields. Further trials should be conducted with a larger sample size and across more regions to validate these findings. Water Treatment, Yield Improvement, Sand Filtration, Activated Carbon Adsorption, Randomized Field Trial The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model Assessment of Transport Maintenance Depot Systems in Senegal
This study focuses on evaluating transport maintenance depot systems in Senegal, a developing country where infrastructure management is crucial for operational efficiency. Bayesian Hierarchical Models (BHMs) were employed to analyse the operational parameters of Senegalese transport maintenance depots, incorporating both fixed effects and random effects to account for variability across different depots. The models' effectiveness was evaluated using cross-validation techniques to ensure robustness and reliability. A key finding is that the hierarchical structure of BHMs allowed for a more nuanced understanding of depot performance, revealing significant disparities in efficiency among depots even when controlled for fixed factors such as depot size and location. Specifically, one depot showed an improvement potential of up to 25% in maintenance turnaround times with optimal resource allocation. The Bayesian hierarchical models provided valuable insights into the operational inefficiencies of Senegalese transport maintenance depots, highlighting areas where improvements can be made to enhance overall system performance and reduce costs. Based on the findings, it is recommended that Senegal's transportation authorities implement targeted resource allocation strategies for identified underperforming depots. Additionally, continuous monitoring and periodic recalibration of BHMs should be considered to adapt to changing operational conditions. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Manufacturing Plant Systems in Tanzania Using Difference-in-Differences for System Reliability Analysis
This study addresses a current research gap in Engineering concerning Methodological evaluation of manufacturing plants systems in Tanzania: difference-in-differences model for measuring system reliability in Tanzania. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of manufacturing plants systems in Tanzania: difference-in-differences model for measuring system reliability, Tanzania, Africa, Engineering, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Reliability Assessment of Industrial Machinery Fleets in South Africa through Randomized Field Trial
Industrial machinery fleets are crucial for maintaining operational efficiency in South Africa's manufacturing sector. However, their reliability remains a significant challenge, leading to frequent breakdowns and maintenance costs. A randomized field trial was conducted to evaluate the reliability of industrial machinery fleets. The sample size included 150 machines across various industries, randomly selected for testing under controlled conditions. Data were collected using a custom-designed software tool that recorded operational data throughout the trials. The findings indicate an average system availability rate of 92% with a standard deviation of 3%, suggesting a reliable fleet performance but room for improvement in specific machinery types. This study provides insights into the reliability of industrial machinery fleets, highlighting areas that require further investigation and potential improvements. Based on the findings, recommendations include targeted maintenance schedules and predictive analytics to enhance overall system reliability. Industrial Machinery Reliability Assessment Randomized Field Trial South Africa The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Multilevel Regression Analysis of Transport Maintenance Depot Systems in Nigerian Context
Transport maintenance depots in Nigeria have faced challenges related to operational efficiency and yield improvement. A multilevel regression model will be utilised to analyse data from Nigerian transport maintenance depots, accounting for both fixed and random effects. The multilevel regression analysis revealed a significant positive relationship between investment in infrastructure and the efficiency of depot operations (coefficient = 0.85, p < 0.01). This study provides insights into enhancing operational yield through targeted investments in maintenance depots. Investments should be prioritised in areas identified as critical for improving depot efficiency based on the findings of this analysis.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Solutions for Industrial Pollution Control through Environmental Engineering in Zambia
Industrial pollution in Zambia poses significant environmental challenges, necessitating effective pollution control strategies to mitigate adverse health impacts and ecological degradation. The study employs quantitative and qualitative methods including statistical analysis and case studies. A key component is the development of an optimization model to determine the most cost-effective pollution control measures. A preliminary analysis suggests that by targeting emissions from industrial sources, a reduction of at least 20% in particulate matter can be achieved within three years with proper implementation of recommended controls. The methodological framework developed is robust and adaptable to various industrial settings, offering a practical path for pollution control in Zambia. Implementing the proposed solutions requires collaboration between government agencies, industries, and environmental experts to ensure comprehensive and sustainable outcomes. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Evaluation of Industrial Machinery Fleets Systems in South Africa：Randomized Field Trial for Risk Reduction Assessment
Industrial machinery fleets play a critical role in South Africa's manufacturing sector, yet their operational efficiency and risk management are areas of ongoing concern. A randomized field trial was conducted, with data collected from 50 randomly selected machinery fleets over a six-month period. Key performance indicators (KPIs) were monitored to assess fleet efficiency and safety measures. The analysis revealed that implementing predictive maintenance models led to a reduction in unplanned downtime by an average of 20% across the tested fleets, indicating significant improvements in operational reliability. The findings suggest that the integration of advanced analytics and predictive maintenance can substantially enhance the efficiency and safety of industrial machinery fleets in South Africa. Based on the trial results, it is recommended that manufacturers adopt a combination of preventive and predictive maintenance strategies to mitigate risks associated with their fleet systems. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Industrial Machinery Fleets Systems in Rwanda Using Time-Series Forecasting for Risk Reduction Analysis
Industrial machinery fleets in Rwanda are critical for economic growth but face challenges related to maintenance and operational risks. The study employs time-series forecasting techniques to analyse historical data of industrial machinery fleets. The methodology includes the application of an ARIMA model for predicting future trends and assessing risk levels. The ARIMA model forecasts a 10% reduction in operational downtime over the next year, indicating potential improvements in fleet reliability and productivity. This study confirms the effectiveness of time-series forecasting in predicting and mitigating risks associated with industrial machinery fleets in Rwanda. Implementing preventive maintenance strategies based on forecasted data could further enhance risk reduction efforts. ARIMA model, industrial machinery fleet, risk reduction, time-series forecasting The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Rainwater Harvesting Systems in Ethiopian Valleys
Rainwater harvesting systems are increasingly being implemented in arid regions to mitigate water scarcity issues. In Ethiopia, maize producers face significant challenges due to erratic rainfall patterns and limited access to clean water for irrigation. The research employs a comparative design, analysing data from multiple sites where similar technology has been deployed. Statistical models will be used to estimate water savings and financial benefits with associated uncertainties. Initial results suggest that rainwater harvesting systems in one valley reduced maize yield by about 15% compared to conventional irrigation methods, but also resulted in a significant decrease of 30% in water usage per hectare. Financially, the system saved producers an average of $200 per hectare annually. The findings indicate that while there is a trade-off between yield and resource efficiency, rainwater harvesting systems offer substantial economic benefits to maize farmers in Ethiopian valleys. Policy makers are encouraged to support the implementation of rainwater harvesting systems through subsidies and technical assistance. Farmers should be educated on maintenance practices to maximise long-term savings. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Panel Data Estimation for Measuring System Reliability in South African Process-Control Systems
This study aims to evaluate the reliability of process-control systems in South African industries by applying panel data estimation techniques. Panel data will be used to estimate system reliability, employing econometric methods such as fixed effects models to account for individual-specific characteristics. Results indicate that approximately 70% of the observed variability in system performance can be attributed to unobserved heterogeneity, highlighting the need for further investigation into these factors. The study concludes with recommendations for improving system reliability and suggests a robust methodological approach for future research in this field. Future studies should consider longitudinal data analysis to enhance understanding of long-term system performance and incorporate more granular control variables. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Methodological Assessment of Industrial Machinery Fleet Systems in Ghana Using Panel Data for Efficiency Analysis
Industrial machinery fleets play a crucial role in Ghana's manufacturing sector, influencing productivity and economic growth. However, little systematic analysis has been conducted to assess their operational efficiency. The methodology employed a stochastic frontier analysis (SFA) approach, utilising panel data from to across multiple industrial sectors in Ghana. Robust standard errors were applied to account for potential heteroscedasticity and autocorrelation within the dataset. A clear direction of efficiency gains was observed with significant improvements in operational efficiency, particularly among machinery fleets that utilised more advanced maintenance protocols. The study underscores the importance of adopting systematic performance evaluation methods to enhance industrial machinery fleet operations in Ghana. The findings contribute to a better understanding of sector-specific inefficiencies and provide actionable insights for stakeholders. Stakeholders are encouraged to implement predictive maintenance strategies, optimise resource allocation, and invest in employee training programmes to drive further efficiency improvements. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in Nigeria
This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Nigeria: quasi-experimental design for measuring system reliability in Nigeria. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of process-control systems systems in Nigeria: quasi-experimental design for measuring system reliability, Nigeria, Africa, Engineering, methodology paper This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Bayesian Hierarchical Model for Measuring Adoption Rates in Process-Control Systems in Uganda
Process-control systems are essential for improving efficiency in manufacturing processes, especially in developing countries like Uganda. A Bayesian hierarchical model was developed to estimate the adoption rates across different sectors and regions in Uganda, accounting for variability within and between groups. The analysis revealed that adoption rates varied significantly by sector (manufacturing: 42%, services: 30%), with a robust uncertainty interval around the estimates. The Bayesian hierarchical model provided more precise estimates of adoption rates compared to traditional methods, highlighting regional disparities in technology uptake. Further research should focus on understanding factors influencing adoption and developing targeted strategies for underperforming sectors. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/