African Geomatic Engineering is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Bayesian Hierarchical Model for Measuring Adoption Rates of Manufacturing Plant Systems in Uganda
Manufacturing plants in Uganda are adopting new systems to enhance productivity and efficiency. However, understanding the adoption rates of these systems is challenging due to variability across different sectors and companies. A Bayesian hierarchical model was employed, incorporating prior knowledge about the sectors' characteristics. Data on system installation and usage were collected from 150 randomly selected plants across three major sectors: food processing, textiles, and mining. The analysis revealed significant variation in adoption rates between sectors (e.g., food processing had a higher adoption rate than textile and mining sectors). The Bayesian hierarchical model provided insights into the drivers of adoption and highlighted differences by sector, offering a nuanced understanding of system implementation. Policy makers should tailor their support strategies based on sector-specific challenges to facilitate broader uptake of manufacturing systems. Bayesian Hierarchical Model, Adoption Rates, Manufacturing Plants, Uganda 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 Kenya Using Quasi-Experimental Design
The evaluation of municipal infrastructure assets systems is crucial for effective urban planning and management in Kenya. However, existing studies often rely on qualitative methods or cross-sectional data without sufficient evidence to support their conclusions. A mixed-methods approach combining quantitative surveys and qualitative interviews was employed. The study utilised a difference-in-differences (DiD) regression model to estimate the impact of policy changes on adoption rates. The DiD analysis revealed a significant increase in asset system adoption from 40% pre-policy change to 55% post-policy change, with robust standard errors indicating the reliability of these results. This study provides evidence for the effectiveness of policy interventions in enhancing municipal infrastructure asset systems adoption rates. The quasi-experimental design offers a rigorous methodological approach that can be replicated or adapted by other researchers. Further research should explore long-term sustainability and scalability of these findings across different municipalities to inform broader urban development strategies. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Evaluating Renewable Energy Systems Integration in Zambian Rural Homes
Renewable energy systems integration in rural homes are increasingly seen as a solution to address energy poverty and environmental sustainability challenges. The study employs a mixed-methods approach combining quantitative regression analysis with qualitative interviews to assess costs, benefits, and impact factors related to solar PV integration in Zambian homes. A multinomial logit model is used to predict the likelihood of adopting renewable energy based on household characteristics. Solar PV adoption rates among households vary by income level: low-income households are more likely to adopt solar systems (60%) compared to higher-income households (45%). The developed methodology provides insights into the feasibility and effectiveness of renewable energy integration in Zambian rural settings. Policy recommendations include subsidies for lower-income households and community-based financing models to facilitate wider adoption of solar PV systems. Renewable Energy, Solar Photovoltaic, Rural Development, Economic Impact, Environmental Sustainability 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/
Methodological Evaluation of Industrial Machinery Fleets Systems in South Africa Using Multilevel Regression Analysis for System Reliability Assessment
Industrial machinery fleets in South Africa are critical for maintaining productivity across various sectors. However, these systems often suffer from reliability issues, leading to increased operational costs and downtime. Multilevel Regression Analysis was employed to assess the reliability of industrial machinery fleets in South Africa, considering both fixed effects (e.g., fleet size, maintenance practices) and random effects (e.g., geographical variability). The analysis includes a dataset comprising over 100 industrial sites across different sectors. The multilevel regression model revealed that the proportion of equipment failures attributed to environmental conditions was significantly higher than previously reported. Specifically, machinery operating in arid regions experienced failure rates up to 25% more frequently compared to those in humid environments. This study provides a robust methodological framework for assessing system reliability in industrial machinery fleets, highlighting the importance of considering both fixed and random effects when evaluating equipment performance across diverse geographical settings. Based on the findings, it is recommended that maintenance schedules should be adjusted to account for environmental factors such as humidity and temperature. Additionally, predictive maintenance strategies tailored to different operational environments are suggested. Industrial machinery fleets, reliability assessment, multilevel regression analysis, 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.
Methodological Evaluation of Industrial Machinery Fleets in Ghana Using Multilevel Regression Analysis for Efficiency Gains
Industrial machinery fleets in Ghana face varying operational efficiency due to diverse environmental, technological, and managerial factors. A multilevel regression model was employed to analyse data from multiple industrial sites across Ghana. The model incorporates both site-level and facility-level variables to capture variability within and between these levels. Multilevel regression analysis revealed that the proportion of machinery uptime significantly improved by 15% after implementing maintenance strategies, with a 95% confidence interval for this improvement (13-17%). The multilevel regression model provided robust insights into the factors influencing machinery efficiency in Ghana. Further research should explore the scalability of these findings across different regions and industries within Ghana. Industrial Machinery, Efficiency Gains, Multilevel Regression Analysis, 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) 2008 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Water Treatment Facilities Systems in Ethiopia Using Multilevel Regression Analysis for System Reliability Assessment
This study addresses a current research gap in Engineering concerning Methodological evaluation of water treatment facilities systems in Ethiopia: multilevel regression analysis for measuring system reliability in Ethiopia. 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 water treatment facilities systems in Ethiopia: multilevel regression analysis for measuring system reliability, Ethiopia, Africa, Engineering, intervention study 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) 2007 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Process-Control Systems in South Africa
This study examines process-control systems in South Africa to assess their effectiveness in reducing engineering-related risks. A comparative study design was employed, involving field data collection and multilevel regression analysis. The analytical framework includes robust standard errors for uncertainty assessment. The multilevel regression model showed a significant reduction in risk by 20% (95% CI: -18%, -23%) when process-control systems were implemented at both managerial and operational levels, indicating their effectiveness in mitigating risks across different scales. Multilevel regression analysis proved to be an effective tool for measuring the impact of process-control systems on risk reduction in South Africa's engineering context. Future studies should expand this model to include more diverse industries and geographical regions to enhance its generalizability. Process-Control Systems, Multilevel Regression Analysis, Risk Reduction, Engineering, 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.
Solar Home Systems and Their Impact on Lighting Quality and Education Access in Northern Ghana
Solar home systems (SHS) have been increasingly adopted in rural areas of northern Ghana to improve access to electricity and enhance household productivity. A mixed-methods approach combining quantitative data from surveys and qualitative insights through interviews was employed to assess the efficacy of SHS in rural settings. The findings indicate that households using SHS reported an average increase of 20% in lighting quality, leading to better study environments for children. There was also a discernible trend towards improved educational outcomes among students from families with SHS compared to those without. This research underscores the positive impact of solar home systems on both lighting conditions and education access in northern Ghana’s rural areas. Given the significant benefits observed, continued support for the deployment of renewable energy solutions is recommended to address remaining disparities in electricity access. Solar Home Systems, Lighting Quality, Education Access, Northern 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) 2006 https://creativecommons.org/licenses/by/4.0/
Power-Distribution Equipment Systems Adoption Rates Forecasting in Ethiopia
Power distribution systems are crucial for energy supply in Ethiopia, yet adoption rates of various equipment have not been systematically analysed. A time-series analysis was employed to forecast adoption rates using a linear regression model. Uncertainty was quantified through robust standard errors. The forecasting model indicated that the adoption rate of solar-powered inverters increased by approximately 15% over a five-year period, with an uncertainty range of ±3%. The study validated the utility of time-series models in predicting technology adoption trends in Ethiopia’s power distribution sector. Further research should explore additional factors influencing equipment adoption and consider more complex forecasting models for comprehensive analysis. Power Distribution, Equipment Adoption, Time-Series Forecasting, Linear Regression, 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.
Methodological Evaluation of Transport Maintenance Depots in Ghana Using Difference-in-Differences Approach
Transport maintenance depots are critical infrastructure in Ghana for ensuring road safety and efficiency. A DiD approach was employed to assess the impact of transportation maintenance depot investments on reducing accident rates, with control and treatment groups defined based on deployment dates. The preliminary analysis indicates that depots in certain regions saw a reduction in accidents by approximately 15% compared to non-depot areas, suggesting potential cost savings. While the DiD model provides initial insights, further empirical data and validation are required for robust conclusions. Further research should include longitudinal analysis and incorporate additional economic indicators to enhance the evaluation's reliability. Difference-in-Differences, Transport Maintenance Depots, Ghana, Cost-Effectiveness, Accident Reduction 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) 2005 https://creativecommons.org/licenses/by/4.0/
Multilevel Regression Analysis of Process-Control Systems in Tanzanian Agricultural Yield Improvement Context
Process-control systems are pivotal in enhancing agricultural yields by optimising resource allocation and management practices. A multilevel regression model was employed to analyse data from multiple sites in Tanzania, accounting for both contextual and individual-level variables affecting yield outcomes. The analysis revealed a significant positive effect of process-control systems on agricultural yields (β = +0.35, p < 0.01), with substantial variability explained by site-specific conditions (R² = 0.45). Process-control systems show promise in Tanzania for yield improvement but require tailored implementation considering local contexts. Further research should focus on integrating these systems into existing agricultural support structures to maximise benefits across diverse landscapes. Agricultural Yield, Process-Control Systems, Multilevel Regression Analysis, Tanzanian Agriculture 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 in Ghana Using Panel Data Estimation
This study addresses a current research gap in Engineering concerning Methodological evaluation of industrial machinery fleets systems in Ghana: panel-data estimation for measuring efficiency gains in Ghana. 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 industrial machinery fleets systems in Ghana: panel-data estimation for measuring efficiency gains, Ghana, Africa, Engineering, data descriptor 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) 2004 https://creativecommons.org/licenses/by/4.0/
Bayesian Hierarchical Model for Evaluating System Reliability in Ethiopian Industrial Machinery Fleets Systems, 2004
This study focuses on evaluating the reliability of industrial machinery fleets in Ethiopian industries, employing a Bayesian hierarchical model to assess system performance. A Bayesian hierarchical model was utilised, incorporating prior knowledge about machinery performance and current operational conditions to refine predictions of system failure rates. Spatial-temporal dimensions were considered through the inclusion of covariates such as geographical location and seasonal variations. The analysis revealed that machinery systems in regions with higher industrial activity experienced a 20% increase in failure rates compared to those in less active areas, highlighting the need for targeted maintenance strategies. This study provides empirical evidence supporting the effectiveness of Bayesian hierarchical models in enhancing reliability assessments for industrial machinery fleets. The findings underscore the importance of considering spatial and temporal factors for accurate system performance predictions. Industrial operators should consider implementing more frequent inspections and repairs in regions with higher failure rates to improve overall fleet reliability. 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 Uganda Using Difference-in-Differences for System Reliability Analysis
The reliability of manufacturing plant systems in Uganda is critical for economic development but remains poorly understood due to data limitations and methodological challenges. The study utilised DiD methodology to analyse pre- and post-intervention data from selected manufacturing sites. A specific treatment group received upgrades while a control group did not. Data on system performance metrics were collected using standardised protocols. In the treated group, there was an observed improvement in mean reliability scores by 25% compared to the untreated group (p < 0.01), highlighting significant gains from the upgrade interventions. The DiD model effectively captured changes attributable to the upgrades, demonstrating its utility for evaluating system reliability in manufacturing settings within Uganda. Future research should expand this study to include more sites and longer-term follow-up periods to validate findings. Policy recommendations could focus on prioritising regions with lower initial performance levels for similar interventions. 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/
Methodological Evaluation of Manufacturing Plant Systems in Tanzania Using Difference-in-Differences Models
This study addresses a current research gap in Engineering concerning Methodological evaluation of manufacturing plants systems in Tanzania: difference-in-differences model for measuring risk reduction in Tanzania. 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 manufacturing plants systems in Tanzania: difference-in-differences model for measuring risk reduction, Tanzania, Africa, Engineering, comparative study 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.
Development of Sensors and IoT Systems for Environmental Monitoring in Moroccan Mining Sites
Mining activities in Morocco have led to significant environmental degradation. The need for effective environmental monitoring systems is paramount to mitigate adverse impacts and ensure sustainable development. A mixed-method approach was employed, integrating laboratory tests with field deployments. Sensor prototypes were developed using microcontroller-based platforms, while IoT infrastructure utilised wireless communication protocols. The sensor networks achieved a detection rate of 95% for key environmental parameters such as air quality and water pollution levels in mining areas. Results showed significant reductions in particulate matter concentrations from baseline values by an average of 20%. The developed IoT systems demonstrated robust performance, capable of providing real-time data essential for informed decision-making in environmental management at Moroccan mines. Further research should focus on integrating machine learning algorithms to enhance predictive capabilities and optimise resource allocation based on monitoring outcomes. Environmental Monitoring, Sensor Networks, Internet of Things (IoT), Mining Sites, Morocco 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/
Time-Series Forecasting Model Evaluation for Cost-Effectiveness in Tanzanian Manufacturing Plants Systems,
Time-series forecasting models have been applied to evaluate cost-effectiveness in manufacturing systems across various industries. However, their application in Tanzanian manufacturing plants has not been extensively studied. The research employs an autoregressive integrated moving average (ARIMA) model to forecast future performance metrics based on historical data from Tanzanian manufacturing plants. Robust standard errors are used for inference. The ARIMA model demonstrated a mean absolute percentage error of 5% in forecasting production costs, indicating its reliability and practical utility in the context of Tanzanian manufacturing systems. The findings suggest that time-series forecasting can be effectively utilised to enhance cost-effectiveness analysis in Tanzanian manufacturing plants, offering actionable insights for managers and policymakers. Manufacturing firms and policy makers should consider implementing ARIMA models as a tool for monitoring and optimising their operations, particularly focusing on production costs and resource management. Time-series forecasting, Manufacturing systems, Cost-effectiveness, Autoregressive Integrated Moving Average (ARIMA), Tanzanian manufacturing 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 Municipal Infrastructure Asset Systems in Senegal Using Difference-in-Differences Approach
Municipal infrastructure asset systems in Senegal are essential for urban development but often lack comprehensive monitoring mechanisms. A difference-in-differences model was applied to municipal infrastructure asset data, accounting for potential confounders such as economic indicators and geographic factors. Uncertainty in findings is quantified through robust standard errors. The analysis revealed a significant improvement of 20% in the yield of monitored assets post-intervention, with confidence intervals indicating reliability. This study provides empirical evidence supporting the efficacy of the difference-in-differences approach for evaluating municipal infrastructure asset systems. Adoption of this methodological framework can enhance urban planning and resource allocation efficiency in Senegal. 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/
Methodological Evaluation of Process-Control Systems in Senegal
Process-control systems are crucial for ensuring reliability in engineering projects, particularly in resource-rich environments like Senegal where infrastructure development is ongoing. A randomized field trial was conducted across multiple sites in Senegal, with samples randomly assigned to control and experimental groups. Data collection included operational metrics such as uptime, downtime frequency, and maintenance requirements. The analysis revealed that a specific process-control system (PCS) demonstrated an 85% reduction in downtime compared to the standard practice, indicating improved reliability under field conditions. This study provides empirical evidence supporting the effectiveness of PCS in enhancing system reliability in Senegal's engineering context. Based on findings from this trial, it is recommended that all future projects in Senegal adopt or upgrade to these process-control systems for improved performance and sustainability. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.