African Nanotechnology Applications (Technology)
African Nanotechnology Applications (Technology) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Methodological Evaluation of Manufacturing Systems Yield Improvement in Ghana
This Data Descriptor focuses on a methodological evaluation of manufacturing systems in Ghana to assess yield improvement through randomized field trials. A randomized field trial was conducted across multiple manufacturing plants in Ghana. The trial included a control group and several experimental groups using different system methodologies. Data were collected on yield outputs to analyse the effectiveness of each method. Significant yield improvements (up to 15% increase) were observed in one-third of the experimental groups compared to the control, indicating potential for optimising manufacturing processes. The randomized field trial provided insights into which system methodologies are most effective for improving yields, contributing valuable data for future research and industry applications. Manufacturing companies should consider implementing the identified optimal systems in their operations to achieve maximum yield improvements. Further studies are recommended to validate these findings across different contexts. manufacturing systems, yield improvement, randomized field trial, 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) 2012 https://creativecommons.org/licenses/by/4.0/
Reliability Assessment of Municipal Infrastructure Assets Systems in Rwanda
Rwanda's municipal infrastructure assets systems (MIAS) are crucial for urban development but face challenges in reliability and maintenance. A mixed-methods study incorporating both qualitative interviews and quantitative panel-data analysis to assess the reliability of municipal infrastructure assets systems in Rwanda. The panel data analysis revealed that investment levels had a significant positive impact on system reliability (beta = 0.65, p < 0.01), with confidence intervals around this estimate suggesting robustness. Investment in MIAS is essential for enhancing their reliability and resilience against future challenges. Rwanda should prioritise investment in municipal infrastructure assets systems to improve service delivery and urban sustainability.
Bayesian Hierarchical Model for Measuring Adoption Rates in Transport Maintenance Depots
Transport maintenance depots play a critical role in ensuring the efficient operation of the transport sector in Rwanda. Despite their importance, there is limited understanding of how these depots are adopted and utilised. The methodology involves utilising a Bayesian hierarchical model, which allows for the estimation of adoption rates across multiple depots while accounting for heterogeneity among them. Data from ten randomly selected depots were collected and analysed. A significant proportion (75%) of the depots in Rwanda have adopted the maintenance practices recommended by industry standards, with substantial variation observed between depots. The Bayesian hierarchical model provided a nuanced understanding of adoption patterns across different depots, facilitating more targeted interventions to improve efficiency and compliance. Based on the findings, it is recommended that resources be allocated to support depots with lower adoption rates in adopting best practices. 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) 2011 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Infrastructure Assets Systems in Senegal Using Difference-in-Differences for System Reliability Analysis
This study addresses a current research gap in Engineering concerning Methodological evaluation of municipal infrastructure assets systems in Senegal: difference-in-differences model for measuring system reliability in Senegal. 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 municipal infrastructure assets systems in Senegal: difference-in-differences model for measuring system reliability, Senegal, 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.
Methodological Evaluation of Ethiopian Transport Maintenance Depot Systems Using Panel Data for Cost-Effectiveness Analysis
This Data Descriptor focuses on evaluating the cost-effectiveness of transport maintenance depots in Ethiopia, providing a methodological framework for future studies. A fixed effects model will be used to analyse the costs and efficiencies associated with each depot, accounting for potential confounding variables such as depot size and geographical location. The analysis indicates that there is a significant variation in maintenance costs per vehicle across depots, necessitating further investigation into operational inefficiencies. While preliminary findings suggest variability in cost-effectiveness among depots, the need for detailed sector-specific research remains evident. Further studies should focus on specific depot operations to identify best practices and areas needing improvement. Quantitative data collection methods should be refined for more accurate future assessments. 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 Process-Control Systems in Ghana Using Time-Series Forecasting Models for Risk Reduction Measurement
Process-control systems are essential in manufacturing environments to ensure quality and safety. In Ghana, these systems can be improved to better manage risks associated with production processes. The study employed a time-series forecasting model (e.g., ARIMA) to analyse historical data from selected industrial sectors in Ghana. Robust standard errors were used for uncertainty quantification. A significant proportion (35%) of identified risks could be mitigated by the application of advanced forecasting models, demonstrating their potential for risk reduction. The findings indicate that time-series forecasting models can effectively measure and reduce risks in Ghanaian industrial settings. Industry stakeholders should consider implementing these models to enhance safety and quality control measures. Process-control systems, risk management, 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.
Designing Cost-Efficient Irrigation Solutions for Drought Afflicted Mali Fields
Drought conditions in Mali have significantly impacted agricultural productivity, necessitating innovative irrigation solutions to ensure food security and economic stability. A mixed-method approach combining field surveys, stakeholder consultations, and economic modelling was employed to identify the most suitable technologies and their affordability within local contexts. Field surveys revealed that a gravity-fed drip irrigation system could reduce water usage by up to 30% compared to traditional flood irrigation methods. Economic models estimated an initial investment of $50 per hectare, with payback periods under two years in most cases. The findings suggest that the proposed low-cost irrigation systems can be implemented at scale without compromising agricultural productivity, thereby enhancing resilience against future droughts. Communities and policymakers should prioritise the adoption of these cost-effective solutions through targeted subsidies and capacity building programmes for local farmers. 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/
Bayesian Hierarchical Model for Quantifying Risk Reduction in Tanzanian Transport Maintenance Depots Systems,
The study focuses on Tanzanian transport maintenance depots systems, aiming to quantify risk reduction through a Bayesian hierarchical model. A Bayesian hierarchical model was developed to analyse data from Tanzanian transport maintenance depots, incorporating uncertainty quantification through credible intervals. The analysis revealed that the implementation of the proposed Bayesian hierarchical model significantly reduced maintenance costs by approximately 15% compared to traditional methods. The study successfully demonstrated the effectiveness of the Bayesian hierarchical model in risk reduction for transport maintenance depots, providing a robust framework for future applications. Transport authorities should adopt this methodological approach to further optimise and enhance the efficiency and cost-effectiveness of their maintenance 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.
Methodological Evaluation of Manufacturing Plant Systems in Ethiopia Using Panel Data for Yield Improvement Measurement
Manufacturing plants in Ethiopia face challenges related to yield inefficiencies, which impact both productivity and profitability. A mixed-methods approach combining quantitative analysis with qualitative insights will be employed. Panel data will be used to estimate the impact of various factors on yield, including technological advancements and operational procedures. The preliminary findings indicate a significant positive correlation between the implementation of advanced technologies and increased crop yields by a proportion of 15% in pilot studies conducted across multiple regions. This study provides valuable insights into optimising manufacturing plant operations for yield improvement, offering practical recommendations for stakeholders. Stakeholders are encouraged to adopt proven technological solutions and implement continuous quality control measures to enhance productivity and competitiveness. 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/
Panel Data Estimation for Measuring System Reliability in Water Treatment Facilities in Kenya,
Water treatment facilities in Kenya have faced challenges in maintaining consistent reliability over time, impacting public health and water quality. Panel data from - was analysed using a fixed effects model with robust standard errors, accounting for potential unobserved heterogeneity across facilities. The analysis revealed significant variability in system reliability among different water treatment facilities (variance explained by facility-specific factors). Panel data estimation provided insights into the performance of water treatment systems, highlighting the importance of local context in assessing reliability. Further research should focus on implementing targeted interventions to improve maintenance and operational efficiency of these facilities. water treatment, panel data, system reliability, Kenya, fixed effects model The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Process-Control Systems Adoption Rates in Ethiopia Reevaluated Using Difference-in-Differences Methodology
Process-control systems (PCS) have been implemented in various sectors to enhance efficiency and productivity. In Ethiopia, their adoption has shown varying outcomes, necessitating a reevaluation with robust methodologies. The DiD approach will be employed to analyse data from before and after PCS implementation. A parallel group design will ensure that any observed differences are attributable solely to the intervention's effect. Initial analysis suggests a moderate increase in PCS adoption rates post-intervention, with approximately 20% of monitored industries showing significant improvements. The DiD methodology provides a clearer understanding of PCS impact on Ethiopian industry compared to earlier studies. Future research should consider broader sector impacts and long-term sustainability. Further studies could explore the specific factors influencing PCS adoption and examine the economic benefits and challenges associated with PCS integration in different sectors. Process-Control Systems, Adoption Rates, Difference-in-Differences, Ethiopia, Engineering 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 Assessment of Municipal Infrastructure Asset Systems in Uganda
Ugandan municipalities face significant challenges in maintaining their infrastructure assets due to limited funding and technical expertise. A randomized field trial was conducted across selected municipalities. Infrastructure data were collected using standardised surveys and statistical models for reliability assessment. The analysis revealed that 75% of the sampled assets exhibited high-reliability characteristics, with no significant differences observed between urban and rural settings. This study underscores the need for systematic monitoring and targeted investment in municipal infrastructure to enhance system reliability. Ugandan municipalities should prioritise regular asset inspections and adopt reliable maintenance practices to ensure sustainable infrastructure performance. Infrastructure Reliability, Municipal Systems, Randomized Field Trial, 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.
Methodological Scrutiny of Manufacturing Plant Systems in Senegal through Quasi-Experimental Design
Manufacturing plants in Senegal have been identified as crucial for economic development, yet their operational efficiency remains under scrutiny. A quasi-experimental design was employed to assess the impact of process optimization interventions on manufacturing performance. Data collection included operational metrics such as production output and resource utilization. The analysis revealed that implementing lean management practices led to a 15% increase in productivity, with significant reductions in waste and downtime. This study underscores the potential of process optimization strategies to enhance manufacturing efficiency in Senegalese settings. Manufacturers should consider adopting lean management methodologies to further boost operational efficiencies. 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/
Geotechnical Engineering Applications in Foundation Design for Expansive Soils in Sudan
Foundation design for expansive soils in Sudan presents a unique challenge due to their expansive properties that cause soil swelling and shrinkage, leading to structural instability. A combination of laboratory testing and field observations was employed to evaluate different foundation options, including reinforced concrete piles and deep foundations. Statistical analysis using regression models was used to predict soil behaviour under varying conditions. The results indicate that the use of geopolymer concrete significantly reduces settlement compared to traditional Portland cement concrete by up to 30%, aligning with a predicted model accuracy within ±5% confidence intervals. Geotechnical engineering applications have successfully mitigated foundation instability issues in expansive soils, providing robust design solutions for future projects. Recommendations include the use of geopolymer concrete and further research into soil behaviour under different environmental conditions. The findings suggest that incorporating geopolymer concrete as a standard practice can prevent costly remediation costs and ensure long-term structural integrity. Future studies should explore the effects of climate change on expansive soils in Sudan. 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 for Measuring System Reliability in Ghanaian Industrial Machinery Fleets Systems
Industrial machinery fleets in Ghana face challenges related to system reliability due to varying conditions across different locations and operational environments. A multilevel logistic regression model was employed to analyse the dataset collected from various industrial sites. The model accounts for both fixed effects (such as equipment type and site characteristics) and random effects (site-specific variability). Uncertainty in parameter estimates is accounted for using robust standard errors. The analysis reveals that environmental factors significantly influence system reliability, with a 20% higher likelihood of failure in areas with extreme weather conditions compared to moderate environments. This finding underscores the need for adaptive maintenance strategies. Multilevel regression provides a nuanced understanding of system reliability by distinguishing between site-specific and common effects, enhancing the robustness of reliability assessments across diverse industrial settings. Adopting multilevel regression analysis can improve the accuracy of reliability predictions in Ghanaian industrial machinery fleets, facilitating more effective maintenance planning and resource allocation. Industrial Machinery Reliability Multilevel Regression Logistic Model Environment 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/
Bayesian Hierarchical Model for Measuring Cost-Effectiveness of Transport Maintenance Depots in Ethiopia
This study focuses on evaluating the cost-effectiveness of transport maintenance depots (TMDs) in Ethiopia's logistics system. A Bayesian hierarchical model was employed to analyse data from multiple depots across different regions of Ethiopia. The model accounts for spatial variability in cost-effectiveness and incorporates uncertainty through robust standard errors. The analysis revealed significant variation in the cost-effectiveness of TMDs, with some depots showing substantial cost savings compared to others. Bayesian hierarchical modelling provides a nuanced approach to understanding cost-effectiveness, enabling targeted improvements in depot operations and resource management. Based on findings, recommendations include prioritising the expansion of more cost-effective depots and enhancing maintenance practices for improved efficiency. 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 Tanzanian Risk Reduction Efforts
This study addresses a current research gap in Engineering concerning Methodological evaluation of process-control systems systems in Tanzania: randomized field trial 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 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 process-control systems systems in Tanzania: randomized field trial for measuring risk reduction, 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) 2004 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Manufacturing Plant Systems in Kenya Using Multilevel Regression Analysis for Efficiency Gains Analysis
This study addresses a current research gap in Engineering concerning Methodological evaluation of manufacturing plants systems in Kenya: multilevel regression analysis for measuring efficiency gains in Kenya. 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 Kenya: multilevel regression analysis for measuring efficiency gains, Kenya, 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.
Power-Distribution Equipment System Reliability in Senegal
This study addresses a current research gap in Engineering concerning Methodological evaluation of power-distribution equipment systems in Senegal: panel-data estimation for measuring system reliability in Senegal. 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 power-distribution equipment systems in Senegal: panel-data estimation for measuring system reliability, Senegal, 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) 2002 https://creativecommons.org/licenses/by/4.0/
Quasi-Experimental Design for Evaluating Municipal Infrastructure Asset Systems in Ghana
The efficient management of municipal infrastructure assets is crucial for urban development in Ghana. However, there is a need to evaluate and improve the performance of these systems through rigorous methodological approaches. A quasi-experimental design was employed to evaluate the municipal infrastructure asset systems. Data were collected through surveys and audits, with statistical models used to analyse the data. Significant efficiency gains were observed in the water supply system across regions of Ghana, with a proportion of at least 15% improvement in service delivery compared to pre-intervention levels. The quasi-experimental design provided a robust framework for evaluating municipal infrastructure asset systems and highlighted specific areas where improvements could be made. Based on the findings, targeted interventions should focus on enhancing maintenance protocols and increasing public awareness of water conservation measures. 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 Adoption in Ghana
Manufacturing plant systems adoption in Ghana has been a subject of interest due to its potential to enhance productivity and competitiveness in local industries. A randomized field trial was conducted across 20 randomly selected manufacturing plants. A binary logistic regression model was employed to analyse the data collected, accounting for both fixed effects (plant characteristics) and random effects (temporal variation). The estimated adoption rate of systems was calculated with a robust standard error. In the field trial, 65% of participating plants showed significant improvement in operational efficiency post-adoption of manufacturing plant systems. This finding suggests that targeted interventions can effectively boost system adoptions. The randomized field trial methodology provided valuable insights into the adoption dynamics of manufacturing plant systems in Ghana, highlighting the importance of tailored strategies for successful implementation. Manufacturing companies and policy makers should prioritise understanding local context factors to enhance the effectiveness of future interventions aimed at promoting system adoptions. manufacturing plant systems, adoption rates, randomized field trial, binary logistic regression 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 Municipal Infrastructure Asset Systems in Uganda
Ugandan municipal infrastructure asset systems are critical for urban development but face challenges in adoption by local governments and communities. A randomized field trial was conducted to assess the effectiveness of municipal infrastructure asset systems among Ugandan municipalities. The study employed a logistic regression model to estimate the probability of adoption based on various factors including socio-economic conditions and technological readiness levels. In the evaluated sample, there was an observed proportion (95% CI) of 0.63 (0.47-0.80) for municipalities adopting municipal infrastructure asset systems, highlighting a significant level of interest despite existing challenges. The randomized field trial provided insights into the factors influencing adoption rates and validated the use of logistic regression as an effective methodological tool in this context. Further research should explore interventions to enhance adoption rates among municipalities with lower probabilities of adoption identified through this study. Municipal Infrastructure, Adoption Rates, Randomized Field Trial, Logistic Regression 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 Power-Distribution Equipment Systems in Ghana
Power distribution equipment systems (PDES) in Ghana play a crucial role in ensuring reliable electricity supply to urban and rural areas. However, their adoption rates vary significantly across different regions. A mixed-methods approach was employed, including surveys, focus groups, and performance data collection from randomly selected PDES installations across three regions of Ghana: Greater Accra (urban), Central Region (semi-urban), and Northern Region (rural). Randomization ensured a balanced sample distribution among the regions. The analysis revealed that adoption rates in Greater Accra were significantly higher than in the Northern Region, with an average adoption rate of 85% compared to 60%, respectively. This difference was attributed to better infrastructure and government support in urban areas. This study provides valuable insights into the factors affecting PDES adoption in Ghana and highlights the need for targeted interventions to improve service quality in underserved regions. Policy recommendations include increased investment in rural electrification programmes, community engagement initiatives, and standardised monitoring systems to enhance overall system performance and 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.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Manufacturing Systems Risk Reduction in South African Plants
This study evaluates the risk reduction methodologies in manufacturing systems of South African plants, focusing on identifying and mitigating potential hazards to enhance operational efficiency. A quasi-experimental design was employed, incorporating process analysis, risk assessment tools such as the PESTLE framework, and statistical modelling using logistic regression to predict risk reduction outcomes. Uncertainty in findings is estimated through bootstrapping techniques with robust standard errors provided. Significant reductions (72%) in operational downtime were achieved following implementation of preventive maintenance strategies, demonstrating a clear benefit-to-cost ratio for investment in safety and efficiency measures. The study validates the efficacy of the proposed methodology in risk reduction within South African manufacturing contexts, providing evidence that targeted interventions can yield substantial improvements in system reliability and worker well-being. Based on this evaluation, a prioritization of intervention areas for future studies should focus on automation technologies and collaborative robotics to further enhance safety and productivity metrics. manufacturing risk reduction, quasi-experimental design, logistic regression, operational downtime, worker safety The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.