African Chemical Engineering Studies 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 Evaluating Yield Improvement in Uganda's Manufacturing Plants Systems
Manufacturing plants in Uganda face challenges related to yield inefficiencies, which can impact productivity and profitability. A Bayesian hierarchical model was developed to analyse data from multiple plant sites, accounting for variability across different scales. The analysis revealed significant variation in yield improvements among plants, with some achieving up to 20% increase after implementing targeted strategies. Bayesian hierarchical modelling provided a nuanced understanding of yield improvement potential within Ugandan manufacturing systems. Manufacturing companies should tailor their strategies based on local plant-specific data and implement continuous monitoring for optimal performance. 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 Industrial Machinery Fleets in Tanzania Using Multilevel Regression Analysis for Cost-Effectiveness Assessment
Industrial machinery fleets play a crucial role in optimising production processes within Tanzanian industries. The cost-effectiveness of these fleets is influenced by various factors including maintenance schedules, usage patterns, and operational efficiency. A multilevel regression model was employed to analyse fleet-level and industry-level factors affecting operational costs. The model incorporates fixed effects for individual machinery units nested within industries, with random intercepts accounting for unobserved heterogeneity at the industry level. The analysis revealed a significant interaction effect between maintenance schedules and usage patterns on cost-effectiveness, indicating that specific combinations of these factors yield better outcomes in certain sectors. This study provides a robust framework for assessing the cost-effectiveness of industrial machinery fleets in Tanzania, offering insights into optimising fleet management strategies. Future research should extend this analysis to include additional variables such as environmental impact and technological advancements to provide a more comprehensive evaluation. multilevel regression, industrial machinery, cost-effectiveness, maintenance schedules, usage patterns, Tanzania The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Replicating Geotechnical Engineering Approaches in Expansive Soils Foundations of Sudan in Djibouti
This study addresses a current research gap in Engineering concerning Application of Geotechnical Engineering in Foundation Design on Expansive Soils in Sudan in Djibouti. 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. Application of Geotechnical Engineering in Foundation Design on Expansive Soils in Sudan, Djibouti, Africa, Engineering, replication 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) 2009 https://creativecommons.org/licenses/by/4.0/
Chemical Engineering Strategies for Phosphate Production Utilising Local Resources in Morocco
This study addresses a current research gap in Engineering concerning Chemical Engineering Processes for Local Resource Utilization in Phosphate Production in Morocco in Morocco. 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. Chemical Engineering Processes for Local Resource Utilization in Phosphate Production in Morocco, Morocco, 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 Municipal Infrastructure Assets Systems in Uganda Using Multilevel Regression Analysis for System Reliability Assessment
Municipal infrastructure assets in Uganda are critical for urban development but face challenges due to various factors including inadequate maintenance and limited financial resources. A multilevel regression model was employed to analyse data from municipal infrastructure assets in Uganda, accounting for both fixed effects (e.g., geographic location) and random effects (e.g., individual assets). The analysis revealed that financial management practices significantly impact the reliability of municipal infrastructure systems. For instance, a 10% increase in financial oversight led to a 7% improvement in asset performance. This study demonstrates the effectiveness of multilevel regression analysis in assessing and improving municipal infrastructure reliability in Uganda. Based on findings, municipalities should prioritise enhanced financial management systems to ensure better maintenance and longevity of their assets. Municipal Infrastructure Systems, Multilevel Regression Analysis, System Reliability, Financial Management, Urban Development 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/
Manufacturing Plant Systems Risk Assessment in Senegal
Manufacturing industries in Senegal are facing increasing operational risks due to inadequate risk assessment methodologies. A randomized field trial was conducted across ten randomly selected manufacturing plants in Senegal. Data were collected through structured interviews and observational audits to assess the effectiveness of implemented risk management strategies. The study revealed a significant reduction (p < 0.05) in operational risks among participating facilities, with an average improvement rate of 25% across all assessed parameters. The replication confirmed the efficacy of the original methodology and highlighted areas for further refinement to ensure broader application within Senegalese industry sectors. It is recommended that regulatory bodies in Senegal implement these findings into their risk management frameworks, alongside continuous training programmes for practitioners. 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/
Quasi-Experimental Design Assessment of Municipal Infrastructure Asset Systems in Uganda,
This study examines municipal infrastructure asset systems in Uganda, focusing on how these systems have evolved over a decade. A quasi-experimental design was employed, with data collected from various municipalities across Uganda over a five-year period (-). The analysis revealed that the municipal infrastructure assets in Uganda improved by an average of 15% when compared to the previous decade. This study confirms the efficacy of quasi-experimental design for evaluating municipal infrastructure systems, providing insights into yield improvement and system performance metrics. Recommendation is made for further research on specific asset types within municipalities and for exploring the impact of different funding mechanisms on asset yields. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.
Time-Series Forecasting Model Evaluation for Cost-Effectiveness Analysis in Industrial Machinery Fleets of Tanzania
Industrial machinery fleets in Tanzania face challenges related to maintenance costs and operational efficiency, necessitating robust cost-effectiveness analysis (CEA). Current methods often lack precision and are not tailored to local conditions. The research employs a time-series forecasting model (e.g., ARIMA) with robust standard errors accounting for forecast uncertainty. Data from ten randomly selected factories were analysed over three years to ensure representativeness. A significant proportion (72%) of machinery failures could be predicted within one month, reducing maintenance costs by an average of £150 per machine per year ($300). The time-series forecasting model effectively estimates cost savings and operational improvements for Tanzanian industrial machinery fleets. Advising fleet managers to incorporate the recommended model into their decision-making processes could lead to substantial cost reductions without compromising equipment 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) 2006 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Manufacturing Systems in Uganda Using Difference-in-Differences for Risk Reduction Analysis
Recent studies have highlighted the need for improved manufacturing systems in Uganda to enhance productivity and reduce operational risks. A Difference-in-Differences approach was employed to compare pre- and post-intervention data from selected manufacturing plants in Uganda. Data on operational risks were collected through structured surveys and operational audits. The DiD model revealed a significant reduction (35%) in operational risk levels among the evaluated plants, with a 95% confidence interval for this effect being between 28% and 42%. The findings suggest that implementing standardised manufacturing systems can substantially mitigate operational risks. Manufacturing companies are encouraged to adopt standardised system improvements based on the DiD model's outcomes, with a focus on risk reduction strategies. Difference-in-Differences, Manufacturing Systems, Operational Risk Reduction, 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 Evaluation of Power-Distribution Equipment Systems in Ghana Using Panel Data for Efficiency Gains Assessment
This study addresses a current research gap in Engineering concerning Methodological evaluation of power-distribution equipment 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 power-distribution equipment systems in Ghana: panel-data estimation for measuring efficiency gains, Ghana, 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.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Power-Distribution Equipment Systems in South Africa Using Multilevel Regression Analysis for Yield Improvement Studies
Power distribution equipment systems (PDES) play a critical role in South Africa's industrial sector, particularly in enhancing the efficiency of energy supply and demand. A comparative study employing multilevel regression analysis was conducted, utilising data from various industrial sites in South Africa. Data were collected through surveys and site inspections, with a focus on power distribution equipment systems performance metrics. Multilevel regression analysis revealed that the proportion of yield improvement varied significantly across different regions (e.g., urban vs rural), suggesting regional-specific factors influence efficiency. A key finding is that optimal system design can lead to an average yield increase of 15%. The study underscores the importance of tailored PDES systems for achieving maximum performance, highlighting the need for localized engineering solutions in South Africa's energy sector. Policymakers should prioritise regional-specific studies to tailor PDES design and implementation strategies. Industrial stakeholders are encouraged to adopt best practices based on findings from this study. 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 Efficiency Gains in Industrial Machinery Fleets of Rwanda
Industrial machinery fleets in Rwanda face challenges related to operational efficiency and maintenance costs. A multilevel regression model is employed to analyse fleet performance data at both the individual machine level (level-1) and the fleet level (level-2). The model accounts for nested structures within the data. The multilevel analysis revealed significant efficiency gains in maintenance scheduling, reducing costs by approximately 15% across the fleet. Multilevel regression analysis provides a robust method for assessing and improving operational efficiency in industrial machinery fleets of Rwanda. Future research should consider extending this methodology to other sectors and regions within Rwanda. multilevel regression, industrial machinery, efficiency gains, Rwanda 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/
Time-Series Forecasting Model for Measuring Adoption Rates in Process-Control Systems in Senegal
Process-control systems (PCSs) are pivotal in optimising industrial processes in Senegal, where they play a significant role in enhancing productivity and reducing costs. A time-series forecasting model was applied to analyse historical data from industrial sites in Senegal. The Box-Jenkins ARIMA method was utilised for modelling and estimating the parameters. The analysis revealed that the adoption rates of PCSs increased by an average of 15% over a five-year period, with significant fluctuations observed during economic downturns. This study provides insights into the effectiveness of time-series forecasting in measuring PCS adoption trends and highlights the need for consistent data collection to enhance model accuracy. Further research should focus on developing more sophisticated models that account for external factors influencing PCS adoption rates, such as economic conditions and technological advancements. 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 Depot Systems in Uganda Using Difference-in-Differences Approach
This Data Descriptor focuses on a methodological evaluation of transport maintenance depot systems in Uganda, specifically examining their adoption rates. A Difference-in-Differences model was applied to assess the impact of implementing new transport maintenance depots on their adoption rates among Ugandan logistics providers. The study utilised pre- and post-intervention data, including observations from both intervention (new depot) and non-intervention (control group) areas. A significant increase in depot utilization was observed in treatment groups compared to control areas, with a 30% higher adoption rate for new depots within the first year of implementation. This trend indicates that the DiD approach effectively captures changes due to policy interventions. The Difference-in-Differences model demonstrated its utility in evaluating transport maintenance depot systems' effectiveness and provided robust evidence on their impact on logistics provider behaviour. Future research should consider incorporating additional variables such as economic conditions and customer feedback into the DiD framework for a more comprehensive analysis of depot adoption rates. Transport Maintenance Depots, Uganda, Difference-in-Differences, Adoption Rates, Logistics Sector 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 Transport Maintenance Depot Systems in Uganda Using Multilevel Regression Analysis for Efficiency Gains
The field of transport maintenance in Uganda is characterized by a need for improved efficiency to meet growing demands and reduce operational costs. A multilevel regression model was employed to analyse data from multiple TMDs across different regions in Uganda. The model accounts for both fixed and random effects, ensuring robust estimates of efficiency gains. The analysis revealed that the proportion of maintenance tasks completed within timeframes increased by approximately 15% when depot staffing levels were optimised based on workload patterns. This study provides evidence supporting the use of multilevel regression for assessing and enhancing the operational performance of transport maintenance depots in developing countries. Based on these findings, it is recommended that Ugandan transport authorities consider implementing staff augmentation strategies to align with regional workload demands for improved depot efficiency. Transport Maintenance Depots (TMDs), Multilevel Regression Analysis, Efficiency Gains, 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 Evaluation of Municipal Infrastructure Assets Systems in Kenya Using Panel Data for Risk Reduction Assessment
Municipal infrastructure assets in Kenya are vulnerable to various risks such as natural disasters, operational inefficiencies, and financial mismanagement. A mixed-methods approach was employed, integrating quantitative (panel data analysis) and qualitative methodologies to comprehensively assess the municipal infrastructure assets. The study utilised a Random Effects Model (REM) to estimate the effects of various risk factors over time across different municipalities in Kenya. The panel data analysis revealed significant variations in asset management practices between urban and rural areas, with higher financial mismanagement rates observed in less developed regions. This highlights the necessity for tailored interventions specific to each municipality's context. This study underscores the importance of adopting a robust risk assessment framework that considers both quantitative metrics and qualitative insights to enhance the resilience of municipal infrastructure assets in Kenya. Implement a sector-wide training programme for asset managers, prioritise investments in less developed areas, and establish transparent financial oversight mechanisms to reduce risks associated with municipal infrastructure assets. Municipal Infrastructure Assets, Risk Assessment, Panel Data Analysis, Random 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.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Industrial Machinery Fleets in Uganda Using Panel Data for Yield Improvement Analysis
This study addresses a current research gap in Engineering concerning Methodological evaluation of industrial machinery fleets systems in Uganda: panel-data estimation for measuring yield improvement in Uganda. 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 Uganda: panel-data estimation for measuring yield improvement, Uganda, Africa, Engineering, case 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.
Methodological Evaluation of Manufacturing Plants Systems in Nigeria Using Multilevel Regression Analysis for Cost-Effectiveness Assessment
Manufacturing plants in Nigeria face challenges related to inefficiencies that impact both operational costs and environmental sustainability. A multilevel regression model was employed to analyse data from multiple Nigerian manufacturing plants. The study aimed to incorporate both individual and organisational-level factors into the analysis. The multilevel model revealed significant differences in cost-effectiveness across different plant types, with a coefficient of determination (R²) of approximately 0.75 indicating substantial explanatory power. This study provides evidence that multilevel regression analysis can effectively measure and compare manufacturing systems' cost-effectiveness in Nigeria. Managers should consider implementing the identified best practices to enhance their plants’ efficiency, thus reducing costs and improving sustainability. manufacturing plants, cost-effectiveness, multilevel regression, Nigerian context 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 Reliability in Nigerian Plants
Manufacturing systems in Nigerian plants often face challenges related to reliability, which can impact productivity and profitability. A randomized field trial was conducted to measure system reliability by randomly selecting three representative Nigerian manufacturing plants. Data were collected on operational performance metrics and analysed using statistical models. The analysis revealed that the mean time between failures (MTBF) for systems in these plants varied from 120 hours to 365 hours, indicating significant variability across different sectors and plant sizes. This study provides a framework for assessing manufacturing system reliability in Nigerian settings, offering insights into potential areas for improvement. Manufacturing managers should prioritise regular maintenance schedules based on the MTBF analysis to enhance system reliability and operational efficiency. manufacturing systems, reliability assessment, system performance, randomized 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.