Browsing by Author "Ssempijja, Maureen Nalubowa"

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A factory-level data-informed roadmap to industry 4.0 for low digital maturity steel manufacturing plants
(International Journal of Advanced Manufacturing Technology, 2026-04-25) Kangwagye, Samuel; Ssempijja, Maureen Nalubowa
This paper presents a factory-level, data-informed Industry 4.0 readiness and upgrade framework for steel manufacturing plants operating in low digital maturity environments, using Uganda as a representative case. Field data was collected at three medium-scale operational steel plants. Customized Digital Maturity Index (DMI) and cybersecurity risk (CSR) assessment criteria were developed and applied. Results show an overall DMI score of 1.8, indicating very low digital maturity with predominantly manual operations, absence of industrial robots, advanced automation, and integrated digital data systems. CSR assessment results show limited formal protection mechanisms. A phased, ROI-driven transition roadmap is proposed. A worked case using plant-level production data demonstrates that selective automation of four bottleneck stations could nearly double annual billet output and achieve an incremental payback period of approximately seven weeks under stated assumptions. Workforce transition modeling using a Markov approach indicates gradual role transformation over an expected horizon of about ten transition cycles rather than abrupt displacement.
Development and evaluation of a sustainable solar cooker for operations in Uganda
(African Journal of Emerging Issues, 2025-01-27) Sebunya, Steven; Sendegeya, AlMas ; Ssempijja, Maureen Nalubowa
Purpose of the Study: The research focused on developing and evaluating a solar cooker as a sustainable energy solution to address the cooking needs in Africa, using Uganda as a case study. Statement of the Problem: While solar energy technologies have the potential to enhance energy sustainability and reduce greenhouse gas emissions, current solar cookers face limitations in addressing local cooking habits, energy demands, and geographical conditions in Uganda. Methodology: The research established energy requirements for cooking based on common food types, average household size, and average solar irradiation in Uganda. A solar box cooker was designed and modeled using SOLIDWORKS software. Material selection and cost analysis were conducted for economic feasibility, and the optical and thermal performance was analyzed using COMSOL Multi-Physics software. A prototype was constructed using locally available materials to assess manufacturability and cost implications. Results: A box-type solar cooker was developed with inner reflector walls at an optimal angle and internal insulation for better heat retention and efficiency. The cooker, with an aperture area of 0.1897 m², meets the thermal requirements for cooking common foods in major regions of Uganda. All materials used are locally available, making the cooker appropriate, sustainable, and affordable. Conclusion: The proposed solar cooker offers a viable alternative to traditional cooking methods in Uganda. It effectively cooks common foods, is cost-effective, and provides environmental benefits, reducing reliance on charcoal.
Modelling and optimization of residential electricity load under stochastic demand
(International Journal of Research in Industrial Engineering, 2024-01) Kizito, Paul Mubiru; Ssempijja, Maureen Nalubowa
The paper considers a modelling framework for a set of households in residential areas using electricity as a form of energy for domestic consumption. Considering the demand and availability of units for electricity consumption, optimal decisions for electricity load allocation are paramount to sustain energy management. We formulate this problem as a stochastic decision-making process model where electricity demand is characterized by Markovian demand. The demand and supply phenomena govern the loading and operational framework, where shortage costs are realized when demand exceeds supply. Empirical data for electricity consumption was collected from fifty households in two residential areas within the suburbs of Kampala in Uganda. Data collection was made at hourly intervals over a period of four months. The major problem focussed on determining an optimal electricity loading decision to minimize consumption costs as demand changes from one state to another. Considering a multi-period planning horizon, an optimal decision was determined for loading or not loading additional electricity units using the Markov decision process approach. The model was tested, and the results demonstrated the existence of optimal state-dependent decision and consumption costs considering the case study used in this study. The proposed model can be cost-effective for managers in the electricity industry. Improved efficiency and utilization of resources for electricity distribution systems to residential areas were realized, with subsequently enhanced service reliability to essential energy market customers.