Optimization of Aluminium Roofing Sheet Production in a Distressed Economy: A Case Study of a Manufacturing Company in Nigeria

This study optimized an aluminum roofing sheet production company in a distressed economic environment characterized by persistent operational inefficiencies. Production and maintenance data, was obtained from the case study company records and structured questionnaires, and was analyzed using Excel and Python software. The regression-based predictive maintenance model and selected lean manufacturing tools were applied to improve operational performance. Analysis of production data from 2017 to 2021 revealed a 28.8% decline in total output and a 20% reduction in production targets, primarily due to machine downtime and external disruptions. Following the implementation of predictive maintenance, average machine downtime decreased by 55.3%, from 1,316.67 hours in 2021 to 588.34 hours in 2023. In addition, the adoption of lean tools such as Heijunka and Takt Time increased total production by 56.2%, rising from 3,170 tons (2017–2021) to 4,950 tons (2022–2023), and resulted in a 30.4% improvement in net revenue. Maintenance costs were also stabilized at approximately ₦6,000,000 during the 2022–2023 period. These findings demonstrate that the combined application of predictive maintenance and lean manufacturing techniques can significantly enhance productivity, cost efficiency, and operational resilience in manufacturing industries operating under economic constraints.