Investigation and Analysis of Maintenance Strategy in Metahara Sugar Factory to Enhance Productivity

Abstract

Metahara Sugar Factory was started sugar production in 1970GC. Productivity of the factory is facing many challenges due to raw material input, machinery performance due to failure, shutdown and professional workers skill and motivation. This thesis research was aimed to collect different data from planning, production, quality and maintenance department to collect primary and secondary data in order to investigate production bottlenecks for five consequentive years from 2017/018 to 2021/022 GC. Questioners were distributed to different sections, documentation of different data on plan, production, down time were gathered. Factory visited and interviews were conducted to concerned section. The data were analyzed using different tools such as bar charts, histogram, line charts, pie charts, Statistical Package for Social Sciences (SPSS) software and others to reach on conclusion. Multiple linear regression models using Minitab software was used to get a regression model of statistically significant with a P value less than 0.001 and had an R-squared value of 98.98%.The performance result of the factory from 2017/2018 to 2021/2022 was 68,137.40tons, 63,311.20 tons, 595.10 tons, 76,266.60 tons and 44,472.60 tons of sugar respectively and the production plan was97,175.10tons, 95,000.00tons, 100,296.20 tons, 97,163.00 tons and 95,000.00tons for each year respectively. While the productivity calculated with ratio of sugar produced to cane crushed was 0.87, 0.848, 0.83, 0.89 and 0.89 respectively for the five consequentive years. The productivity bottleneck of the factory were machineries downtimes due to mechanical, electrical, instrument, blockage, others, preventive maintenance, pressure drop and lack of steam. Among total factory down times recorded for consequentive five years, the 2019/20 campaign year was the highest which was 3721.83hours and exposed for high maintenance cost which was 576,154,477.22 birr. In current study to improve productivity multiple linear regression model was developed by Minitab software from factory down time data to predict the optimum value at which the productivity will be high by 10.2% profitability margin. Finally, to achieve the optimized down time the organization should use the preventive and predictive maintenance strategy and rather breakdown maintenance which was highly used in the factory.