Abstract
Optimal maintenance is supreme for organizations striving to remain competitive, sustainable, and improve productivity. Maintenance of infrastructures in any corporate establishment is subject to limitations in employees' perspectives. Contributing factors include financial constraints, education, resource constraints, technologies, training, and most importantly the cost of replacing infrastructure. The inabilities to deal with these and other limitations have resulted in significant constraints on the fossil power generating plants. This paper extensively investigates protocols relating to employees' perspectives suitable for effective implementations resulting in sustainable optimal maintenance mainly focusing on engineering modification processes and total productive maintenance. The Electricity Supply Commission (Eskom) located in the Republic of South Africa (RSA) is investigated to present a case. Eskom has embarked on different design constraints, rollout skills, and systems-based solutions towards ensuring sustainable total productive maintenance. This paper comparatively reviews these traditional total productive maintenance implementations at Eskom and propose feasible sustainable strategies collected from best practice literature to improve current conventional applications. For purpose of effective research, detail data in a form of questionnaires is collected and performance analysis information by employees at Duvha power station located in Mpumalanga was shared. The aim was to gather information and develop innovative approach that provides methodology for obtaining optimal maintenance schedules for the systems subject to deterioration and external influences. These optimal schedules result in reduction of expected costs. Using the suggested probabilistic methodology and processes, the impact of environment was analysed theoretically and the possible applications is discussed as well. The presented data analysis was focused on overall measures of performance of generating units.
M.Tech. (Industrial Engineering)