Six sigma design, analysis and evaluation of unscheduled bogie faults in freight wagons for optimum performance and enhanced logistics management

Abstract

In the South African economy, the rail industry is crucial in transporting large freight or consigning goods and humans at an affordable and safe cost. The rail industry's poor operational outlook and lack of competitiveness, caused by the frequent breakdowns of commuter trains, have become a significant challenge in recent years. This perceived level of unreliability, associated with the rail industry, has significantly impacted the downward trend of the competitiveness of trade and industry-related economic benchmarks. Improving the reliability of rolling stock in the rail industry has become a subject of great concern in many logistics and freighting organisations globally. Improving reliability implies better quality rolling stock will be available for running the trains. Improving rolling stock maintenance in the rail industry can be achieved through continuous supervision utilising effective business management techniques. Freight logistics companies need to be more proactive in the maintenance of their rolling stock. The complex competitive business challenges emanate from fluctuating market demands, globalisation, and economic uncertainty. The literature suggests that the adoption of DMAIC (Define-Measure-Analyse-Improve-Control) tools is a highly effective initiative that can help the rail industry compete with other sectors in the national economy by reducing operational costs, enhancing productivity, and improving overall quality. The rail industry, being a backbone industry, is the backbone of the South African economy and is significantly lacking in these areas. The purpose of this study was to enhance the overall performance of wagons through process optimisation in the sole rail logistics and freighting industry of the Republic of South Africa by exploring, understanding, and analysing the unscheduled bogie faults and identifying the factors that contribute to high number of faults using the Six Sigma DMAIC problem solving methodology. A case study was presented to validate the DMAIC framework. Twelve months of data, covering the 2023/2024 financial year for bogies faults, was collected and analysed. The study revealed that 2551 faults were recorded for twelve months. Using the Pareto chart, the findings showed that unscheduled bogie faults contributed 48% with a total of 618, followed by brakes with 460 faults and followed by hallow wear with 239 faults. Therefore, the findings disclosed a significant link between Six Sigma structural effectiveness and Six Sigma enablers. Furthermore, Six Sigma enablers play a pivotal role in enhancing organisational prosperity. This study recommends exploring Six Sigma flexibility, expanding the research scope, and investigating its application across diverse South African industries.