Evaluation of Train Maintenance and Reliability at Siemens
Introduction
Siemens AG is a Germany-based multinational building and automation company that focuses on providing mobility services in both rail and road transport industries. Siemens prides itself as one of the pioneers of innovations within the rail transport industry. Siemens mobility services focus on improving train throughput by reducing the time wasted on maintenance and increasing train reliability (Siemens, 2020). In today’s internationally dynamic business environment, companies are under constant strain to improve their standard of consumer satisfaction and reduce operational expenses. In addressing these demands, companies often implement performance assessment programs to assess existing functions and establish targets for optimizing potential results. According to Neely (2002), management effectiveness in enhancing efficiency is dependent on a structured performance assessment framework that provides managers with clear short-term and long-term performance goals. In this regard, Siemens focuses on assessing and optimizing the availability of its trains, eventually leading to increased throughput and generated revenue. The company employs a cloud-based system for monitoring and assessing the various components in each train unit (Siemens Industry Inc, 2018).
Operational management is the administrative role responsible for organizing, managing and coordinating the functions required for the manufacture of products and services. It is the management’s role to deal with the critical capability of the enterprise. According to various scholars, effective management should incorporate efficient process assessment mechanisms into decision-making processes inside the organizational and supply chain functions (Heizer et al., 2014; Bozarth, 2013). Formal quantitative models for performance measurement are required for efficient measuring performance. Quality assessment indicators are essential for the application of a standardized performance evaluation model.
In Siemens mobility services, the management uses the Railigent system to carry out standardized performance evaluation of its fleet of rail vehicles. The railigent systems utilize a cloud-based parts monitoring system, which constantly provides feedback regarding every part or system of the train. Siemens invested in the systems to achieve its vision of having a hundred per cent availability of its trains (Siemens Mobility, 2020). The system increases train reliability through several management goals. First, the company aims to lower maintenance costs by extending maintenance intervals. The company achieves this by reducing unnecessary maintenance schedules that were common before the incorporation of the monitoring system. Additionally, the company has reduced unplanned downtimes o its trains by more than thirty per cent by making only necessary maintenance schedules.
This report reviews and analyses the reliability of Siemens’ class 350-2 trains between the periods of 8-10. Each period consists of four weeks. Therefore, the four periods run from 13th October 2019 to 4th January 2020. The evaluation aims to compare the performances of the class 350-2 fleets within the three periods of operation. It also intends to determine the most detrimental defects within the fleets that has the greatest impact on fleet reliability. The report will also use root cause analysis to determine the various reoccurrence of trends or defects that could have been avoided. Finally, the report will use the Moving Annual Average (MAA) data provided to appraise the performance of other Electrical Multiple Units (EMUs) within Siemens fleet of trains.
Analysis of Fleet Performances
References
Evens, T., Schuurman, D., De Marez, L. and Verleye, G., 2010. Forecasting broadband Internet adoption on trains in Belgium. Telematics and Informatics, 27(1), pp.10-20.
Hansen, C., Daim, T., Ernst, H. and Herstatt, C., 2016. The future of rail automation: A scenario-based technology roadmap for the rail automation market. Technological Forecasting and Social Change, 110, pp.196-212.
Janić, M., 2016. A multidimensional examination of performances of HSR (High-Speed Rail) systems. Journal of Modern Transportation, 24(1), pp.1-21.
Kharlamov, E., Mailis, T., Mehdi, G., Neuenstadt, C., Özçep, Ö., Roshchin, M., Solomakhina, N., Soylu, A., Svingos, C., Brandt, S., Giese, M., Ioannidis, Y., Lamparter, S., Möller, R., Kotidis, Y. and Waaler, A., 2017. Semantic access to streaming and static data at Siemens. Journal of Web Semantics, 44, pp.54-74.
Kharlamov, E., Mehdi, G., Savković, O., Xiao, G., Kalaycı, E. and Roshchin, M., 2019. Semantically-enhanced rule-based diagnostics for industrial Internet of Things: The SDRL language and case study for Siemens trains and turbines. Journal of Web Semantics, 56, pp.11-29.
Neely, A., 2002. Business Performance Measurement: Theory And Practice. Cambridge: Cambridge University Press.
Siemens Industry Inc, 2018. Maintaining Drive Train Systems in the Digital Age: Drive Train AnalyticsSieme. Siemens, [online] Available at: <https://assets.new.siemens.com/siemens/assets/api/uuid:696ac631-54dc-4d04-a331-da5595f24dd9/version:1542223705/df-cs-drive-train-analytics-en-lores.pdf> [Accessed 22 June 2020].
Siemens Mobility, 2020. Railigent – The Solution To Manage Assets. [online] Siemens Mobility Global Website. Available at: <https://www.mobility.siemens.com/global/en/portfolio/rail/services/digital-services/railigent.html#:~:text=Railigent%20makes%20intelligent%20use%20of,more%20out%20of%20your%20systems.> [Accessed 24 June 2020].
Siemens, 2020. Siemens Annual Report 2019. Siemens.com, [online] Available at: <https://assets.new.siemens.com/siemens/assets/api/uuid:59a922d1-eca0-4e23-adef-64a05f0a8a61/siemens-ar2019.pdf> [Accessed 23 June 2020].