• Sectors: Transportation, Rail and Urban Transport

London Underground’s 10 year Asset Management Plans

London, United Kingdom - London Underground (LU) carries more than one billion passengers a year; as many as the entire UK National Rail network. With around 3.5 million journeys per day, on 11 lines serving 270 stations, LU now runs more services than ever before on the 150-year old network. In late 2012, LU wanted to investigate how it could improve its asset investment planning, as the original spreadsheet based system had reached the limit of what it enabled the organisation to do. The answer was to work with SEAMS, an Arcadis company, to turn the spreadsheet model into a full Whole Life Cost calculation model to help improve decision making.

£80m

Reduction in cost of track assets a year

61%

Reduction in passenger delays due to track failures
London Underground’s 10 year Asset Management Plans

Railways operate within rigid funding envelopes, so it was essential to LU that SEAMS’ Enterprise Decision Analytics® (EDA) software was able to hit performance benchmarks. This included working across a number of different asset types and assessing the impact of decisions relative to other changes, such as the introduction of new rolling stock.  This meant that the railway can decide how to allocate a constrained budget to create the best possible increase in value.  

The London Underground Track Model has been developed progressively over the last five years, gaining in both capability and complexity as it is continuously improved to support Asset Management maturity.  It now sits at the heart of Track Asset Management decision making within London Underground. 

It’s used to run scenarios to calculate the expected impact of potential cuts to the investment budget and communicate this with management during the business planning round. It captures the total cost of ownership for track assets, incorporating performance and maintenance benefit for each renewal option.  Plus, it quantifies the impact of future timetable changes associated with providing services for the increasing population of London. 

LU also uses the Track Model to calculate the design life for each asset depending on its type, curvature, installation year and the type of rolling stock.  Using condition information collected by the maintainer, LU then adds a measured life overlay to normalise for areas which are performing differently to the forecast or where poor asset information exists. 

It further allows the team to understand the ownership liability of building new lines in terms of maintenance, renewals and risk. The team can then model the impact of future rolling stock technology changes and understand the impact on the track. 

The optimal timing for asset replacements can be identified using the best value interventions, ensuring key objectives such as safety, minimal service disruption and cost savings are met. The model gives greater flexibility and ensures multiple ‘what if’ scenarios can be run quickly.

SEAMS EDA® has been used by London Underground to successfully defend budgets and demonstrate how changes in investments will affect operational costs and the health of the network. As a result, London Underground has been able to deliver its strategy of focussed investment in track infrastructure to lower ongoing costs through reduced maintenance requirements, improved reliability and safety and enable higher speed train operations to increase fare revenue and meet the demands of an ever-growing population.

“SEAMS Enterprise Decision Analytics® has provided LU with a secure and stable platform from which to calculate the expected cost, risk and performance profiles associated with the ownership of our track assets.” 

Elizabeth Lasserre, Track Asset Manager, Transport for London


Outcomes

£80m

Reduction in cost of track assets a year

61%

Reduction in passenger delays due to track failures

52%

Reduction in life expired assets

Questions about this project

Dan Scott

Head of Asset Management Transformation, SEAMS Ask me a question
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