Asset Management, Railway Infrastructure, Predictive Maintenance

Updated: Jun 10

One of the largest controllable expenditures for all companies that own, manage, or operate public infrastructure industry relates to maintenance engineering. These expenditures can represent anywhere between 18% and 53% of revenues for businesses that have a large tangible asset base.

Client Requirements

Authentic Evidence was hired to support one of the largest infrastructure asset management companies in the transport sector, which owns, operates, and manages over USD $92 billion in property, plant, and equipment. The senior asset management and front-line maintenance engineering teams set themselves a goal to integrate and harmonise their organisational management processes for collecting, storing, analysing, and communicating large quantities of data to improve management reporting, accelerate organisational decision making, strengthen governance controls in risk management, and achieve an organisational performance target of USD $4 billion in efficiency savings between the years 2019 and 2024.


Our Work

Our technical director worked closely with a variety of internal stakeholders to understand their individual needs for information, operational challenges, and technological constraints. By engaging with a diverse group of stakeholders he was able to gain insights into how the team could empower decision makers with the evidence, analytical tools, and technology integration solutions to achieve their strategic objectives.

The team worked closely with a variety of engineering teams to understand their precise needs for information and set forth a plan to help them analyse the degradation of specific assets based on how important they were to the overall transport network, the intensity to which they were being used, and how complex they were to maintain.


We helped the front-line engineers link their failure mode effects and criticality analysis (FMECA) metrics to organisational performance metrics that could be translated across the organisation to increase awareness that maintenance engineering activities do not only impact costs, safety, reliability, efficiency, and growth in capacity, but maintenance engineering is significantly important to quality control, environment sustainability, customer satisfaction, and the overall health of the national transport system.


Successful Outcome

We aggregated over 454 terabytes of structured and unstructured data from over 71 different enterprise software systems and advanced sensory equipment to identify where we could automate consistent routines in manual data processing between different business units and management support functions.


This process uncovered a significant opportunity to accelerate the delivery of analytical dashboards and decision support tools that would accelerate information flows between different parts of the organisation and improve the existing processes related to monitoring, predicting, planning, and tracking the maintenance of critical infrastructure, including electrification and power distribution assets, railway tracks, and signalling systems.


Based on the observed data, we built an extension of a generalised proportional intensities model to offer maintenance engineers an enhanced ability to rank their decisions in scheduling preventive maintenance tasks according to failure mode effects and the criticality of an asset as well as the availability and procurement of spare parts. We included a number of predictor variables in the model, such as:


  • Quality of last maintenance action;

  • Time since last maintenance action;

  • Environmental indicators;

  • Asset condition indicators.


The 'quality of maintenance' is a subjective measure based on the expert assessment of senior maintenance engineers, and its provides a qualitative score which can be monitored and measured to assess the functionality and future performance of a complex system of assets. Our justification for including the 'time since last maintenance' was to allow for the possibility that maintenance interventions can introduce problems similar to the burn-in of new components. The inclusion of ‘environmental indicators’ were introduced to measure the effects of temperatures and other weather variables that will accelerate the degradation of assets. The 'asset condition indicators', when available, were integrated from remote condition monitoring devices to provide consistent measurements and direct guidance on the likely occurrence of potential failures, which allows maintenance engineers to plan any maintenance interventions in advanced of any assets failing. This advanced notice of potential failures is also expected to result in better cost performance for the procurement of spare parts and engineering services.


Winning Thesis

Our holistic approach to designing a solution for the client improved data quality by over 89% and is now being incorporated into the client‘s enterprise asset management strategy with staged roll-outs across regional business units to gauge user acceptance in advance of an extensive roll-out that will be managed by one of the largest information and technology services companies in world. Our analytical dashboards are also helping the client gain a deeper understanding of their maintenance engineering operations, providing them with detailed insights into how they measure cost-benefits associated with major capital expenditures, which typically range between USD $5-9 billion per year.


Topics Associated with this Project

#InfrastructureAssetManagement

#EnterpriseAssetManagement

#MaintenanceStrategy

#ReliabilitCenteredMaintenance

#MaintenanceManagementSystems

#ComplexSystemAnalysis

#SparePartsManagement

#FailureModeEffectsCriticalityAnalysis #FMECA

#PreventativeMaintenance

#PredictiveMaintenance

#ProactiveMaintenance

#AssetManagement

#HumanResources

#StatisticalPatternRecognition

#MachineLearning

#4IR #FourthIndustrialRevolution

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