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Silver Edge delivers mature data analytics solutions ready to solve asset management problems. We combine domain knowledge with predictive and prescriptive analytics through application of Machine Learning and Artificial Intelligence.

We aim to harness existing data, breakdown traditional thinking and support the effective use of technology to enhance asset performance and reliability.

Asset Management Services

Strategic Asset Management

Operational Asset Management

Service Delivery

Asset Management Strategy,
Framework and Policy Practical

Engineering Management Support

  • Standards, Code of Practice
    and Policy Advisory

  • Performance Metrics
    and Key Performance Indicators

Asset Register Data

  • Asset information, asset location,
    description, technical data,
    maintenance records,
    condition measures, performance

  • Data Management, confidence and

  • Data capture

  • Asset Visualisation & Hierarchies

  • Asset Condition Management

Asset Risk Management

  • Risk Framework Development

  • Risk Registers, Analysis
    and Decision Support

Decision Support

  • Current State Analysis

  • Asset Health Modelling

  • Deterioration Forecast

  • Data Science and Analysis

  • Predictive Analytics

Asset Management Information

Reliability Engineering

  • Root Cause Analysis


  • RCM

  • Maintenance Strategy
    Development and Review

Pre-feasibility and Feasibility Study Support and Education

Service Delivery Support

  • Project Management

  • Project Engineering

  • Desktop Engineering and Analysis

  • Tools and Process Development

  • Estimation

  • Field Verification

Asset Management Services
Asset Management Technologies

Asset Management Technologies

A combination of innovative technologies with tailored strategic and operational asset management services, assists clients by analysing and visualising the current and future condition of assets to make an impact on maintenance strategy, to develop greater reliability of their asset and to optimise the asset lifecycle.

Silver Edge works with leading asset technology companies to develop and apply innovative and modern solutions to support smart asset management.

As an exclusive distributor for Dual Inventive, we are improving safety and efficiency while reducing downtime in the rail environment. We are working at the forefront of trackworker safety and enabling IoT applications for rail.

Digital Transformation via IoT

Safe & real-time access to asset condition status

Enabling pre-emptive maintenance response

Low-cost technology - increase coverage of the network

Data security via secure cloud platforms

Improving both worker safety and efficiency and reducing downtime on the operating railway

Digital Twin

Your journey to Digital Transformation

Digital Twin

Silver Edge are leaders in taking infrastructure owners on a journey to digital twin implementation.  Through our deep domain knowledge and asset management expertise, we lead customers through the complex process of preparing data and business processes for digital transformation.

“Predictive maintenance to optimise the availability of your assets through digital twin technology”.

We understand the business value of delivering real time asset management with a digital twin solution, and how to embed new technologies within existing business processes. A digital twin can be developed for all levels of asset operations and network complexity. Our approach looks to match the needs of asset owners with a practical outcome aligned to the business needs and level of sophistication.

The key outcome of a digital twin is optimised availability of your assets, automation of asset reliability and health, achieving your KPIs through real time data led planning, improved safety, predictive maintenance and a reduction in faults and maintenance costs.

With an integrated data approach you’re able to drive efficiencies through real time decision making. This creates optimised operational performance, management of faults and failures through insights and actions, the journey from condition-based maintenance to predictive maintenance through machine learning and the use of dashboards to analyse the data against KPIs.

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