Intelligent Asset Management: Enhancing Decisions with Analytics

1. Intelligent Asset ManagementEmbedding Analytics to Improve Asset Maintenance and Renewal Decisions London, 30 April 2014, Russell Hodge

2. Success or otherwise of the Asset Intensive Enterprise is driven by the value they deliver from those assets Network Rail • How we have helped Network Rail make better decisions on managing the UK railway Analytics • The role of Big Data, Analytics and the analytics practitioner Intelligent Asset Management • Wider role of Analytics in delivering value from assets through the asset life Critical role of analytics in delivering tangible value from assets.

3. My background Experience in AM What we hear from clients Role of Analytics • Leading engagements in Rail and Utilities • 10 years experience in delivering consulting led transformation • Leader in Business Analytics • Post granulate research degree in ‘Reliability and Maintainability in Aerospace’ • Undergraduate in Engineering and Business Analytics • Corporate member of IAM and active engagement • Engaging with CXOs and heads of Asset Management • Our clients recognise the need for Asset Management transformation • End to end solutions require a focus on the: • People; capability build • Process; changed ways of working • Technology; enabling data & apps • Personal focus on Business Analytics • Core capability in Asset Management • Delivers insight to make better decisions how assets are managed • IAM Competency alignment: • Risk Management & Performance Management • Policy Development, Strategy Development, AM Planning • Asset Knowledge Management Principal, Head of Intelligent Asset Management, Capgemini Consulting UK

4. Like all asset intensive organisations Network Rail’s ability to manage their assets directly impacts performance • Network Rail have huge investments tied up in their assets • Own and run UK wide rail infrastructure • 22, 000 miles of track • Annual asset spend of £4bn • Core business processes are focused on maximising the availability and uptime while minimising whole life cost • Recognised they were not making well informed decisions through the asset lifecycle • Require a step change in their asset management function • Requires the right people capabilities, process and enabling technology Embedding Analytics in the heart of your organisation drives tangible value; For Network Rail we have demonstrated £125m benefits.

5. Data & Analytics at the core of programme to transform how they manage the infrastructure through the asset lifecycle

6. Linear Asset Decision Support (LADS) provides the capability to deliver true predictive insight for Asset Management LADS provides visual layered view of multiple information sources providing root cause analysis For example, better understanding of underlying cause of problems relating to track geometry Data collected from monitoring fleet, manual inspections and other sources LADS enables NR to deliver more effective maintenance, fewer renewals of the right specification for at least the same level of performance More reliable decisions around track maintenance processes, refurbishment and renewals processes LADS enables consistent, evidence-based decision making and application of policy over time through use of algorithms

7. Consolidates existing data and delivers additional insight to those that are making key decisions when and where they need it “Data – Insight – Action – Outcome” “Right Work, Right Place, Right Time” Renewals Less complete renewals by better targeted single component replacement Planned maintenance Proactive maintenance management through better understanding asset condition Unplanned maintenance More effect treatments through better root cause analysis Better, more informed decisions at heart of the business.

8. Getting the foundations in place; an integrated single source of accurate asset data, is key to delivering improved decision making Get the data foundation in place Consolidating Diverse Data into One Place Deliver insight from the data Turn insight into actions and outcomes

9. With the data in place we deliver insight that supports key investment decisions through analytics Get the data foundation in place Using Analytics to Deliver the insight Deliver insight from the data Turn insight into actions and outcomes

10. It is then important to clearly articulate the business outcome and benefits that are driven from making better decisions Get the data foundation in place Delivering Measurable Benefit from Better Asset Decision Making Deliver insight from the data • All data in one place • Data that users will not have seen • Geometry trace data aligned • Able to overlay data/see trends • iPad as well as PC usage • Able to predict asset degradation • Able to compare sites/assets • Able to pinpoint specific locations Turn insight into actions and outcomes Delivers over £125m in direct benefit

11. Success or otherwise of the Asset Intensive Enterprise is driven by the value they deliver from those assets Network Rail • How we have helped Network Rail make better decisions on managing the UK railway Analytics • The role of Big Data, Analytics, Mobility and the analytics practitioner Intelligent Asset Management • Wider role of Analytics in delivering more from your assets through the asset life Critical role of analytics in delivering tangible value from assets.

12. Achieving the vision requires a step change in how an enterprise manages its assets • Developing the People Capability • Shortage of analytics talent • Immature, disparate in-house capability • Define the analytics operating model • Provide expertise in sophisticated techniques to develop ‘engines’ • Define capability requirements • Build local capability (e.g. super users) to develop the analytics ‘engines’ in house • Deliver Analytics as a Service • Embedding in Business Process • Poor “alignment” between analytics and the business • Develop the processes that allow organisations to act on analytics • Empower the organisation to act real time on insight • Integrate analytics insight into Asset Management functions • Embed processes to deliver sustainable value • Develop the governance around the analytics operating model Process People Technology Data • Technology • Need for faster decision making and greater flexibility • Need for analytical technologies – descriptive, predictive and prescriptive • Data and Governance • Integration of new data sources • No single version of the truth • Data quality and data ownership Transforming the People and Process components are key to delivering business change and business outcomes

13. With the right Asset data in place, Analytics provides the capability to make better, more informed decisions • Data Modelling is used to collect, store and cut the asset data in an efficient way • Visualisation to integrate, consolidate and present asset information in a meaningful way to the right people at the right time Data Descriptive insight • Human Interaction Decision Action Outcome Business Benefit Diagnostic insight Predictive analytics Prescriptive analytics Decision Support Decision Automation • Optimising whole life cost for asset portfolio • Simulation of asset performance based on known environmental conditions • Optimise long term workbank • Predict asset degradation and exceedance • Predict failure likelihood • Predict impact of intervention type

14. There are many key data sources included to support improved decision making

15. LADS Operating Model defines people, processes, technology and data to deliver as a cohesive managed service • Define LADS-as-a-service “up front” • Define guiding principles to operate as managed service (i.e. customer-focused, owned, innovative, sustainable, valuable, affordable) • Determine drivers, parameters, scope and overall “shape” of service • Agree ownership, governance rules and policy constraints (e.g. safety, information security) • Build organisational capabilities and processes • Create outcome-focussed target operating model to define “end state” for service implementation • Develop process decomposition for business & support processes, with swimlaned process flows designed to Level 3 • Design business & support roles based on process swimlanes, develop RACI matrix and define skills & knowledge requirements for each role • Define expectations for users, customers and (internal and external) suppliers • Identify and assess change impacts, and plan actions required to address them • Analyse skill & capability requirements by role, to determine organisational training needs • Utilise process model to design service support model, solution test scenarios and end user training course content • Define value proposition, service architecture, KPI framework and SLAs for managed service element • Develop framework for commercial operation of managed service • Establish governance to last over CP5 • Implement new governance components in sustainable structure (customer board, super user group, expert user scripting capability) • Embed into existing governance framework for AI services • Confirm reporting relationships into continuing programme Combined with training, business change, operational process definition.

16. Success or otherwise of the Asset Intensive Enterprise is driven by the value they deliver from those assets Network Rail • How we have helped Network Rail make better decisions on managing the UK railway Analytics • The role of Big Data, Analytics, Mobility and the analytics practitioner Intelligent Asset Management • Wider role of Analytics in delivering more from your assets through the asset life Critical role of analytics in delivering tangible value from assets.

17. We recognise the challenges and expectations that these organisations must meet in driving value from their assets Market Expectations Aging Infrastructure Increasing Stakeholder Pressure • Years of underinvestment • Historic asset spec • Often safety critical or huge cost impact of failure • Delivery efficiency & effectiveness • Cost reduction • Safety criticality Workforce Capability Increasing Customer Expectations • Lack of trust in asset and don't know how to use the data that does exist • Base decisions on judgement alone, over maintain over renew • Aging workforce, reduction in expertise Challenges & Expectations • Increased service level expectations • Willingness to share comment • Personalised service Quality of Asset Data Diversity of Asset Portfolio • Age range of assets • Varying criticality; impact of failure • Asset knowledge and specification • Mix of continuous and fixed • Historic assets, minimal data • Legacy systems and data management • Limited diagnostics Big Data Challenge • Connected smart assets • New assets streaming data from multiple diagnostics • Standalone systems • Unstructured data Business Challenges

18. There are a number of factors that will enable better decisions through planning and executing the asset lifecycle Analytical Capability Strategy & Vision Business Outcomes • AM Policy AM Strategy Operating Model Business Operations Asset Org Design & Workforce Capability Workforce Enablement & Tooling Process Optimisation Resourcing Strategy and Optimisation Demand Analysis Asset Management Decision Making Asset Investment Planning & Management Asset Performance Management and BI • Strategic Planning Framework Life Cycle Cost and Value Optimisation Criticality, Risk Assessment & Management Acquire / Create Utilise Maintain Renew / Dispose Operations & Maintenance Decision-Making • Aging Assets Strategy • Condition led renewal • Refurbish rather than renew • Capital Investment Decision-Making • Enhanced policy & standards • Design for reliability and maintainability • Shutdowns & Outage Strategy & Optimisation • Reliability Engineering & Root Cause Analysis • Automated Inspection • Reliability-Centred Maintenance and FMEA • Risk-Based Maintenance • Maintenance effectiveness Data & Asset Information Asset Knowledge and Enablers Asset Information Strategy Asset Knowledge Standards Asset Data & Knowledge (including Big Data) Asset Information Systems

19. Delivering value from Asset data through Analytics is at the core of the ‘Intelligent Asset Management’ framework Analytical Capability Strategy and Vision Business Outcomes Asset Information Vision & Value Discovery Business Outcomes Operating Model Business Operations Business Operations Asset Management Transformation Service Asset Management Target Operating Model Workforce Planning & Optimisation ISO 55000 Strategic Alignment Asset Investment Planning & Management Asset Management Decision Making Asset Investment Planning Risk Assessment & Management Asset Performance Management Regulatory Support Acquire / Create Renew / Dispose Utilise Maintain Asset Decision Support Big Data & Real-time Analytics Predictive Asset Maintenance Energy Optimisation Asset Knowledge and Enablers Digital industrial Asset Lifecycle Management (iALM) Asset Knowledge and Enablers Enabling Analytics & BI platforms Asset Information Framework Asset Data Quality

20. Better decisions through the asset lifecycle enable Network Rail to achieve multiple business outcomes • Improved investment planning • Sustainably reduce whole life cost of renewing and maintaining assets Financial benefit • More effective use of existing infrastructure • Improve the availability of assets • Meet the demands of customers, regulators and shareholders Performance IAM Value Drivers Reputation Regulatory compliance Safety and risk • Safety risk modelling to reliably identify critical assets • Analysis of operational safety-related risk precursors • Meet regulatory obligations to avoid penalties • Evidence to support regulator negotiations

21. RussellHodge Principalrussell.hodge@capgemini.com Capgemini London 40 Holborn Viaduct, London, EC1N 2PB +44789 115 0186 Insert contact picture Questions?

23. Network Rail: Asset Management System Transformation What was the client situation? Network Rail, an organisation of 35,000 employees, owns and operates Britain’s rail infrastructure. With an estimated 1.3 billion journeys made on Britain’s railways each year it is essential that Network Rail maintain the level of service expected by the travelling public and the Office of Rail Regulation (ORR), its industry regulator. With an anticipated future increase in rail usage, both higher passenger numbers and more trains on the track, Network Rail must find new ways to optimise the management of its core assets to meet this increased demand. What was the solution? As part of Network Rail’s Asset Information programme Offering Rail Better Information Services (ORBIS), Capgemini have worked with Network Rail and Bentley Systems to deliver a Linear Asset Decision Support system for Track assets. This solution utilises industry leading capabilities to consolidate Network Rail’s complex engineering data and provide insight from that data to the engineer, enabling them to make better decisions on managing the track. Importantly, the Linear Asset Decision Support system ensures this information is available when and where the engineers need it and in a visual format that is easy to interpret and act upon. The solution combines data from 14 asset information systems into a single digital solution, providing a consolidated and aligned view of all rail asset data. Engineers can view, manipulate and analyse this data. How did we collaborate? To deliver a solution that meets the needs of the business in such a complex area it was critical that the design and deployment of the solution was business led. Capgemini and Network Rail used a "Model Office" approach to harness the capabilities and expertise of the engineering Subject Matter Experts from the business. This approach was centred on engaging a cross section of business users to provide the depth of understanding required and design how best to embed these new technologies and ways of working in the business. This collaborative approach delivered business defined requirements and a business designed solution. What was the impact? With the deployment of a Linear Asset Decision Support solution Network Rail engineers now have access to enhanced insight to ensure they are doing the right work, in the right place at the right time. Through utilising new, digital technologies in the Asset Management function Network Rail is now able to make better decisions on how they manage their track assets, realising hundreds of improved decisions every day. Such improved decisions are resulting in more preventative track maintenance and renewal resulting in fewer asset faults and failures. In addition, where issues do occur better decisions are leading to more first time fixes and fewer repeat faults across the asset estate. All of this is contributing to a reduced number of separate interventions and less intrusive work on the track asset. Importantly this leads to increased asset availability and therefore and improved service for Network Rail customers, the train operators and ultimately the travelling public seeing less disruptions to train journeys and a subsequent improved customer experience "Network Rail is transforming how it manages its infrastructure assets. We are moving from paper-based working, time-based asset renewals and a 'find and fix' approach to asset management to a proactive digitally-enabled 'predict and prevent'. This requires insight into how different assets work and perform together as an asset system, along with historical condition and workbank data that enables reliable analytical predictions to be made. The Linear Asset Decision Support system developed and implemented by Network Rail's £330m ORBIS programme does just that. Our track engineers across the country can now access critical asset-related data where and when they need it most, enabling them to better target the most appropriate type of work to the right place. Getting our asset interventions right first time saves cost and helps us run an even safer, better performing railway.“ - Patrick Bossert, Director, Asset Information at Network Rail

24. The level 1 ‘logical application architecture’ illustrates the main technical components that enable insight through the asset lifecycle Business Outcomes Applications: Asset Management Decision Making Business Operations Business Operations GIS BI / Presentation Tier Unstructured Data Workforce Scheduling Internet Big Data Real time Analytics Asset Performance Management ADS Asset Decision Support tools AIP Asset Investment Planning MDMS ERP SCADA Images & Video Investment Management Asset Tech. Drawing Project Management Integration Layer - Asset Data Mart Network Model Maintenance Management Asset User Data Asset Knowledge and Enablers Finance Mobile EAM Workforce Management Asset Register & Condition Weather Work History & Plans