london, 30 april 2014, russell hodge intelligent asset management embedding analytics to improve...

London, 30 April 2014, Russell Hodge

Intelligent Asset ManagementEmbedding Analytics to Improve Asset Maintenance and Renewal Decisions

2Copyright © Capgemini 2014. All Rights Reserved

Intelligent Asset Management | April 2014

Success or otherwise of the Asset Intensive Enterprise is driven by the value they deliver from those assets

Network Rail

Analytics

Intelligent Asset Management

How we have helped Network Rail make better decisions on managing the UK railway

The role of Big Data, Analytics and the analytics practitioner

Wider role of Analytics in delivering value from assets through the asset life

Critical role of analytics in delivering tangible value from assets.



My background

Principal, Head of Intelligent Asset Management, Capgemini Consulting UK

Experience in AM

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

What we hear from clients

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

Role of Analytics

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



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.



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



Linear Asset Decision Support (LADS) provides the capability to deliver true predictive insight for Asset Management

Data collected from monitoring fleet, manual inspections and

other sources

LADS provides visual layered view of multiple information sources

providing root cause analysis

More reliable decisions around track maintenance processes, refurbishment and renewals

processes

For example, better understanding of underlying cause of problems relating to

track geometry

LADS enables NR to deliver more effective maintenance,

fewer renewals of the right specification for at least the same

level of performance

LADS enables consistent, evidence-based decision making

and application of policy over time through use of algorithms



Consolidates existing data and delivers additional insight to those that are making key decisions when and where they need it

Renewals

Planned maintenance

Unplanned maintenance

Less complete renewals by better targeted single component replacement

Proactive maintenance management through better understanding asset condition

More effect treatments through better root cause analysis

“Data – Insight – Action – Outcome” “Right Work, Right Place, Right Time”

Better, more informed decisions at heart of the business.



Getting the foundations in place; an integrated single source of accurate asset data, is key to delivering improved decision making

Deliver insight from

the data

Get the data foundation in

place

Turn insight into actions

and outcomes

Consolidating Diverse Data into One Place




the data


place


and outcomes

With the data in place we deliver insight that supports key investment decisions through analytics

Using Analytics to Deliver the insight



It is then important to clearly articulate the business outcome and benefits that are driven from making better decisions


the data


place


and outcomes

Delivering Measurable Benefit from Better Asset Decision Making

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

Delivers over £125m in direct benefit




Network Rail

Analytics



The role of Big Data, Analytics, Mobility and the analytics practitioner

Wider role of Analytics in delivering more from your assets through the asset life




Transforming the People and Process components are key to delivering business change and business outcomes

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

Achieving the vision requires a step change in how an enterprise manages its assets

People Process

Technology DataTechnology Need for faster decision making and

greater flexibility Need for analytical technologies –

descriptive, predictive and prescriptive

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

Data and Governance Integration of new data sources No single version of the truth Data quality and data ownership



With the right Asset data in place, Analytics provides the capability to make better, more informed decisions

13

Human Interaction

Dec

isio

n

Dat

a

Descriptive insight

Diagnostic insight

Predictive analytics

Prescriptive analyticsDecision Support

Decision Automation

Actio

n

Out

com

eBu

sine

ss B

enefi

t

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

Predict asset degradation and exceedance Predict failure likelihood Predict impact of intervention type

Optimising whole life cost for asset portfolio Simulation of asset performance based on known

environmental conditions Optimise long term workbank



Asset Objects (Geographical Data)• Age and type of components (Rail/Ballast/Sleeper)• Geographical conditions and boundaries• Infrastructure types (e.g. Embankments, cuttings etc.)• Weak embankment information and drainage• Cumulative tonnage over the track• Start and finish locations of S&Cs and structures (e.g. Bridges, tunnels etc.)• Tight Clearances

Condition data• Track Geometry• Fine content in Ballast (GPR)• Rail breaks and defects• Track Photos and Video

Intervention History and Plans• Intervention Records and Plans• Planned renewal works• Aspirational renewal works

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



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

LADS Operating Model defines people, processes, technology and data to deliver as a cohesive managed service

15

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)

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

LADS Service

OptramSolution

LADS Data Model

Business Processes

Reference Data

Functional Requirements

Condition Data

Role Based Training

Modeled (Derived) Data

Data Specification

Solution Build, Test & Deploy

Products Delivered By

Visualisation of Data

Business Algorithms

Future Enhancements

Knowledge Transfer

LADS Strategy

Data Loading & Alignment

Services & CapabilitiesOperating

Model

Transactional Data

Data Stewardship

LADS Customer Board

LADS Service Owner

LADS System Owner

Customer BRIG(x 10)

Design Authority Corporate Communications

Benefits Governance

Board

Asset Information Operational

Review

Expert Users (Scripting &

Analysis)

Super Users

Group Business Services

Bentley(Software Vendor)

Combined with training, business change, operational process definition.




Network Rail

Analytics



The role of Big Data, Analytics, Mobility and the analytics practitioner

Wider role of Analytics in delivering more from your assets through the asset life




We recognise the challenges and expectations that these organisations must meet in driving value from their assets

Challenges & Expectations

Increasing Customer Expectations

• Increased service level expectations• Willingness to share comment • Personalised service

Increasing Stakeholder Pressure• Delivery efficiency

& effectiveness• Cost reduction• Safety criticality

Aging Infrastructure

• Years of underinvestment• Historic asset spec• Often safety critical or huge

cost impact of failure

Diversity of Asset Portfolio• Age range of assets• Varying criticality; impact of failure• Asset knowledge and specification• Mix of continuous and fixed

Quality of Asset Data• 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

Market Expectations

Workforce Capability

• 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

17



Analytical Capability

There are a number of factors that will enable better decisions through planning and executing the asset lifecycle

Operating Model

Data & Asset Information

Business Outcomes

Asset Investment Planning & Management

Asset Management

DecisionMaking

Asset Knowledge

and Enablers

Strategy & Vision

Acquire / Create

Utilise Maintain Renew / Dispose

Business Operations

Workforce Enablement & Tooling

Process Optimisation

Asset Org Design & Workforce Capability

Resourcing Strategy and Optimisation

Asset Performance Management and BILife Cycle Cost and Value

OptimisationCriticality, Risk Assessment

& Management

Demand Analysis

AM Policy

Strategic Planning Framework

AM Strategy

• Aging Assets Strategy• Condition led renewal• Refurbish rather than

renew

• Shutdowns & Outage Strategy & Optimisation

• Reliability Engineering & Root Cause Analysis

• Automated Inspection

• Reliability-Centred Maintenance and FMEA

• Risk-Based Maintenance• Maintenance

effectiveness

• Capital Investment Decision-Making

• Enhanced policy & standards

• Design for reliability and maintainability

Operations & Maintenance Decision-Making

Asset Data & Knowledge (including Big Data)

Asset Knowledge Standards

Asset Information Systems

Asset Information

Strategy

18



Analytical Capability

Delivering value from Asset data through Analytics is at the core of the ‘Intelligent Asset Management’ framework

Operating ModelBusiness Operations

Asset Investment Planning & Management

Business Outcomes

Asset Knowledge and Enablers

Strategy and Vision

Business Outcomes

Asset Management

DecisionMaking

Asset Knowledge

and Enablers

Business Operations

Asset Investment Planning

Asset Performance Management Regulatory Support

Asset Information Vision & Value Discovery

Risk Assessment & Management

Asset Data Quality

Asset Management Transformation Service

Digital industrial Asset Lifecycle Management (iALM)

Asset Information Framework

Workforce Planning & Optimisation

ISO 55000Strategic Alignment

Enabling Analytics & BI platforms

Acquire / Create

Utilise Maintain Renew / Dispose

Asset Decision Support

Energy Optimisation

Big Data & Real-time Analytics

Predictive Asset Maintenance

Asset Management Target Operating Model



Better decisions through the asset lifecycle enable Network Rail to achieve multiple business outcomes

20

IAMValue Drivers

Financial benefit

Performance

Regulatory compliance

Safety and risk

Reputation

• Improved investment planning• Sustainably reduce whole life cost

of renewing and maintaining assets

• More effective use of existing infrastructure

• Improve the availability of assets

• 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

• Meet the demands of customers, regulators and shareholders

Questions?

[email protected]

Capgemini London40 Holborn Viaduct,London, EC1N 2PB+44789 115 0186

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The information contained in this presentation is proprietary.© 2014 Capgemini. All rights reserved.

Rightshore® is a trademark belonging to Capgemini.

www.capgemini.com

About Capgemini

With more than 130,000 people in 44 countries, Capgemini is one of the world's foremost providers of consulting, technology and outsourcing services. The Group reported 2012 global revenues of EUR 10.3 billion.

Together with its clients, Capgemini creates and delivers business and technology solutions that fit their needs and drive the results they want. A deeply multicultural organization, Capgemini has developed its own way of working, the Collaborative Business Experience™, and draws on Rightshore®, its worldwide delivery model.

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Network Rail: Asset Management System Transformation

"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

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

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.

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.

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 client situation?

What was the

solution?

How did we

collaborate?

What was the

impact?



Business Operations

Asset Knowledge and Enablers

Business Operations

Mobile

Applications: Asset Management Decision Making

The level 1 ‘logical application architecture’ illustrates the main technical components that enable insight through the asset lifecycle

24

ERP

EAM

Integration Layer - Asset Data Mart

AIPAsset

Investment Planning

ADSAsset Decision Support tools

Real

tim

e A

naly

tics

Asset Performance Management

Big

Dat

a

Business Outcomes

InvestmentManagement

ProjectManagement

MaintenanceManagement

Finance

Workforce Management

Asset Register & Condition Work History & Plans

BI / Presentation TierUnstructured

Data

Images & Video

Asset Tech. Drawing

Network Model

Asset User Data

SCAD A

GIS

MD

M S

Workforce Scheduling

Weather

Internet

london, 30 april 2014, russell hodge intelligent asset management embedding analytics to improve...

Documents

heads of asset management

asset lifecycle

asset maintenance

asset intensive enterprise

business analytics post

miles of track annual

performance network

better decisions