Asset Management Decision Making

Report
Intelligent Asset Management
Embedding Analytics to Improve Asset
Maintenance and Renewal Decisions
London, 30 April 2014, Russell Hodge
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.
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
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My background
Principal, Head of Intelligent Asset
Management, Capgemini Consulting UK
Experience in AM
What we hear from clients
Role of Analytics
 Leading engagements in Rail and
Utilities
 Engaging with CXOs and heads of
Asset Management
 Personal focus on Business
Analytics
 10 years experience in delivering
consulting led transformation
 Our clients recognise the need for
Asset Management transformation
 Core capability in Asset
Management
 Leader in Business Analytics
 End to end solutions require a focus
on the:
 Delivers insight to make better
decisions how assets are managed
 Post granulate research degree in
‘Reliability and Maintainability in
Aerospace’
 Undergraduate in Engineering and
Business Analytics
 Corporate member of IAM and
active engagement
 People; capability build
 IAM Competency alignment:
 Process; changed ways of
working
 Risk Management & Performance
Management
 Technology; enabling data &
apps
 Policy Development, Strategy
Development, AM Planning
 Asset Knowledge Management
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
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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.
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
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Data & Analytics at the core of programme to transform how they
manage the infrastructure through the asset lifecycle
Intelligent Asset Management | April 2014
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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
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
More reliable decisions around
track maintenance processes,
refurbishment and renewals
processes
Intelligent Asset Management | April 2014
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Consolidates existing data and delivers additional insight to those that
are making key decisions when and where they need it
“Data – Insight – Action – Outcome”
Renewals
Planned maintenance
Unplanned
maintenance
“Right Work, Right Place, Right Time”
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
Better, more informed decisions at heart of the business.
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
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Getting the foundations in place; an integrated single source of accurate
asset data, is key to delivering improved decision making
Consolidating Diverse Data into One Place
Get the data
foundation in
place
Deliver
insight from
the data
Turn insight
into actions
and
outcomes
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
With the data in place we deliver insight that supports key investment
decisions through analytics
Using Analytics to Deliver the insight
Get the data
foundation in
place
Deliver
insight from
the data
Turn insight
into actions
and
outcomes
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
It is then important to clearly articulate the business outcome and benefits
that are driven from making better decisions
Delivering Measurable Benefit from Better Asset Decision Making
Get the data
foundation in
place
Deliver
insight from
the data




Turn insight
into actions
and
outcomes




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
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
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, Mobility and the analytics
practitioner
 Wider role of Analytics in delivering more from your assets
through the asset life
Critical role of analytics in delivering tangible value from assets.
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
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
Technology
 Need for faster decision making and
greater flexibility
 Need for analytical technologies –
descriptive, predictive and
prescriptive
People
Process
Technology
Data
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
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
Intelligent Asset Management | April 2014
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With the right Asset data in place, Analytics provides the capability to
make better, more informed decisions
Diagnostic insight
Predictive analytics
Prescriptive analytics
 Predict asset degradation and exceedance
 Predict failure likelihood
 Predict impact of intervention type
Decision Support
Action
Data
Human Interaction
Decision
Descriptive
insight
Outcome
Business Benefit
 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
Decision Automation
 Optimising whole life cost for asset portfolio
 Simulation of asset performance based on known
environmental conditions
 Optimise long term workbank
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13
There are many key data sources included to support improved
decision making
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
Intelligent Asset Management | April 2014
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LADS Operating Model defines people, processes, technology and
data to deliver as a cohesive managed service
Products
Delivered By
Business
Processes
LADS
Service
Services &
Capabilities
Operating
Model
LADS Strategy
Optram
Solution
Functional
Requirements
Visualisation
of Data
Data Loading
& Alignment
Reference
Data
Condition
Data
Transactional
Data
Customer BRIG
(x 10)
Role Based Training
Knowledge Transfer
LADS Data
Model
Design Authority
Corporate
Communications
Solution Build,
Test & Deploy
Business
Algorithms
Future
Enhancements
Modeled
(Derived) Data
Data
Specification
Data
Stewardship
Benefits
Governance
Board
LADS Customer Board
Super Users
LADS Service Owner
LADS System Owner
Expert Users
(Scripting &
Analysis)
Asset Information
Operational
Review
Group Business
Services
Bentley
(Software Vendor)
Define LADS-as-a-service “up front”
 Define guiding principles to operate
as managed service (i.e. customerfocused, 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
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
Combined with training, business change, operational process definition.
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
15
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, Mobility and the analytics
practitioner
 Wider role of Analytics in delivering more from your assets
through the asset life
Critical role of analytics in delivering tangible value from assets.
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
We recognise the challenges and expectations that these
organisations must meet in driving value from their assets
Market Expectations
Increasing Stakeholder
Pressure
Increasing Customer
Expectations
• Delivery efficiency
& effectiveness
• Cost reduction
• Safety criticality
• Increased service level expectations
• Willingness to share comment
• Personalised service
Aging Infrastructure
• Years of underinvestment
• Historic asset spec
• Often safety critical or huge
cost impact of failure
Challenges &
Expectations
Diversity of Asset Portfolio
• Age range of assets
• Varying criticality; impact of failure
Big Data Challenge
• Asset knowledge and specification
• Connected smart assets
• Mix of continuous and fixed
• New assets streaming data
from multiple diagnostics
•
Standalone systems
Business Challenges
• Unstructured data
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
Quality of Asset Data
• Historic assets, minimal data
• Legacy systems and data
management
• Limited diagnostics
Intelligent Asset Management | April 2014
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17
There are a number of factors that will enable better decisions
through planning and executing the asset lifecycle
Analytical Capability
Strategy & Vision
Business
Outcomes
Business
Operations
Asset Org Design &
Workforce Capability
Acquire /
Create
• Capital Investment
Decision-Making
• Enhanced policy &
standards
• Design for reliability and
maintainability
Asset
Information
Strategy
Workforce Enablement &
Tooling
Resourcing Strategy and
Optimisation
Asset Investment Planning & ManagementAsset Performance
Strategic Planning
Framework
Asset
Knowledge
and Enablers
Operating Model
Process Optimisation
Demand Analysis
Asset
Management
Decision
Making
AM Strategy
AM Policy
Life Cycle Cost and Value
Optimisation
Utilise
Criticality, Risk Assessment
& Management
Maintain
Operations & Maintenance Decision-Making
• Shutdowns & Outage
Strategy & Optimisation
• Reliability Engineering &
Root Cause Analysis
• Automated Inspection
• Reliability-Centred
Maintenance and FMEA
• Risk-Based Maintenance
• Maintenance
effectiveness
Management and BI
Renew /
Dispose
• Aging Assets Strategy
• Condition led renewal
• Refurbish rather than
renew
Data & Asset Information
Asset Knowledge
Standards
Asset Information
Systems
Asset Data & Knowledge
(including Big Data)
Intelligent Asset Management | April 2014
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18
Delivering value from Asset data through Analytics is at the core of
the ‘Intelligent Asset Management’ framework
Analytical Capability
Strategy and Vision
Business
Business
Outcomes
Outcomes
Business
Business
Operations
Operations
Asset Information Vision
& Value Discovery
Operating Model
Asset Management
Transformation Service
Asset Management Target
Operating Model
Workforce Planning &
Optimisation
ISO 55000
Strategic Alignment
Asset Investment
Planning Planning
& Management
Asset Investment
Risk Assessment &
Management
Asset
Management
Decision
Making
Asset Performance
Management
Acquire /
Create
Utilise
Maintain
Asset Decision Support
Big Data & Real-time
Analytics
Regulatory Support
Renew /
Dispose
Predictive Asset
Maintenance
Energy Optimisation
Asset
Asset
Knowledge
Knowledge
and Enablers
and Enablers
Digital industrial Asset Lifecycle Management (iALM)
Asset Data Quality
Asset Information
Framework
Enabling Analytics &
BI platforms
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
‹#›
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
• Meet the demands
of customers,
regulators and
shareholders
Financial
benefit
Reputation
IAM
Value Drivers
Safety and
risk
• Safety risk modelling to
reliably identify critical assets
• Analysis of operational safetyrelated risk precursors
Performance
• More effective use of
existing infrastructure
• Improve the availability
of assets
Regulatory
compliance
• Meet regulatory obligations to
avoid penalties
• Evidence to support regulator
negotiations
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
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20
Questions?
Insert
contact
picture
Russell
Hodge
Principal
[email protected]
Capgemini London
40 Holborn Viaduct,
London, EC1N 2PB
+44789 115 0186
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.
Learn more about us at www.capgemini.com.
www.capgemini.com
The information contained in this presentation is proprietary.
© 2014 Capgemini. All rights reserved.
Rightshore® is a trademark belonging to Capgemini.
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
Copyright © Capgemini 2014. All Rights Reserved
23
The level 1 ‘logical application architecture’ illustrates the main
technical components that enable insight through the asset lifecycle
Business Outcomes
Business
Operations
Applications: Asset Management Decision Making
BI / Presentation Tier
Unstructured
Data
Workforce
Scheduling
Asset
User Data
Weather
Real time
Analytics
Asset
Performance
Management
ADS
Asset Decision
Support tools
AIP
Asset
Investment
Planning
ERP
Investment
Management
Project
Management
Integration Layer - Asset Data Mart
MDMS
Network
Model
SCADA
Images &
Video
Big Data
GIS
Internet
Asset Tech.
Drawing
Business
Operations
Maintenance
Management
Asset Knowledge and Enablers
Mobile
EAM
Asset Register
& Condition
Work History & Plans
Finance
Workforce
Management
Intelligent Asset Management | April 2014
Copyright © Capgemini 2014. All Rights Reserved
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